JPL TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 03 in netCDF/ASCII/GeoTIFF Formatsdata.nasa.gov | Last Updated 2020-03-30T04:26:46.000Z
The JPL monthly land mass grids contain land water mass anomaly given as equivalent water thickness derived from GRACE time-variable gravity observations during the specified timespan in ASCII/netCDF/GeoTIFF formats. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. The time coverage for the monthly grids are determined by GRACE months ( https://podaac-tools.jpl.nasa.gov/drive/files/allData/tellus/L3/docs/GraceMonths.html ). A glacial isostatic adjustment correction has been applied, and standard corrections for geocenter (degree-1) and C20 (degree-20) are incorporated. Post-processing filters have been applied to reduce correlated errors. From the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in compliance with the PO.DAAC metadata best practice.
- API data.nasa.gov | Last Updated 2020-01-29T04:19:14.000Z
The goal of this project is to mature the technology readiness of a radiation tolerant smallsat computer system for a subsequent orbital flight demonstration. The system is implemented with commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) to provide space-computing performance that improves upon existing radiation hardened processors. Commercial FPGAs are now yielding acceptable levels of total ionizing dose immunity due to the thinning of gate oxides and relative deepening of isolation trenches. If a single event effect mitigation strategy can be implemented on COTS FPGAs, then reliable, high performance space computing can be accomplished at a fraction of the cost of existing radiation hardened processors.
Lightweight and Compace Multifunction Computer-Controlled Strength and Aerobic Training Device, Phase Idata.nasa.gov | Last Updated 2020-01-29T04:57:18.000Z
TDA Research proposes to develop a computer-controlled lightweight and compact device for aerobic and resistive training (DART) to counteract muscular atrophy and bone loss and to improve the overall wellness of astronauts operating in microgravity. The DART will be able to provide resistive loads up to 350 lbf and will accurately simulate the load profile of a mass in a 1-g environment. It will also be capable of applying custom load profiles such as eccentric overloading. In aerobic training mode, the DART will simulate the loads of a rowing machine with loads up to 175. The system will computer-controlled and can automatically calibrate to a user's range of motion. The total weight of the device will be less than 20 lbs and have a compact form factor to enable integration into a small crew module. By using a regenerative energy recovery system, the average power consumption of the DART will be less than 100 W during an exercise session. TDA is able to build on previous experience building exercise equipment for NASA and develop the DART in a short timeframe. TDA will prove the feasibility of providing effective aerobic and resistive training with a single device that is lightweight and compact in Phase I. At the end of Phase I a prototype will be delivered to NASA for evaluation. In Phase II we will advance the technology and provide the second generation prototype to NASA for testing on the International Space Station.
- API data.nasa.gov | Last Updated 2020-01-29T02:13:09.000Z
Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify fault modes, and to produce system estimates for State of Health (SoH) and Remaining Useful Life (RUL). The base technology has been applied for prognostic purposes for various government-sponsored programs, but FFP signature modeling and processing has not been applied for the area of Fault Management, nor does it include such features as fault dictionaries, lookup tables, and management algorithms. The technology includes Ridgetop-designed and developed algorithms to do the following: (1) perform Kalman Filtering to reduce noise; (2) transform sensor signal data to reveal underlying (hidden) FFP signatures; (3) normalize units-of-measure dependent signal data into dimensionless FFP signatures to facilitate re-use and reduce the time to characterize and define new FFP signatures; (4) define and use model definitions that reduce memory requirements and support fast and accurate processing and calculations; (5) two forms of trajectory curve characterization, both straight-line and curvilinear; (6) a fast yet accurate, graphics-based mathematical routine to adapt an FFP model to received data; (7) amplitude and time updates similar to Extended Kalman Filtering to estimate how long it will take an adapted FFP model to reach a defined failure threshold; and (8) produce SoH and RUL estimates that rapidly converge to the estimated time-to-failure (TTF) solution. The FFP signature modeling and processing will include additional innovation to support FM to minimize application-specific programming, those include algorithms to simplify fault identification and isolation.
