1 00:00:00,050 --> 00:00:04,090 [data sounds] 2 00:00:04,110 --> 00:00:08,150 The GPM mission is a sophisticated 3 00:00:08,170 --> 00:00:12,190 network of satellites, covering the entire globe in less than three hours, 4 00:00:12,210 --> 00:00:16,230 giving us an unprecedented picture of precipitation, 5 00:00:16,250 --> 00:00:20,270 from rain to falling snow, hurricanes to monsoons, 6 00:00:20,290 --> 00:00:24,310 droughts and floods. So how do we get all of that information 7 00:00:24,330 --> 00:00:28,340 out of this? The short answer: Tons of data 8 00:00:28,360 --> 00:00:32,350 from all over. 9 00:00:32,370 --> 00:00:36,360 As GPM takes snapshots at precipitation, 10 00:00:36,380 --> 00:00:40,400 like in a major storm, the data gathered is transmitted 11 00:00:40,420 --> 00:00:44,420 to a network of satellites called TDRSS. 12 00:00:44,440 --> 00:00:48,440 Erich Stocker: The important thing to recognize is that the GPM satellite does not 13 00:00:48,460 --> 00:00:52,470 talk directly to the Earth; it talks to the communications satellite 14 00:00:52,490 --> 00:00:56,520 which is known as TDRSS. And the TDRSS satellites 15 00:00:56,540 --> 00:01:00,550 talk to a ground station, which is at White Sands, New Mexico, 16 00:01:00,570 --> 00:01:04,570 and that's a very effective way to get 17 00:01:04,590 --> 00:01:08,580 continuous data, which cannot be gotten otherwise, unless you do 18 00:01:08,600 --> 00:01:12,680 direct broadcast and have many, many ground stations, which isn't 19 00:01:12,700 --> 00:01:16,740 as effective as going through the TDRSS system. 20 00:01:16,760 --> 00:01:20,890 The White Sands Ground Station then sends information about the health and 21 00:01:20,910 --> 00:01:24,940 geolocation of the GPM Core satellite to the hub of all 22 00:01:24,960 --> 00:01:29,020 of this activity, the Missions Operations Center, located at NASA's 23 00:01:29,040 --> 00:01:33,080 Goddard Space Flight Center. The raw data streams 24 00:01:33,100 --> 00:01:37,190 into Goddard's Precipitation Processing System, or PPS. 25 00:01:37,210 --> 00:01:41,220 The data from the radar is routed through GPM's partner, 26 00:01:41,240 --> 00:01:45,250 the Japan Aerospace Exploration Agency, for initial processing 27 00:01:45,270 --> 00:01:49,270 and then is sent back to the PPS. George Huffman: The data that come down from 28 00:01:49,290 --> 00:01:53,410 the satellite are actually not precipitation. They're in the form of radiances, 29 00:01:53,430 --> 00:01:57,510 in the case of the microwave instruments, or reflectivities, in the case 30 00:01:57,530 --> 00:02:01,650 of the radar. The computer codes I've been talking about--the algorithms-- 31 00:02:01,670 --> 00:02:05,700 are the way we get from numbers 32 00:02:05,720 --> 00:02:09,750 that nobody including me can directly interpret to the thing we care about, 33 00:02:09,770 --> 00:02:13,790 which is precipitation. The GPM mission is not just the Core 34 00:02:13,810 --> 00:02:17,840 spacecraft, but also a constellation of existing satellites 35 00:02:17,860 --> 00:02:21,890 from partners around the world. Each constellation member may have its own 36 00:02:21,910 --> 00:02:25,910 unique scientific objectives, but they all contribute data to the PPS 37 00:02:25,930 --> 00:02:29,940 in order to develop global precipitation products. 38 00:02:29,960 --> 00:02:33,950 Erich Stocker: The Precipitation Processing System gets data from the satellite and 39 00:02:33,970 --> 00:02:38,000 various other sources and creates the science products 40 00:02:38,020 --> 00:02:42,050 that are going to be used for both applications purposes, that is 41 00:02:42,070 --> 00:02:46,060 societal benefits, and scientific research. The PPS then produces 42 00:02:46,080 --> 00:02:50,110 a suite of data products, including both instrument specific and 43 00:02:50,130 --> 00:02:54,150 merged data, unifying the data gathered by the international 44 00:02:54,170 --> 00:02:58,170 partner satellites that make up the GPM constellation. 45 00:02:58,190 --> 00:03:02,210 George Huffman: You could compare this to making soup. We have 46 00:03:02,230 --> 00:03:06,240 carrots, and we have onions, and we have potatoes. 47 00:03:06,260 --> 00:03:10,250 They're all vegetables. And so you have to wash them, peel them, 48 00:03:10,270 --> 00:03:14,300 take out the bad spots. That's a really important step, you don't want your soup to 49 00:03:14,320 --> 00:03:18,360 taste bad. When you get done, of course, you have to taste test it to make sure the 50 00:03:18,380 --> 00:03:22,410 seasoning right, and then you have to serve it. And so each of those steps 51 00:03:22,430 --> 00:03:26,460 in a mathematical sense, is what we have to do in order to take all the 52 00:03:26,480 --> 00:03:30,490 diverse sources of information and end up with a unified product 53 00:03:30,510 --> 00:03:34,550 which the user finds to be useful. These precipitation 54 00:03:34,570 --> 00:03:38,580 products will be useful in many societal applications, like 55 00:03:38,600 --> 00:03:42,610 hydrologic modeling, mapping potential natural disasters, 56 00:03:42,630 --> 00:03:46,630 agricultural modeling, weather prediction, 57 00:03:46,650 --> 00:03:50,680 and climate research. Erich Stocker: As we improve the precipitation 58 00:03:50,700 --> 00:03:54,690 retrievals that form the basis for these merged products 59 00:03:54,710 --> 00:03:58,710 that will get better and better, and we'll be seeing actual satellite data 60 00:03:58,730 --> 00:04:02,760 rather than just forecasts. [raindrops falling] 61 00:04:02,780 --> 00:04:06,790 62 00:04:06,810 --> 00:04:10,820 63 00:04:10,840 --> 00:04:14,840 64 00:04:14,860 --> 00:04:17,838