{ "id": 40348, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/", "page_type": "Gallery", "title": "ESD data for Societal Benefit", "description": "No description available.", "release_date": "2018-04-24T00:00:00-04:00", "update_date": "2018-04-26T00:00:00-04:00", "main_image": { "id": 388785, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030400/a030496/current_earth_observing_fleet_searchweb.png", "filename": "current_earth_observing_fleet_searchweb.png", "media_type": "Image", "alt_text": "HD resolution movies of NASA's Earth Observing fleet.", "width": 180, "height": 320, "pixels": 57600 }, "media_groups": [ { "id": 371236, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/#media_group_371236", "widget": "Card gallery", "title": "General", "caption": "", "description": "", "items": [ { "id": 410115, "type": "media_group", "extra_data": null, "title": "Current Earth Observing Fleet", "caption": "Like orbiting sentinels, NASA’s Earth-observing satellites vigilantly monitor our planet’s ever-changing pulse from their unique vantage points in orbit. This animation shows the orbits of all of the current satellite missions. The flight paths are based on actual orbital elements. These missions—many joint with other nations and/or agencies—are able to collect global measurements of rainfall, solar irradiance, clouds, sea surface height, ocean salinity, and other aspects of the environment. Together, these measurements help scientists better diagnose the “health” of the Earth system.

This animation will be regularly updated to show the orbits of the current earth observing fleet.

This most recent version, published in March 2014 includes the recently launched GPM satellite and removes Jason-1 which was decommissioned in 2013.

\nPrevious versions from recent years include:

\n

entry 4274 a February 2015 version including SMAP\n

entry 3996 a spring 2014 version including GPM \n

entry 4070 a May 2013 version which added Landsat-8\n

entry 3892 a Dec 2011 version which added Suomi NPP and Aquarius\n

entry 3725 a version from June 2010\n

", "instance": { "id": 388785, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030400/a030496/current_earth_observing_fleet_searchweb.png", "filename": "current_earth_observing_fleet_searchweb.png", "media_type": "Image", "alt_text": "HD resolution movies of NASA's Earth Observing fleet.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410116, "type": "media_group", "extra_data": null, "title": "Blue Marble 2015", "caption": "Satellites like Suomi National Polar-orbiting Partnership (NPP) get a complete view of our planet each day, which allows us to create beautiful images of Earth like the one shown here. While it might seem simple, it is actually a rather complex process. Multiple, adjacent swaths of satellite data are pieced together like a quilt to make one global image. Suomi NPP was placed in a unique polar-orbit around the planet that takes the satellite over the equator at the same local (ground) time every orbit. The satellite passes are generally separated by 90 minutes and the instruments image the Earth’s surface in long wedges, called swaths. The swaths from each successive orbit overlap one another, so that at the end of the day, the satellite has a complete view of the world. \r\rThis composite image, captured by Suomi NPP’s Visible Infrared Imaging Radiometer Suite (VIIRS), shows how the Earth looked from space on October 14, 2015—a day the contiguous United States had mostly clear skies. The movement of clouds is not easily visible between consecutive swaths of data; however, by the end of the day, the cumulative movement of clouds can be seen at the vertical seam located near the center of the Pacific Ocean. The vertical lines of haze near the equator are caused by sunglint, the reflection of sunlight off the ocean.", "instance": { "id": 425600, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030700/a030763/R_earth_viirs_1080p.00001_searchweb.png", "filename": "R_earth_viirs_1080p.00001_searchweb.png", "media_type": "Image", "alt_text": "The Blue Marble, October 2015", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410117, "type": "media_group", "extra_data": null, "title": "Rotating Earth at Night", "caption": "This new space-based view of Earth’s city lights is a composite assembled from data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took the satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth’s land surface and islands. This new data was then mapped over existing MODIS Blue Marble imagery to provide a realistic view of the planet. The view was made possible by the “day-night band” of Suomi NPP’s Visible Infrared Imaging Radiometer Suite. VIIRS detects light in a range of wavelengths from green to near-infrared and uses “smart” light sensors to observe dim signals such as city lights, auroras, wildfires, and reflected moonlight. This low-light sensor can distinguish night lights tens to hundreds of times better than previous satellites.", "instance": { "id": 428529, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030000/a030082/viirs_dnb_night_lights_rotating_earth_searchweb.png", "filename": "viirs_dnb_night_lights_rotating_earth_searchweb.png", "media_type": "Image", "alt_text": "Earth at night created with Suomi NPP data from April and October 2012.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 414221, "type": "media_group", "extra_data": null, "title": "NASA Earth Science Division Missions", "caption": "In order to study the Earth as a whole system and understand how it is changing, NASA develops and supports a large number of Earth observing missions. These missions provide Earth science researchers the necessary data to address key questions about global climate change. \n\n

Missions begin with a study phase during which the key science objectives of the mission are identified, and designs for spacecraft and instruments are analyzed. Following a successful study phase, missions enter a development phase whereby all aspects of the mission are developed and tested to insure it meets the mission objectives. Operating missions are those missions that are currently active and providing science data to researchers. Operating missions may be in their primary operational phase or in an extended operational phase.\n\n

Missions begin with a study phase during which the key science objectives of the mission are identified, and designs for spacecraft and instruments are analyzed. Following a successful study phase, missions enter a development phase whereby all aspects of the mission are developed and tested to insure it meets the mission objectives.", "instance": { "id": 1, "url": "https://svs.gsfc.nasa.gov/images/no_preview_web_black.png", "filename": "no_preview_web_black.png", "media_type": "Image", "alt_text": "Current Airborne Fleet", "width": 320, "height": 180, "pixels": 57600 } }, { "id": 410119, "type": "media_group", "extra_data": null, "title": "Remotely Sensing Our Planet", "caption": "The term \"remote sensing” is commonly used to describe the science—and art—of identifying, observing, and measuring an object without coming into direct contact with it. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials, by which they may be identified and categorized by class/type, substance, and spatial distribution. \r\rRemote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines; it also has military, intelligence, commercial, economic, planning, and humanitarian applications. This diagram reveals the variety of remote sensing platforms used today—offering a multi-scale, multi-resolution view of our planet. Remote sensing instruments are of two primary types—active and passive. Active sensors, provide their own source of energy to illuminate the objects they observe. An active sensor emits radiation in the direction of the target to be investigated. The sensor then detects and measures the radiation that is reflected or backscattered from the target. Passive sensors, on the other hand, detect natural energy (radiation) that is emitted or reflected by the object or scene being observed. Reflected sunlight is the most common source of radiation measured by passive sensors. \r\rTo access and download NASA Earth-observing data, visit earthdata.nasa.gov.", "instance": { "id": 411958, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030892/remote_sensing_diagram_hw_searchweb.png", "filename": "remote_sensing_diagram_hw_searchweb.png", "media_type": "Image", "alt_text": "The term \"remote sensing” is commonly used to describe the science—and art—of identifying, observing, and measuring an object without coming into direct contact with it. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials, by which they may be identified and categorized by class/type, substance, and spatial distribution. \r\rRemote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines; it also has military, intelligence, commercial, economic, planning, and humanitarian applications. This diagram reveals the variety of remote sensing platforms used today—offering a multi-scale, multi-resolution view of our planet. Remote sensing instruments are of two primary types—active and passive. Active sensors, provide their own source of energy to illuminate the objects they observe. An active sensor emits radiation in the direction of the target to be investigated. The sensor then detects and measures the radiation that is reflected or backscattered from the target. Passive sensors, on the other hand, detect natural energy (radiation) that is emitted or reflected by the object or scene being observed. Reflected sunlight is the most common source of radiation measured by passive sensors. \r\rTo access and download NASA Earth-observing data, visit earthdata.nasa.gov.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410120, "type": "details_page", "extra_data": null, "instance": { "id": 30590, "url": "https://svs.gsfc.nasa.gov/30590/", "page_type": "Hyperwall Visual", "title": "From Observations to Models", "description": "NASA’s Global Modeling and Assimilation Office (GMAO) uses the Goddard Earth Observing System Model, Version 5 Data Assimilation System (GEOS­-5 DAS) to produce global numerical weather forecasts on a routine basis. GMAO forecasts play important roles in managing NASA’s fleet of science satellites and in researching the impact of new satellite observations. In order to provide timely information about the state of the atmosphere for NASA instrument teams and researchers, the GMAO runs the GEOS-­5 DAS four times each day in real time. For each forecast, it is necessary to provide accurate initial conditions that drive the GEOS-­5 forecasts. To do this, the best estimate of the full, three-dimensional atmospheric state is determined by combining the latest observations and a short-term, 6-­hour forecast—a process known as data assimilation. The GEOS-­5 DAS assimilates more than 5 million observations during each 6-hour assimilation period.These observations are assembled from a number of sources from around the globe, including NASA, NOAA, EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), commercial airlines, the US Department of Defense, and many others. Similarly, each observation type has its own sampling characteristics. It can be seen in the animation how different observation types have different strategies. One of the main challenges of data assimilation is to understand how all these observations are alike, how they differ, and how they interact with each other.Funding for the development of the GEOS-5 model and data assimilation system development comes from NASA's Modeling, Analysis, and Prediction Program and the NASA Weather Focus Area's contribution to the Joint Center for Satellite Data Assimilation.The GEOS-5 DAS runs at the NASA Center for Climate Simulation, which is funded by NASA’s High-End Computing Program.For More Information:http://gmao.gsfc.nasa.gov/http://www.nccs.nasa.gov/images/data_assim_story_072815.pdf || ", "release_date": "2015-05-07T10:00:00-04:00", "update_date": "2025-03-03T00:03:01.288967-05:00", "main_image": { "id": 431584, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030500/a030590/fig-wleg-wm-00720_print.jpg", "filename": "fig-wleg-wm-00720_print.jpg", "media_type": "Image", "alt_text": "This animation shows the global observations assimilated into the GEOS-5 data assimilation system over 6 hours. Data assimilation occurs four times per day.", "width": 1024, "height": 576, "pixels": 589824 } } } ], "extra_data": {} }, { "id": 371237, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/#media_group_371237", "widget": "Tile gallery", "title": "Water & Ice", "caption": "", "description": "", "items": [ { "id": 410121, "type": "media_group", "extra_data": null, "title": "Near Real-Time Global Precipitation from the Global Precipitation Measurement Constellation", "caption": "The Global Precipitation Measurement (GPM) mission produces NASA's most comprehensive global rain and snowfall product to date, called the Integrated Multi-satellite Retrievals for GPM (IMERG). It is computed using data from the GPM constellation of satellites — a network of international satellites that currently includes the GPM Core Observatory, GCOM-W1, NOAA-18, NOAA-19, DMSP F-16, DMSP F-17, DMSP F-18, Metop-A, and Metop-B. The global IMERG dataset provides precipitation rates for the entire world every 30 minutes. Although the process to create the combined dataset is intensive, the GPM team creates a preliminary, near-real-time dataset of precipitation within several hours of data acquisition. This visualization shows the most currently available precipitation data from IMERG, depicting how rain and snowstorms move around the planet. As scientists work to understand all the elements of Earth's climate and weather systems, and how they could change in the future, GPM provides a major step forward in providing comprehensive and consistent measurements of precipitation for scientists and a wide variety of user communities.", "instance": { "id": 375209, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004200/a004285/imergert_1080p_30_searchweb.png", "filename": "imergert_1080p_30_searchweb.png", "media_type": "Image", "alt_text": "This gallery brings together the data visualizations that are updated daily for NASA's Earth Information Center (EIC).", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410122, "type": "media_group", "extra_data": null, "title": "Painting the World with Water", "caption": "The ten satellites in the Global Precipitation Measurement Constellation provide unprecedented information about the rain and snow across the entire Earth. This visualization shows the constellation in action, taking precipitation measurements underneath the satellite orbits. As time progresses and the Earth's surface is covered with measurements, the structure of the Earth's preciptation becomes clearer, from the constant rainfall patterns along the Equator to the storm fronts in the mid-latitudes. The dynamic nature of the precipitation is revealed as time speeds up and the satellite data swaths merge into a continuous animation of changing rain and snowfall. Finally, the video fades into an animation of IMERG, the newly available data set of global precipitation every thirty minutes that is derived from this satellite data.", "instance": { "id": 444553, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004200/a004283/GPM_Fleet_IMERG_globe.00556_searchweb.png", "filename": "GPM_Fleet_IMERG_globe.00556_searchweb.png", "media_type": "Image", "alt_text": "An animation depicting the build-up of precipitation data on the globe from the Global Precipitation Measurement constellation of satellites, resulting in the IMERG global precipitation data set.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410123, "type": "details_page", "extra_data": null, "instance": { "id": 3912, "url": "https://svs.gsfc.nasa.gov/3912/", "page_type": "Visualization", "title": "Global Sea Surface Currents and Temperature", "description": "This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization. || ", "release_date": "2012-03-16T10:00:00-04:00", "update_date": "2025-02-18T00:01:26.447913-05:00", "main_image": { "id": 479018, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003900/a003912/flat_global_ecco2_2028x1024.25000.jpg", "filename": "flat_global_ecco2_2028x1024.25000.jpg", "media_type": "Image", "alt_text": "Global sea surface currents colored by temperature. These are the assembled (contiguous) versions of the animation. There are several resolutions to choose from, some are cropped for various purposes. The 6840x3420 version is the complete, full resolution visualization at the appropriate 2x1 aspect ratio and has not been cropped or resized. The time range for these visualizations is from 2007-03-25T12:00Z to 2008-03-03T12:00Z.", "width": 2048, "height": 1024, "pixels": 2097152 } } }, { "id": 410124, "type": "details_page", "extra_data": null, "instance": { "id": 4544, "url": "https://svs.gsfc.nasa.gov/4544/", "page_type": "Visualization", "title": "2015-2016 El Niño: Daily Sea Surface Temperature Anomaly and Ocean Currents", "description": "This visualization shows 2015-2016 El Nino through changes in sea surface temperature and ocean currents. Blue regions represent colder temperatures and red regions represent warmer temperatures when compared with normal conditions. Yellow arrows illustrate eastward currents and white arrows are westward currents. || GMAO_elNino_oceanTemperatureAnomaly_currents__1300_print.jpg (1024x576) [175.5 KB] || GMAO_elNino_oceanTemperatureAnomaly_currents__1300_searchweb.png (320x180) [97.1 KB] || GMAO_elNino_oceanTemperatureAnomaly_currents__1300_thm.png (80x40) [6.7 KB] || GMAO_elNino_oceanTemperatureAnomaly_currents_1080p.webm (1920x1080) [163.5 KB] || with_colorbar (3840x2160) [256.0 KB] || GMAO_elNino_oceanTemperatureAnomaly_currents_1080p.mp4 (1920x1080) [159.4 MB] || GMAO_oceanTemperatureAnomaly_withColorbar.mp4 (3840x2160) [166.0 MB] || ", "release_date": "2017-05-26T10:30:00-04:00", "update_date": "2024-10-06T22:39:35.752061-04:00", "main_image": { "id": 426723, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004544/GMAO_elNino_oceanTemperatureAnomaly_currents__1300_print.jpg", "filename": "GMAO_elNino_oceanTemperatureAnomaly_currents__1300_print.jpg", "media_type": "Image", "alt_text": "This visualization shows 2015-2016 El Nino through changes in sea surface temperature and ocean currents. Blue regions represent colder temperatures and red regions represent warmer temperatures when compared with normal conditions. Yellow arrows illustrate eastward currents and white arrows are westward currents.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410125, "type": "details_page", "extra_data": null, "instance": { "id": 4443, "url": "https://svs.gsfc.nasa.gov/4443/", "page_type": "Visualization", "title": "NASA-USDA-FAS Soil Moisture / IMERG", "description": "Soil Moisture / Precipitation in Australia, Absolute || australia_abs.0001_print.jpg (1024x576) [100.7 KB] || australia_abs.0001_searchweb.png (320x180) [64.4 KB] || australia_abs.0001_thm.png (80x40) [5.8 KB] || australia_abs (1920x1080) [0 Item(s)] || australia_abs_1080p30.webm (1920x1080) [14.4 MB] || australia_abs_1080p30.mp4 (1920x1080) [117.6 MB] || ", "release_date": "2016-03-30T00:00:00-04:00", "update_date": "2025-03-02T00:06:45.638270-05:00", "main_image": { "id": 425681, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004443/australia_abs.0001_print.jpg", "filename": "australia_abs.0001_print.jpg", "media_type": "Image", "alt_text": "Soil Moisture / Precipitation in Australia, Absolute", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410126, "type": "media_group", "extra_data": null, "title": "Soil Moisture and Rainfall", "caption": "This visualization compares weekly soil moisture and sea surface salinity data (over land and water, respectively) from NASA’s Soil Moisture Active Passive Satellite (SMAP) mission [top map] with a precipitation product called Integrated Multi-satellite Retrievals for GPM, or IMERG [bottom map], from April 17 to August 2, 2015. IMERG is derived using data from the Global Precipitation Measurement (GPM) international constellation of satellites. \r\rThese maps reveal how precipitation amounts influence soil moisture conditions and sea surface salinity. For example, high amounts of precipitation along the equator coincide with relatively moist soil conditions on land (blue shades) and low salinity values in the ocean (green and blue shades). Conversely, areas that receive little or no precipitation, such as the Sahara Desert in northern Africa, coincide with dry soils (dark yellow shades). Scientists can use data from SMAP and IMERG to develop improved flood prediction and drought monitoring capabilities. Societal benefits include improved water-resource management, agricultural productivity, and wildfire and landslide predictions. Data from SMAP also allow us to extend the data record of the highly successful 3-year Aquarius sea surface salinity mission into the future.", "instance": { "id": 432988, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030600/a030698/smap_and_imerg_searchweb.png", "filename": "smap_and_imerg_searchweb.png", "media_type": "Image", "alt_text": "Soil Moisture and Ocean Salinity are compared to Rainfall", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410127, "type": "media_group", "extra_data": null, "title": "California Gets Slammed Again", "caption": "California has been experiencing a drought since 2012, but the first months of 2017 have brought some relief in the form of torrential rains. These rains have been brought to California in a series of atmospheric rivers, long narrow channels of water vapor in the atmosphere that reach from tropical latitudes to the coast of California. These channels bring rainfall to the state when they are disrupted by atmospheric conditions over California's eastern mountains. This visualization of atmospheric water vapor and precipitation during the first three weeks of February clearly show the successive atmospheric rivers and the resulting rainfall.

