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SBUV2/NOAA-17 Ozone (O3) Profile and Total Column Ozone 1 Month Zonal Mean L3 Global 5.0 degree Latitude Zones V1 (SBUV2N17L3zm) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:51:02.000ZThe Solar Backscattered Ultraviolet (SBUV) from NOAA-17 Level-3 monthly zonal mean (MZM) product (SBUV2N17L3zm) 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 126 months of data from August 2002 through January 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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Bio-optical properties of the different water masses in the Gulf of St. Lawrence
data.nasa.gov | Last Updated 2023-04-17T13:03:21.000ZThe St. Lawrence ecosystem is a complex environment influenced by a variety of physical forces (runoff, winds, tides, bathymetry) that sustains a diverse food web going from phytoplankton to whales. Chlorophyll concentration is thus an important variable to measure at the scale of the ecosystem. Because of its large size, remote sensing is the only available tool to measure chlorophyll distribution in the St. Lawrence using ocean color imagery. To fully utilize this type of data, it is however important to have a sound knowledge of the bio-optical properties of the different water masses in the system. A St. Lawrence SeaWiFS program was thus built to gather this knowledge beginning in 1997.
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Model Adaptation for Prognostics in a Particle Filtering Framework
data.nasa.gov | Last Updated 2020-01-29T02:10:29.000ZOne of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
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PHOENIX MARS ROBOTIC ARM CAMERA 5 NORMAL OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:52:37.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains normal data from the Robotic Arm Camera (RAC).
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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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LIS 0.1 DEGREE VERY HIGH RESOLUTION GRIDDED LIGHTNING MONTHLY CLIMATOLOGY (VHRMC) V1
data.nasa.gov | Last Updated 2022-05-03T14:29:10.000ZThe LIS 0.1 Degree Very High Resolution Gridded Lightning Monthly Climatology (VHRMC) dataset consists of gridded monthly climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.
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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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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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TES/Aura L3 Water Vapor Monthly Gridded V006
data.nasa.gov | Last Updated 2022-01-17T05:58:53.000ZTL3H2OM_6 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Water Vapor Monthly Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of monthly atmospheric temperature and volume mixing ratios (VMRs) for the Water Vapor atmospheric species, which are provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products are composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir; Nadir observations, which point directly to the surface of the Earth, are different from limb observations, which are pointed at various off-nadir angles into the atmosphere. The product file may contain, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing were the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are complete Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represent Global Surveys that are initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly.
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TES/Aura L3 Ozone Daily Gridded V006
data.nasa.gov | Last Updated 2022-01-17T05:58:51.000ZTL3O3D_6 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Ozone Daily Gridded Version 6 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are completed Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceeded the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).