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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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Additive Manufacturing Technology Development Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:39:10.000Z<p>The 3D Printing In Zero-G (3D Print) technology demonstration project is a proof-of-concept test designed to assess the properties of melt deposition modeling additive manufacturing in the microgravity environment experienced on the International Space Station (ISS). The lessons learned from this technology demonstration will be used for the next generation of melt deposition modeling in the permanent NanoRacks Additive Manufacturing Facility (AMF) as well as for any future additive manufacturing technology NASA plans to use, such as metals or electronics in-space manufacturing, on both the ISS and Deep Space Missions. This demonstration is the first step towards realizing a &ldquo;machine shop&rdquo; in space, a critical enabling component of any Deep Space Mission.</p><p>The 3D Print payload consists of a 3D printer (a two-axis extruder mobility system, a single-axis print tray mobility system, the extruder and accompanying feedstock cartridge, the print tray, Environmental Control Unit (ECU, a prototype for the permanent AMF), an electronics box, and all of the necessary cables and bolts to attach the device to the ISS Microgravity Science Glovebox&nbsp;(MSG) cold plate, MSG laptop computer, and MSG power supply) and all identified spare parts. The 3D Print payload will operate within the MSG. The payload uses extrusion-based additive manufacturing technology to fabricate objects. Additive manufacturing is the process of creating three dimensional objects from a Computer Aided Design (CAD) model where material is deposited layer by layer. The 3D Print payload will extrude a bead of thermo-polymer material from a larger diameter feedstock material. When one layer is complete, the next layer is printed on top and bonded to the lower layer while still molten. This creates an adhesive bond as opposed to a solid material extrusion.</p><p>Performance goals were defined realizing the 3D Print is a technology demonstration. The following is a list of minimum success criteria:<br />1. Successful integration and safe operation in the MSG on the ISS<br />2. Demonstration of extrusion based additive manufacturing using polymeric material<br />3. Successful extrusion and traversing<br />4. Printing of one part while in ISS microgravity<br />5. Mitigation of functional risks for future facilities<br />6. Comparison of ISS printed parts with those printed on Earth (dimensional and strength testing).</p>
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Surface Turbulent Fluxes, 1x1 deg Daily Grid, Set1 V2c
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T08:49:10.000ZThese data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF2c) Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project, http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects/surface-turbulent-fluxes-esdr http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects GSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). However, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the "artifactitious" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. GSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful "Rice Cooker Theory" by Shie (2010a,b), i.e., "To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c. This is the Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. The GSSTF, Version 2c, daily fluxes have first been produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15). Then, the Combined daily fluxes are produced by averaging (equally weighted) over available flux data/files from various satellites. These Combined daily flux data are considered as the "final" GSSTF, Version 2c, and are stored in this HDF-EOS5 collection. There are only one set of GSSTF, Version 2c, Combined data, "Set1" It contains 9 variables: "E" 'latent heat flux' (W/m**2), "STu" 'zonal wind stress' (N/m**2), "STv" 'meridional wind stress' (N/m**2), "H" 'sensible heat flux' (W/m**2), "Qair" 'surface air (~10-m) specific humidity' (g/kg), "WB" 'lowest 500-m precipitable water' (g/cm**2), "U" '10-m wind speed' (m/s), "DQ" 'sea-air humidity difference' (g/kg) "Tot_Precip_Water" 'total precipitable water' (g/cm**2) The double-quoted labels are the short names of the data fields in the HDF-EOS5 files. The "individual" daily flux data files, produced for each individual satellite, are also available in HDF-EOS5, although from differe...
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Vibration-Free Cooling Cycle Pump for Space Vehicles and Habitats Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:26:51.000ZMainstream Engineering Corporation completed the design of a high-speed pump for International Space Station (ISS) Environmental Control and Life Support Systems and future spacecraft and extraterrestrial outpost applications. Specifications for this pump were derived from an existing pump currently operating as part of the thermal control loop on the ISS. The design includes magnetic bearings so that a vibration-reducing control algorithm can be implemented. A digital controller was designed, which measured and reduced vibration-causing fluctuations in shaft displacement due to rotor unbalance in multiple axes. The controller was tested over an operating speed range of 600 to 7200 rpm with excellent results. The controller reduced mean shaft displacement by 71% over the entire operating range, and reduced it by more than 80% at higher operating speeds where synchronous vibration was dominant. In Phase II the magnetic bearing equipped cooling loop pump designed in Phase I will be fabricated and tested. Mainstream will demonstrate the added efficiency, reliability, and low vibration of the system as compared with the existing pump. The pump assembly will undergo vibration characterization testing with support from Marshall Space Flight Center.