", "instance": { "id": 416010, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004555/atriver_pacific.00780_searchweb.png", "filename": "atriver_pacific.00780_searchweb.png", "media_type": "Image", "alt_text": "This visualization combines precipitation data from the Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals (IMERG) and water vapor data from the Goddard Earth Observing System Model (GEOS). These datasets show the extreme rainfall that occurred in California during the first three weeks of February 2017 and the atmospheric rivers that transported the rain to the area.This video is also available on our YouTube channel.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410128, "type": "media_group", "extra_data": null, "title": "California Drought", "caption": "The NASA Gravity Recovery and Climate Experiment (GRACE) mission, launched in 2002, maps changes in Earth's gravity field resulting from the movement of water over the planet. As water moves around the globe — for example, due to flooding in some regions and drought in others — GRACE acts like a 'scale in the sky,' mapping the regions of Earth that are gaining or losing water each month. The GRACE mission has been particularly successful in monitoring the melting of the Greenland and Antartic ice sheets, and in mapping changing freshwater storage on land. \n\nThis animation shows how the total amount of water (all of the snow, surface water, soil moisture and groundwater) varies in space and time, with the passage of dry seasons and wet seasons as well as with flooding, drought and transport due to water management Blue colors represent wetter than average conditions (relative to the 2002-2013 time period) and the red colors represent drier than average conditions. The graph at the left shows the monthly changes for the average of map region outlined in yellow. The yellow line in the graph at the left shows interannual variations.\n\nThe Sacramento and San Joaquiin River basins are outlined in yellow and the rivers and their tributaries are shown by the blue lines. The basins include California's Central Valley, the most productive agricultural region in the United States. Ongoing drought in California has drained the state of nearly 15 cubic kilometers (12 miillion acre feet; 4 trillion gallons) of water in each of the last 3 years. Much of the loss is a result of groundwater depletion. Limited rainfall and snowmelt throughout the state has forced agriculture and cities to rely more heavily on groundwater reserves, resulting in rapid depletion of the aquifer beneath the Central Valley. At least 50% of the annual water loss is due to the removal of groundwater.", "instance": { "id": 430604, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030500/a030521/grace_ca_drought_v4_0128_searchweb.png", "filename": "grace_ca_drought_v4_0128_searchweb.png", "media_type": "Image", "alt_text": "GRACE gravity data reveals water deficit in California.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410129, "type": "media_group", "extra_data": null, "title": "Irrigation and Groundwater Depletion", "caption": "A time series of global irrigation and groundwater depletion maps reveals geographical patterns in the use of fresh water for agriculture.\n\nThe amount of water involved is enormous. Worldwide, the irrigation of farmland accounts for about 70% of the fresh water diverted by human activity. We might each drink only a few liters (quarts) of water per day, but the food we eat can require a thousand times as much water to produce. Some of the underground aquifers tapped for irrigation replenish so slowly that they are considered a non-renewable resource. The overuse of this groundwater could have long-term consequences for food security and the stability of global markets in food, cotton, and other agricultural products.\n\nA new study by researchers at University College London and NASA's Goddard Institute of Space Studies in New York City combines trade data and a global water usage model to determine which crops are grown with non-renewable groundwater and where those crops are consumed. The study appears in the March 30, 2017 issue of Nature.", "instance": { "id": 418371, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004523/usa_west.1780_searchweb.png", "filename": "usa_west.1780_searchweb.png", "media_type": "Image", "alt_text": "Irrigation and groundwater depletion are shown side-by-side in the western United States.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410130, "type": "media_group", "extra_data": null, "title": "Global Terrestrial Water Storage Anomaly", "caption": "GRACE (Gravity Recovery and Climate Experiment) maps variations in Earth's gravity field. GRACE consists of two identical spacecraft that fly about 220 kilometers (137 miles) apart in a polar orbit 500 kilometers (310 miles) above Earth. GRACE maps Earth's gravity field by making accurate measurements of the distance between the two satellites, using GPS and a microwave ranging system. It is providing scientists from all over the world with an efficient and cost-effective way to map Earth's gravity field with unprecedented accuracy. The results from this mission are yielding crucial information about the distribution and flow of mass within Earth and its surroundings.\n\nThe gravity variations studied by GRACE can be used to determine ground water storage on land masses. By comparing current data to an average over time, scientists can generate an anomaly map to see where ground water storage has been depleted or increased.\n\nGRACE is a joint partnership between the National Aeronautics and Space Administration (NASA) in the United States and Deutsche Forschungsanstalt für Luft und Raumfahrt (DLR) in Germany. Project management and systems engineering activities are carried out by the Jet Propulsion Laboratory.", "instance": { "id": 441374, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004300/a004338/grace_world_anom_searchweb.png", "filename": "grace_world_anom_searchweb.png", "media_type": "Image", "alt_text": "Print resolution map of the world depicting GRACE Terrestrial Water Storage Anomaly as of April 2015 relative to a 2002-2015 mean.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410131, "type": "media_group", "extra_data": null, "title": "Sea Surface Temperature Anomaly and Terrestrial Water Storage Anomaly Comparison", "caption": "Every two to seven years, an unusually warm pool of water, sometimes two to three degrees Celsius higher than normal, develops across the eastern tropical Pacific Ocean to create a natural short-term climate change event. This warm condition, known as El Niño, affects the local aquatic environment, but also spurs extreme weather patterns around the world, from flooding in California to droughts in Australia. \n\nSea Surface Temperature Anomalies (SSTA) show ocean regions with warmer or colder temperatures than the long-term average for a given month. Globally, SSTA are an important driver of atmospheric circulation and rainfall patterns. Climate modes such as the El Niño Southern Oscillation (ENSO) in the tropical Pacific Ocean, including El Niño (warm SSTA) and La Niña (cold SSTA) phases, give us rise to predictable changes in rainfall patterns. The strong El Niño event that developed in 2015 appears as warm SSTA in the eastern Pacific Ocean. \n\nFor more information on the GEOS5 mission please visit http://www.nasa.gov/mission_pages/Grace/ ", "instance": { "id": 436527, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004413/grace_w_ssta_rob2.4991_searchweb.png", "filename": "grace_w_ssta_rob2.4991_searchweb.png", "media_type": "Image", "alt_text": "Every two to seven years, an unusually warm pool of water, sometimes two to three degrees Celsius higher than normal, develops across the eastern tropical Pacific Ocean to create a natural short-term climate change event. This warm condition, known as El Niño, affects the local aquatic environment, but also spurs extreme weather patterns around the world, from flooding in California to droughts in Australia. \n\nSea Surface Temperature Anomalies (SSTA) show ocean regions with warmer or colder temperatures than the long-term average for a given month. Globally, SSTA are an important driver of atmospheric circulation and rainfall patterns. Climate modes such as the El Niño Southern Oscillation (ENSO) in the tropical Pacific Ocean, including El Niño (warm SSTA) and La Niña (cold SSTA) phases, give us rise to predictable changes in rainfall patterns. The strong El Niño event that developed in 2015 appears as warm SSTA in the eastern Pacific Ocean. \n\nFor more information on the GEOS5 mission please visit http://www.nasa.gov/mission_pages/Grace/ ", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410132, "type": "media_group", "extra_data": null, "title": "Normalized Differential Vegetation Index critical to Agricultural Monitoring in the United States", "caption": "On April 29-30, 2012 the G8 International Conference on Open Data for Agriculture brought together open data and agriculture experts along with the U.S. Agriculture Secretary U.S. Chief Technology Officer, and the World Bank Vice President for Sustainable Development to explore more opportunities for open data and knowledge sharing. Governments want to help their farmers protect crops from pests and extreme weather, monitor water supplies and anticipate planting seasons that are shifting due to climate change.

New satellite technologies offer enhanced capabilities for early forecasting of food production at national, regional, and global scales. The Group on Earth Observations (GEO) Global Agricultural Monitoring (GEOGLAM) program aims to strengthen national capacity in all countries from freely available data.

