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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.

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At least 109 records · Page 6
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NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS2005

Use of Multiangle Satellite Observations to Retrieve Aerosol Properties and Ocean Color

A new technique is described for retrieving aerosol over ocean water and the associated ocean color using multiangle satellite observations. Unlike current satellite aerosol retrieval algorithms which only utilize observations at red wavelengths and longer, with the assumption that these wavelengths have a negligible ocean (water-leaving radiance), this new algorithm uses all available spectral bands and simultaneously retrieves both aerosol properties and the spectral ocean color. We show some results of case studies using MISR data, performed over different water conditions (coastal water, blooms, and open water).

ocean↗
NASA NTRS2014

Assessment of NASA GISS CMIP5 and Post-CMIP5 Simulated Clouds and TOA Radiation Budgets Using Satellite Observations: TOA Radiation Budget and CREs - Part 2

In Part I of this study, the NASA GISS Coupled Model Intercomparison Project (CMIP5) and post-CMIP5 (herein called C5 and P5, respectively) simulated cloud properties were assessed utilizing multiple satellite observations, with a particular focus on the southern midlatitudes (SMLs). This study applies the knowledge gained from Part I of this series to evaluate the modeled TOA radiation budgets and cloud radiative effects (CREs) globally using CERES EBAF (CE) satellite observations and the impact of regional cloud properties and water vapor on the TOA radiation budgets. Comparisons revealed that the P5- and C5-simulated global means of clear-sky and all-sky outgoing longwave radiation (OLR) match well with CE observations, while biases are observed regionally. Negative biases are found in both P5- and C5-simulated clear-sky OLR. P5-simulated all-sky albedo slightly increased over the SMLs due to the increase in low-level cloud fraction from the new planetary boundary layer (PBL) scheme. Shortwave, longwave, and net CRE are quantitatively analyzed as well. Regions of strong large-scale atmospheric upwelling/downwelling motion are also defined to compare regional differences across multiple cloud and radiative variables. In general, the P5 and C5 simulations agree with the observations better over the downwelling regime than over the upwelling regime. Comparing the results herein with the cloud property comparisons presented in Part I, the modeled TOA radiation budgets and CREs agree well with the CE observations. These results, combined with results in Part I, have quantitatively estimated how much improvement is found in the P5-simulated cloud and radiative properties, particularly over the SMLs and tropics, due to the implementation of the new PBL and convection schemes.

Model comparison↗
NASA NTRS1992

The use of satellite observations in ice cover simulations

Satellites can furnish observational data on ice cover over greater areas and for longer periods than field observations; a combination of visible or IR fine-resolution satellite imagery and radar altimetry, in conjunction with coarse-resolution data from passive microwave sources, have been used to verify and validate many numerical models of ice cover. Satellite passive microwave data are especially useful in the case of large-scale models used in studies of sea ice interannual variability. A number of remotely-sensed data sources are used by the NOAA's Joint Ice Center to create a qualitative weekly analysis of ice concentrations.

Preller, Ruth H.↗
NASA NTRS1977

Satellite observations of new infrared sources

Preliminary results are reported for satellite observations of about 1000 IR sources at a wavelength of 2.7 microns. The observations were made in the declination zone between +10 and -10 deg to a limiting flux of 4 by 10 to the -16th power W/sq cm per micron. Of 83 previously unobserved sources, twenty are identified with late-type stars having visual magnitudes of 6 to 9. The nine sources with available luminosity classifications are found to be giant stars, and flux densities at 2.2 and 3.6 microns are predicted for four unreddened stars among these. Three other detected sources are positively identified with red variable stars; twenty more are tentatively identified as such. It is suggested that some of the 40 blank-field 2.7-micron sources detected at high galactic latitude may be Mira variables with thick dust shells located at large distances from the galactic plane.

Heinsheimer, T. F.↗
NASA NTRS2008

Opportunities for Coordinated Observations of CO2 with the Orbiting Carbon Observatory (OCO) and Greenhouse Gases Observing Satellite (GOSAT)

The Orbiting Carbon Observatory (OCO) and the Greenhouse Gases Observing Satellite (GOSAT) are the first two satellites designed to make global measurements of atmospheric carbon dioxide (CO2) with the precision and sampling needed identify and monitor surface sources and sinks of this important greenhouse gas. Because the operational phases of the OCO and GOSAT missions overlap in time, there are numerous opportunities for comparing and combining the data from these two satellites to improve our understanding of the natural processes and human activities that control the atmospheric CO2 and it variability over time. Opportunities for cross-calibration, cross-validation, and coordinated observations that are currently under consideration are summarized here.

climate↗
NASA NTRS2022

Homogenization of the Observatoire de Haute Provence electrochemical concentration cell (ECC) ozonesonde data record: comparison with lidar and satellite observations

