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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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NASA NTRS1984

Results of the joint NASA/NOAA AMTS/HIRS2 sounding simulation test

The relative accuracies of atmospheric temperature profiles retrieved from HIRS2, the current operational infra-red temperature sounder, and AMTS, a proposed advanced high-spectral resolution infra-red sounder were compared. The sounding simulation test compared retrievals generated by GLAS, using their physical retrieval algorithm, and NESDIS, using their operational statistical regression algorithm, for both instruments under clear and cloudy conditions. Test results for both conditions are summarized in tables showing RMS temperature errors for 18 tropospheric layers, 4 stratospheric layers, and surface skin temperature. The improvement of the AMTS instrument over HIRS2, particularly for cloudy conditions is demonstrated. The GLAS retrievals are more accurate than the operational NESDIS retrieval, especially with increasing cloud cover.

Susskind, J.↗
NASA NTRS2009

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

Zabodsky, Brad↗
NASA NTRS2007

Validation of the Aura Microwave Limb Sounder Temperature and Geopotential Height Measurements

This paper describes the retrievals algorithm used to determine temperature and height from radiance measurements by the Microwave Limb Sounder on EOS Aura. MLS is a "limbscanning" instrument, meaning that it views the atmosphere along paths that do not intersect the surface - it actually looks forwards from the Aura satellite. This means that the temperature retrievals are for a "profile" of the atmosphere somewhat ahead of the satellite. Because of the need to view a finite sample of the atmosphere, the sample spans a box about 1.5km deep and several tens of kilometers in width; the optical characteristics of the atmosphere mean that the sample is representative of a tube about 200-300km long in the direction of view. The retrievals use temperature analyses from NASA's Goddard Earth Observing System, Version 5 (GEOS-5) data assimilation system as a priori states. The temperature retrievals are somewhat deperrde~zt on these a priori states, especially in the lower stratosphere. An important part of the validation of any new dataset involves comparison with other, independent datasets. A large part of this study is concerned with such comparisons, using a number of independent space-based measurements obtained using different techniques, and with meteorological analyses. The MLS temperature data are shown to have biases that vary with height, but also depend on the validation dataset. MLS data are apparently biased slightly cold relative to correlative data in the upper troposphere and slightly warm in the middle stratosphere. A warm MLS bias in the upper stratosphere may be due to a cold bias in GEOS-5 temperatures.

Schwartz, M. J.↗
NASA NTRS2008

Near-Real Time Cloud Retrievals from Operational and Research Meteorological Satellites

A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications.

Minnis, Patrick↗
NASA NTRSDate not supplied

A Radiometric Consistent Spectral Fingerprinting Algorithm for Continuity Products of Hyperspectral Sounders

A radiometric consistent climate fingerprinting methodology has been developed to derive long-term temperature, water vapor, cloud, trace gases, and surface skin temperature anomaly time series from the hyper-spectral sounder measurements of multiple platforms. The spectral fingerprinting methodology requires the use of radiative kernels that are radiometrically consistent with observations. Radiative kernels are built using space-time averaged Jacobians that are physically retrieved from observations under all sky conditions. The physical retrieval algorithm uses the Principal Component based Radiative Transfer Model (PCRTM) for the forward simulation. The incorporation of multiple scattering simulation in PCRTM allows the direct radiative relationship between single field-of-view (FOV) radiance observations and corresponding thermal dynamic variables including cloud properties to be established. Therefore, radiance ?closure? can be achieved under all-sky conditions by the fingerprinting scheme. This methodology has been used to derive climate anomalies from the space-time averaged spectra of AIRS/AMSU and CrIS/ATMS. The use of a consistent fingerprinting scheme provides an effective mean of generating continuity product by merging observations from different platforms and therefore facilitating the long-term climate trend study.

Wan Wu↗
NASA NTRS2004

A Comparison of TOMS Version 8 Total Column Ozone Data with Data from Groundstations

The Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) data have been reprocessed with a new retrieval algorithm, (Version 8) and an updated calibration procedure. These data have been systematically compared to total ozone data from Brewer and Dobson spectrophotometers for 73 individual ground stations. The comparisons were made as a function of latitude, solar zenith angle, reflectivity and total ozone. Results show that the accuracy of the TOMS retrieval'is much improved when aerosols are present in the atmosphere, when snow/ice and sea glint are present, and when ozone in the northern hemisphere is extremely low. TOMS overpass data are derived from the single TOMS best match measurement, almost always located within one degree of the ground station and usually made within an hour of local noon. The version 8 Earth Probe TOMS ozone values have decreased by an average of about 1% due to a much better understanding of the calibration of the instrument. The remaining differences between TOMS and ground stations suggest that there are still small errors in the TOMS retrievals. But if TOMS is used as a transfer standard to compare ground stations, the large station-to-station differences suggest the possibility of significant instrument errors at some ground stations.

