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NLDAS Mosaic Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:56:09.000ZThis monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. Brief description about the NLDAS-2 hourly and monthly Mosaic LSM data can be found from the GCMD DIFs for GES_DISC_NLDAS_MOS0125_H_V002 and GES_DISC_NLDAS_MOS0125_M_V002 at http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_MOS0125_H_V002 and http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_MOS0125_M_V002. Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). The NLDAS-2 Mosaic model monthly climatology data set contains thirty-seven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_MOS.002.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. There are six vertical levels for the Soil Moisture (PDS 086) in the Mosaic GRIB files. For more information, please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/NLDAS/NLDAS_MOS0125_MC.002.ctl.
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LBA Regional Monthly Climatology for the 20th Century (New et al.)
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T08:10:02.000ZThis data set is a subset of "Global Monthly Climatology for the 20th Century (New et al.)" (2000a). This subset characterizes mean monthly surface climate over the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W) during nearly all of the 20th Century. The data are gridded at 0.5-degree latitude/longitude resolution and include seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and further data will be added by the data originators. In constructing the monthly grids, the authors used an anomaly approach that attempts to maximize station data in space and time (New et al. 2000b). In this technique, grids of monthly historic anomalies are derived in relation to a standard normal period. Station measurement data for the years 1961-1990 were extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN) and used in constructing the normal period (New et al. 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data are in ASCII GRID format for ArcInfo. Information on creating this LBA subset is available in ftp://daac.ornl.gov/data/lba/physical_climate/GIS_EastAngliaClimateMonthly/comp/eastanglia_readme.pdf.Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre.To access the complete year-by-year monthly data set or data more recent than posted here, users may make a request with the Climate Impacts LINK Project at the Climatic Research Unit (e-mail: d.viner@uea.ac.uk; web site: www.cru.uea.ac.uk/link). LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.
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SBUV2/NOAA-16 Ozone (O3) Profile and Total Column Ozone 1 Month Zonal Mean L3 Global 5.0 degree Latitude Zones V1 (SBUV2N16L3zm) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:51:01.000ZThe Solar Backscattered Ultraviolet (SBUV) from NOAA-16 Level-3 monthly zonal mean (MZM) product (SBUV2N16L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 154 months of data from October 2000 through July 2013. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). The MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions: 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees). 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone). NOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm. The zonal means computed for each month are screened according to the following statistical criteria: 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month. 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band. 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).
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Approximate Cartesian Control for Robotic Tool Usage with Graceful Degradation Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:39.000ZMany of NASA's exploration scenarios include important roles for autonomous or partially autonomous robots. It is desirable for them to utilize human tools when possible, rather than needing to build custom tools for each robot. Control of robotic manipulators for tool usage generally requires a very precise Cartesian-space trajectory of the tool tip (e.g., moving a marker along the surface of a whiteboard or rotating a screwdriver about an axis). Well-known techniques exist for manipulator control in Cartesian space, most of which necessitate solving a series of Inverse Kinematics (IK) problems. Closed-form IK solvers work well for 7-degree-of-freedom (DOF) arms with rigid tool attachments, but cannot handle non-rigid tools that slip in the robot's hands. Numerical IK approaches are more generic and can handle non-rigid links to tools, but can be slow to converge. More importantly, if any joints fail or become limited in their range of motion, the robot arm essentially becomes 6-DOF or lower. IK solvers often fail in these lower DOF spaces because the configuration space becomes non-continuous and full of "holes". As a result, a 7-DOF robotic arm in space might be rendered largely useless if a single joint fails or even loses mobility until it can be serviced. TRACLabs proposes to investigate an alternative approach to traditional Cartesian control approaches, which rely on complex IK solvers that go from Cartesian space backwards to joint space. We propose to leverage cheap memory and modern processing speeds to instead perform simple computations that go from joint space forwards to Cartesian space. Such techniques should overcome common changes to a manipulation chain caused by tool slippage or the grasping of a new tool and to overcome uncommon changes to a chain caused by joint failures, reduced joint mobility, changes in joint geometry or range of motion, or added joints.
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CogGauge Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:22:36.000ZCog-Gauge is a portable hand-held game that can be used by astronauts and crew members during space exploration missions to assess their cognitive workload decrements that possibly result from fatigue, stress, or neurocognitive deficits. Cog-Gauge combines behavioral workload assessment using a dual-task approach with predictive workload models to counter the effects of game learning. The game will be built using an iterative usability driven approach where emphasis will be placed on building an engaging relevant game that builds from contextual task analysis and user profiling. The specific technical challenges foreseen are integrating two approaches of cognitive workload modeling, and using learning curves to model game learning, then using algorithms to determine a user's workload as soon as they complete a timed interaction with the game. Specific questions to address pertain to feasibility of proposed solution and hardware/software requirements.