The Observatoire de Haute Provence (OHP) weekly Electrochemical Concentration Cell (ECC) ozonesonde data have been homogenized for the period 1991-2021 according to the recommendations of the Ozonesonde Data Quality Assessment (O3S-DQA) panel. The assessment of the ECC homogenization benefit has been carried out using comparisons with other ozone measuring ground based instruments at the same station (lidar, surface measurements) and with collocated satellite observations of the O3vertical profile by Microwave Limb Sounder (MLS). The major differences between uncorrected and5homogenized ECC data are related to a change of ozonesonde type in 1997, removal of the pressure dependency of the ECC background current and correction of internal pump temperature. The original 3-4 ppbv positive bias between ECC and Lidar in the troposphere is corrected with the homogenization. The ECC 30-years trends of the seasonally adjusted ozone concentrations are also significantly improved in both the troposphere and the stratosphere after the ECC homogenization, as shown by the ECC/lidar or ECC/surface ozone trend comparisons. A -0.19% per year negative trend of the normalization factor (NT)10calculated using independent measurements of the total ozone column (TOC) at OHP disappears after homogenization of the ECC data. There is however a remaining -3.7% negative bias in the TOC which is likely related to an underestimate of the ECC concentrations in the stratosphere above 50 hPa. Differences between TOC measured by homogenized ECC and satellite observations show a smaller bias of -1%. Comparisons between homogenized ECC and OHP stratospheric lidar and MLS observations below 26 km are slightly negative (-2%) or positive (+2%), respectively. Both the comparisons with lidar and15satellite observations suggest that homogenization increases the negative bias of the ECC to values lower than -6% above 28km. The reason for this bias is still unclear, but a possible explanation might be related to freezing or evaporation of the sonde solution in the stratosphere.

Ozonesondes↗
NASA NTRS2010

Classification of Clouds and Deep Convection from GEOS-5 Using Satellite Observations

With the increased resolution of global atmospheric models and the push toward global cloud resolving models, the resemblance of model output to satellite observations has become strikingly similar. As we progress with our adaptation of the Goddard Earth Observing System Model, Version 5 (GEOS-5) as a high resolution cloud system resolving model, evaluation of cloud properties and deep convection require in-depth analysis beyond a visual comparison. Outgoing long-wave radiation (OLR) provides a sufficient comparison with infrared (IR) satellite imagery to isolate areas of deep convection. We have adopted a binning technique to generate a series of histograms for OLR which classify the presence and fraction of clear sky versus deep convection in the tropics that can be compared with a similar analyses of IR imagery from composite Geostationary Operational Environmental Satellite (GOES) observations. We will present initial results that have been used to evaluate the amount of deep convective parameterization required within the model as we move toward cloud system resolving resolutions of 10- to 1-km globally.

Putman, William↗
NASA NTRS1988

Non-seasonal changes in total column ozone from satellite observations, 1970-86

Analyses of nonseasonal changes in total column ozone from satellite observations during the period 1970-86 suggest, when combined with analyses of Dobson network data, that the largest reduction in global ozone since 1959 occurred between 1978 and 1986. The existence of an Arctic region of enhanced ozone depletion with similar but less pronounced characteristics of the Antarctic region is described. Before 1983 large rates of decrease were confined to high latitudes, subsequently regions of large rates of ozone depletion have appeared at midlatitudes.

Heath, Donald F.↗
NASA NTRS1975

Solar energy microclimate as determined from satellite observations

A method is presented for determining solar insolation at the earth's surface using satellite broadband visible radiance and cloud imagery data, along with conventional in situ measurements. Conventional measurements are used to both tune satellite measurements and to develop empirical relationships between satellite observations and surface solar insolation. Cloudiness is the primary modulator of sunshine. The satellite measurements as applied in this method consider cloudiness both explicitly and implicitly in determining surface solar insolation at space scales smaller than the conventional pyranometer network.

Vonder Haar, T. H.↗
NASA NTRS2004

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

Kleb, Mary M.↗
NASA NTRS1983

Interactive analysis of a large aperture Earth observations satellite

A system level design and analysis has been conducted on an Earth Observation Satellite (EOS) system using the Interactive Design and Evaluation of Advanced Spacecraft (IDEAS) computer-aided design and analysis program. The IDEAS program consists of about 40 user-friendly technical modules and an interactive graphics display. The reflector support system and feed mast of the EOS spacecraft are constructed with box-truss structural concept, a lattice configuration which can be packaged for delivery in a single Shuttle flight and deployed in orbit. The deployed spacecraft consists of a 120-m by 60-m parabolic focal axis. The spacecraft was modeled for structural, thermal, and control systems analysis and structural elements were designed. On-orbit dynamic and thermal loading analyses were conducted; spacecraft weights and developmental and first unit costs were determined.

Wright, R. L.↗