These visuals show MODIS' satellite-derived crop NDVI Anomaly relative to average (2000-2011). Orange and brown indicate crop with below average conditions. Green indicates crop with above averate conditions. The visual compares the crop conditions or NDVI anomaly from year 2011-2012 to year 2012-2013. In the 2012-2013 year 7,342 more metric tons (MT) of wheat were produced then in the previous year, but 40,086 fewer metric tons of corn were produced.", "instance": { "id": 465700, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004000/a004072/zoomtoUSAcomposite2100_web.png", "filename": "zoomtoUSAcomposite2100_web.png", "media_type": "Image", "alt_text": "This sequence shows NASA MODIS' derived crop NDVI anomaly relative to the average (2000-2011) with the USDAA's end of season crop production for wheat and corn in the United States.Orange and brown indicate below average and green indicates above average crop production. This still image is from March 30, 2013 showing below average conditions for most of the United States.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410133, "type": "media_group", "extra_data": null, "title": "Weekly Animation of Arctic Sea Ice Age with Graph of Ice Age By Area: 1984 - 2016", "caption": "One significant change in the Arctic region in recent years has been the rapid decline in perennial sea ice. Perennial sea ice, also known as multi-year ice, is the portion of the sea ice that survives the summer melt season. Perennial ice may have a life-span of nine years or more and represents the thickest component of the sea ice; perennial ice can grow up to four meters thick. By contrast, first year ice that grows during a single winter is generally at most two meters thick.\n\nBelow is an animation of the weekly sea ice age between 1984 and 2016. The animation shows the seasonal variability of the ice, growing in the Arctic winter and melting in the summer. In addition, this also shows the changes from year to year, depicting the age of the sea ice in different colors. Younger sea ice, or first-year ice, is shown in a dark shade of blue while the ice that is four years old or older is shown as white. A color scale identifies the age of the intermediary years.\n\nA graph in the lower, right corner the quantifies the change over time by showing the area in millions of square kilometers covered by each age category of perennial sea ice. This graph also includes a memory bar - the green line that here represents the current maximum value seen thus far in the animation for the particular week displayed. For example, when showing the first week in September, the memory bar will show the maximum value seen for all prior years' first week of September since the beginning of the animation (January 1, 1984).\n\nCorrection: The original release on 10/28/2016 incorrectly labeled the oldest category on the graph as \"5+\". This was corrected to read \"4+\" on 10/30/2016.", "instance": { "id": 419045, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004510/weeklySeaIceAge_withSqKmGraph_4k.4944_searchweb.png", "filename": "weeklySeaIceAge_withSqKmGraph_4k.4944_searchweb.png", "media_type": "Image", "alt_text": "This visualization shows the age of the Arctic sea ice between 1984 and 2016. Younger sea ice, or first-year ice, is shown in a dark shade of blue while the ice that is four years old or older is shown as white. A bar graph displayed in the lower right corner quantifies the area in square kilometers covered by each age category of perennial sea ice.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410134, "type": "media_group", "extra_data": null, "title": "Sea Ice Maximum extent 2018", "caption": "Sea ice in the Arctic grew to its annual maximum extent on March 17, 2018, joining 2015, 2016, and 2017 as the years with the lowest maximum extents on record, according to scientists at the National Snow and Ice Data Center (NSIDC) and NASA. The Arctic sea ice cover peaked at 5.59 million square miles (14.48 million square kilometers), making it the second lowest maximum on record, at about 23,000 square miles (60,000 square kilometers) higher than the record low maximum reached on March 7, 2017. \n\nThis animation runs from October 1, 2017 to March 17, 2018, the date that the maximum sea ice extent occurred. The images shown here portray the sea ice as it was observed by the AMSR2 instrument onboard the Japanese Shizuku satellite. The opacity of the sea ice shown in this animation is derived from the AMSR2 sea ice concentration. The blueish white color shown on the sea ice is derived from the AMSR2 89 GHz brightness temperature data.", "instance": { "id": 405514, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004628/SeaIceMax_2018.1071_searchweb.png", "filename": "SeaIceMax_2018.1071_searchweb.png", "media_type": "Image", "alt_text": "This image shows the maximum extent of the Arctic sea ice that occurred on March 17th, 2018. The yellow line indicates the 30 year average maximum extent calculated from 1981 through 2010. The date is shown in the upper left corner.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410135, "type": "media_group", "extra_data": null, "title": "Greenland Ice Loss 2002-2016", "caption": "The mass of the Greenland ice sheet has rapidly declined in the last several years due to surface melting and iceberg calving. Research based on observations from the NASA/German Aerospace Center’s twin Gravity Recovery and Climate Experiment (GRACE) satellites indicates that between 2002 and 2016, Greenland shed approximately 280 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year. These images, created from GRACE data, show changes in Greenland ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. In general, higher-elevation areas near the center of Greenland experienced little to no change, while lower-elevation and coastal areas experienced up to 13.1 feet (4 meters) of ice mass loss (expressed in equivalent-water-height; dark red) over a 14-year period. The largest mass decreases of up to 11.8 inches (30 centimeters (equivalent-water-height) per year occurred along the West Greenland coast. The average flow lines (grey; created from satellite radar interferometry) of Greenland’s ice converge into the locations of prominent outlet glaciers, and coincide with areas of high mass loss.", "instance": { "id": 414688, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030879/grace_greenland_201608_searchweb.png", "filename": "grace_greenland_201608_searchweb.png", "media_type": "Image", "alt_text": "Greenland Ice Loss as measured by GRACE", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410136, "type": "details_page", "extra_data": null, "instance": { "id": 30880, "url": "https://svs.gsfc.nasa.gov/30880/", "page_type": "Hyperwall Visual", "title": "Antarctic Ice Loss 2002-2016", "description": "The mass of the Antarctic ice sheet has changed over the last several years. Research based on observations from NASA’s twin NASA/German Aerospace Center’s twin Gravity Recovery and Climate Experiment (GRACE) satellites indicates that between 2002 and 2016, Antarctica shed approximately 125 gigatons of ice per year, causing global sea level to rise by 0.35 millimeters per year.These images, created with GRACE data, show changes in Antarctic ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. In general, areas near the center of Antarctica experienced small amounts of positive or negative change, while the West Antarctic Ice Sheet experienced a significant ice mass loss (dark red) over the fourteen-year period. Floating ice shelves whose mass GRACE doesn't measure are colored gray. || ", "release_date": "2017-05-11T00:00:00-04:00", "update_date": "2025-02-02T00:37:04.497978-05:00", "main_image": { "id": 414496, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030880/grace_antarctica_black_w_vel_v3_201608_print.jpg", "filename": "grace_antarctica_black_w_vel_v3_201608_print.jpg", "media_type": "Image", "alt_text": "Ice sheet mass loss with superimposed ice sheet velocity streamlines.", "width": 1024, "height": 574, "pixels": 587776 } } }, { "id": 410137, "type": "media_group", "extra_data": null, "title": "Operation Icebridge Studies Changes in Greenland's Helheim Glacier", "caption": "These visualizations show data from the Helheim Glacier in Greenland collected by Pre-Icebridge in 1998 and Operation Icebridge in 2013. Data from both the Airborne Topographic Mapper (ATM) and the Digital Mapping System (DMS) are included.\n\nThe first visualization shows how the scanner on the aircraft acquired the data, building up a representation of the 3d laser scanned points as we go. Once the calving front from 1998 is revealed, the 2013 data is faded in showing the differences between the years. The dots are colored initially by absolute height with reds higher and blues lower; after the 2013 data is added, the dot colors change to a localized scheme with reds higher than nearby points and blues lower than nearby points. ATM data is added at the end for some context.\n\nThe second visualization shows the DMS data with ATM data at the 2013 calving front. The DMS data is overlayed onto photogrametrically determined altitudes which don't precisely correspond to the ATM data. The heights of the ATM data are the 'true' heights.", "instance": { "id": 415411, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004566/dms20.3800_searchweb.png", "filename": "dms20.3800_searchweb.png", "media_type": "Image", "alt_text": "Flying down the Helheim Glacier in Greenland as ATM altimetry date is shown - first with data from 1998 then data from 2013 is added", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410138, "type": "details_page", "extra_data": null, "instance": { "id": 30781, "url": "https://svs.gsfc.nasa.gov/30781/", "page_type": "Hyperwall Visual", "title": "The Earth Observing Fleet by Theme", "description": "The current Earth Observing Fleet with all satellites capturing data related to Sea Ice Cover highlighted, combined with key visualizations showing the significance of the data || fleet_data_precipitation_1080p.00001_print.jpg (1024x576) [227.2 KB] || fleet_data_precipitation_720p.mp4 (1280x720) [51.9 MB] || fleet_data_precipitation_1080p.webm (1920x1080) [3.7 MB] || fleet_data_precipitation_1080p.mp4 (1920x1080) [95.8 MB] || fleet_precipitation (4104x2304) [0 Item(s)] || fleet_data_precipitation_2304p.mp4 (4096x2304) [281.0 MB] || ", "release_date": "2017-05-31T00:00:00-04:00", "update_date": "2025-03-03T00:15:59.457213-05:00", "main_image": { "id": 413831, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030700/a030781/fleet_data_atmo_chem_1080p.00001_print.jpg", "filename": "fleet_data_atmo_chem_1080p.00001_print.jpg", "media_type": "Image", "alt_text": "The current Earth Observing Fleet with all satellites capturing data related to Aerosols & Atmospheric Chemistry highlighted, combined with key visualizations showing the significance of the data", "width": 1024, "height": 576, "pixels": 589824 } } } ], "extra_data": {} }, { "id": 371238, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/#media_group_371238", "widget": "Tile gallery", "title": "Atmosphere", "caption": "", "description": "", "items": [ { "id": 410139, "type": "details_page", "extra_data": null, "instance": { "id": 30637, "url": "https://svs.gsfc.nasa.gov/30637/", "page_type": "Hyperwall Visual", "title": "GEOS-5 Aerosols Simulation for SC 2014", "description": "GEOS-5 aerosols shown at SC 2014. || aerosols-sc2014-preview.jpg (1024x512) [140.7 KB] || aerosols_globe_c1440_NR_BETA9-SNAP_20070228_2200z_searchweb.png (180x320) [97.6 KB] || aerosols_globe_c1440_NR_BETA9-SNAP_20070228_2200z_thm.png (80x40) [7.4 KB] || aerosols (1920x1080) [0 Item(s)] || aerosols-sc14.webm (1920x1080) [10.2 MB] || aerosols-sc14.mp4 (1920x1080) [155.5 MB] || 30637_aerosols_sim_1920x1080.mp4 (1920x1080) [204.3 MB] || aerosols (5760x2881) [0 Item(s)] || 30637_aerosols_sim_4K.mp4 (4096x2048) [206.8 MB] || 30637_aerosols_sim_UHD_large.mp4 (3840x2160) [206.3 MB] || 30637_aerosols_sim_1280x720_prores.mov (1280x720) [1.5 GB] || 30637_aerosols_sim_UHD_youtube_hq.mov (3840x2160) [4.0 GB] || 30637_aerosols_sim_UHD.mov (3840x2160) [11.2 GB] || 30637_aerosols_sim_MASTER.mov (5760x2881) [23.5 GB] || ", "release_date": "2014-12-10T00:00:00-05:00", "update_date": "2025-01-06T02:25:05.777504-05:00", "main_image": { "id": 432604, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030600/a030637/aerosols_globe_c1440_NR_BETA9-SNAP_20070228_2200z_searchweb.png", "filename": "aerosols_globe_c1440_NR_BETA9-SNAP_20070228_2200z_searchweb.png", "media_type": "Image", "alt_text": "GEOS-5 aerosols shown at SC 2014.", "width": 180, "height": 320, "pixels": 57600 } } }, { "id": 410140, "type": "media_group", "extra_data": null, "title": "Carbon Dioxide from GMAO using Assimilated OCO-2 Data", "caption": "Carbon dioxide is the most important greenhouse gas released to the atmosphere through human activities. It is also influenced by natural exchange with the land and ocean. This visualization provides a high-resolution, three-dimensional view of global atmospheric carbon dioxide concentrations from September 1, 2014 to August 31, 2015. The visualization was created using output from the GEOS modeling system, developed and maintained by scientists at NASA. The height of Earth’s atmosphere and topography have been vertically exaggerated and appear approximately 400 times higher than normal to show the complexity of the atmospheric flow. Measurements of carbon dioxide from NASA’s second Orbiting Carbon Observatory (OCO-2) spacecraft are incorporated into the model every 6 hours to update, or “correct,” the model results, called data assimilation.

\n\rAs the visualization shows, carbon dioxide in the atmosphere can be mixed and transported by winds in the blink of an eye. For several decades, scientists have measured carbon dioxide at remote surface locations and occasionally from aircraft. The OCO-2 mission represents an important advance in the ability to observe atmospheric carbon dioxide. OCO-2 collects high-precision, total column measurements of carbon dioxide (from the sensor to Earth’s surface) during daylight conditions. While surface, aircraft, and satellite observations all provide valuable information about carbon dioxide, these measurements do not tell us the amount of carbon dioxide at specific heights throughout the atmosphere or how it is moving across countries and continents. Numerical modeling and data assimilation capabilities allow scientists to combine different types of measurements (e.g., carbon dioxide and wind measurements) from various sources (e.g., satellites, aircraft, and ground-based observation sites) to study how carbon dioxide behaves in the atmosphere and how mountains and weather patterns influence the flow of atmospheric carbon dioxide. Scientists can also use model results to understand and predict where carbon dioxide is being emitted and removed from the atmosphere and how much is from natural processes and human activities.