- API data.nasa.gov | Last Updated 2020-01-29T04:07:32.000Z
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
- API data.nasa.gov | Last Updated 2020-01-29T04:11:51.000Z
In this proposal, we describe a program to develop a high-performance, cost-effective and robust microwave receiver prototype for multi-purpose Non-Destructive Evaluation (NDE). Currently, NDE of space transportation vehicles is primarily carried out on the ground, between missions. For future space missions, as duration and frequency increases, more inspection will need to be performed in space in order to monitor the aging process of the structure and to insure its integrity. For this purpose, NDE equipment that is compact, lightweight, easily operated by human with limited mobility or robot, and that exhibits low power consumption is required. Furthermore, in order to minimize the quantity of embarked equipment, the inspection equipment must be able to perform as many different inspection tasks as possible. Our innovative receiver is based on the integration of a microwave interferometer coupled with a pulsed laser to generate the ultrasound. . Based on the results obtained during Phase 1, we strongly think that we will be able to overcome the limitation generally associated with classical optical receiver: 1) Inability to work in factory environment where thermal, mechanical and optical propagation (fumes, water drops,..) perturbations are present; 2) Reduction in sensitivity caused by the speckle nature of the light reflected from rough surfaces; 3) High system cost due the price of the probe lasers, optics and engineering to develop an optical system working in a harsh environment (fumes, water drops, strong mechanical vibration) and 4) high maintenance cost (Lasers and optics need to be checked and re-aligned frequently). Our proposed approach will lead to a cost-effective prototype with good sensitivity and performances in industrial environment.
- API data.nasa.gov | Last Updated 2020-03-30T04:01:56.000Z
This data set contains active-layer and permafrost temperatures from two stations in Soendre Stroemfjord, Greenland. Snow depth and snow extent were also recorded. Thermometers at Station A (67 deg N, 50.8 deg W, 50 m asl) recorded temperatures once a day from September 1967 to February 1976. Thermometers at Station B (67 deg N, 50.8 deg W, 38 m asl) recorded temperatures once a day from September 1967 to August 1970; however, only bi-weekly averages are given for Station B. Data are in tab-delimited ASCII text format and are available via FTP.
- API data.nasa.gov | Last Updated 2020-03-30T04:10:51.000Z
CAL_LID_L2_333mCLay-ValStage1-V3-02 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1/3km (333m) cloud layer data. Version 3.02 represents a transition of the Lidar, IIR, and WFC processing and browse code to a new cluster computing system. No algorithm changes were introduced and very minor changes were observed between V3.01 and V3.02 as a result of the compiler and computer architecture differences. Within the Lidar Cloud Layer Product there are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each one of which is associated with a variable number of layer descriptors. The column descriptors specify the temporal and geo-physical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud and/or aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. New parameters for the V3-01 product include: column optical depths, layer top pressure, layer base pressure, layer mid-point pressure, layer top temperature, and layer base temperature. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.
- API data.nasa.gov | Last Updated 2020-03-30T04:10:53.000Z
CAL_LID_L2_05kmALay-Prov-V3-40 data are CALIPSO Lidar Level 2 5km aerosol layer data. Within the Lidar Aerosol Layer Product there are two general classes of data:- Column Properties (including position data and viewing geometry)- Layer PropertiesThe lidar layer products consist of a sequence of column descriptors, each one of which is associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. The science algorithms used to produce the V3.40 CALIOP data products are identical to those used to generate the V3.01 and V3.02 products; however, some of the ancillary data used in the V3.40 analyses is different. All CALIOP data products rely on meteorological data provided by NASA's Global Modeling and Assimilation Office (GMAO). The V3.01 and V3.02 data products were produced using the GMAO's GEOS 5.2 data products. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.
- API data.nasa.gov | Last Updated 2020-03-30T04:11:13.000Z
The lidar level 3 (L3) CALIPSO Global Energy and Water cycle Experiment (GEWEX) Cloud product is a reformatted version of CALIPSO’s contribution to the GEWEX cloud assessment of global cloud datasets from satellites (https://climserv.ipsl.polytechnique.fr/gewexca/index.html). The data submitted by the CALIPSO team this project had to conform to a very specific format; yearly netCDF files organized by parameter. In order to be compatible with other publicly orderable lidar L3 CALIPSO aerosol and cloud products, which are reported as monthly HDF files, this new lidar L3 CALIPSO GEWEX cloud product was created. These files report global distributions of cloud amount and cloud top as averages and histograms on a uniform 2-dimentional (2D) spatial grid. All level 3 parameters are derived from the CALIPSO version 4.x level 2 5 km cloud merged layer products, with a temporal averaging of one month.