Earth Science Overview Oct 2018 Briefing

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Earth Observing System - A planetary system of systems

  • Earth Observing Fleet (June 2018)
    This animation shows the orbits of NASA's fleet of Earth observing spacecraft that are considered operational as of June 2018. New elements in this version include the GRACE Follow-On 1 and 2. The clouds used in this version are from a high resolution GEOS model run at 10 minute time steps interpolated down to the per-frame level.
    Spacecraft included: Aqua Aura CALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation CYGNSS-1: Cyclone Global Navigation Satellite System 1 CYGNSS-2: Cyclone Global Navigation Satellite System 2 CYGNSS-3: Cyclone Global Navigation Satellite System 3 CYGNSS-4: Cyclone Global Navigation Satellite System 4 CYNGSS-5: Cyclone Global Navigation Satellite System 5 CYGNSS-6: Cyclone Global Navigation Satellite System 6 CYGNSS-7: Cyclone Global Navigation Satellite System 7 CYGNSS-8: Cyclone Global Navigation Satellite System 8 Cloudsat DSCOVR: Deep Space Climate Observatory GPM: Global Precipitation Measurement GRACE-FO-1: Gravity Recovery and Climate Experiment Follow On-1 GRACE-FO-2: Gravity Recovery and Climate Experiment Follow On-2 ISS: International Space Station Jason 2 Jason 3 Landsat 7 Landsat 8 OCO-2: Orbiting Carbon Observatory-2 SMAP: Soil Moisture Passive Active SORCE: Solar Radiation and Climate Experiment Suomi NPP: Suomi National Polar-orbiting Partnership Terra
  • Earth: A System of Systems
    This visualization reveals that the Earth system, like the human body, comprises diverse components that interact in complex ways. Heat absorbed by the ocean is transported by ocean currents—shown here as ECCO2 model output. This energy is constantly released into Earth’s atmosphere. Heat and moisture from the ocean and land influence Earth’s weather patterns—represented here as 500 mb wind speeds from GEOS-5. Moisture in the atmosphere—represented as precipitable water from the GEOS-5 model—forms clouds and precipitation—shown here using the GPM IMERG product. Precipitation significantly impacts water availability, which influences soil moisture and ocean salinity—shown here as data from SMAP. Lastly, data from multiple satellites show the density of plant growth on land and chlorophyll concentrations in the ocean. While scientists learn a great deal from studying each of these components individually, improved observational and computational capabilities increasingly allow them to study the interactions between these interrelated geophysical and biological parameters, leading to unprecedented insight into how the Earth system works—and how it might change in the future. List of visualizations used: Slice 1: Sea Surface Currents & Temperature (ECCO2 model) Slice 2: Winds (GEOS-5 model) Slice 3: Precipitable Water (GEOS-5 model) Slice 4: Clouds (GEOS-5 model) Slice 5: Precipitation (IMERG data) Slice 6: Soil Moisture (SMAP data) Slice 7: Biosphere (Multiple satellite datasets) Slice 8: Blue Marble (MODIS data)
  • 20 Years of Global Biosphere (updated)
    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.
  • Global Sea Surface Currents and Temperature
    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.

  • Near Real-Time Global Precipitation from the Global Precipitation Measurement Constellation
    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.
  • Hurricane Jose lingers in the Atlantic as Hurricane Maria approaches Puerto Rico
    The Global Precipitation Measurement (GPM) mission shows the rainfall distribution for two major storms churning in the Atlantic and Caribbean basins. The visualization shows Hurricane Jose as it continues to slowly move northward off the US East Coast east of the Outer Banks of North Carolina. At one time, Jose was a powerful category 4 border line category 5 storm with maximum sustained winds reported at 155 mph by the National Hurricane Center back on the 9th of September as it was approaching the northern Leeward Islands. Jose passed northeast of the Leeward Islands as a category 4 storm on a northwest track and then began to weaken due to the effects of northerly wind shear. Remaining over warm water allowed Jose to strengthen back into a hurricane on September 15th as wind shear across the storm diminished. At this time, Jose was still only midway between the central Bahamas and Bermuda, having just completed its loop, and moving to the northwest. On the 16th, Jose turned northward as it moved around the western edge of a ridge of high pressure near Bermuda and began to parallel the US East Coast well away from shore. An overpass by the GPM Core Observatory captured an image of Jose overnight at 3:36 UTC 18 September (11:36 pm EST 17 September) as the storm was moving due north at 9 mph well off shore from the coast of North Carolina. The GPM image estimated areas of very heavy rain on the order of 75 mm/hr (~3 inches per hour). The GPM Core Observatory satellite also had an excellent view of Hurricane Maria when it passed almost directly above the hurricane on September 17, 2017 at 1001 PM AST (September 18, 2017 0201 UTC). GPM's Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) showed that Maria had well defined bands of precipitation rotating around the eye of the tropical cyclone. GPM's radar (DPR Ku band) found rain falling at a rate of over 6.44 inches (163.7 mm) per hour in one of these extremely powerful storms northeast of Maria's eye. Intense thunderstorms were found towering to above 9.7 miles (15.7 km). This kind of chimney cloud is also called a "hot tower" (as it releases a huge quantity of latent heat by condensation). These tall thunderstorms in the eye wall are often a sign that a tropical cyclone is becoming more powerful. Maria rapidly intensified following this view to a Category 5 storm on September 19th.
  • Pinpointing Where the Lights Went Out in Puerto Rico
    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.