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GPM Level 3 IMERG Monthly 0.1 x 0.1 degree V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:01:54.000ZThis Directory Interchange Format (DIF) describes a collection of fields for the GPM Level 3 IMERG *Final* Monthly 0.1 x 0.1 degree V03 (GPM_3IMERGM) at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The Integrated Multi-satelliE Retrievals for GPM (*IMERG*) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 0.1°x0.1° fields. These are provided to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated “even-odd” geo- IR fields and forward them to PPS for use in the CMORPH-KF Lagrangian time interpolation scheme and the PERSIANN-CCS computation routines. The PERSIANN-CCS estimates are computed (supported by an asynchronous re-calibration cycle) and sent to the CMORPH-KF Lagrangian time interpolation scheme. The CMORPH-KF Lagrangian time interpolation (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The IMERG system is run twice in near-real time • “Early” multi-satellite product ~4 hr after observation time and • “Late” multi-satellite product ~12 hr after observation time, and once after the monthly gauge analysis is received • “Final” satellite-gauge product ~2 months after the observation month. The baseline is for the (near-)real-time Early and Late half-hour estimates to be calibrated with climatological coefficients that vary by month and location, while in the Final post-real-time run the multi-satellite half-hour estimates are adjusted so that they sum to a monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. Release document: http://pmm.nasa.gov/sites/default/files/document_files/IMERG_FinalRun_Day1_release_notes.pdf Other key documents: Technical document and acronyms: http://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf Algorithm Theoretical Basis Document (ATBD): http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf In brief, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the GPM Combined Instrument product (because it is presumed to be the best snapshot GPM estimate), then “morphed” and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with monthly surface precipitation gauge analysis data (where available) to provide half-hourly and monthly precipitation estimates on a 0.1° lat./long. grid over the domain 60°N-S. Precipitation phase is diagnosed using analyses of surface temperature, humidity, and pressure. The current period of record is mid-March 2014 to the present (delayed by about 2 months).The Integrated Multi-Satellite Retrievals for GPM (IMERG) algorithm is designed to leverage the international constellation of precipitation-relevant satellites to create a long record of uniformly time/space gridded precipitation estimates for the globe. The algorithm is focused on creating the best estimate at each time step, meaning that it is not a Climate Data Record, although the ideal is as homogenous a record as possible.
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Integrated System Management and Reconfigurable Control Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:43:36.000ZThe team proposes to develop an onboard, real-time health management capability that monitors a flight control system (for spacecraft, fixed or rotary wing aircraft) in a highly dynamic environment and responds to anomalies with suggested recovery or mitigation actions. The goal of the proposed capability is to take system/component level health status information and aggregate this information across all channels and subsystems to the flight control system for anomaly mitigation, failure accommodation, and control re-configuration, based on mission objectives. In Phase I, the research will be focused on a preliminary design of the component-to-system health capability correlation and the anomaly mitigation strategy. In Phase II, the team will conduct a prototype demonstration for a relevant space vehicle as the target application.
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SAFARI 2000 Surface Atmospheric Radiative Transfer (SMART), Dry Season 2000
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T08:43:42.000ZSurface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena. This allows investigators to participate in coordinated measurement campaigns, such as SAFARI 2000.The SMART instruments were deployed to the Skukuza Airport from August 15 to September 17, 2000 to take part in the SAFARI 2000 Dry Season Aircraft Campaign. The SMART-COMMIT mission is designed to pursue the following goals: Earth Observing System (EOS) validation; innovative investigations; and long-term atmospheric monitoring. The results reported in this data set are for the following instruments deployed and measurements recorded at the Skukuza Airport site within the Kruger National Park: several broadband radiometers, for global, diffuse, direct downward solar irradiance and global infrared downward irradiance; meteorological sensors, for surface air temperature, pressure, relative humidity, and wind; and a Solar Spectral Flux Radiometer (NASA Ames) for spectral solar downward irradiance.
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NLDAS Mosaic Land Surface Model L4 Monthly 0.125 x 0.125 degree V002
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:56:10.000ZThis data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly Mosaic model data were generated from the NLDAS-2 hourly Mosaic model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, and snow melt, and monthly average for other variables. Monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, except the first month (Jan. 1979) that starts from 00Z 02 Jan 1979. Brief description about the NLDAS-2 monthly Mosaic model can be found from the GCMD DIF for NLDAS-2 hourly Mosaic data GES_DISC_NLDAS_MOS0125_H_V002 at http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_MOS0125_H_V002. Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012). The NLDAS-2 monthly Mosaic model data contain thirty-seven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_MOS.002.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. For information about the vertical layers of the Soil Moisture Content (PDS 086) and Soil Temperature (PDS 085), please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/NLDAS/NLDAS_MOS0125_M.002.ctl.
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NLDAS VIC Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T08:29:26.000ZAbstract: This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The file format is WMO GRIB-1. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980-2009) of the NLDAS-2 monthly data. Brief description about the NLDAS-2 hourly and monthly VIC LSM data can be found from the GCMD DIFs for GES_DISC_NLDAS_VIC0125_H_V002 and GES_DISC_NLDAS_VIC0125_M_V002 at http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_VIC0125_H_V002 and http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_VIC0125_M_V002. Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003). The NLDAS-2 VIC monthly climatology data contain forty-three fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_VIC.002.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. For information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151), please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/NLDAS/NLDAS_VIC0125_MC.002.ctl.