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The MODIS VIIRS 2016 Science Team Meeting was held June 6-10, 2016 at the Sheraton in Silver Spring, MD. The organizers plan to post the presentations and posters here: http:modis.gsfc.nasa.govsci_teammeetings201606.
NASA's Earth Science Data Information System (ESDIS) Project has been operating NASA's Suomi National Polar-Orbiting Partnership (SNPP) Science Data Segment (SDS) since the launch in October 2011. At launch, the SDS focused primarily on the evaluation of Sensor Data Records (SDRs) and Environmental Data Records (EDRs) produced by the Joint Polar Satellite System (JPSS), a National Oceanic and Atmosphere Administration (NOAA) Program, as to their suitability for Earth system science. During the summer of 2014, NASA transitioned to the production of standard Earth Observing System (EOS)-like science products for all instruments aboard Suomi NPP. The five Science Investigator-led Processing Systems (SIPS): Land, Ocean, Atmosphere, Ozone, and Sounder were established to produce the NASA SNPP standard Level 1, Level 2, and global Level 3 products developed by the SNPP Science Teams and to provide the products to NASA's Distributed Active Archive Centers (DAACs) for archive and distribution to the user community. The processing, archiving and distribution of data from NASA's Clouds and the Earth's Radiant Energy System (CERES) and Ozone Mapper/Profiler Suite (OMPS) Limb instruments will continue. With the implementation of the JPSS Block 2 architecture and the launch of JPSS-1, the SDS will receive SNPP data in near real-time via the JPSS Stored Mission Data Hub (JSH), as well as JPSS-1 and future JPSS-2 data. The SNPP SIPS will ingest EOS compatible Level 0 data from the EOS Data Operations System (EDOS) element for their data processing, enabling the continuous EOS-SNPP-JPSS Satellite Data Record.
Monthly mean August 2014 Version-6.28 AIRS and CrIS products agree well with OMPS and CERES, and reasonably well with each other. Version-6.28 CrIS total precipitable water is biased dry compared to AIRS. AIRS and CrIS Version-6.36 water vapor products are both improved compared to Version-6.28. Version-6.36 AIRS and CrIS total precipitable water also shows improved agreement with each other. AIRS Version-6.36 total ozone agrees even better with OMPS than does AIRS Version-6.28, and gives reasonable results during polar winter where OMPS does not generate products. CrIS and ATMS are high spectral resolution IR and Microwave atmospheric sounders currently flying on the SNPP satellite, and are also scheduled for flight on future NPOESS satellites. CrIS/ATMS have similar sounding capabilities to those of the AIRS/AMSU sounder suite flying on EOS Aqua. The objective of this research is to develop and implement scientifically equivalent AIRS/AMSU and CrIS/ATMS retrieval algorithms with the goal of generating a continuous data record of AIRS/AMSU and CrIS/ATMS level-3 data products with a seamless transition between them in time. To achieve this, monthly mean AIRS/AMSU and CrIS/ATMS retrieved products, and more importantly their interannual differences, should show excellent agreement with each other. The currently operational AIRS Science Team Version-6 retrieval algorithm has generated 14 years of level-3 data products. A scientifically improved AIRS Version-7 retrieval algorithm is expected to become operational in 2017. We see significant improvements in water vapor and ozone in Version-7 retrieval methodology compared to Version-6.We are working toward finalization and implementation of scientifically equivalent AIRS/AMSU and CrIS/ATMS Version-7 retrieval algorithms to be used for the eventual processing of all AIRS/AMSU and CrIS/ATMS data. The latest version of our retrieval algorithm is Verison-6.36, which includes almost all the improvements we want in Version-7. Version-6.28 has been used to process both AIRS and CrIS data for August 2014. This poster compares August 2014 monthly mean Version-6.28 AIRS/AMSU and CrIS/ATMS products with each other, and also with monthly mean products obtained using AIRS Version-6. AIRS and CrIS results using Version-6.36 are presented for April 15, 2016. These demonstrate further improvements since Version-6.28. The new results also show improved agreement of Version-6.36 AIRS and CrIS products with each other. Version-6.36 is not yet optimized for CrIS ozone products.
We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.
