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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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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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CORONA Satellite Photographs from the U.S. Geological Survey
data.nasa.gov | Last Updated 2022-01-17T05:16:00.000ZThe first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.
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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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OMI/Aura Level 1B UV Zoom-in Geolocated Earthshine Radiances 1-orbit L2 Swath 13x12 km V003 (OML1BRUZ) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:46:59.000ZThe Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Zoom-in-Mode (OML1BRUZ) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 215 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. The shortname for this OMI Level-1B Product is OML1BRUZ. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRUZ files are stored in HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.
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Probabilistic Model-Based Diagnosis for Electrical Power Systems
data.nasa.gov | Last Updated 2020-01-29T02:07:39.000ZWe present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
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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.