Labow, G. J.↗
NASA NTRS2022

Defining Pupil Knowledge Requirements for Roman Space Telescope Integrated Payload Assembly Testing

This thesis examines the use of phase retrieval to characterize an optical system under testing conditions with pupil knowledge error. The purpose of this research is to define pupil knowledge requirements for the integrated payload testing of the Roman Space Telescope (RST). I have conducted a Monte Carlo study to determine how uncertainty in the location of the ground support equipment relative to the payload will affect the ability to use image-based wavefront sensing during the test to characterize system alignment. This test will be conducted in the Space Environment Simulator (SES) with the presence of Wavefront Error (WFE) due to gravity sag on the primary mirror. In this study, I incrementally decentered a piece of ground support equipment, the SubAperture Metrology System (SAMS), and evaluated how the phase retrieval algorithm attempted to fit the wavefront with the pupil mismatch. The results of this study are a factor in the overall Integrated Payload Assembly (IPA) testing WFE budget.

Roman Space Telescope↗
NASA NTRS2008

Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

Durand, Michael↗
NASA NTRS1981

The feasibility of using time-dependent photochemical calculations to infer radical species concentrations from solar occultation absorption measurements

In connection with fast chemical reactions, short-lived stratospheric species experience rapid concentration variations at sunset and sunrise. For solar occultation absorption measurements, these rapid concentration variations may introduce significant errors with respect to the inference of atmospheric abundances for some species due to asymmetrical concentration distributions. Most retrieval algorithms assume that concentration distributions are spherically symmetric. The effect of this assumption on the accuracy of inferred concentrations has been studied by Kerr et al. (1977). The present investigation considers the feasibility of using a time-dependent one-dimensional photochemical model to provide detailed information about the asymmetrical distribution for use in the retrieval procedure. As shown by Boughner et al. (1980), diurnal effects can be represented by an inhomogeneity factor. It is found that the NO retrieval improves considerably with the inclusion of a correction factor containing the asymmetrical variations.

Larsen, J. C.↗
NASA NTRS2007

GIFTS SM EDU Level 1B Algorithms

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) SensorModule (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the GIFTS SM EDU Level 1B algorithms involved in the calibration. The GIFTS Level 1B calibration procedures can be subdivided into four blocks. In the first block, the measured raw interferograms are first corrected for the detector nonlinearity distortion, followed by the complex filtering and decimation procedure. In the second block, a phase correction algorithm is applied to the filtered and decimated complex interferograms. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected spectrum. The phase correction and spectral smoothing operations are performed on a set of interferogram scans for both ambient and hot blackbody references. To continue with the calibration, we compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. We now can estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. The correction schemes that compensate for the fore-optics offsets and off-axis effects are also implemented. In the third block, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation. Finally, in the fourth block, the single pixel algorithms are applied to the entire FPA.

Tian, Jialin↗
NASA NTRS2007

GIFTS SM EDU Data Processing and Algorithms

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Sensor Module (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three Focal Plane Arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the processing algorithms involved in the calibration stage. The calibration procedures can be subdivided into three stages. In the pre-calibration stage, a phase correction algorithm is applied to the decimated and filtered complex interferogram. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected blackbody reference spectra. In the radiometric calibration stage, we first compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. During the post-processing stage, we estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. We then implement a correction scheme that compensates for the effect of fore-optics offsets. Finally, for off-axis pixels, the FPA off-axis effects correction is performed. To estimate the performance of the entire FPA, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation.

Tian, Jialin↗
NASA NTRS1998

US Participation in the GOME and SCIAMACHY Projects

This report summarizes research done under NASA Grant NAGW-2541 through September 30, 1997. The research performed under this grant includes development and maintenance of scientific software for the GOME retrieval algorithms, consultation on operational software development for GOME, sensitivity and instrument studies to define GOME and SCIAMACHY instruments, consultation on optical and detector issues for both GOME and SCIAMACHY, consultation and development for SCIAMACHY near-real-time (NRT) and off-line (OL) data products, and development of infrared line-by-line atmospheric modeling and retrieval capability for SCIAMACHY. The European Space Agency selected the SAO to participate in GOME validation and science studies, part of the overall ERS AO. This provided access to all GOME data; The SAO activities that are carried out as a result of selection by ESA were funded by the present grant. The Global Ozone Monitoring Experiment was successfully launched on the ERS- 2 satellite on April 20, 1995, and remains working in normal fashion. SCIAMACHY is currently scheduled for launch in early 2000. The first two European ozone monitoring instruments (OMI), to fly on the q series of operational meteorological satellites being planned by Eumetsat, have been selected to be GOME-type instruments (the first, in fact, will be the refurbished GOME flight spare). K. Chance is the U.S. member of the OMI Users Advisory Group.