\r\rCarbon dioxide variations are largely controlled by fossil fuel emissions and seasonal fluxes of carbon between the atmosphere and land biosphere. For example, dark red and orange shades represent regions where carbon dioxide concentrations are enhanced by carbon sources. During Northern Hemisphere fall and winter, when trees and plants begin to lose their leaves and decay, carbon dioxide is released in the atmosphere, mixing with emissions from human sources. This, combined with fewer trees and plants removing carbon dioxide from the atmosphere, allows concentrations to climb all winter, reaching a peak by early spring. During Northern Hemisphere spring and summer months, plants absorb a substantial amount of carbon dioxide through photosynthesis, thus removing it from the atmosphere and change the color to blue (low carbon dioxide concentrations). This three-dimensional view also shows the impact of fires in South America and Africa, which occur with a regular seasonal cycle. Carbon dioxide from fires can be transported over large distances, but the path is strongly influenced by large mountain ranges like the Andes. Near the top of the atmosphere, the blue color indicates air that last touched the Earth more than a year before. In this part of the atmosphere, called the stratosphere, carbon dioxide concentrations are lower because they haven’t been influenced by recent increases in emissions.", "instance": { "id": 418955, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004514/co2_30.with_labels.2000_searchweb.png", "filename": "co2_30.with_labels.2000_searchweb.png", "media_type": "Image", "alt_text": "Carbon Dioxide from the GEOS-5 modelThis video is also available on our YouTube channel.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410141, "type": "details_page", "extra_data": null, "instance": { "id": 12772, "url": "https://svs.gsfc.nasa.gov/12772/", "page_type": "Produced Video", "title": "2017 Hurricanes and Aerosols Simulation", "description": "Tracking aerosols over land and water from August 1 to November 1, 2017. Hurricanes and tropical storms are obvious from the large amounts of sea salt particles caught up in their swirling winds. The dust blowing off the Sahara, however, gets caught by water droplets and is rained out of the storm system. Smoke from the massive fires in the Pacific Northwest region of North America are blown across the Atlantic to the UK and Europe. This visualization is a result of combining NASA satellite data with sophisticated mathematical models that describe the underlying physical processes.Music: Elapsing Time by Christian Telford [ASCAP], Robert Anthony Navarro [ASCAP]Complete transcript available.Watch this video on the NASA Goddard YouTube channel. || 12772_hurricanes_and_aerosols_1080p_youtube_1080.00001_print.jpg (1024x576) [161.7 KB] || 12772_hurricanes_and_aerosols_1080p_youtube_1080.00001_searchweb.png (180x320) [108.8 KB] || 12772_hurricanes_and_aerosols_1080p_youtube_1080.00001_thm.png (80x40) [7.5 KB] || 12772_hurricanes_and_aerosols_appletv.m4v (1280x720) [78.1 MB] || 12772_hurricanes_and_aerosols_twitter_720.mp4 (1280x720) [34.1 MB] || 12772_hurricanes_and_aerosols.webm (960x540) [65.0 MB] || 12772_hurricanes_and_aerosols_appletv_subtitles.m4v (1280x720) [78.1 MB] || 12772_hurricanes_and_aerosols_1080p_large.mp4 (1920x1080) [163.1 MB] || 12772_hurricanes_and_aerosols_facebook_720.mp4 (1280x720) [184.9 MB] || 12772_hurricanes_and_aerosols_youtube_1080.mp4 (1920x1080) [247.2 MB] || 12772_hurricanes_and_aerosols_youtube_720.mp4 (1280x720) [247.9 MB] || 12772_hurricanes_aerosols_captions.en_US.srt [3.1 KB] || 12772_hurricanes_aerosols_captions.en_US.vtt [3.1 KB] || 12772_hurricanes_and_aerosols_UHD.mp4 (3840x2160) [739.9 MB] || 12772_hurricanes_and_aerosols_1080p-prores.mov (1920x1080) [4.3 GB] || 12772_hurricanes_and_aerosols_UHD_4444.mov (3840x2160) [40.1 GB] || ", "release_date": "2021-05-05T10:25:00-04:00", "update_date": "2025-03-02T23:42:53.438902-05:00", "main_image": { "id": 409572, "url": "https://svs.gsfc.nasa.gov/vis/a010000/a012700/a012772/12772_hurricanes_and_aerosols_1080p_youtube_1080.00001_print.jpg", "filename": "12772_hurricanes_and_aerosols_1080p_youtube_1080.00001_print.jpg", "media_type": "Image", "alt_text": "Tracking aerosols over land and water from August 1 to November 1, 2017. Hurricanes and tropical storms are obvious from the large amounts of sea salt particles caught up in their swirling winds. The dust blowing off the Sahara, however, gets caught by water droplets and is rained out of the storm system. Smoke from the massive fires in the Pacific Northwest region of North America are blown across the Atlantic to the UK and Europe. This visualization is a result of combining NASA satellite data with sophisticated mathematical models that describe the underlying physical processes.Music: Elapsing Time by Christian Telford [ASCAP], Robert Anthony Navarro [ASCAP]Complete transcript available.Watch this video on the NASA Goddard YouTube channel.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410142, "type": "details_page", "extra_data": null, "instance": { "id": 3947, "url": "https://svs.gsfc.nasa.gov/3947/", "page_type": "Visualization", "title": "Watching the Earth Breathe:
An Animation of Seasonal Vegetation and its effect on Earth's Global Atmospheric Carbon Dioxide", "description": "In this animation, NASA instruments show the seasonal cycle of vegetation and the concentration of carbon dioxide in the atmosphere. The animation begins on January 1, when the northern hemisphere is in winter and the southern hemisphere is in summer. At this time of year, the bulk of living vegetation, shown in green, hovers around the equator and below it, in the southern hemisphere.As the animation plays forward through mid-April, the concentration of carbon dioxide, shown in orange-yellow, in the middle part of Earth's lowest atmospheric layer, the troposphere, increases and spreads throughout the northern hemisphere, reaching a maximum around May. This blooming effect of carbon dioxide follows the seasonal changes that occur in northern latitude ecosystems, in which deciduous trees lose their leaves, resulting in a net release of carbon dioxide through a process called respiration. Carbon dioxide is also released in early spring as soils begin to warm. Almost 10 percent of atmospheric carbon dioxide passes through soils each year.After April, the northern hemisphere moves into late spring and summer and plants begin to grow, reaching a peak in the late summer. The process of plant photosynthesis removes carbon dioxide from the air. The animation shows how carbon dioxide is scrubbed out of the atmosphere by the large volume of new and growing vegetation. Following the peak in vegetation, the drawdown of atmospheric carbon dioxide due to photosynthesis becomes apparent, particularly over the boreal forests.Note that there is roughly a three-month lag between the state of vegetation at Earth's surface and its effect on carbon dioxide in the middle troposphere.Data like these give scientists a new opportunity to better understand the relationships between carbon dioxide in Earth's middle troposphere and the seasonal cycle of vegetation near the surface.Creating the AnimationThis animation was created with data taken from two NASA spaceborne instruments. The concentration of carbon dioxide data from the Atmospheric Infrared Sounder (AIRS), a weather and climate instrument that flies aboard NASA's Aqua spacecraft, is overlain on measurements of vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, also on NASA's Aqua spacecraft, to better understand how photosynthesis and respiration influences the atmospheric carbon dioxide cycle over the globe. The animation runs from January through December and repeats. The AIRS tropospheric carbon dioxide seasonal cycle values were made by averaging AIRS data collected between 2003 and 2010, from which the annual carbon dioxide growth trend of 2 parts per million per year has been removed. For example, the data used for January 1 is actually an average of eight years of AIRS carbon dioxide data taken each year on January 1. The vegetation values were made using data averaged over a four-year period, from 2003 to 2006.Further DetailAIRS uses infrared technology to determine the concentration of atmospheric water vapor and several important trace gases as well as information about temperature and clouds. AIRS orbits Earth from pole-to-pole at an altitude of 438 miles (705 kilometers), measuring Earth's infrared spectrum in 3,278 channels spanning a wavelength range from 3.74 microns to 15.4 microns. Originally designed to improve weather forecasts, AIRS has improved operational five-day weather forecasts more than any other single instrument over the past decade. AIRS has also been found to be sensitive to atmospheric carbon dioxide in the middle troposphere, at an altitude of 5 to 10 kilometers or 3 to 6 miles. AIRS is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena. For further information, access the AIRS projectThe MODIS instrument is managed by NASA's Goddard Space Flight Center, Greenbelt, Md. For further information, access the MODIS project. || ", "release_date": "2012-07-08T00:00:00-04:00", "update_date": "2024-10-09T00:02:20.883006-04:00", "main_image": { "id": 474848, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003900/a003947/airsc02_land_connectionV070244.jpg", "filename": "airsc02_land_connectionV070244.jpg", "media_type": "Image", "alt_text": "The concentration of CO2 measured by AIRS is overlain on measurements of vegetation index from the Moderate Resolution Imaging Spectroradiaometer (MODIS), also on the Aqua spacecraft, in an effort to understand the influence of photosynthesis and respiration on the atmospheric CO2 cycle over the globe. The AIRS tropospheric CO2 seasonal cycle displayed is an average over 8 years of AIRS data, from which the annual growth trend of 2 ppm/year has been removed. The animation shows the buildup of tropospheric CO2 in the Northern Hemisphere with a maximum around May. The maximum in the vegetation cycle follows, occurring in the late summer. Following the peak in vegetation, the drawdown of atmospheric CO2 due to photosynthesis is apparent, particularly over the Boreal Forests.This video is also available on our YouTube channel.", "width": 1278, "height": 719, "pixels": 918882 } } }, { "id": 410143, "type": "details_page", "extra_data": null, "instance": { "id": 4412, "url": "https://svs.gsfc.nasa.gov/4412/", "page_type": "Visualization", "title": "NASA Images Show Human Fingerprint on Global Air Quality – Release Materials", "description": "This video provides an overview of the study findings. An HD version of this video is available here: Human Fingerprint on Global Air Quality || 12096-MASTER_appletv_print.jpg (1024x576) [139.8 KB] || 12096-MASTER_appletv.m4v (1280x720) [60.8 MB] || 12096-MASTER_appletv.webm (1280x720) [13.0 MB] || ", "release_date": "2015-12-17T00:00:00-05:00", "update_date": "2025-02-02T00:08:09.068083-05:00", "main_image": { "id": 436910, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004412/global_abs_2005_print.jpg", "filename": "global_abs_2005_print.jpg", "media_type": "Image", "alt_text": "This global map shows the concentration of nitrogen dioxide in the atmosphere as detected by the Ozone Monitoring Instrument aboard the Aura satellite, averaged over 2005.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410144, "type": "media_group", "extra_data": null, "title": "Nitrogen Dioxide from Aura/OMI, 2013-2014", "caption": "Major sources of tropospheric NO2 include industrial emissions, automobile traffic, forest and brush fires, microbiological soil emissions, lightning, and aircraft. More than half of the total NO2 emissions are estimated to be anthropogenic, mainly from the burning of fossil fuels for energy production, transportation, and industrial activities. NO2 has a relatively short lifetime (about a day) and is therefore concentrated near its sources.", "instance": { "id": 427745, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030000/a030014/omi_trop_no2_20140715_searchweb.png", "filename": "omi_trop_no2_20140715_searchweb.png", "media_type": "Image", "alt_text": "This yearlong timeseries of NO₂ from OMI run from July 2013 to July 2014.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410145, "type": "details_page", "extra_data": null, "instance": { "id": 30556, "url": "https://svs.gsfc.nasa.gov/30556/", "page_type": "Hyperwall Visual", "title": "Atmospheric CO₂ Trends", "description": "Fossil fuel combustion and other human activities are now increasing the atmospheric carbon dioxide (CO2) abundance to unprecedented rates. It is estimated that approximately 36 billion tons of CO2 are added to the atmosphere each year. The large graph shown here is an animated version of the standard Keeling curve from 1980 to September 2014. The red line denotes ground-based measurements from the Mauna Loa Observatory in Hawaii, while yellow denotes observations from the South Pole Observatory. Purple denotes the global trend. The smaller graph in the upper left shows satellite measurements of tropospheric CO2 concentrations (white dots) at different latitudes from September 2002 to September 2014, obtained by the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) instruments. Note how the Northern Hemisphere has greater variably and generally higher levels of CO2 than the Southern Hemisphere. In May of 2013, these emissions pushed the monthly average CO2 concentrations above 400 parts per million (ppm)—a level that has not been reached during the past 800,000 years. These ever-increasing levels are raising concerns about greenhouse-gas-induced climate change. || ", "release_date": "2014-12-10T00:00:00-05:00", "update_date": "2025-01-06T02:14:28.196859-05:00", "main_image": { "id": 1093701, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030500/a030556/pumphandle_hyper_2022_1080p.00001_print.jpg", "filename": "pumphandle_hyper_2022_1080p.00001_print.jpg", "media_type": "Image", "alt_text": "Full and complete visualization: the pump handle + Keeling data + Law dome and Siple ice core + Vostok and EPICA Dome C ice core", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410146, "type": "media_group", "extra_data": null, "title": "Simulated Sulfur Dioxide and Sulfate Aerosols", "caption": "Sulfur dioxide (SO2) is an atmospheric pollutant that poses significant threats to human health. High concentrations of SO2 irritate the eyes, nose, and lungs, and can result in temporary breathing impairment. It is also a precursor to sulfuric acid, a major constituent of acid rain. SO2 is produced by the combustion of coal, fuel oil, and gasoline (since these fuels contain sulfur), and in the oxidation of naturally occurring sulfur gases, such as in volcanic eruptions. Volcanic plumes, rich in ash and SO2, are a hazard to aviation. Emitted SO2 is oxidized to form sulfate aerosols that that can alter the brightness of clouds and precipitation. Sulfate aerosols persist for long periods of time and can contribute to climate change. This simulation, produced by the Goddard Earth Observing System Model Version 5 (GEOS-5), shows SO2 and sulfate aerosols at 7-kilometer resolution from September 1, 2006 to December 31, 2006. Simulations such as this allow scientists to better understand how SO2 and sulfate aerosols travel through the atmosphere and impact Earth’s climate.", "instance": { "id": 432490, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030600/a030641/sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z_searchweb.png", "filename": "sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z_searchweb.png", "media_type": "Image", "alt_text": "Sulfur and Sulfates animation of Sept 1 - Dec 31, 2006", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410147, "type": "media_group", "extra_data": null, "title": "Carbon Monoxide", "caption": "AIRS' global carbon monoxide measurements are important because scientists can monitor the transport of fire emissions around the globe on a daily basis. Previously, carbon monoxide measurements came from satellite instruments that saw only part of the Earth each day or from weather balloons. Prior to AIRS, scientists had to integrate those observations with computer models to infer the day-to-day impact of fire emissions on the atmosphere. AIRS provides daily, global coverage. AIRS also measures some of the key atmospheric gases that affect climate, including ozone, methane, and dust and other aerosols.

Tropospheric CO abundances are retrieved from the 4.67 m region of AIRS spectra as one of the last steps of the AIRS team algorithm. AIRS' 1600 km cross-track swath and cloud-clearing retrieval capabilities provide daily global CO maps over approximately 70% of the Earth.

The streak of red, orange, and yellow across South America, Africa, and the Atlantic Ocean in this animation points to high levels of carbon monoxide, as measured by the Atmospheric Infrared Sounder (AIRS) instrument flying on NASA's Aqua satellite. The carbon monoxide primarily comes from fires burning in the Amazon basin, with some additional contribution from fires in southern Africa. The animation shows carbon monoxide transport sweeping east throughout August, September, and October 2005.", "instance": { "id": 482200, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003800/a003882/airsdatecarbonmono_searchweb.png", "filename": "airsdatecarbonmono_searchweb.png", "media_type": "Image", "alt_text": "The streak of red, orange, and yellow across South America, Africa, and the Atlantic Ocean in this image points to high levels of carbon monoxide on September 30, 2005.This product is available through our Web Map Service.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410148, "type": "details_page", "extra_data": null, "instance": { "id": 30918, "url": "https://svs.gsfc.nasa.gov/30918/", "page_type": "Hyperwall Visual", "title": "Total Column Ozone from EP-TOMS and MERRA-2 GMI", "description": "Total Column Ozone from EP-TOMS and MERRA-2 GMIThe ozone layer is Earth’s protection from harmful ultraviolet radiation. NASA has a long history of measuring total column ozone using a variety of instruments, typically with polar orbiting satellites measuring backscattered solar radiation. This produces near global coverage over the course of a day over the sunlit portion of Earth. Some missing data occurs between swaths, over the polar region during winter, and during satellite outages. This animation shows the evolution of daily composites of total column ozone as observed with Earth Probe Total Ozone Mapping Spectrometer (EP-TOMS), on the right panel, from July 1, 2002 to Oct. 31, 2002. On the left panel is the total column ozone from the MERRA-2 GMI simulation, with hourly time resolution over the same time period. MERRA-2 GMI is a Goddard Earth Observing System version 5 (GEOS-5) “replay” simulation at 0.5° (~50km) horizontal resolution, driven by MERRA-2 reanalyzed winds, temperature, and pressure, coupled to the comprehensive Global Modeling Initiative (GMI) stratosphere-troposphere chemical mechanism. This animation shows the onset of the Antarctic ozone hole formation during austral winter of the dynamically active 2002 season and its breakdown during spring. In September 2002, the Antarctic polar vortex split into 2 lobes following the first and only observed major stratospheric warming in the Southern Hemisphere over our observational record. By combining NASA’s observations and chemistry simulations we have a clearer view of the evolution of Earth’s ozone layer over the recent past. || oman_toz_2002_pngs_1080.00001_print.jpg (1024x576) [117.1 KB] || oman_toz_2002_pngs_1080.00001_searchweb.png (320x180) [61.2 KB] || oman_toz_2002_pngs_1080.00001_web.png (320x180) [61.2 KB] || oman_toz_2002_pngs_1080.00001_thm.png (80x40) [6.0 KB] || oman_toz_2002_pngs_1080.webm (1920x1080) [10.5 MB] || oman_toz_2002_pngs_1080.mp4 (1920x1080) [187.7 MB] || ", "release_date": "2017-12-04T00:00:00-05:00", "update_date": "2025-01-06T02:53:47.702917-05:00", "main_image": { "id": 409224, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030900/a030918/oman_toz_2002_pngs_1080.00001_print.jpg", "filename": "oman_toz_2002_pngs_1080.00001_print.jpg", "media_type": "Image", "alt_text": "Total Column Ozone from EP-TOMS and MERRA-2 GMIThe ozone layer is Earth’s protection from harmful ultraviolet radiation. NASA has a long history of measuring total column ozone using a variety of instruments, typically with polar orbiting satellites measuring backscattered solar radiation. This produces near global coverage over the course of a day over the sunlit portion of Earth. Some missing data occurs between swaths, over the polar region during winter, and during satellite outages. This animation shows the evolution of daily composites of total column ozone as observed with Earth Probe Total Ozone Mapping Spectrometer (EP-TOMS), on the right panel, from July 1, 2002 to Oct. 31, 2002. On the left panel is the total column ozone from the MERRA-2 GMI simulation, with hourly time resolution over the same time period. MERRA-2 GMI is a Goddard Earth Observing System version 5 (GEOS-5) “replay” simulation at 0.5° (~50km) horizontal resolution, driven by MERRA-2 reanalyzed winds, temperature, and pressure, coupled to the comprehensive Global Modeling Initiative (GMI) stratosphere-troposphere chemical mechanism. This animation shows the onset of the Antarctic ozone hole formation during austral winter of the dynamically active 2002 season and its breakdown during spring. In September 2002, the Antarctic polar vortex split into 2 lobes following the first and only observed major stratospheric warming in the Southern Hemisphere over our observational record. By combining NASA’s observations and chemistry simulations we have a clearer view of the evolution of Earth’s ozone layer over the recent past.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410149, "type": "media_group", "extra_data": null, "title": "KORUS-AQ: Surface Ozone Levels Over the Korean Peninsula in June 2013", "caption": "These visuals were created in anticipation of the 2016 Korean United States Air Quality study (KORUS-AQ) field campaign which will combine observations from aircraft, satellties, ships and ground stations with air quality models to assess and monitor air quality acorss urban, rural and coastal areas.\n\nOzone gas and particle pollution are two of the main factors that contribute to poor air quality around the world. \n\nWhile ozone gas located high in the stratosphere protects us from the sun’s harmful UV rays, pollution from cars and other human emissions near ground level can cause chemical reactions that lead to ozone formation near the surface. Breathing in high levels of ozone is also bad for human health, causing lung diseases and health impacts on sensitive populations such as children, the elderly and people with asthma. \n\nThese visuals are showing the ozone that formed near the surface, or 'surface ozone', over the Korean peninsula in June 2013 according to the GEOS-5 Nature Run chemistry model data. Peak ozone in Korea occurs between April and June.\n\nSince Seoul is located on a peninsula, the metropolitan area and the pollution produced here are separated from other sources of emissions. In addition, Seoul’s human-produced emissions are concentrated in its urban areas but are surrounded by more rural agricultural areas. The contrast between urban and rural zones on the peninsula allow scientists to study and differentiate human and naturally-produced emissions and better understand how they interact chemically. Understanding the chemical reactions between urban and agricultural emissions is extremely important for improving models that forecast air quality.", "instance": { "id": 424871, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004447/O3_L72_betterOutlines_1080p30.00034_searchweb.png", "filename": "O3_L72_betterOutlines_1080p30.00034_searchweb.png", "media_type": "Image", "alt_text": "Surface ozone over Korean peninsula in June 2013.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410150, "type": "media_group", "extra_data": null, "title": "Hazardous Air Quality Conditions in Singapore", "caption": "Each year, peat fires start to burn in Indonesia because farmers engage in slash and burn agriculture—a technique that involves frequent burning of rainforest to clear the way for crops or grazing animals. The intent is often to make room for new plantings of oil palm and acacia pulp. In October 2015 more than 94,000 fires had burned across the island nation, affecting the health of millions of people in Indonesia, Malaysia, and Singapore. On September 24, 2015, dense haze carried by southerly winds was blown into Singapore. The PSI reading at 7:00 PM local time rose into the \"Hazardous\" range for the first time in 2015 with a reading of 313. It rose further to 317 at 8:00 PM, which prompted the Ministry of Education to close all primary and secondary schools on September 25. The haze deteriorated further by September 25, reaching a record high for the year at 5:00 AM with a reading of 341. Anti-pollution masks were distributed to the elderly and other vulnerable people. The smoke—which is an annual problem for the region—is a serious health hazard, especially for the elderly, children, and those with breathing problems.

