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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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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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Metagenomic analysis of feces from mice flown on the RR-6 mission
data.nasa.gov | Last Updated 2023-01-26T18:46:14.000ZThe objective of the Rodent Research-6 (RR-6) study was to evaluate muscle atrophy in mice during spaceflight and to test the efficacy of a novel therapeutic to mitigate muscle wasting. The experiment involved an implantable subcutaneous nanochannel delivery system (nDS; between scapula) which delivered the drug formoterol (FMT; a selective xce xb22 adrenoceptor agonist) over the course of time. To this end a cohort of forty 32-weeks-old female C57BL/6NTac mice were either sham operated or implanted with vehicle or treatment-filled nDS launched in two Transporters (20 mice per Transporter) on SpaceX-13 on December 15 2017. They were transferred to Rodent Habitats onboard the International Space Station (ISS) and maintained in microgravity for 29 days (N=20 Live Animal Return Spaceflight [LAR FLT]) or >50 days (N=20 ISS Terminal Spaceflight [ISS-T FLT]). After 29 days the 20 LAR FLT animals were returned live to back to Earth on January 13 2018. After splashdown the animals were ambulatory on-ground for ~4 days until all subjects were processed during one day of dissections. There were two Basal (BSL) groups of animals sacrificed (LAR BSL & ISS-T BSL; N=20; 40 animals; ~36 weeks old) at Kennedy Space Center (KSC; 12/9/17). LAR BSL animals were dissected and samples were collected upon euthanasia. A Ground Control (GC) group LAR GC mimicked the LAR FLT group which was housed at KSC then shipped alive to Novartis xe2 x80 x99s Facilities where both the LAR FLT and LAR GC groups were processed (~41 weeks old; 1/16/18). All were anesthetized with isoflurane blood samples were obtained by closed-chest cardiac puncture and the animals were euthanized by exsanguination and thoracotomy. The 20 ISS-T FLT mice were anesthetized via intraperitoneal injection of ketamine/xylazine/acepromazine over the course of a four days of dissections (2/6/18 until 2/9/18; 53-56 days after launch; 44 weeks old at time of on-orbit dissections). Blood samples and euthanasia were conducted the same as LAR groups. Following blood draw and hind limb dissection the ISS-T FLT animal carcasses were wrapped in aluminum foil placed in a ziploc bag and placed in storage at -80 xcb x9aC or colder until return. The ISS-T Ground Control (ISS-T GC) (at KSC) followed the same euthanasia timeline methods and preservation. The final processing of frozen ISS-T FLT frozen ISS-T GC and frozen 0-day ISS-T BSL animals were completed at Houston Methodist Research Institute in Houston TX (5/21/18 until 5/24/18). GeneLab received feces from only sham treated animals (no drug treated animals) from the following groups. FLT: LAR (n=9) ISS-T (n=7); GC: LAR (N=7) ISS-T (N=9); BSL: LAR (n=7) ISS-T (n=9). DNA was extracted and analyzed by sequencing using a variety of different targeted and un-targeted metagenome profiling assays.
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Transcriptional analysis of dorsal skin from mice flown on the RR-6 mission
data.nasa.gov | Last Updated 2023-01-26T18:45:51.000ZThe objective of the Rodent Research-6 (RR-6) study was to evaluate muscle atrophy in mice during spaceflight and to test the efficacy of a novel therapeutic to mitigate muscle wasting. The experiment involved an implantable subcutaneous nanochannel delivery system (nDS; between scapula) which delivered the drug formoterol (FMT; a selective Beta-2 adrenoceptor agonist) over the course of time. To this end a cohort of forty 32-weeks-old female C57BL/6NTac mice were either sham operated. or implanted with vehicle or treatment-filled nDS and launched in two Transporters (20 mice per Transporter) on SpaceX-13 on December 15 2017. They were transferred to Rodent Habitats onboard the International Space Station (ISS) and maintained in microgravity for 29 days (N=20 Live Animal Return [LAR]) or >50 days (N=20 ISS Terminal). After 29 days the 20 LAR animals were returned live to back to Earth on January 13 2018. After splashdown the animals were ambulatory on-ground for ~4 days until all subjects were processed during one day of dissections. There were two Baseline groups of animals sacrificed (LAR Baseline & FLT Baseline; N=20; 40 animals; ~36 weeks old) at Kennedy Space Center (KSC; 12/9/17). A Ground Control group mimicked the Flight LAR group which was housed at KSC then shipped alive to Novartis facilities where both the LAR and LAR Ground Control groups were processed (~41 weeks old; 1/16/18). All were anesthetized with isoflurane blood samples were obtained by closed-chest cardiac puncture and the animals were euthanized by