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Auditory Presentation of H/OZ Critical Flight Data Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:08:03.000ZAutomation of a flight control system to perform functions normally attributed to humans is often not robust and limited to specific operating conditions and types of operation and a small set of fixed behaviors (i.e. modes). eSky has shown that metrics such as the time delay between a required control input from the crew and the actual input is sensitive to crew functional degradation through external distraction. We are currently developing strategies for using such crew state metrics to modulate the level of automation support provided to the flight crew. Dynamic reallocation of function between crew and automation can reduce the cognitive workload on the crew, enhance their ability to supervise the automation and help them intervene in the event of any failure or operation outside the desired operating conditions. eSky is exploring function reallocation in a collaborative flight control system (HFCS) design pioneered at NASA Langley. HFCS combines precise flight control automation with rudimentary intelligence that the flight crew can guide using relatively simple mechanisms. HFCS automation manages short-term control tasks (e.g. path following) while the crew is required to direct every significant trajectory change using flight controls rather than an FMS interface to keep them engaged in conduct of the flight. The automation communicates intentions to the pilot through visual and haptic (tactile) feedback; the crew communicates intentions to the automation through conventional controls. The HFCS user interface is primarily visual and tactile with limited auditory elements, mainly limited to a few alerts and warnings. eSky proposes to establish the auditory channel as a key element in providing flight dynamic information and cueing of required crew in puts in addition to envelope protection warnings. These new interface elements will be integrated into eSky's evolving strategies for functionality reallocation of between automation and crew.
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AtOrAboveProficient
datahub.smcgov.org | Last Updated 2016-08-30T23:34:30.000ZSTAR Third Grade Reading Scores as a Percentage of Economically Disadvantaged Students classified by the State of California Department of Education as being "At or Above Proficient" reading level for 2013 school year. Hillsborough Unified School District reported a third grade enrollment of 183 students, with 1 student tested. Test results cannot be reported due to identity protection requirements. Las Lomitas Elementary School District reported a third grade enrollment of 170 with 3 students tested. Test results cannot be reported due to identity protection requirements. Menlo Park City Elementary District reported a third grade enrollment of 352 with 6 students tested. Test results cannot be reported due to identity protection requirements. Portola Valley Elementary School District reported a third grade enrollment of 80 with 3 students tested. Test results cannot be reported due to identity protection requirements. Woodside School District reported a third grade enrollment of 51 with 2 students tested. Test results cannot be reported due to identity protection requirements.
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PHOENIX MARS ROBOTIC ARM CAMERA 5 NORMAL OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:52:37.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains normal data from the Robotic Arm Camera (RAC).
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Global Navigation Satellite System (GNSS) IGS Clock Combination Product from Real-Time AC Submissions from NASA CDDIS
data.nasa.gov | Last Updated 2022-01-17T05:22:33.000ZThis derived product set consists of Global Navigation Satellite System satellite and receiver clock combination product (30-second granularity, daily files, generated daily) from the real-time IGS analysis center submissions available from 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. The CDDIS provides access to products generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. IGS analysis centers (ACs) access GNSS real-time data streams to produce GNSS satellite and ground receiver clock values in real-time. The product streams are combination solutions generated by processing individual real-time solutions from participating IGS Real-time ACs. The IGS Real-Time Analysis Center Coordinator (RTACC) uses these individual AC solutions to generate this real-time IGS combined satellite and receiver clock product. The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. This clock solution is a batch combination based on daily clock submissions by these IGS real-time analysis centers and have been provided since February 2009, shortly after real-time streams were routinely available through the IGS Real-Time Pilot Project and prior to the availability of real-time product streams. Clock solution files consist of decoded clock results from the real time stream at 30-second intervals. This combination is a daily solution available approximately one to three days 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.
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Global Navigation Satellite System (GNSS) Rapid Clock Product (30 second resolution, daily files, generated daily) from NASA CDDIS
data.nasa.gov | Last Updated 2023-02-28T19:25:38.000ZThis derived product set consists of Global Navigation Satellite System Rapid Satellite and Receiver Clock Product (30-second granularity, daily files, generated daily) 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 satellite and receiver clock 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. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products.
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NOAA - Severe weather warnings for tornadoes: Storm based accuracy (%)
performance.commerce.gov | Last Updated 2024-03-28T20:34:54.000ZTornado Warnings are issued to enable the public to get out of harm’s way and mitigate preventable loss. NWS forecasters issue approximately 2,900 Tornado Warnings per year, primarily between the Rockies and Appalachian Mountains. Tornado Warning statistics are based on a comparison of warnings issued and weather spotter observations of tornadoes and/or storm damage surveys from Weather Forecast Offices in the United States. Accuracy or probability of detection (POD) is the percentage of time a tornado actually occurred in an area that was covered by a tornado warning. The difference between the accuracy percentage figure and 100% represents the percentage of events occurring without warning. Most tornadoes cannot be visually tracked from beginning to end and post-storm damage surveying is the official method with which the NWS categorizes tornado characteristics (intensity, path length & width) but must rely on radar data to estimate the timing of the tornado track.