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VIIRS/NPP BRDF/Albedo Snow Status Daily L3 Global 30ArcSec CMG V001
data.nasa.gov | Last Updated 2024-05-27T13:06:38.000ZThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD). VNP43D52 contains the snow status quality layer, which identifies each pixel as either “Snow-free Albedo Retrieved” or “Snow Albedo Retrieved” for the acquisition period.
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MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006
data.nasa.gov | Last Updated 2024-05-20T13:05:25.000ZThe Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006). The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. The MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation. Improvements/Changes from Previous Versions Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. * MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. * Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. * The MCD43 products use L2G-lite surface reflectance as input. * In cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. * CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the
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Classification of Aeronautics System Health and Safety Documents
data.nasa.gov | Last Updated 2020-01-29T01:57:57.000ZMost complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP to enable identification of system problems. However, repository volume and complexity make human analysis difficult. Multiple experts are needed, and they often disagree on classifications. Even the same person has classified the same document differently at different times due to evolving experiences. Consistent classification is necessary to support tracking trends in problem categories over time. A decision support system that performs consistent document classification quickly and over large repositories would be useful. We discuss the results of two algorithms we have developed to classify ASRS and ASAP documents. The first is Mariana---a support vector machine (SVM) with simulated annealing, which is used to optimize hyperparameters for the model. The second method is classification built on top of nonnegative matrix factorization (NMF), which attempts to find a model that represents document features that add up in various combinations to form documents. We tested both methods on ASRS and ASAP documents with the latter categorized two different ways. We illustrate the potential of NMF to provide document features that are interpretable and indicative of topics. We also briefly discuss the tool that we have incorporated Mariana into in order to allow human experts to provide feedback on the document categorizations.
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Northern Hemisphere Snow Cover Monthly Statistics at 1 Degree Resolution V001 (NHSNOWM) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:45:22.000ZThis product is Snow Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly snow statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period from January 2000 to November 2014. Monthly data were derived from daily snow cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).
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High Mountain Asia UCLA Daily Snow Reanalysis V001
data.nasa.gov | Last Updated 2022-01-17T05:28:32.000ZSnowpack plays a significant role in the hydrologic cycle over High Mountain Asia (HMA). As a vital water resource, the distribution of snowpack volume also impacts the water availability for downstream populations. To assess the regional water balance, it is important to characterize the spatio-temporal distribution of water storage in the HMA snowpack. This HMA snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow covered area (fSCA), snow depth (SD), etc.
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ASO L4 Lidar Snow Water Equivalent 50m UTM Grid V001
data.nasa.gov | Last Updated 2022-01-17T05:08:23.000ZThis data set contains 50 m gridded snow water equivalent (SWE) values collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns. The data were derived from the <a href="https://nsidc.org/data/aso_50m_sd">ASO L4 Lidar Snow Depth 50m UTM Grid</a> data product and from modeled snow density.
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Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
data.nasa.gov | Last Updated 2022-01-17T05:15:59.000ZThis data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
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SnowEx17 Boise State University Terrestrial Laser Scanner (TLS) Point Cloud V001
data.nasa.gov | Last Updated 2022-01-17T05:55:02.000ZThis data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2016) and snow-on (February 2017) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.
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MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0 V001
data.nasa.gov | Last Updated 2022-01-17T05:34:41.000ZThis data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively. The fourth variable merges the three source products into a single representation of snow cover.
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SnowEx17 Community Snow Pit Measurements V001
data.nasa.gov | Last Updated 2022-01-17T05:55:04.000ZThis data set contains measurements of snow temperature, density, stratigraphy, grain size, wetness, depth, and snow water equivalent (SWE) for snow pits at two study sites in Colorado, USA: Grand Mesa and Senator Beck Basin. Data were collected during the NASA SnowEx 2017 campaign.