    These 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.

    One 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.

  • GOES and GPM Capture Florence Trying to Intensify Over the Atlantic
    Hurricane Florence originally formed from an African Easterly wave that emerged off the west coast of Africa back on the 30th of August. When it reached the vicinity of the Cape Verde Islands the next day, it was organized enough to become a tropical depression. The following day the depression strengthened enough to become a tropical storm and Florence was born on the 1st of September. Over the next 3 days, Florence gradually strengthened as it moved in a general west-northwest direction into the central Atlantic. Then, on the 4th of September, Florence began to rapidly intensify. By the morning of the 5th, Florence was a Category 3 hurricane before reaching Category 4 intensity later that afternoon with maximum sustained winds estimated at 130 mph by the National Hurricane Center (NHC). At this point, Florence became the victim of increasingly strong southwesterly wind shear, which greatly weakened the storm all the way back down to a tropical storm the by evening of the 6th. The following GOES-East Infrared (IR) loop shows Florence from 17:54 UTC (1:54 pm EDT) 6 September to 19:27 UTC (3:27 pm EDT) 7 September when it was struggling against the strong southwesterly wind shear in the Central Atlantic. A very interesting looking feature is the arc-shaped cloud that propagates outward from the storm towards the west. This cloud feature is occurring at upper-levels and is likely tied to a gravity wave propagating outward from an area of intense convection that erupted from deep within the storm. When the tops of these smaller scale storms within a storm reach the upper troposphere, they can trigger gravity waves. As these waves progagate outward they can enhance cloud formation where they induce rising motion and erode cloud where they induce downward motion or subsidence. As this arc-shaped cloud is able to propagate outward uniformly from the center, it must be occurring above the shear layer. Compensating areas of subsidence can also surround the strong rising motion occurring within the tall convective clouds. This can help to erode surrounding clouds and may be contributing to the clearing that occurs between the arc-shaped cloud and the main area of convection. The end of the loop shows surface rainfall and a 3D flyby of Florence courtesy of the GPM core satellite, which passed over the storm at around 19:21 UTC (3:21 pm EDT) on the 7th. At the surface, two areas of intense rain (shown in magenta) reveal the presence of two areas of strong thunderstorms within Florence north and northeast of the center. The flyby shows a 3D rendering of the radar structure of the storm. The darker blue tower indicates an area of deep convection that has penetrated well over 10 km high and is associated with the southernmost area of intense rain just north of the center. It is these areas of deep convection that fuel the storm by releasing heat, known as latent heat, mainly from condensation, near the core. Although it would be nearly 2 days before Florence re-gained hurricane intensity, these convective towers are what helped Florence to survive the effects of the wind shear and eventually grow back into a Category 4 hurricane. GPM is a joint mission between NASA and the Japanese space agency JAXA. Caption by Stephen Lang (SSAI/NASA GSFC) and Joe Munchak (GSFC). A short 360 video flying under Florence is available here:
    Look for a longer narrated 360 video flying through Hurricane Maria in the coming weeks!
  • Global Landslide Hazard Assessment Model (LHASA) with Global Landslide Catalog (GLC) data
    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: The Global Landslide Catalog is currently available here:
  • 2017 Hurricanes and Aerosols Simulation
    Tracking the aerosols carried on the winds let scientists see the currents in our atmosphere. This visualization follows sea salt, dust, and smoke from July 31 to November 1, 2017, to reveal how these particles are transported across the map.

    The first thing that is noticeable is how far the particles can travel. Smoke from fires in the Pacific Northwest gets caught in a weather pattern and pulled all the way across the US and over to Europe. Hurricanes form off the coast of Africa and travel across the Atlantic to make landfall in the United States. Dust from the Sahara is blown into the Gulf of Mexico. To understand the impacts of aerosols, scientists need to study the process as a global system.

    The Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center has developed the Goddard Earth Observing System (GEOS), a family of mathematical models. Combined with data from NASA's Earth observing satellites, the supercomputer simulations enhance our scientific understanding of specific chemical, physical, and biological processes.

    During the 2017 hurricane season, the storms are visible because of the sea salt that is captured by the storms. Strong winds at the surface lift the sea salt into the atmosphere and the particles are incorporated into the storm. Hurricane Irma is the first big storm that spawns off the coast of Africa. As the storm spins up, the Saharan dust is absorbed in cloud droplets and washed out of the storm as rain. This process happens with most of the storms, except for Hurricane Ophelia. Forming more northward than most storms, Ophelia traveled to the east picking up dust from the Sahara and smoke from large fires in Portugal. Retaining its tropical storm state farther northward than any system in the Atlantic, Ophelia carried the smoke and dust into Ireland and the UK.

    Computer simulations using the GEOS models allow scientists to see how different processes fit together and evolve as a system. By using mathematical models to represent nature we can separate the system into component parts and better understand the underlying physics of each. GEOS runs on the Discover supercomputer at the NASA Center for Climate Simulation (NCCS) For more information: NASA@SC17: Glimpse at the Future of Global Weather Prediction and Analysis at NASA