exsanguination and thoracotomy. The 20 ISS Terminal mice were anesthetized via intraperitoneal injection of ketamine/xylazine/acepromazine over the course of a four days of dissections (2/6/18 until 2/9/18; 53-56 days after launch; 44 weeks old at time of on-orbit dissections). Blood samples and euthanasia were conducted the same as LAR and Baseline. Following blood draw and hind limb dissection the ISS-terminal animal carcasses were wrapped in aluminum foil placed in a ziploc bag and placed in storage at -80C or colder until return. The ISS-terminal Ground Controls (at KSC) followed the same euthanasia timeline methods and preservation. The final processing of frozen ISS-terminal frozen ISS-terminal Ground Controls and frozen 0-day FLT baseline animals were completed at Houston Methodist Research Institute in Houston TX (5/21/18 until 5/24/18). GeneLab received samples of dorsal skin from only sham treated animals (no drug treated animals) from the following groups Flight: LAR (n=9) ISS Terminal (n=9); Ground Controls: LAR GC (N=9) ISS Terminal GC (N=10) LAR Baseline (n=10) ISS Terminal Baseline (n=6). Total RNA was extracted and sequenced at a target depth of 60 M clusters per sample (ribodepleted paired end 150).
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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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Transcriptional analysis of liver from mice flown on the RR-6 mission
data.nasa.gov | Last Updated 2023-01-26T18:45:47.000ZThe objective of the Rodent Research-6 (RR-6) study was to evaluate muscle atrophy in mice during spaceflight and to test the efficacy of a novel therapeutic to mitigate muscle wasting. The experiment involved an implantable subcutaneous nanochannel delivery system (nDS; between scapula) which delivered the drug formoterol (FMT; a selective Beta-2 adrenoceptor agonist) over the course of time. To this end a cohort of forty 32-weeks-old female C57BL/6NTac mice were either sham operated or implanted with vehicle or treatment-filled nDS launched in two Transporters (20 mice per Transporter) on SpaceX-13 on December 15 2017. They were transferred to Rodent Habitats onboard the International Space Station (ISS) and maintained in microgravity for 29 days (N=20 Live Animal Return [LAR]) or >50 days (N=20 ISS Terminal). After 29 days the 20 LAR animals were returned live to back to Earth on January 13 2018,. After splashdown the animals were ambulatory on-ground for ~4 days until all subjects were processed during one day of dissections. There were two Baseline groups of animals sacrificed (LAR Baseline & FLT Baseline; N=20; 40 animals; ~36 weeks old) at Kennedy Space Center (KSC; 12/9/17). A Ground Control group mimicked the Flight LAR group which was housed at KSC then shipped alive to Novartis Facilities where both the LAR and LAR Ground Control groups were processed (~41 weeks old; 1/16/18). All were anesthetized with isoflurane blood samples were obtained by closed-chest cardiac puncture and the animals were euthanized by exsanguination and thoracotomy. The 20 ISS Terminal mice were anesthetized via intraperitoneal injection of ketamine/xylazine/acepromazine over the course of a four days of dissections (2/6/18 until 2/9/18; 53-56 days after launch; 44 weeks old at time of on-orbit dissections). Blood samples and euthanasia were conducted the same as LAR and Baseline. Following blood draw and hind limb dissection the ISS-terminal animal carcasses were wrapped in aluminum foil placed in a ziploc bag and placed in storage at -80C or colder until return. The ISS-terminal Ground Controls (at KSC) followed the same euthanasia timeline methods and preservation. The final processing of frozen ISS-terminal frozen ISS-terminal Ground Controls and frozen 0-day FLT baseline animals were completed at Houston Methodist Research Institute in Houston TX (5/21/18 until 5/24/18). GeneLab received samples of liver from only sham treated animals (no drug treated animals) from the following groups Flight: LAR (n=10) ISS Terminal (n= 10); Ground Controls: LAR GC (N=9) ISS Terminal GC (N=10) LAR Baseline (n=10) ISS Terminal Baseline (n=10). Total RNA was extracted and sequenced at a target depth of 60 M clusters per sample (ribodepleted paired end 150).
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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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Using Decision Trees to Detect and Isolate Leaks in the J-2X
data.nasa.gov | Last Updated 2020-01-29T04:53:31.000Z**Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine** Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center **Abstract** The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. **Introduction** The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool [http://www.gensym.com](http://www.gensym.com) to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro