- API data.nasa.gov | Last Updated 2020-02-10T05:30:39.000Z
The Global Precipitation Climatology Project (GPCP) Version 2 data set includes global, monthly precipitation rates and associated random errors (RMSE), and a monthly precipitation climatology derived as an average from all GPCP data sets from January 1979 to December 1999. The data are derived from measured gauge data and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data sets and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.
- API data.nasa.gov | Last Updated 2019-12-13T00:12:54.000Z
The SSM/I Derived Oceanic Monthly Rainfall Indices data set is a GlobalbPrecipitaton Climate Project (GPCP) product. Monthly rainfall indices overnthe oceans are derived from Special Sensor Microwave Imager (SSM/I) data from the Defense Meteorological Satellite Program (DMSP) satellites F8 and F11 on channels 19 and 22 V. The data set covers the period from July 1987 to December 1995. The monthly rainfall indices are on a 5 degree by 5 degree grid extending from 50 N to 50 S. The Wilheit, Chang and Chiu (1991) method used to derive the indices gives valid values only over ocean areas. Land pixels (including island pixels) and erroneous pixels return a -10 flag. The data are stored on a 72 x 20 grid. Grid point (1,1) contains the index for 45-50 N, 0-5 E, grid point (2,1) contains the index for 45-50 N, 5-10 E, ... and grid point (72,20) contains the index for 45-50 S, 175-180 W. In the data set, each month starts with an ASCII header to identify the year and month. The data is in 10F8.1 format. Each value is the average of AM and PM estimates and corrected for beam filling error. The equation used is: (AM PM)/2.0 * 1.8. Land pixels are set to -10.0. Also there are 33 pixels blocked out due to island contamination (-10.0). If the rain retrieval did not converge, a -10.0 is assigned to the pixel. The objective of this data set is to provide a long term monthly rainfall data set to be used in EOS global change and GEWEX related research. The data set can be accessed through the IMS. Data maintained in an off-line archive will also be listed in the IMS, and orders will be filled as the data is requested. The SSM/I Derived Oceanic Monthly Rainfall Indices data set will be reviewed every six months to ensure that the most current version of the data is available.
- API data.nasa.gov | Last Updated 2019-12-12T23:50:14.000Z
CAL_LID_L2_05kmCPro-Prov-V3-40 data are CALIPSO Lidar Level 2 Cloud Profile data. The Lidar Level 2 Cloud Profile data product contains cloud profile data and ancillary data. The cloud profile product is produced at 5 km horizontal resolution and is written in HDF. Note that there is no atmospheric volume characterization associated with the cloud profile products. Also, the 1064 calibration scheme assumes that both the extinction and the backscatter from clouds are spectrally independent. Consistent with this assumption, extinction and backscatter profiles will be reported for clouds only at 532 nm. Additionally, it is important to note that the aerosol profile product extends upward to 30.1 km, while the cloud profile product ceases at 20.2. Therefore, users interested in polar stratospheric clouds will need to order the aerosol profile data product. 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 2020-01-29T02:14:25.000Z
The objective of this research is to create a suite of tools for monitoring airport gate activities with the objective of improving aircraft turnaround. Airport ramp areas are the most crowded and cluttered spaces in the entire National Airspace System (NAS). Activities related to turnaround of the aircraft from the gate represent a significant source of delay and therefore impact the predictability of NAS operations. Optimal Synthesis Inc., seeks to leverage its expertise in monitoring aircraft in the ramp areas using video surveillance data and advanced computer vision algorithms towards building an advanced gate activity monitoring that will in turn enable a gate turnaround prediction tool. The tool suite will specifically identify the various stages of turnaround such as refueling, luggage unloading/loading, catering, and deicing. It will further create a probabilistic model of the times associated with each of these events, that will be used for predicting the future sequence of events and their predicted times of completion. Phase I research will demonstrate the core ideas of gate activity recognition using state-of-the-art computer vision and machine learning algorithms. Phase II research will elevate the technology readiness level of this tool suite to work with real-time video surveillance streams.