\n\r\rThis set of images shows Singapore and the nearby region on May 29, 2015, when air quality conditions were normal, and on September 25, 2015, when a thick smoky haze covered the nation. Each image reveals a true-color image [top] from the Moderate Resolution Imaging Spectroradiometer (MODIS) and atmospheric cross-section [bottom] from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The CALIPSO image from September 25 reveals the thick layer of smoke (dark orange) in the atmosphere.", "instance": { "id": 433041, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030600/a030699/singapore_smog_24_1080p_searchweb.png", "filename": "singapore_smog_24_1080p_searchweb.png", "media_type": "Image", "alt_text": "Singapore region on September 24 and May 25, 2015, MODIS data only", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410151, "type": "media_group", "extra_data": null, "title": "The Earth Observing Fleet by Theme", "caption": "The current Earth Observing Fleet with all satellites capturing data related to Sea Ice Cover highlighted, combined with key visualizations showing the significance of the data", "instance": { "id": 413828, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030700/a030781/fleet_data_atmo_chem_1080p.00001_searchweb.png", "filename": "fleet_data_atmo_chem_1080p.00001_searchweb.png", "media_type": "Image", "alt_text": "The current Earth Observing Fleet with all satellites capturing data related to Aerosols & Atmospheric Chemistry highlighted, combined with key visualizations showing the significance of the data", "width": 180, "height": 320, "pixels": 57600 } } ], "extra_data": {} }, { "id": 371239, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/#media_group_371239", "widget": "Tile gallery", "title": "Biosphere & Population", "caption": "", "description": "", "items": [ { "id": 410152, "type": "media_group", "extra_data": null, "title": "20 Years of Global Biosphere (updated)", "caption": "By monitoring the color of reflected light via satellite, scientists can determine how successfully plant life is photosynthesizing. A measurement of photosynthesis is essentially a measurement of successful growth, and growth means successful use of ambient carbon. This data visualization represents twenty years' worth of data taken primarily by SeaStar/SeaWiFS, Aqua/MODIS, and Suomi NPP/VIIRS satellite sensors, showing the abundance of life both on land and in the sea. In the ocean, dark blue to violet represents warmer areas where there is little life due to lack of nutrients, and greens and reds represent cooler nutrient-rich areas. The nutrient-rich areas include coastal regions where cold water rises from the sea floor bringing nutrients along and areas at the mouths of rivers where the rivers have brought nutrients into the ocean from the land. On land, green represents areas of abundant plant life, such as forests and grasslands, while tan and white represent areas where plant life is sparse or non-existent, such as the deserts in Africa and the Middle East and snow-cover and ice at the poles.", "instance": { "id": 551528, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004596/biosphere7_mollweide.4507_searchweb.png", "filename": "biosphere7_mollweide.4507_searchweb.png", "media_type": "Image", "alt_text": "This Mollweide projected data visualization shows 20 years of Earth's biosphere starting in September 1997 going through September 2017. Data for this visualization was collected from multiple satellites over the past twenty years.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410153, "type": "media_group", "extra_data": null, "title": "Watching the Earth Breathe:
An Animation of Seasonal Vegetation and its effect on Earth's Global Atmospheric Carbon Dioxide", "caption": "In this animation, NASA instruments show the seasonal cycle of vegetation and the concentration of carbon dioxide in the atmosphere. The animation begins on January 1, when the northern hemisphere is in winter and the southern hemisphere is in summer. At this time of year, the bulk of living vegetation, shown in green, hovers around the equator and below it, in the southern hemisphere.

As the animation plays forward through mid-April, the concentration of carbon dioxide, shown in orange-yellow, in the middle part of Earth's lowest atmospheric layer, the troposphere, increases and spreads throughout the northern hemisphere, reaching a maximum around May. This blooming effect of carbon dioxide follows the seasonal changes that occur in northern latitude ecosystems, in which deciduous trees lose their leaves, resulting in a net release of carbon dioxide through a process called respiration. Carbon dioxide is also released in early spring as soils begin to warm. Almost 10 percent of atmospheric carbon dioxide passes through soils each year.

After April, the northern hemisphere moves into late spring and summer and plants begin to grow, reaching a peak in the late summer. The process of plant photosynthesis removes carbon dioxide from the air. The animation shows how carbon dioxide is scrubbed out of the atmosphere by the large volume of new and growing vegetation. Following the peak in vegetation, the drawdown of atmospheric carbon dioxide due to photosynthesis becomes apparent, particularly over the boreal forests.

Note that there is roughly a three-month lag between the state of vegetation at Earth's surface and its effect on carbon dioxide in the middle troposphere.

Data like these give scientists a new opportunity to better understand the relationships between carbon dioxide in Earth's middle troposphere and the seasonal cycle of vegetation near the surface.

Creating the Animation

This animation was created with data taken from two NASA spaceborne instruments. The concentration of carbon dioxide data from the Atmospheric Infrared Sounder (AIRS), a weather and climate instrument that flies aboard NASA's Aqua spacecraft, is overlain on measurements of vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, also on NASA's Aqua spacecraft, to better understand how photosynthesis and respiration influences the atmospheric carbon dioxide cycle over the globe. The animation runs from January through December and repeats. The AIRS tropospheric carbon dioxide seasonal cycle values were made by averaging AIRS data collected between 2003 and 2010, from which the annual carbon dioxide growth trend of 2 parts per million per year has been removed. For example, the data used for January 1 is actually an average of eight years of AIRS carbon dioxide data taken each year on January 1. The vegetation values were made using data averaged over a four-year period, from 2003 to 2006.

Further Detail

AIRS uses infrared technology to determine the concentration of atmospheric water vapor and several important trace gases as well as information about temperature and clouds. AIRS orbits Earth from pole-to-pole at an altitude of 438 miles (705 kilometers), measuring Earth's infrared spectrum in 3,278 channels spanning a wavelength range from 3.74 microns to 15.4 microns. Originally designed to improve weather forecasts, AIRS has improved operational five-day weather forecasts more than any other single instrument over the past decade. AIRS has also been found to be sensitive to atmospheric carbon dioxide in the middle troposphere, at an altitude of 5 to 10 kilometers or 3 to 6 miles. AIRS is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif., under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena. For further information, access the AIRS project

The MODIS instrument is managed by NASA's Goddard Space Flight Center, Greenbelt, Md. For further information, access the MODIS project.", "instance": { "id": 474844, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003900/a003947/airsc02_land_connectionV070244_web.png", "filename": "airsc02_land_connectionV070244_web.png", "media_type": "Image", "alt_text": "The concentration of CO2 measured by AIRS is overlain on measurements of vegetation index from the Moderate Resolution Imaging Spectroradiaometer (MODIS), also on the Aqua spacecraft, in an effort to understand the influence of photosynthesis and respiration on the atmospheric CO2 cycle over the globe. The AIRS tropospheric CO2 seasonal cycle displayed is an average over 8 years of AIRS data, from which the annual growth trend of 2 ppm/year has been removed. The animation shows the buildup of tropospheric CO2 in the Northern Hemisphere with a maximum around May. The maximum in the vegetation cycle follows, occurring in the late summer. Following the peak in vegetation, the drawdown of atmospheric CO2 due to photosynthesis is apparent, particularly over the Boreal Forests.This video is also available on our YouTube channel.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410154, "type": "media_group", "extra_data": null, "title": "Vegetation Greening Trend in Canada and Alaska: 1984-2012", "caption": "High-latitude regions have been warming rapidly since the last century, at a rate higher than the global average. At continental scales, satellite data since the 1980s have indicated increased vegetation productivity (greening) across northern high latitudes, and a productivity decline (browning) for certain areas of undisturbed boreal forest of Canada and Alaska. These remote sensing results have been corroborated by in-situ evidence. \n \nThis research provides a spatially complete view of the vegetation greenness change for all of Canada and Alaska by calculating per-pixel NDVI trend from all available 1984–2012 peak-summer Landsat-5 and -7 surface reflectance data. By incorporating observations from overlapping scenes, researchers obtained up to 160 valid NDVI values for certain areas from this 29-year period, establishing the mid-Summer greenness trend. \n\nThis animation shows the resulting greenness trend over Canada and Alaska with special attention focused on the regions of Quebec and northern Alaska.", "instance": { "id": 424239, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004452/AG_v0020_Final.3975_searchweb.png", "filename": "AG_v0020_Final.3975_searchweb.png", "media_type": "Image", "alt_text": "This animation examines the change in the vegetation trend over Canada and Alaska between 1984 and 2012.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410155, "type": "media_group", "extra_data": null, "title": "Monitoring Chimpanzee Habitats in western Tanzania", "caption": "In partnership with the Jane Goodall Institute (JGI), NASA scientists have provided assistance in the monitoring, forecasting and conservation of natural resources in regions surrounding the Gombe National Park in Tanzania. Between 1972 and 1999, significant deforestation had occurred in the regions outside the boundary of the Gombe National Park to the detriment of the park's chimpanzee population as well as to the that of the villagers living in the region. \n\nIn 2005, JGI initiated a forest monitoring program training and in 2009 equipped community members with GPS-enabled Android smart phones and tablets to report their observations on forests threats and wildlife. Combining NASA remote sensing data with citizen science observations facilitated local people to develop and implement land use plans, leading to improved decision making and facilitating the establishment of village forest reserves.\n\nIn 2016, the Metropolitan Group developed a 4.5-minute video detailing the story of this collaboration between NASA and JGI. The video shows footage of project activities in Tanzania along with planning meetings using DigitalGlobe and NASA satellite data. The animations shown here were developed to support this production. The complete video is available here.", "instance": { "id": 422306, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004483/Gombe_zoomOut.3050_searchweb.png", "filename": "Gombe_zoomOut.3050_searchweb.png", "media_type": "Image", "alt_text": "This visualization pulls out from Tanzania to show global view of the planet.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410156, "type": "media_group", "extra_data": null, "title": "NOAA Coral Reef Watch 2015", "caption": "The NOAA Coral Reef Watch program's satellite data provide current reef environmental conditions to quickly identify areas at risk for coral bleaching, where corals lose the symbiotic algae that give them their distinctive colors. If a coral is severely bleached, disease and partial mortality become likely, and the entire colony may die.\n\nThe satellite data used to create these products includes the polar orbiters Suomi-NPP/VIIRS and MetOp-B/AVHRR, and the geostationary satellites MSG-3, MTSAT-2, GOES-East, and GOES-West.", "instance": { "id": 433250, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030700/a030728/coral_reef_bleaching_alert_searchweb.png", "filename": "coral_reef_bleaching_alert_searchweb.png", "media_type": "Image", "alt_text": "The NOAA Coral Reef Watch (CRW) daily 5-km satellite Bleaching Alert Area product presented here outlines the areas where coral bleaching thermal stress currently reaches various bleaching stress levels, based on our satellite sea surface temperature (SST) monitoring. ", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410157, "type": "media_group", "extra_data": null, "title": "Five-Year Global Temperature Anomalies from 1880 to 2016", "caption": "Earth’s 2016 surface temperatures were the warmest since modern recordkeeping began in 1880, according to independent analyses by NASA and the National Oceanic and Atmospheric Administration (NOAA).\n\nGlobally-averaged temperatures in 2016 were 1.78 degrees Fahrenheit (0.99 degrees Celsius) warmer than the mid-20th century mean. This makes 2016 the third year in a row to set a new record for global average surface temperatures. \n\nThe 2016 temperatures continue a long-term warming trend, according to analyses by scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York. NOAA scientists concur with the finding that 2016 was the warmest year on record based on separate, independent analyses of the data. \n\nBecause weather station locations and measurement practices change over time, there are uncertainties in the interpretation of specific year-to-year global mean temperature differences. However, even taking this into account, NASA estimates 2016 was the warmest year with greater than 95 percent certainty. \n\n“2016 is remarkably the third record year in a row in this series,” said GISS Director Gavin Schmidt. “We don’t expect record years every year, but the ongoing long-term warming trend is clear.”\n\nThe planet’s average surface temperature has risen about 2.0 degrees Fahrenheit (1.1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide and other human-made emissions into the atmosphere.\n\nMost of the warming occurred in the past 35 years, with 16 of the 17 warmest years on record occurring since 2001. Not only was 2016 the warmest year on record, but eight of the 12 months that make up the year – from January through September, with the exception of June – were the warmest on record for those respective months. October and November of 2016 were the second warmest of those months on record – in both cases, behind records set in 2015.\n\nPhenomena such as El Niño or La Niña, which warm or cool the upper tropical Pacific Ocean and cause corresponding variations in global wind and weather patterns, contribute to short-term variations in global average temperature. A warming El Niño event was in effect for most of 2015 and the first third of 2016. Researchers estimate the direct impact of the natural El Nino warming in the tropical Pacific increased the annual global temperature anomaly for 2016 by 0.2 degrees Fahrenheit (0.12 degrees Celsius).\n\nWeather dynamics often affect regional temperatures, so not every region on Earth experienced record average temperatures last year. For example, both NASA and NOAA found the 2016 annual mean temperature for the contiguous 48 United States was the second warmest on record. In contrast, the Arctic experienced its warmest year ever, consistent with record low sea ice found in that region for most of the year.\n\nNASA’s analyses incorporate surface temperature measurements from 6,300 weather stations, ship- and buoy-based observations of sea surface temperatures, and temperature measurements from Antarctic research stations. These raw measurements are analyzed using an algorithm that considers the varied spacing of temperature stations around the globe and urban heating effects that could skew the conclusions. The result of these calculations is an estimate of the global average temperature difference from a baseline period of 1951 to 1980.\n\nNOAA scientists used much of the same raw temperature data, but with a different baseline period, and different methods to analyze Earth’s polar regions and global temperatures.\n\nGISS is a laboratory within the Earth Sciences Division of NASA’s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University’s Earth Institute and School of Engineering and Applied Science in New York.\n\nNASA monitors Earth's vital signs from land, air and space with a fleet of satellites, as well as airborne and ground-based observation campaigns. The agency develops new ways to observe and study Earth's interconnected natural systems with long-term data records and computer analysis tools to better see how our planet is changing. NASA shares this unique knowledge with the global community and works with institutions in the United States and around the world that contribute to understanding and protecting our home planet.\n\nThe full 2016 surface temperature data set and the complete methodology used to make the temperature calculation are available at: http://data.giss.nasa.gov/gistemp", "instance": { "id": 417031, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004546/robinson2_1213_searchweb.png", "filename": "robinson2_1213_searchweb.png", "media_type": "Image", "alt_text": "This color-coded map displays a progression of changing global surface temperatures anomalies from 1880 through 2016. The final frame represents global temperature anomalies averaged from 2012 through 2016 in degrees Celsius.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410158, "type": "media_group", "extra_data": null, "title": "Black Marble 2016", "caption": "Satellite images of Earth at night—often referred to as \"night lights\"—have been a gee-whiz curiosity for the public and a tool for fundamental research for nearly 25 years. They have provided a broad, beautiful picture, showing how humans have shaped the planet and lit up the darkness. Produced every decade or so, such maps have spawned hundreds of pop-culture uses and dozens of economic, social science, and environmental research projects.

