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Global Navigation Satellite System (GNSS) Final Clock Product (30 second resolution, daily files, generated weekly) from NASA CDDIS
data.nasa.gov | Last Updated 2023-02-28T19:25:26.000ZThis derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (30-second granularity, daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.
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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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Global Navigation Satellite System (GNSS) Rapid Orbit/Clock/ERP Product Summary from NASA CDDIS
data.nasa.gov | Last Updated 2023-03-01T00:51:24.000ZThis derived product set consists of Global Navigation Satellite System Rapid Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined orbit, satellite and receiver clock, and ERP products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily rapid products.
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Optimal Alarm Systems
data.nasa.gov | Last Updated 2020-01-29T03:25:13.000ZAn optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It currently use Kalman filtering for dynamic systems to provide a layer of predictive capability for the forecasting of adverse events. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. Due to the fact that the alarm regions for an optimal level-crossing predictor cannot be expressed in closed form, one of our aims has been to investigate approximations for the design of an optimal alarm system. Approximations to this sort of alarm region are required for the most computationally efficient generation of a ROC curve or other similar alarm system design metrics. Algorithms based upon the optimal alarm system concept also require models that appeal to a variety of data mining and machine learning techniques. As such, we have investigated a serial architecture which was used to preprocess a full feature space by using SVR (Support Vector Regression), implicitly reducing it to a univariate signal while retaining salient dynamic characteristics (see AIAA attachment below). This step was required due to current technical constraints, and is performed by using the residual generated by SVR (or potentially any regression algorithm) that has properties which are favorable for use as training data to learn the parameters of a linear dynamical system. Future development will lift these restrictions so as to allow for exposure to a broader class of models such as a switched multi-input/output linear dynamical system in isolation based upon heterogeneous (both discrete and continuous) data, obviating the need for the use of a preprocessing regression algorithm in serial. However, the use of a preprocessing multi-input/output nonlinear regression algorithm in serial with a multi-input/output linear dynamical system will allow for the characterization of underlying static nonlinearities to be investigated as well. We will even investigate the use of non-parametric methods such as Gaussian process regression and particle filtering in isolation to lift the linear and Gaussian assumptions which may be invalid for many applications. Future work will also involve improvement of approximations inherent in use of the optimal alarm system of optimal level-crossing predictor. We will also perform more rigorous testing and validation of the alarm systems discussed by using standard machine learning techniques and consider more complex, yet practically meaningful critical level-crossing events. Finally, a more detailed investigation of model fidelity with respect to available data and metrics has been conducted (see attachment below). As such, future work on modeling will involve the investigation of necessary improvements in initialization techniques and data transformations for a more feasible fit to the assumed model structure. Additionally, we will explore the integration of physics-based and data-driven methods in a Bayesian context, by using a more informative prior.
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MODIS/Aqua Cloud Properties 5-min L2 Swath 1km
data.nasa.gov | Last Updated 2024-06-10T13:02:53.000ZThe MODIS/Aqua Cloud Properties 5-min L2 Swath 1km product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS Cloud Properties product does not use algorithms identical to those used in the standard MODIS product (MOD06/MYD06). The product consists of cloud optical and physical parameters derived using observations in visible through infrared spectral channels. MODIS infrared channels that are common with VIIRS are primarily used to derive cloud-top temperature, cloud-top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. The MODIS solar reflectances channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The MODIS Cloud Properties product is a Level-2 product generated at 1 km (at nadir) spatial resolution. The current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties’ thermodynamic phase that caused erroneous liquid water cloud phase results.
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VIIRS/NOAA20 Cloud Properties 6-min L2 Swath 750m
data.nasa.gov | Last Updated 2024-09-16T13:03:45.000ZThe VIIRS/NOAA20 Cloud Properties 6-min L2 Swath 750m product is a continuity product similar to its counterpart product from the Suomi National Polar-orbiting Partnership (SNPP) VIIRS. Judiciously leveraging a common set of spectral channels, they help sustain the long-term records of both MODIS and VIIRS heritages. A commonly applicable algorithm to both MODIS and VIIRS inputs is the hallmark of this continuity approach. CLDPROP_L2_VIIRS_NOAA20 is the shortname for the NOAA20 VIIRS incarnation of the orbital swath-based Cloud Properties continuity product. For more information, visit product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_L2_VIIRS_NOAA20
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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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SIAM 2007 Text Mining Competition dataset
data.nasa.gov | Last Updated 2020-01-29T04:25:03.000Z**Subject Area:** Text Mining **Description:** This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining algorithms for document classification. The documents in question were aviation safety reports that documented one or more problems that occurred during certain flights. The goal was to label the documents with respect to the types of problems that were described. This is a subset of the Aviation Safety Reporting System (ASRS) dataset, which is publicly available. **How Data Was Acquired:** The data for this competition came from human generated reports on incidents that occurred during a flight. **Sample Rates, Parameter Description, and Format:** There is one document per incident. The datasets are in raw text format. All documents for each set will be contained in a single file. Each row in this file corresponds to a single document. The first characters on each line of the file are the document number and a tilde separats the document number from the text itself. **Anomalies/Faults:** This is a document category classification problem.
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Next Generation Microshutter Arrays Project
data.nasa.gov | Last Updated 2020-01-29T03:35:26.000ZWe propose to develop the next generation MicroShutter Array (MSA) as a multi-object field selector for missions anticipated in the next two decades. For many applications, this field selector improves instrument efficiency proportionally to the number of shutters opened simultaneously. We have successfully developed and built the JWST microshutter array system, which increases the observing efficiency of the Near Infrared Spectrometer by two orders of magnitude. As a result of this development, there is a significant scientific demand for these devices for space-based and ground based applications. The availability of large format microshutters can significantly increase the scientific reach of spectroscopic survey instrument such as WFIRST and future missions such as ATLAST. The basic design of the JWST MSA cannot be extended to such a large scales due to the design limitations set by the required magnetic actuation. We have recently demonstrated shutter operation using DC plus AC resonant pumping. The breakthrough demonstrates that we are able to eliminate bulky permanent magnets used for JWST MSA actuation, thus opening an avenue to create a very large focal plane field selector that can be built at much lower cost. in this program, we will fabricate electrostatically actuated microshutter arrays and demonstrate their performance for use as UV, visible, and infrared field selectors.
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MODIS/Aqua Calibrated Radiances 5-Min L1B Swath 500m - NRT
data.nasa.gov | Last Updated 2023-07-24T13:04:42.000ZThe 500 meter MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for these solar reflective bands through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channel locations for the MODIS 500 meter data are as follows: Band Center Wavelength (um) Primary Use ---- ---------------------- ----------- 1 0.620 - 0.670 Land/Cloud Boundaries 2 0.841 - 0.876 Land/Cloud Boundaries 3 0.459 - 0.479 Land/Cloud Properties 4 0.545 - 0.565 Land/Cloud Properties 5 1.230 - 1.250 Land/Cloud Properties 6 1.628 - 1.652 Land/Cloud Properties 7 2.105 - 2.155 Land/Cloud Properties Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500 m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MYD02QKM) and 1 km resolution channels (MYD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of: 1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands 3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD http://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf . 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization The MODIS L1B 500 m data are stored in the Earth Observing System Hierarchical Data Format (HDF-EOS) which is an extension of HDF as developed by the National Center for Supercomputer Applications (NCSA) at the University of Illinois. A ty