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TRMM (TMPA-RT) Near Real-Time Precipitation L3 1 day 0.25 degree x 0.25 degree V7 (TRMM_3B42RT_Daily) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:59:46.000ZTMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06). This daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt
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India Annual Winter Cropped Area, 2001-2016
data.nasa.gov | Last Updated 2022-01-17T05:29:43.000ZThe India Annual Winter Cropped Area, 2001 - 2016 consists of annual winter cropped areas for most of India (except the Northeastern states) from 2000-2001 to 2015-2016. This data set utilizes the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; spatial resolution: 250m) for the winter growing season (October-March). The methodology uses an automated algorithm identifying the EVI peak in each pixel for each year and linearly scales the EVI value between 0% and 100% cropped area within that particular pixel. Maps were then resampled to 1 km and were validated using high-resolution QuickBird, RapidEye, SkySat, and WorldView-2 images spanning 2008 to 2016 across 11 different agricultural regions of India. The spatial resolution of the data set is 1 km, resampled from 250m. The data are distributed as GeoTIFF and NetCDF files and are in WGS 84 projection.
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OWLETS-1 Ozonesonde Data
data.nasa.gov | Last Updated 2022-07-18T13:04:47.000ZOWLETS1_Sondes_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone data collected via synchronous ozonesonde launches at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete. Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.
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GPM Ground Validation SEA FLUX ICE POP V1
data.nasa.gov | Last Updated 2022-06-07T06:12:15.000ZThe GPM Ground Validation SEA FLUX ICE POP dataset includes estimates of ocean surface latent and sensible heat fluxes, 10m wind speed, 10m air temperature, 10m air humidity, and skin sea surface temperature in support of the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to the Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This data file is available in netCDF-4 format from September 1, 2017 through April 30, 2018.
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CORONA Satellite Photographs from the U.S. Geological Survey
data.nasa.gov | Last Updated 2022-01-17T05:16:00.000ZThe first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.
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2008 Environmental Performance Index (EPI)
data.nasa.gov | Last Updated 2022-01-17T05:02:20.000ZThe 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents �at target�). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission.
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2002 Environmental Sustainability Index (ESI)
data.nasa.gov | Last Updated 2022-01-17T05:02:16.000ZThe 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN).
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MISR Level 3 FIRSTLOOK Global Land product in netCDF format covering a day V002
data.nasa.gov | Last Updated 2023-01-19T22:32:40.000ZThis file contains the MISR Level 3 FIRSTLOOK Component Global Land product in netCDF format covering a day
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Transcriptional analysis of liver from mice flown on the RR-6 mission
data.nasa.gov | Last Updated 2023-01-26T18:45:47.000ZThe objective of the Rodent Research-6 (RR-6) study was to evaluate muscle atrophy in mice during spaceflight and to test the efficacy of a novel therapeutic to mitigate muscle wasting. The experiment involved an implantable subcutaneous nanochannel delivery system (nDS; between scapula) which delivered the drug formoterol (FMT; a selective Beta-2 adrenoceptor agonist) over the course of time. To this end a cohort of forty 32-weeks-old female C57BL/6NTac mice were either sham operated or implanted with vehicle or treatment-filled nDS launched in two Transporters (20 mice per Transporter) on SpaceX-13 on December 15 2017. They were transferred to Rodent Habitats onboard the International Space Station (ISS) and maintained in microgravity for 29 days (N=20 Live Animal Return [LAR]) or >50 days (N=20 ISS Terminal). After 29 days the 20 LAR animals were returned live to back to Earth on January 13 2018,. After splashdown the animals were ambulatory on-ground for ~4 days until all subjects were processed during one day of dissections. There were two Baseline groups of animals sacrificed (LAR Baseline & FLT Baseline; N=20; 40 animals; ~36 weeks old) at Kennedy Space Center (KSC; 12/9/17). A Ground Control group mimicked the Flight LAR group which was housed at KSC then shipped alive to Novartis Facilities where both the LAR and LAR Ground Control groups were processed (~41 weeks old; 1/16/18). All were anesthetized with isoflurane blood samples were obtained by closed-chest cardiac puncture and the animals were euthanized by exsanguination and thoracotomy. The 20 ISS Terminal mice were anesthetized via intraperitoneal injection of ketamine/xylazine/acepromazine over the course of a four days of dissections (2/6/18 until 2/9/18; 53-56 days after launch; 44 weeks old at time of on-orbit dissections). Blood samples and euthanasia were conducted the same as LAR and Baseline. Following blood draw and hind limb dissection the ISS-terminal animal carcasses were wrapped in aluminum foil placed in a ziploc bag and placed in storage at -80C or colder until return. The ISS-terminal Ground Controls (at KSC) followed the same euthanasia timeline methods and preservation. The final processing of frozen ISS-terminal frozen ISS-terminal Ground Controls and frozen 0-day FLT baseline animals were completed at Houston Methodist Research Institute in Houston TX (5/21/18 until 5/24/18). GeneLab received samples of liver from only sham treated animals (no drug treated animals) from the following groups Flight: LAR (n=10) ISS Terminal (n= 10); Ground Controls: LAR GC (N=9) ISS Terminal GC (N=10) LAR Baseline (n=10) ISS Terminal Baseline (n=10). Total RNA was extracted and sequenced at a target depth of 60 M clusters per sample (ribodepleted paired end 150).
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Probabilistic Model-Based Diagnosis for Electrical Power Systems
data.nasa.gov | Last Updated 2020-01-29T02:07:39.000ZWe present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.