\r\rThis image of Earth at night in 2016 was created with data from the Suomi National Polar-orbiting Partnership (NPP) satellite launched in October 2011 by NASA, the National Oceanic and Atmospheric Administration, and the U.S. Department of Defense. Each pixel shows roughly 0.46 miles (742 meters) across.

\r\rScientists use the Suomi NPP night-lights dataset in many ways. Some applications include: forecasting a city’s energy use and carbon emissions; eradicating energy poverty and fostering sustainable energy development; providing immediate information when disasters strike; and monitoring the effects of conflict and population displacement. Scientists at NASA are working to automate nighttime VIIRS data processing so that data users are able to view nighttime imagery within hours of acquisition, which could lead to other potential uses by research, meteorological, and civic groups.", "instance": { "id": 414843, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030876/BlackMarble_2016_global_7km_searchweb.png", "filename": "BlackMarble_2016_global_7km_searchweb.png", "media_type": "Image", "alt_text": "An composite image shows a cloud-free view of Earth", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410159, "type": "media_group", "extra_data": null, "title": "Black Marble 2016 (Regions)", "caption": "Satellite images of Earth at night—often referred to as \"night lights\"—have been a gee-whiz curiosity for the public and a tool for fundamental research for nearly 25 years. They have provided a broad, beautiful picture, showing how humans have shaped the planet and lit up the darkness. Produced every decade or so, such maps have spawned hundreds of pop-culture uses and dozens of economic, social science, and environmental research projects.

\r\n\n\rThis region of the a global image of Earth at night in 2016 was created with data from the Suomi National Polar-orbiting Partnership (NPP) satellite launched in October 2011 by NASA, the National Oceanic and Atmospheric Administration, and the U.S. Department of Defense. Each pixel shows roughly 0.46 miles (742 meters) across. Other regional images are available.\n\n

\r\rScientists use the Suomi NPP night-lights dataset in many ways. Some applications include: forecasting a city’s energy use and carbon emissions; eradicating energy poverty and fostering sustainable energy development; providing immediate information when disasters strike; and monitoring the effects of conflict and population displacement. Scientists at NASA are working to automate nighttime VIIRS data processing so that data users are able to view nighttime imagery within hours of acquisition, which could lead to other potential uses by research, meteorological, and civic groups.", "instance": { "id": 414848, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030877/BlackMarble_2016_928m_mediterranean_labeled_searchweb.png", "filename": "BlackMarble_2016_928m_mediterranean_labeled_searchweb.png", "media_type": "Image", "alt_text": "A section of the Black Marble 2016 image centered on the Mediterranean.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410160, "type": "media_group", "extra_data": null, "title": "A Changing Earth at Night", "caption": "This image shows the change in lighting intensity from 2012 to 2016. The map was created using two separate night lights datasets (from 2012 and 2016) derived using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the National Oceanic and Atmospheric Administration (NOAA)-NASA Suomi National Polar-orbiting Partnership (NPP) satellite. Each pixel represents 500 meters (1640 feet), or approximately six city blocks. Dark purple represents areas with new light since 2012, while dark orange represents areas where light existed in 2012 but no longer exists in 2016. Areas where lighting intensity stayed the same between 2012 and 2016 appear white. Varying shades of purple and orange indicate areas that have become brighter or dimmer since 2012, respectively.\r\n\r\nScientists use the Suomi NPP night lights dataset in many ways. Some applications include: forecasting a city’s energy use and carbon emissions, eradicating energy poverty and fostering sustainable energy development, providing immediate information when disasters strike, and monitoring the effects of conflict and population displacement.\r\n\r\nIn recent years, India has undergone rapid electrification (purple). In Syria, six years of war have had a devastating effect on millions of its people. One of the most catastrophic impacts has been on the country’s electricity network. Lights have gone out (orange) during the course of the conflict, leaving people to survive with little to no power. In Nigeria, light from gas flaring activity decreased from 2012 to 2016 (orange), largely due to international agreements acted on by the country.", "instance": { "id": 409186, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030900/a030919/BlackMarble20162012diff500m_cb_searchweb.png", "filename": "BlackMarble20162012diff500m_cb_searchweb.png", "media_type": "Image", "alt_text": "Changes in lights from 2012 to 2016", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410161, "type": "media_group", "extra_data": null, "title": "Urban Growth in Las Vegas", "caption": "The city of Las Vegas—meaning the meadows—was established in 1905. Its grassy meadows and artesian springs attracted settlers traveling across the arid Desert Southwest in the early 1800s. In the 1930s, gambling became legalized and construction of the Hoover Dam began, resulting in the city's first growth spurt. Since then, Las Vegas has not stopped growing. Population has reached nearly two million over the past decade, becoming one of the fastest growing metropolitan areas in the world. These false-color images show the rapid urbanization of Las Vegas between 1984 and 2014. The city streets and other impervious surfaces appear gray, while irrigated vegetation appears red. Over the years, the expansion of irrigated vegetation (e.g., lawns and golf courses) has stretched the city’s desert bounds.", "instance": { "id": 397078, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030200/a030215/landsat_las_vegas_animation_searchweb.png", "filename": "landsat_las_vegas_animation_searchweb.png", "media_type": "Image", "alt_text": "Animation of timeseries of Landsat data of Las Vegas", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410162, "type": "media_group", "extra_data": null, "title": "Sprawling Shanghai", "caption": "The surge in urbanization began in the 1980s when the Chinese government began opening the country to foreign trade and investment. As markets developed in “special economic zones,” villages morphed into booming cities and cities grew into sprawling megalopolises.\n\nPerhaps no city epitomizes the trend better than Shanghai. What had been a relatively compact industrial city of 12 million people in 1982 had swollen to 24 million in 2016, making it one of the largest metropolitan areas in the world.\n\nFor more than four decades, Landsat satellites have collected images of Shanghai. These composite images show how cities in the Yangtze River Delta have expanded since 1984. \n\nThese “best-pixel mosaics” are made up of small parts of many images captured over five-year periods. The first image is a mosaic of scenes captured between 1984 and 1988; the second shows the best pixels captured between 2013 and 2017. This technique makes it possible to strip away clouds and haze, which are common in Shanghai.\n\nA 2015 World Bank report noted that 7,734 square kilometers in the Yangtze River Delta Economic Zone—which includes Shanghai, Suzhou, Wuxi, and several other cities—became urban between 2000 and 2010. That is an area equivalent to 88 Manhattans. During that period, population in that zone increased by 21 million people.", "instance": { "id": 858819, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030874/woc_shanghai_1080p.00001_searchweb.png", "filename": "woc_shanghai_1080p.00001_searchweb.png", "media_type": "Image", "alt_text": "The surge in urbanization began in the 1980s when the Chinese government began opening the country to foreign trade and investment. As markets developed in “special economic zones,” villages morphed into booming cities and cities grew into sprawling megalopolises.\n\nPerhaps no city epitomizes the trend better than Shanghai. What had been a relatively compact industrial city of 12 million people in 1982 had swollen to 24 million in 2016, making it one of the largest metropolitan areas in the world.\n\nFor more than four decades, Landsat satellites have collected images of Shanghai. These composite images show how cities in the Yangtze River Delta have expanded since 1984. \n\nThese “best-pixel mosaics” are made up of small parts of many images captured over five-year periods. The first image is a mosaic of scenes captured between 1984 and 1988; the second shows the best pixels captured between 2013 and 2017. This technique makes it possible to strip away clouds and haze, which are common in Shanghai.\n\nA 2015 World Bank report noted that 7,734 square kilometers in the Yangtze River Delta Economic Zone—which includes Shanghai, Suzhou, Wuxi, and several other cities—became urban between 2000 and 2010. That is an area equivalent to 88 Manhattans. During that period, population in that zone increased by 21 million people.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410163, "type": "media_group", "extra_data": null, "title": "Mountaintop Mining, West Virginia", "caption": "These images illustrate the growth of the Hobet mine in Boone County, WV as it moves from ridge to ridge between 1984 and 2015. The natural forested landscape appears dark green, creased by steams and indented by hollows. Active mining areas, however, appear off-white and areas being reclaimed with vegetation appear light green. The law requires coal operators to restore the land to its approximate original shape, but the rock debris generally can’t be securely piled as high or graded as steeply as the original mountaintop. There is always too much rock left over, and coal companies dispose of it by building valley fills in hollows, gullies, and streams. While the image from 2015 shows apparent green-up of restored lands, it also shows expanded operations in the west. The resulting impacts to stream biodiversity, forest health, and ground-water quality are high, and may be irreversible.", "instance": { "id": 428410, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030000/a030059/mountaintop_mining_1984-2012_720p_web.png", "filename": "mountaintop_mining_1984-2012_720p_web.png", "media_type": "Image", "alt_text": "This time-series illustrates the growth of the Hobet mine in Boone County, WV as it moves from ridge to ridge between 1984 and 2015.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410164, "type": "media_group", "extra_data": null, "title": "Using Satellite and Ground-based Data to Develop Malaria Risk Maps", "caption": "Malaria is a major problem in the Amazon where malaria mosquitoes tend to prefer wet, hot areas with more standing water. Seasonal occupational movement along rivers and in forested areas increases transmission and concentrates malaria in specific regions.\r

\rThe objective of Malaria Project, an ongoing study led by William Pan and Ben Zaitchik, is to develop a detection and early warning system for malaria risk in the Amazon. Using data from NASA satellites and a Land Data Assimilation System (LDAS), the scientists hope that their research can help health officials pinpoint where to deploy resources and what resources to deploy during a disease outbreak. \r

\rBy incorporating NASA data such as precipitation, soil moisture, air temperature, and humidity into their new system, scientists are better able to predict where malaria-spreading mosquitoes are breeding. These climate factors in conjunction with a population density and human movement model will help scientists better understand where and when people are at high risk for malaria. The malaria warning system will predict outbreaks and simulate response to help a country's health care system to more strategically determine where to deploy their resources. \r