- API data.nasa.gov | Last Updated 2020-01-29T03:39:20.000Z
<p>The primary objective of this activity is to develop, design, and test (DD&T) the QUAD-core siTARA (QUATARA) computer to distribute computationally intensive processes such as: communication, sensors, attitude determination, attitude control, cameras, robotic manipulators, and science payloads. An example of the current state-of-the art for a COTS CubeSat flight computer is, a 16 bit 80 MHz Microchip dsPIC33 microcontroller capable of managing the satellite attitude determination, control system, communication system, power, and science payloads. Adding more capability to these COTS flight computers required the development, under a previous CIF proposal, of the Modular Attitude Determination System (MADS) board. MADS lessened the I/O load from the flight computer so it could focus on higher priority tasks such as managing a Real-Time Operating System (RTOS) or carrying out an attitude control algorithm. The MADS board utilized a 16 bit 80 MHz Texas Instruments ARM Cortex-M4 Stellaris microcontroller to execute the attitude determination algorithm independently of the dsPIC33 flight computer. Once the MADS board processes the data, the dsPIC33 receives the estimated states and determines the desired attitude control.</p><p>The addition of cameras, proximity sensors, robotic manipulators, thruster systems, complex science payloads and video guidance systems, would cause current CubeSat flight computers to be overwhelmed. Because of the desire to expand the capabilities of CubeSats, the innovation of the QUATARA architecture enhances the capabilities of data handling and computer processing by replacing the 16 bit 80 MHz microcontrollers with four 64 bit 1 GHz microprocessors. The QUATARA allows for tasks to be processed at a faster rate not only because of the difference in clock speed between the platforms but also because of the fact that there are four individual microprocessors which can run these tasks independently without the need to serialize the execution of the code like in a single microcontroller.</p><p>The QUATARA computer aims to be fault-tolerant by means of a software voting scheme to guard against the effects of Single Event Effects (SEE) such as Single Event Upsets (SEU). Each ‘node’ (Gumstix Computer-On-Modules (COM)) of the QUATARA computer will be connected to its own set of sensors and actuators. These individual nodes will collect their respective data and share it between themselves over a data bus (such as RS-485). Once each node has all the data from all of the other nodes it will process it and come up with a result. This result can then be used to determine if a node is considered as ‘failed’ and that node then needs to be disabled, (this can be done by ignoring future data received from that node or by completely shutting it off). In the case a node is lost a support node is available to be switched in for the failed node. This support node will focus on low priority tasks, (such as housekeeping), if it is not required as a voting node. Synchronization between the nodes can be maintained by having a precise timing source on each of the processors, (such as a ticking timer interrupt routine), that ticks at a set time interval. This timing information will be passed between the nodes and the tick rate of the interrupt routine will be modified as required to ensure that all of the nodes data remains in sync.</p>
- API data.nasa.gov | Last Updated 2020-01-29T04:12:36.000Z
Current-day capabilities for performing post operations analysis (POA) of air traffic operations at airports, airlines and FAA facilities are mostly limited to creating reporting type of analysis results which compare mean values of key performance indicators against the respective expected nominal levels (e.g., average daily delay). This single point comparison method does not directly enable a POA analyst to identify the root-cause for a particular observed inefficiency, nor does it help in identifying a solution for mitigating that inefficiency. This SBIR develops a machine learning based approach for improving POA and for potentially making it more autonomous. We call this tool Collective Inference based Data Analytics System for POA (CIDAS-P). CIDAS-P will provide airport, airline, FAA and NASA personnel with a fast, flexible and streamlined process for analyzing the day-of-operations, rapidly pinpointing exact causes for any observed inefficiencies, as well as recommending actions to be taken to avoid the same inefficiencies in the future. It does this by developing an innovative, collective inference algorithm for cross-comparing performance of the same facility on different days as well as cross-comparing performance across different facilities. The algorithm leverages sophisticated probabilistic modeling techniques that consider the subtle nuances by which cross-facility and cross-day operational scenarios differ to enable apples-to-apples comparisons across traffic scenarios and identify what works well and what does not in similar situations. User acceptance of NASA Trajectory Based Operations research products stands to benefit from CIDAS-P because CIDAS-P's automated recommendations can help identify and fix problems with these products early on in their deployment life-cycle.
- API data.nasa.gov | Last Updated 2020-01-29T04:04:16.000Z
slowed rotor / compound (SL/C) aircraft offer VTOL combined with fixed-wing flight-efficiencies. They are safer than any other type aircraft -- with much lower acquisition, maintenance and operational cost than helicopters and tiltrotors. Carter Aviation Technologies began developing SL/C aircraft in 1994 and began flying a prototype, the CarterCopter Technology Demonstrator (CCTD) in 1998. This proposal, using CCTD data, will provide a prototype 2-seat SR/C, VTOL aircraft that meets NASA?s PAVE goals. Reduced community noise is provided by a computerized propeller, designed for quietness, which operates at low tip-speeds and is protected by tail-booms. The non-stalling autorotating rotor provides low tip-speeds, eliminates the helicopter ?dead man zone? and provides the equivalent of an emergency parachute. Low cost per seat mile is provided by simplified construction, reduced parts count and high flight-efficiency. During VTOL and low-speed flight, SR/C aircraft fly like an autogyro having the same hp to weight ratio. Autogyros are the easiest aircraft to learn to fly safely. Pilot workload is simplified by an automated tilting pylon that keeps the wings in best L/D, an automated boosted collective and automated rotor flapping controls. The landing gear absorbs 24 ft/sec impacts. Only the tilting pylon is untested.
Microparticle, Conductivity, and Density Measurements from the WAIS Divide Deep Ice Core, Antarctica, Version 1data.nasa.gov | Last Updated 2020-02-10T05:35:38.000Z
This data set includes microparticle concentration, electrical conductivity, and density measurements from the West Antarctic Ice Sheet (WAIS) Divide deep ice core, WDC06A. Microparticle concentration data are reported as total particles per ml of meltwater. Concentration was measured using a laser detector and the University of Maine WAIS Melt Monitor system. Conductivity is measured in micro-Siemens per cm (uS/cm). Density data were collected on 3 by 3 by 100 cm sticks from the WDC06A core, using the Maine Automated Density Gauge Experiment (MADGE). Density data span 0 to 160 m in depth, while the particle and conductivity measurements span the upper 577 m of the core. Data are available via FTP in ASCII text format (.txt).
- API data.nasa.gov | Last Updated 2020-01-29T01:45:46.000Z
One of the most demanding and high-stakes crew tasks aboard the International Space Station (ISS) is the capture of a visiting spacecraft by manual operation of the Space Station Robotic Manipulator System (SSRMS, or Canadarm2). The cost of a missed capture or improper arm/vehicle contact is likely to be very high. Since these operations may be performed up to six months after the most recent ground-based training, crews aboard the ISS prepare for such manual robotic tasks with the Robotics On-Board Trainer, a laptop-based graphical/dynamic simulator using NASA Dynamic Onboard Ubiquitous Graphic (DOUG) software from Johnson Space Center's Virtual Reality Laboratory. This system, however, does not utilize any real-world, 3-D, out-the-window views. Building upon recent advances in head-mounted augmented reality systems, the team of Systems Technology, Inc. and Dr. Stephen Robinson of UC Davis propose the Station Manipulator Arm Augmented Reality Trainer (SMAART) that will offer ISS crews significantly more realistic on-board refresher training for vehicle capture by manipulating the actual SSRMS with real out-the-Cupola-window views, but with a graphically-simulated vehicle overlaid on the astronaut's non-simulated view via a head-mounted display. Providing multi-sensory realism in on-board training for such high cognitive-demand skills is expected to increase crew readiness and therefore reduce operational risk for visiting vehicle capture.
- API data.nasa.gov | Last Updated 2020-01-29T02:13:20.000Z
Given that SysML is becoming a standard for model-based systems engineering and Integration (SE&I), system health management (SHM)-related models will either be done in SysML, or be done outside of SysML but enabled by conversion, mapping, and traceability of information across SysML and SHM models. Given that current implementations of SysML are not particularly useful to perform analyses, and that SHM analyses are not identical to typical SE&I-related analyses, there will need to be connectivity between SysML representations and SHM models that perform SHM-related analyses. Qualtech Systems, Inc. (QSI), with Dr. Stephen Johnson as a consultant intends to explore and develop the integration of model-based systems engineering and Integration (SE&I) using SysML with system health management (SHM) modeling and analysis using QSI's Testability Engineering and Maintenance System (TEAMS). An overarching objective of this proposal is to reduce the duplicative and disjoint effort by NASA's subject matter experts in the development of systems engineering and design models as well as systems health management/fault management models. The intent is to leverage the success space or intent based system design models and transform them for developing fault management models and ensuring changes in design have a natural flow-through to the FM domain, thereby keeping FM models in sync with the design through a semi-automated process. This is one step in the larger set of issues that will need to be addressed in the development of the model-based Discipline of Systems Engineering and its concurrent integration with SHM to achieve higher-quality designs while reducing the costs of SE&I.