This paper will describe the results that have been obtained to date during the MMS mission concerning conjunction assessment. MMS navigation makes use of a weak-signal GPS-based system: this allows signals to be received even when MMS is flying above the GPS orbits, producing a highly accurate determination of the four MMS orbits. This data is downlinked to the MMS Mission Operations Center (MOC) and used by the Flight Dynamics Operations Area (FDOA) for both maneuver design and conjunction assessment. The MMS fly in tetrahedron formations around apogee, in order to collect simultaneous particles and fields science data. The original plan was to fly tetrahedra between 10 and 160 km in size; however, after Phase 1a of the mission, the science team requested that smaller sizes be flown if feasible. After analysis (to be detailed in a companion paper), a new minimum size of 7 km was decided upon. Flying at this reduced scale size makes conjunction assessment between the MMS spacecraft even more important: the methods that are used by the MMS FDOA to address this problem will be described in the paper, and a summary given of the previous analyses that went into the development of these techniques. Details will also be given of operational experiences to date. Finally, two CA mitigation maneuver types that have been designed (but never yet required to actually be performed) will also be outlined.
This document, the Kepler Instrument Handbook (KIH), is for Kepler and K2 observers, which includes the Kepler Science Team, Guest Observers (GOs), and astronomers doing archival research on Kepler and K2 data in NASAs Astrophysics Data Analysis Program (ADAP). The KIH provides information about the design, performance, and operational constraints of the Kepler flight hardware and software, and an overview of the pixel data sets available. The KIH is meant to be read with these companion documents:1. Kepler Data Processing Handbook (KSCI-19081) or KDPH (Jenkins et al., 2016). The KDPH describes how pixels downlinked from the spacecraft are converted by the Kepler Data Processing Pipeline (henceforth just the pipeline) into the data products delivered to the MAST archive. 2. Kepler Archive Manual (KDMC-10008) or KAM (Thompson et al., 2016). The KAM describes the format and content of the data products, and how to search for them.3. Kepler Data Characteristics Handbook (KSCI-19040) or KDCH (Christiansen et al., 2016). The KDCH describes recurring non-astrophysical features of the Kepler data due to instrument signatures, spacecraft events, or solar activity, and explains how these characteristics are handled by the pipeline.4. Kepler Data Release Notes 25 (KSCI-19065) or DRN 25 (Thompson et al., 2015). DRN 25 describes signatures and events peculiar to individual quarters, and the pipeline software changes between a data release and the one preceding it.Together, these documents supply the information necessary for obtaining and understanding Kepler results, given the real properties of the hardware and the data analysis methods used, and for an independent evaluation of the methods used if so desired.
The ARTEMIS mission, part of the THEMIS extended mission, is the first to fly spacecraft in the Earth-Moon Lissajous regions. In 2009, two of the five THEMIS spacecraft were redeployed from Earth-centered orbits to arrive in Earth-Moon Lissajous orbits in late 2010. Starting in August 2010, the ARTEMIS Pl spacecraft executed numerous station-keeping maneuvers, initially maintaining a lunar L2 Lissajous orbit before transitioning into a lunar LI orbit. The ARTEMIS P2 spacecraft entered a LI Lissajous orbit in October 2010. In April 2011, both ARTEMIS spacecraft will suspend Lissajous station-keeping and will be maneuvered into lunar orbits. The success of the ARTEMIS mission has allowed the science team to gather unprecedented magnetospheric measurements in the lunar Lissajous regions.
NASA's Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. This poster presents the use of satellite data from NASA's inventory that have the potential for use in identification and analysis of forest fire risk and subsequent after-effects within a geographic information system (GIS). The challenge is that Earth Observation data is complicated. There is plenty of data available, however, the science teams have had a "top-down" approach: define what it is you are trying to study -select a set of satellite(s) and sensor(s), and drill down for the data.
Aquarius is an L-band radar/radiometer instrument combination that has been designed to measure ocean salinity. It was launched on 10 June 2011 as part of the Aquarius/SAC-D observatory. The observatory is a partnership between the United States National Aeronautics and Space Agency (NASA), which provided Aquarius, and the Argentinian space agency, Comisin Nacional de Actividades Espaciales (CONAE), which provided the spacecraft bus, Satelite de Aplicaciones Cientificas (SAC-D). The observatory was lost four years later on 7 June 2015 when a failure in the power distribution network resulted in the loss of control of the spacecraft. The Aquarius Mission formally ended on 31 December 2017. The last major milestone was the release of the final version of the salinity retrieval (Version 5). Version 5 meets the mission requirements for accuracy, and reflects the continuing progress and understanding developed by the science team over the lifetime of the mission. Further progress is possible, and several issues remained unresolved at the end of the mission that are relevant to future salinity retrievals. The understanding developed with Aquarius is being transferred to radiometer observations over the ocean from NASA's Soil Moisture Active Passive (SMAP) satellite, and salinity from SMAP with accuracy approaching that of Aquarius are already being produced.
Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAsSEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing C burn = Δ Ctot added to the fire plume, where ≈ Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individual species i, δέλτα-xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AÅE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.
Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAs SEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing Cburn Ctot added to the fire plume, where Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individualspecies i, xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.
Since the beginning of the Mars Science Laboratory (MSL) mission, Vera Rubin Ridge (VRR) has been a location of interest to the MSL science team because of its apparent erosional resistance and strong near-IR (~860 nm) absorption feature seen from orbit in the Mars Reconnaissance Orbiter mission's Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data. The strong CRISM absorption feature along VRR was hypothesized to be primarily associated with an increased abundance of crystal-line hematite compared to lower Mt. Sharp units. How-ever, surface multispectral and mineralogic data, from the Mastcam and CheMin instruments onboard the Curiosity rover, suggest hematite is not the only mineral contributing to the near-IR absorption feature measured in VRR or the reason for its relative hardness.
xGDS supports rapid scientific decision making by synchronizing the time and mapped location of observation notes, instrument data, photos, video, samples and other data. xGDS is a suite of web tools, developed at NASA Ames Research Center to support remote science operations in analog missions and prototype new ideas for planetary exploration. During operations, xGDS displays science data in real-time with geographic context, supporting key decisions (e.g. sample site selection). Post-operations, xGDS enables efficient analysis of data by correlating data products in time and on the map. xGDS has been developed in close collaboration with science teams since 2009, and used enthusiastically by upwards of 100 scientists to support scientific field research and data analysis.
The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs"—Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). GPM is well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free.A few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of maps, then extends to preliminary analyses of dataset characteristics. For example, the diurnal cycle demonstrates improvements over V05.
Proceedings of NASA Atmospheric Infrared Sounder Spring Science Team Meeting (AIRS 2016)
The SERVIR program, a joint development initiative of National Aeronautics and Space Administration (NASA) and United States Agency for International Development (USAID), works in partnership with leading regional organizations world-wide to help developing countries use information provided by Earth observing satellites and geospatial technologies for managing climate risks and land use. SERVIR empowers decision-makers with tools, products, and services to act locally on critical issues related to four key areas: Food Security and Agriculture, Water and Water-related Disasters, Land Use/Land Cover and Ecosystems, Weather and Climate. SERVIR is improving awareness, increasing access to information, and supporting analysis to help people in West Africa, Eastern and Southern Africa, Hindu Kush-Himalaya (HKH), the Lower Mekong, South America and Mesoamerica. NASA funds an Applied Sciences Team (AST), composed of experts from universities and research institutions around the United States. Through AST, SERVIR adopted the Tethys platform introduced by Brigham Young University (BYU) for the implementation of tools to deliver scientific data to stakeholders in the Hindu Kush-Himalaya region. Several Tethys applications developed within SERVIR have raised interest amongst decision makers in the countries served by our regional hubs. They see these new apps as key vehicles to disseminate complex information related to weather models, hydrological models, and air quality monitoring, and as such the scope of our Tethys efforts continue to expand to other regions. As of July 2019, each one of the SERVIR hubs (with the exception of Amazonia, which has only recently started), have stood up Tethys portals and have at least one individual trained in the implementation and maintenance of a Tethys portal and in the creation of basic applications. HKH, with BYU's valuable support, has implemented very sophisticated applications that showcase the possibilities and the benefits that the Tethys platform brings.
The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs" Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). The longer latencies increase the available input data for the resulting estimates, most notably the use of monthly precipitation gauge analyses in the Final run. The Early and Late runs use a climatological gauge adjustment as a proxy for the monthly gauge analyses. At meeting time GPM should be well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free. In this talk a few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of near-real-time maps, then extends to preliminary analyses of dataset characteristics. For example, the accumulations during Hurricane Harvey around Houston, Texas, USA, tended to be low, while accumulations along the Texas/Louisiana border to the northeast tended to be high. Furthermore, these opposite-sign differences occurred more or less simultaneously over much of the accumulation period. The working hypothesis is that there were systematic differences in the convective "regime" in the two places. The talk will end with a quick summary of the processing status and the future course of IMERG development.
This presentation provides the future plans for the Earth Observing System (EOS) Aura satellite to the Aura Science Team community. It provides a summary of predictions for future fuel usage, maneuver schemes, and changes to the orbit parameters through the end of mission.