\rVisualizations focus on Peru, one of the central areas of malaria transmission in the Amazon. Four LDAS data sets -- precipitation, soil moisture, air temperature, and humidity are illustrated below. Combined with public health data, the animations show how these factors may affect the outbreak and evolvement of the disease.", "instance": { "id": 412878, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004581/comp_malariaCase_mask_mmddyy_1080p30.00302_searchweb.png", "filename": "comp_malariaCase_mask_mmddyy_1080p30.00302_searchweb.png", "media_type": "Image", "alt_text": "Locations in Peru where malaria cases were reported in 2005", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410165, "type": "media_group", "extra_data": null, "title": "Mosquito Spread and Health", "caption": "An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti. A group of researchers (some from NASA) employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact. The results show that meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (Dec-Mar), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (Jul-Sep), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. \r\rThis map shows 1) Aedes aegypti potential abundance for Jan/July (colored circles), 2) approximate maximum known range of Aedes aegypti (shaded regions) and Aedes albopictus (gray dashed lines), and 3) monthly average number arrivals to the U.S. by air and land from countries on the CDC Zika travel advisory.", "instance": { "id": 419171, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030824/gupta_slide_5_1080p.00001_searchweb.png", "filename": "gupta_slide_5_1080p.00001_searchweb.png", "media_type": "Image", "alt_text": "Seasonal Occurrence and Abundance of Zika Virus Vector Mosquito ", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410166, "type": "media_group", "extra_data": null, "title": "Malaria Modeling and Transmission", "caption": "Despite efforts to eradicate malaria in the 1950s and 1960s, the disease has remained endemic in many tropical areas of the world. Because wide spread spraying of insecticides to control the mosquitos which spread malaria is costly and damaging to the environment, better methods of mosquito control are needed. \n\nOne approach to solving this problem involves modeling the lifecycle of the mosquitos which transmit malaria in order to predict when and where they will be most abundant. Remote sensing data, such as maps of temperature, vegetation, and rainfall are combined with models of the mosquito's lifecycle in order to predict where the mosquitos will live and breed. Insecticide and other treatments can then be targeted to those times and areas when they will have the most effect.", "instance": { "id": 431575, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030500/a030593/malaria_modeling_w_sat_data_searchweb.png", "filename": "malaria_modeling_w_sat_data_searchweb.png", "media_type": "Image", "alt_text": "Remote sensing data products which are input into malaria model.", "width": 180, "height": 320, "pixels": 57600 } } ], "extra_data": {} }, { "id": 371240, "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/#media_group_371240", "widget": "Tile gallery", "title": "Disasters", "caption": "", "description": "", "items": [ { "id": 410167, "type": "details_page", "extra_data": null, "instance": { "id": 4631, "url": "https://svs.gsfc.nasa.gov/4631/", "page_type": "Visualization", "title": "Global Landslide Hazard Assessment Model (LHASA) with Global Landslide Catalog (GLC) data", "description": "Landslides occur when an environmental trigger like an extreme rain event, often a severe storm or hurricane, and gravity's downward pull sets soil and rock in motion. Conditions beneath the surface are often unstable already, so the heavy rains act as the last straw that causes mud, rocks, or debris- or all combined- to move rapidly down mountains and hillsides. Unfortunately, people and property are often swept up in these unexpected mass movements. Landslides can also be caused by earthquakes, surface freezing and thawing, ice melt, the collapse of groundwater reservoirs, volcanic eruptions, and erosion at the base of a slope from the flow of river or ocean water. But torrential rains most commonly activate landslides. A new model has been developed to look at how potential landslide activity is changing around the world. A global Landslide Hazard Assessment model for Situational Awareness (LHASA) has been developed to provide an indication of where and when landslides may be likely around the world every 30min. This model uses surface susceptibility (including slope, vegetation, road networks, geology, and forest cover loss) and satellite rainfall data from the Global Precipitation Measurement (GPM) mission to provide moderate to high “nowcasts.” This visualization shows the landslide nowcast results leveraging nearly two decades of Tropical Rainfall Measurement Mission (TRMM) rainfall over 2001-2016 to identify a landslide climatology by month at a 1 km grid cell. The average nowcast values by month highlight the key landslide hotspots, such as the Southeast Asia during the monsoon season in June through August and the U.S. Pacific Northwest in December and January. Overlaid with these nowcasts values are a Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impact, or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The visualization shows the distribution of landslides each month based on the estimated number of fatalities the event caused. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center and contains over 11,000 reports and growing. A new project called the Community the Cooperative Open Online Landslide Repository, or COOLR, provides the opportunity for the community to view landslide reports and contribute their own. The goal of the COOLR project is to create the largest global public online landslide catalog available and open to for anyone everyone to share, download, and analyze landslide information. More information on this system is available at: https://landslides.nasa.govThe Global Landslide Catalog is currently available here: https://catalog.data.gov/dataset/global-landslide-catalog-export || ", "release_date": "2018-04-26T10:00:00-04:00", "update_date": "2025-02-02T00:10:44.587766-05:00", "main_image": { "id": 405649, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004631/01_ClimatologyMonthly_Fatalities_1920x1080_00000_print.jpg", "filename": "01_ClimatologyMonthly_Fatalities_1920x1080_00000_print.jpg", "media_type": "Image", "alt_text": "This set of 12 still images showcases the landslide climatology by month overlaid with the distribution of landslides each month based on the estimated number of fatalities the event caused. The estimated number of fatalities is based on values from the Global Landslide Catalog (GLC) for the period 2007-2017.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410168, "type": "details_page", "extra_data": null, "instance": { "id": 12897, "url": "https://svs.gsfc.nasa.gov/12897/", "page_type": "Produced Video", "title": "New NASA Model Finds Landslide Threats in Near Real-Time During Heavy Rains", "description": "A new model has been developed to look at how potential landslide activity is changing around the world. A global Landslide Hazard Assessment model for Situational Awareness (LHASA) has been developed to provide an indication of where and when landslides may be likely around the world every 30 minutes. This model uses surface susceptibility (including slope, vegetation, road networks, geology, and forest cover loss) and satellite rainfall data from the Global Precipitation Measurement (GPM) mission to provide moderate to high “nowcasts.” This visualization shows the landslide nowcast results leveraging nearly two decades of Tropical Rainfall Measurement Mission (TRMM) rainfall over 2001-2016 to identify a landslide climatology by month at a 1 km grid cell. The average nowcast values by month highlight the key landslide hotspots, such as the Southeast Asia during the monsoon season in June through August and the U.S. Pacific Northwest in December and January. Overlaid with these nowcasts values are a Global Landslide Catalog(GLC) that was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impact, or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The visualization shows the distribution of landslides each month based on the estimated number of fatalities the event caused. The GLC has been compiled since 2007 at NASA's Goddard Space Flight Center and contains over 11,000 reports and growing. A new project called the Community the Cooperative Open Online Landslide Repository, or COOLR, provides the opportunity for the community to view landslide reports and contribute their own. The goal of the COOLR project is to create the largest global public online landslide catalog available and open to for anyone everyone to share, download, and analyze landslide information. More information on this system is available at: https://landslides.nasa.gov. Landslides occur when an environmental trigger like an extreme rain event, often a severe storm or hurricane, and gravity's downward pull sets soil and rock in motion. Conditions beneath the surface are often unstable already, so the heavy rains act as the last straw that causes mud, rocks, or debris- or all combined- to move rapidly down mountains and hillsides. Unfortunately, people and property are often swept up in these unexpected mass movements. Landslides can also be caused by earthquakes, surface freezing and thawing, ice melt, the collapse of groundwater reservoirs, volcanic eruptions, and erosion at the base of a slope from the flow of river or ocean water. But torrential rains most commonly activate landslides.For more information: https://www.nasa.gov/feature/goddard/2018/new-from-nasa-tracking-landslide-hazards-new-nasa-model-finds-landslide-threats-in-near-real || ", "release_date": "2018-03-22T10:30:00-04:00", "update_date": "2025-02-02T00:22:14.206492-05:00", "main_image": { "id": 405741, "url": "https://svs.gsfc.nasa.gov/vis/a010000/a012800/a012897/LARGE_MP4_3-19_CLIMATOLOGY_ONLY_1.5sec_LOOPED_1080_large.00001_print.jpg", "filename": "LARGE_MP4_3-19_CLIMATOLOGY_ONLY_1.5sec_LOOPED_1080_large.00001_print.jpg", "media_type": "Image", "alt_text": "Data visualizationThis version shows only the landslide climatology (no overlaid fatalities) in order to show seasonality. This version loops two times.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410169, "type": "details_page", "extra_data": null, "instance": { "id": 4575, "url": "https://svs.gsfc.nasa.gov/4575/", "page_type": "Visualization", "title": "NASA Studies Hurricane Matthew", "description": "This data visualization follows Hurricane Matthew throughout its destructive run in the Caribbean and Southeast U.S. coast. By utilizing different data sets from NOAA's GOES satellite, NASA/JAXA's GPM, MERRA-2 model runs, IMERG, Goddard's soil moisture product, and sea surface temperatures, scientists are able to put together a clearer picture of how this hurricane quickly intensified and eventually weakened. || matthew_narrated_v106.5800_print.jpg (1024x576) [189.6 KB] || matthew_narrated_v106.5800_searchweb.png (320x180) [114.8 KB] || matthew_narrated_v106.5800_thm.png (80x40) [7.8 KB] || matthew (1920x1080) [0 Item(s)] || matthew_narrated_v106.webm (1920x1080) [22.0 MB] || matthew_narrated_v106.mp4 (1920x1080) [140.5 MB] || 3840x2160_16x9_30p (3840x2160) [0 Item(s)] || matthew_narrated_v106_4k.mp4 (3840x2160) [443.1 MB] || matthew_narrated_nosound.hwshow || ", "release_date": "2017-07-31T00:00:00-04:00", "update_date": "2025-02-02T00:09:53.910126-05:00", "main_image": { "id": 413735, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004575/matthew_narrated_v106.5800_print.jpg", "filename": "matthew_narrated_v106.5800_print.jpg", "media_type": "Image", "alt_text": "This data visualization follows Hurricane Matthew throughout its destructive run in the Caribbean and Southeast U.S. coast. By utilizing different data sets from NOAA's GOES satellite, NASA/JAXA's GPM, MERRA-2 model runs, IMERG, Goddard's soil moisture product, and sea surface temperatures, scientists are able to put together a clearer picture of how this hurricane quickly intensified and eventually weakened.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410170, "type": "details_page", "extra_data": null, "instance": { "id": 4543, "url": "https://svs.gsfc.nasa.gov/4543/", "page_type": "Visualization", "title": "Monitoring Hurricane Matthew", "description": "This example visualization shows how all of the below data visualizations could be arranged on NASA's 3x3 hyperwall display. || MatthewHyperwall9.01110_print.jpg (1024x576) [227.7 KB] || MatthewHyperwall9.01110_searchweb.png (320x180) [116.5 KB] || MatthewHyperwall9.01110_thm.png (80x40) [8.0 KB] || MatthewHyperwall9.mp4 (1920x1080) [61.9 MB] || MatthewHyperwall9.webm (1920x1080) [4.8 MB] || MatthewHyperwall9_4543.key [64.9 MB] || MatthewHyperwall9_4543.pptx [64.4 MB] || MatthewHyperwall9.mp4.hwshow [206 bytes] || ", "release_date": "2017-01-23T00:00:00-05:00", "update_date": "2025-02-02T00:09:21.049613-05:00", "main_image": { "id": 420300, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004543/MatthewHyperwall9.01110_print.jpg", "filename": "MatthewHyperwall9.01110_print.jpg", "media_type": "Image", "alt_text": "This example visualization shows how all of the below data visualizations could be arranged on NASA's 3x3 hyperwall display.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410171, "type": "media_group", "extra_data": null, "title": "Pinpointing Where the Lights Went Out in Puerto Rico", "caption": "After Hurricane Maria tore across Puerto Rico, it quickly became clear that the destruction would pose daunting challenges for first responders. Most of the electric power grid and telecommunications network was knocked offline. Flooding, downed trees, and toppled power lines made many roads impassable.

\r\n\r\nThese before-and-after images of Puerto Rico’s nighttime lights are based on data captured by the Suomi NPP satellite. The data were acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) “day-night band,” which detects light in a range of wavelengths from green to near-infrared, including reflected moonlight, light from fires and oil wells, lightning, and emissions from cities or other human activity.

\r\n\r\nOne pair of images shows differences in lighting across the entire island, while the other pair shows lighting around San Juan, capital of the commonwealth. One image in each pair shows a typical night before Maria made landfall, based upon cloud-free and low moonlight conditions; the second image is a composite that shows light detected by VIIRS on the nights of September 27 and 28, 2017. By compositing two nights, the image has fewer clouds blocking the view. (Note: some clouds still blocked light emissions during the two nights, especially across southeastern and western Puerto Rico.) The images show widespread outages around San Juan, including key hospital and transportation infrastructure.", "instance": { "id": 410435, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030900/a030908/maria_pr_1080p.00001_searchweb.png", "filename": "maria_pr_1080p.00001_searchweb.png", "media_type": "Image", "alt_text": "Night lights across Puerto Rico before and after Hurricane Maria, 2017", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410172, "type": "media_group", "extra_data": null, "title": "Hurricane Tracks from 2017 with Precipitation and Cloud Data", "caption": "These visualizations show the tracks of Atlantic hurricanes during 2017. Data from the Global Precipitation Mission called IMERG is used to show rainfall and data from NOAA's GOES East shows clouds. Storm position and wind speed data from UNISYS are used to show the track lines. The numbers 1 through 5 as well as \"T\" are displayed when storms change categories. The \"T\" stands for tropical storm.\n\nThere are 2 visualizations at various resolutions:\n\t- a wide Atlantic view that shows all of the hurricane tracks\n\t- a view that follows and zooms in only on Hurricane Harvey\n\nThese visualizaitons were created to support NASA talks given at the National Air and Space Musuem (NASM) in October 2017. The wide Atlantic view was updated at the end of hurricane season to include all Atlantic hurricanes in 2017 for display at the American Geophysical Union (AGU) conference.\n\nThese visualizations only go through October 2017 because there were no Atlantic hurricanes in November or December 2017.", "instance": { "id": 410811, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004586/hurricane_tracks2017_09cpc.2500_searchweb.png", "filename": "hurricane_tracks2017_09cpc.2500_searchweb.png", "media_type": "Image", "alt_text": "2017 Atlantic Hurricane season storm tracks with IMERG precipitation and GOES clouds (01 Aug 2017 to 31 Oct 2017)", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410173, "type": "media_group", "extra_data": null, "title": "Where Does Lightning Strike?", "caption": "You’ve probably heard the old saying: “lightning never strikes the same place twice.” However, this common phrase is a myth. Lightning often strikes the same place repeatedly—especially tall, pointy, and isolated objects. According NOAA, the Empire State Building is hit approximately 23 times a year.

\r\rThis visualization tallies up all the lightning strikes detected by the Lightning Imaging Sensor (LIS) onboard the now defunct Tropical Rainfall Measuring Mission (TRMM) from April 1998 to January 2015—more than 16 years’ worth of lightning data. In orbit, LIS recorded the time of occurrence of a lightning event, measured the radiant energy, and estimated the location during both day and night conditions with high detection efficiency.

\r\rLightning occurs more often over land than over the ocean because land absorbs sunlight and heats up faster than water. This means there is stronger convection and greater atmospheric instability, leading to the formation of more lightning-producing storms over land. The visualization starts out showing how the LIS sensor (green square) sweeps out an orbit path from approximately 35 degrees north to south latitude (blue), detecting lightning flashes as it passes overhead. As the sensor completes more orbits, a long-term count of the number of flashes observed at each location can be accumulated. Adjusting the total number of counts by the number of times the location has been observed gives the average flash rate, also known as a climatology (shown in the last frame of the visualization).", "instance": { "id": 412854, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030872/ligtning_v1_720p.01138_searchweb.png", "filename": "ligtning_v1_720p.01138_searchweb.png", "media_type": "Image", "alt_text": "Lightning flash counts are accumulated to create a long-term average lightning flash rate.", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410174, "type": "media_group", "extra_data": null, "title": "A Menacing Line of Hurricanes", "caption": "Meteorologists struggled to find the right words to describe the situation as a line of three hurricanes—two of them major and all of them threatening land—brewed in the Atlantic basin in September 2017.

\r\rForecasters were most concerned about Irma, which was on track to make landfall in densely populated South Florida on September 10 as a large category 4 storm. Meanwhile, category 2 Hurricane Katia was headed for Mexico, where it was expected to make landfall on September 9. And just days after Irma devastated the Leeward Islands, the chain of small Caribbean islands braced for another blow—this time from category 4 Hurricane Jose.

\r\rThe Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite captured the data for a mosaic of Katia, Irma, and Jose as they appeared in the early hours of September 8, 2017. The images were acquired by the VIIRS “day-night band,” which detects light signals in a range of wavelengths from green to near-infrared, and uses filtering techniques to observe signals such as city lights, auroras, wildfires, and reflected moonlight. In this case, the clouds were lit by the nearly full Moon. The image is a composite, showing cloud imagery combined with data on city lights.", "instance": { "id": 411291, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030898/hurricanes_vir_2017251_lrg_searchweb.png", "filename": "hurricanes_vir_2017251_lrg_searchweb.png", "media_type": "Image", "alt_text": "VIIRS imagery of Katia, Irma, and Jose", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410175, "type": "media_group", "extra_data": null, "title": "Three Consecutive Swaths of Data, Three Different Hurricanes", "caption": "It is extremely rare for a hurricane to show up in three consecutive swaths of data acquired by the same satellite. On September 7, 2017, hurricanes Katia (left, Category 1), Irma (center, Category 5), and Jose (right, Category 3) lined up across the Atlantic basin. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite acquired each image around 11:00 AM local time. The Atlantic hasn’t had three hurricanes at once since 2010 when hurricanes Igor, Julia, and Karl marched across the tropics—storms that also begin with letters I, J, and K.

\r\rOn September 5, Irma was labeled as an “extremely dangerous” Category 5 storm. Irma passed north of the Dominican Republic on September 7. This historically intense hurricane, which maintained winds of 185 miles per hour longer than any storm ever recorded on Earth, made landfall on Cuba’s Camaguey archipelago as a Category 5 hurricane on September 8, again at Cudjoe Key in lower Florida Keys as a Category 4 on September 10, and a final time in Florida later that day on Marco Island as a Category 3 storm. On September 6, Katia had strengthened over the southwestern Gulf of Mexico and was upgraded from tropical storm to Category 1 hurricane status. Katia shortly became a Category 2 storm on September 8, making landfall later that evening as a Category 1 storm north of Tecolutla, Mexico. Jose became a Category 1 storm on September 6 and rapidly intensified into a Category 4 storm by September 8. It remained a Category 4 storm until September 10. As of September 12, Jose is a Category 1 storm. The National Hurricane Center predicts that the storm will not make landfall in the next five days.", "instance": { "id": 411346, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030897/MODIS_terra_three_storms_20170907_9600x3240_searchweb.png", "filename": "MODIS_terra_three_storms_20170907_9600x3240_searchweb.png", "media_type": "Image", "alt_text": "MODIS imagery of Irma, Jose and Katia", "width": 180, "height": 320, "pixels": 57600 } }, { "id": 410176, "type": "details_page", "extra_data": null, "instance": { "id": 12221, "url": "https://svs.gsfc.nasa.gov/12221/", "page_type": "Produced Video", "title": "Tracking Volcanic Ash With Satellites", "description": "Data from the Suomi NPP satellite is used by NASA scientists to map the full three-dimensional structure of volcanic clouds, allowing a more accurate forecast of where the volcanic ash is spreading. The information will be used by air traffic management to re-route flights around the hazardous ash clouds, which can damage airplane engines.Complete transcript available.Music: \"Dangerous Clouds\" by Guy & Zab Skornik [SACEM]Watch this video on the NASA Goddard YouTube channel. || 12221_Volcanic_ash_MASTER_youtube_hq.00596_print.jpg (1024x576) [66.2 KB] || 12221_Volcanic_ash_MASTER_youtube_hq.00596_searchweb.png (180x320) [43.0 KB] || 12221_Volcanic_ash_MASTER_youtube_hq.00596_web.png (320x180) [43.0 KB] || 12221_Volcanic_ash_MASTER_youtube_hq.00596_thm.png (80x40) [4.0 KB] || 12221_Volcanic_ash_MASTER_appletv.m4v (1280x720) [60.8 MB] || 12221_Volcanic_ash_MASTER.webm (960x540) [46.9 MB] || 12221_Volcanic_ash_MASTER_appletv_subtitles.m4v (1280x720) [60.8 MB] || 12221_Volcanic_ash_MASTER_ipod_sm.mp4 (320x240) [21.9 MB] || 12221_Volcanic_ash_captions.en_US.srt [2.2 KB] || 12221_Volcanic_ash_captions.en_US.vtt [2.2 KB] || 12221_Volcanic_ash_MASTER_youtube_hq.mov (1920x1080) [149.2 MB] || 12221_Volcanic_ash_MASTER_large.mp4 (1920x1080) [119.1 MB] || 12221_Volcanic_ash_MASTER.mpeg (1280x720) [394.4 MB] || 12221_Volcanic_ash_MASTER_prores.mov (1280x720) [1.6 GB] || ", "release_date": "2016-05-12T13:30:00-04:00", "update_date": "2024-10-06T23:40:55.466912-04:00", "main_image": { "id": 425055, "url": "https://svs.gsfc.nasa.gov/vis/a010000/a012200/a012221/CalbucoEruption_Ash-SO2.2710_print.jpg", "filename": "CalbucoEruption_Ash-SO2.2710_print.jpg", "media_type": "Image", "alt_text": "Visualization of results from a supercomputer model of ash and sulfur dioxide spreading from an eruption of the Calbuco volcano in April 2015. The supercomputer combines the physics and chemistry of the atmosphere with data from the NASA/NOAA/DoD Suomi NPP satellite to model the full three-dimensional structure of the volcanic cloud.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410177, "type": "details_page", "extra_data": null, "instance": { "id": 4542, "url": "https://svs.gsfc.nasa.gov/4542/", "page_type": "Visualization", "title": "CATS studies volcanic plumes, wildfires, and hurricanes", "description": "NASA’s Cloud-Aerosol Transport System, or CATS, is a lidar remote-sensing instrument taking measurements of atmospheric aerosols and clouds from the International Space Station (ISS). Launched to the ISS in January 2015, CATS is specifically intended to demonstrate a low-cost, streamlined approach to developing ISS science payloads. The CATS mission extends the data record of space-based aerosol and cloud measurements to ensure the continuity of lidar climate observation.Data from CATS will help scientists model the structure of dust plumes and other atmospheric features, which can travel far distances and impact air quality. Climate scientists will also use the CATS data, along with data from other Earth-observing instruments, to look at trends and interactions in clouds and aerosols over time.Calbco EruptionCATS and the ISS provide critical measurements of volcanic plume heights. In late April 2015, the Calbuco Volcano in Chile erupted multiple times; sending plumes of sulfur dioxide and ash into the upper troposphere. Volcanic plumes pose a substantial risk to aviation safety, leading to prolonged flight cancellations that cause ripple effects in the airline industry’s economy and on personal travel. Rerouting air traffic requires accurate forecasts of volcanic plume transport from models such as the NASA GEOS-5 shown here. Utilizing the near-real-time data downlinking capabilities on ISS the CATS team can produce useful data products within six hours of data collection. || ", "release_date": "2017-01-25T00:00:00-05:00", "update_date": "2025-01-05T23:16:41.064155-05:00", "main_image": { "id": 417141, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004500/a004542/cats_calbuco_1355_print.jpg", "filename": "cats_calbuco_1355_print.jpg", "media_type": "Image", "alt_text": "The ISS passes over a plume of ash and sulfur dioxide (SO2) from the Calbuco Volcano eruption. The volcano plume can be seen in attenuated backscatter data collected by the CATS instrument, onboard the ISS. This video is also available on our YouTube channel.", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410178, "type": "details_page", "extra_data": null, "instance": { "id": 3783, "url": "https://svs.gsfc.nasa.gov/3783/", "page_type": "Visualization", "title": "Iceland's Eyjafjallajökull Volcanic Ash Plume May 6-8, 2010 - Stereoscopic Version", "description": "During April and May, 2010, the Eyjafjallajökull volcano on Iceland's southern coast erupted, creating an expansive ash cloud that disrupted air traffic throughout Europe and across the Atlantic. This animation shows the flow of this ash cloud for three days in early May on an hourly basis as sensed from a geostationary satellite. The ash cloud heights were determined using an approach developed by NOAA/NESDIS/STAR for the next generation of Geostationary Operational Environmental Satellite (GOES-R). Data from EUMETSAT's Spinning Enhanced Visible and Infrared Imager (SEVIRI) was used as a proxy for GOES-R Advanced Baseline Imager (ABI) data. This data is shown intersecting with the CALIPSO Parallel Attenuated Backscatter curtain on May 6th. In this page the visualization content is offered in two different modes to accommodate stereoscopic systems as: Left and Right Eye separate and Left and Right Eye side-by-side combined on the same frame. || ", "release_date": "2010-10-21T00:00:00-04:00", "update_date": "2024-10-09T16:01:22.308073-04:00", "main_image": { "id": 489466, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003700/a003783/volcanicAsh_comp_L.0413_web.png", "filename": "volcanicAsh_comp_L.0413_web.png", "media_type": "Image", "alt_text": "This set provides stereoscopic visualization content (Left and Right Eye separate) of the composite animation including the foreground, star background and date overlay.", "width": 320, "height": 180, "pixels": 57600 } } }, { "id": 410179, "type": "details_page", "extra_data": null, "instance": { "id": 4273, "url": "https://svs.gsfc.nasa.gov/4273/", "page_type": "Visualization", "title": "CALIPSO observes Saharan dust crossing the Atlantic Ocean", "description": "Subtitled visualization depicting Saharan dust travelling across the Atlantic Ocean to the Amazon Basin. MODIS imagery shows a 2D representation of the dust cloud, which is then compared to CALIPSO data curtains showing dust throughout the air column. Seasonal dust flux measurements are visualized using particles systems. Finally, average annual dust deposition into the Amazon Basin is shown by Amazon boundary import/export measurements. || Dust_Entire_1080p_60fps.3072_print.jpg (1024x576) [124.9 KB] || Dust_Entire_1080p_60fps.3072_searchweb.png (180x320) [69.8 KB] || Dust_Entire_1080p_60fps.3072_web.png (320x180) [69.8 KB] || Dust_Entire_1080p_60fps.3072_thm.png (80x40) [5.4 KB] || SaharanDust_720p_60fps.mp4 (1280x720) [73.6 MB] || SaharanDust_1080p_60fps.webm (1920x1080) [12.3 MB] || SaharanDust_1080p_60fps.mp4 (1920x1080) [189.6 MB] || entire_4k (3840x2160) [0 Item(s)] || Dust_4k_30fps_2160p.mp4 (3840x2160) [365.9 MB] || ", "release_date": "2015-02-24T09:55:00-05:00", "update_date": "2025-01-05T22:43:59.754494-05:00", "main_image": { "id": 445890, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004200/a004273/4273_African_Dust_Still.png", "filename": "4273_African_Dust_Still.png", "media_type": "Image", "alt_text": "SIGGRAPH VersionFor complete transcript, click here.", "width": 1920, "height": 1080, "pixels": 2073600 } } }, { "id": 410180, "type": "details_page", "extra_data": null, "instance": { "id": 30797, "url": "https://svs.gsfc.nasa.gov/30797/", "page_type": "Hyperwall Visual", "title": "Landsat 8 Views the Soberanes Fire", "description": "By chance, Landsat 8 acquired imagery of the Soberanes fire burning near the California coast between Monterey and Big Sur a few hours after it started on July 22, 2016. Seven days later, on July 29, the fire had grown so much that the surrounding area is almost entirely covered by smoke. This set of Landsat images shows the region on [left to right] July 22, July 29, and August 8 in true color (using bands 4, 3, and 2) and also in shortwave and near-infrared light (using bands 7, 5, and 4). Active fires, which can be detected based on calculations using the shortwave infrared and near-infrared bands, are shown in red on the true color images. The shortwave and near-infrared images penetrate the smoke to provide a clearer view of the burn scar. In this false-color view, active fires are bright red and orange, scarred land is dark red, and intact vegetation and human development are shades of green. || ", "release_date": "2016-08-08T00:00:00-04:00", "update_date": "2024-10-10T00:26:40.164820-04:00", "main_image": { "id": 422061, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030700/a030797/soberanes_fire_landsat_432_vs_754_print.jpg", "filename": "soberanes_fire_landsat_432_vs_754_print.jpg", "media_type": "Image", "alt_text": "An animation compares Infrared band and true color images from Landsat-8 to reveal details of the Soberanes fire", "width": 1024, "height": 574, "pixels": 587776 } } }, { "id": 410181, "type": "details_page", "extra_data": null, "instance": { "id": 3868, "url": "https://svs.gsfc.nasa.gov/3868/", "page_type": "Visualization", "title": "Global Fire Observations and MODIS NDVI", "description": "This visualization leads viewers on a narrated global tour of fire detections beginning in July 2002 and ending July 2011. The visualization also includes vegetation and snow cover data to show how fires respond to seasonal changes. The tour begins in Australia in 2002 by showing a network of massive grassland fires spreading across interior Australia as well as the greener Eucalyptus forests in the northern and eastern part of the continent. The tour then shifts to Asia where large numbers of agricultural fires are visible first in China in June 2004, then across a huge swath of Europe and western Russia in August, and then across India and Southeast Asia through the early part of 2005. It moves next to Africa, the continent that has more abundant burning than any other. MODIS observations have shown that some 70 percent of the world's fires occur in Africa alone. In what's a fairly average burning season, the visualization shows a huge outbreak of savanna fires during the dry season in Central Africa in July, August, and September of 2006, driven mainly by agricultural activities but also by the fact that the region experiences more lightning than anywhere else in the world. The tour shifts next to South America where a steady flickering of fire is visible across much of the Amazon rainforest with peaks of activity in September and November of 2009. Almost all of the fires in the Amazon are the direct result of human activity, including slash-and-burn agriculture, because the high moisture levels in the region prevent inhibit natural fires from occurring. It concludes in North America, a region where fires are comparatively rare. North American fires make up just 2 percent of the world's burned area each year. The fires that receive the most attention in the United States, the uncontrolled forest fires in the West, are less visible than the wave of agricultural fires prominent in the Southeast and along the Mississippi River Valley, but some of the large wildfires that struck Texas earlier this spring are visible. More information on the Fire Information for Resource Management System (FIRMS) is available at http://maps.geog.umd.edu/firms/. || ", "release_date": "2011-10-18T01:00:00-04:00", "update_date": "2025-02-02T00:01:39.789904-05:00", "main_image": { "id": 481530, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003800/a003868/africaNDVIPrintRes.1996_web.png", "filename": "africaNDVIPrintRes.1996_web.png", "media_type": "Image", "alt_text": "A 10 year sequence of global fires as seen by NASA's MODIS instruments.", "width": 180, "height": 320, "pixels": 57600 } } }, { "id": 410182, "type": "details_page", "extra_data": null, "instance": { "id": 4484, "url": "https://svs.gsfc.nasa.gov/4484/", "page_type": "Visualization", "title": "Global Fires 2015-2016 Visualizations", "description": "Global Fires 2015-2016, with Dates and Colorbar || global_fires_statelines_0000_print.jpg (1024x576) [73.9 KB] || global_fires_statelines_0000_searchweb.png (320x180) [41.4 KB] || global_fires_statelines_0000_thm.png (80x40) [4.6 KB] || global_fires_statelines (1920x1080) [0 Item(s)] || global_fires_statelines_1080p30.mp4 (1920x1080) [8.5 MB] || global_fires_statelines_1080p30.webm (1920x1080) [2.3 MB] || global_fires_statelines_1080p30.mp4.hwshow [197 bytes] || ", "release_date": "2016-08-16T00:00:00-04:00", "update_date": "2025-01-05T23:08:31.987113-05:00", "main_image": { "id": 421883, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004400/a004484/global_fires_statelines_0000_print.jpg", "filename": "global_fires_statelines_0000_print.jpg", "media_type": "Image", "alt_text": "Global Fires 2015-2016, with Dates and Colorbar", "width": 1024, "height": 576, "pixels": 589824 } } }, { "id": 410183, "type": "details_page", "extra_data": null, "instance": { "id": 30888, "url": "https://svs.gsfc.nasa.gov/30888/", "page_type": "Hyperwall Visual", "title": "A Human-Driven Decline in Global Burned Area", "description": "Global Burned Area annual change, plus overall trend || time_series_fraction_hw_1080p.00001_print.jpg (1024x576) [205.5 KB] || time_series_fraction_hw_1080p.00001_searchweb.png (320x180) [102.4 KB] || time_series_fraction_hw_1080p.00001_thm.png (80x40) [7.3 KB] || time_series_fraction_hw_1080p.mp4 (1920x1080) [8.0 MB] || time_series_fraction_hw_720p.mp4 (1280x720) [4.0 MB] || time_series_fraction_hw_1080p.webm (1920x1080) [2.2 MB] || time_series_fraction_hw_2304p.mp4 (4096x2304) [26.3 MB] || hw (4104x2304) [128.0 KB] || ", "release_date": "2017-08-01T15:00:00-04:00", "update_date": "2024-12-15T23:54:59.478826-05:00", "main_image": { "id": 413236, "url": "https://svs.gsfc.nasa.gov/vis/a030000/a030800/a030888/time_series_fraction_hw_01424.png", "filename": "time_series_fraction_hw_01424.png", "media_type": "Image", "alt_text": "Overall Trend Map", "width": 1368, "height": 768, "pixels": 1050624 } } }, { "id": 410184, "type": "details_page", "extra_data": null, "instance": { "id": 30627, "url": "https://svs.gsfc.nasa.gov/30627/", 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