Search NASA⌕ Search

SEARCH · Search NASA

Results for “data set”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Experiments at SRT Using the NOAA CrIS/ATMS Proxy Data Set

The objectives of the talk are: (1) Assess the performance of NGAS Version-1.5.03.00 CrIS/ATMS retrieval algorithm as delivered by LaRC, modified to include the MW and IR tuning coefficients and new CrIS noise model (a) Percent acceptance (b) RMS and mean differences of T(p) vs. ECMWF truth as a function of % yield (2) Compare performance of NGAS retrieval algorithm with an AIRS Science Team Version-6 like retrieval algorithm modified at Sounder Research Team (SRT) for CrIS/ATMS

Susskind, Joel↗
NASA NTRS2013

Trends and Variations of Ocean Surface Latent Heat Flux: Results from GSSTF2c Data Set

Trends and variations of Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) version 2c (GSSTF2c) latent heat flux (LHF) are examined. This version of LHF takes account of the correction in Earth incidence angle. The trend of global mean LHF for GSSTF2c is much reduced relative to GSSTF version 2b Set 1 and Set 2 for the same period 1988-2008. Temporal increase of GSSTF2c LHF in the two decades is 11.0%, in which 3.1%, 5.8%, and 2.1% are attributed to the increase in wind, the increase in sea surface saturated air humidity, and the decrease in near-surface air humidity, respectively. The first empirical orthogonal function of LHF is a conventional El Nino Southern Oscillation (ENSO) mode. However, the trends in LHF are independent of conventional ENSO phenomena. After removing ENSO signal, the pattern of LHF trends is primarily determined by the pattern of air-sea humidity difference trends.

global mean↗
NASA NTRS2014

Global Distribution and Variability of Surface Skin and Surface Air Temperatures as Depicted in the AIRS Version-6 Data Set

In this presentation, we will briefly describe the significant improvements made in the AIRS Version-6 retrieval algorithm, especially as to how they affect retrieved surface skin and surface air temperatures. The global distribution of seasonal 1:30 AM and 1:30 PM local time 12 year climatologies of Ts,a will be presented for the first time. We will also present the spatial distribution of short term 12 year anomaly trends of Ts,a at 1:30 AM and 1:30 PM, as well as the spatial distribution of temporal correlations of Ts,a with the El Nino Index. It will be shown that there are significant differences between the behavior of 1:30 AM and 1:30 PM Ts,a anomalies in some arid land areas.

Retrieval algorithm↗
NASA NTRS2012

CASA Forest Cover Change Data Sets

Deforestation and forest fires are global land cover changes that can be caused by both natural and human factors. Although monitoring forest fires in near-real time is critical for operational wildfire management, mapping historical wildfires in a spatially explicit fashion is also important for a number of reasons, including climate change studies (e.g., examining the relationship between rising temperatures and frequency of fires), fuel load management (e.g., deciding when and where to conduct controlled burns), and carbon cycle studies (e.g., quantifying how much CO2 is emitted by fires and for emissions reduction efforts under the United Nations programs for Reducing Emissions from Deforestation and Degradation -- REDD).

Mapping↗