Chance, K. V.↗
NASA NTRS2009

NASA GPM GV Science Implementation

Pre-launch algorithm development & post-launch product evaluation: The GPM GV paradigm moves beyond traditional direct validation/comparison activities by incorporating improved algorithm physics & model applications (end-to-end validation) in the validation process. Three approaches: 1) National Network (surface): Operational networks to identify and resolve first order discrepancies (e.g., bias) between satellite and ground-based precipitation estimates. 2) Physical Process (vertical column): Cloud system and microphysical studies geared toward testing and refinement of physically-based retrieval algorithms. 3) Integrated (4-dimensional): Integration of satellite precipitation products into coupled prediction models to evaluate strengths/limitations of satellite precipitation producers.

Petersen, W. A.↗
NASA NTRS2011

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Moncet, Jean-Luc↗
NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗
NASA NTRS2015

Retrieved Products from Simulated Hyperspectral Observations of a Hurricane

Retrievals were run using the AIRS Science Team Version-6 AIRS Only retrieval algorithm, which generates a Neural-Net first guess (T(sub s))(sup 0), (T(p))(sup 0), and (q(p))(sup 0) as a function of observed AIRS radiances. AIRS Science Team Neural-Net coefficients performed very well beneath 300 mb using the simulated radiances. This means the simulated radiances are very realistic. First guess and retrieved values of T(p) above 300 mb were biased cold, but both represented the model spatial structure very well. QC'd T(p) and q(p) retrievals for all experiments had similar accuracies compared to their own truth fields, and were roughly consistent with results obtained using real data. Spatial coverage of retrievals, as well as the representativeness of the spatial structure of the storm, improved dramatically with decreasing size of the instrument's FOV. We sent QC'd values of T(p) and q(p) to Bob Atlas at AOML for use as input to OSSE Data Assimilation experiments.

CrIS ATMS↗
NASA NTRS2011

Passive Microwave Remote Sensing of Falling Snow and Associated GPM Field Campaigns

Retrievals of falling snow from space represent one of the next important challenges for the atmospheric, hydrological, and energy budget scientific communities. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past. We emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are more sensitive to the ice in clouds and have been used to estimate falling snow from space. While satellite-based remote sensing provides global coverage of falling snow events and the science is relatively new, retrievals are still undergoing development with challenges remaining. There are several current satellite sensors, though not specifically designed for estimating falling snow, are capable of measuring snow from space. These include NOAA's AMSU-B, the MHS sensors, and CloudSat radar. They use high frequency (greater than 85 GHz) passive and active microwave and millimeter-wave channels that are sensitive to the scattering from ice and snow particles in the atmosphere. Sensors with water vapor channels near 183 GHz center line provide opaqueness to the Earth's surface features that can contaminate the falling snow signatures, especially over snow covered surface. In addition, the Global Precipitation Measurement (GPM) mission scheduled for launch in 2013 is specifically designed to measure both liquid rain and frozen snow precipitation. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, and surface types. For example, can a lake effect snow system with low cloud tops having an ice water content (IWC) at the surface of 1.0 gram per cubic meter be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. In this work, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases [9] since simulations provide all the information to determine the measurements from space and the ground truth. This analysis relies on data from the Canadian CloudSat/CALIPSO Validation Program (C3VP) field campaign held from October 31, 2006 through March 1, 2007. In January 2012 GPM will return to the C3VP area for the GPM Cold Precipitation Experiment (GCPEx). This presentation will describe the thresholds-of-detection procedure and results, as well as the field campaign details.

Skofronick-Jackson, Gail↗
NASA NTRS1994

Physical Retrievals of Over-Ocean Rain Rate from Multichannel Microwave Imagery: Theoretical Characteristics of Normalized Polarization and Scattering Indices - Part 1

Microwave rain rate retrieval algorithms have most often been formulated in terms of the raw brightness temperatures observed by one or more channels of a satellite radiometer. Taken individually, single-channel brightness temperatures generally represent a near-arbitrary combination of positive contributions due to liquid water emission and negative contributions due to scattering by ice and/or visibility of the radiometrically cold ocean surface. Unfortunately, for a given rain rate, emission by liquid water below the freezing level and scattering by ice particles above the freezing level are rather loosely coupled in both a physical and statistical sense. Furthermore, microwave brightness temperatures may vary significantly (approx. 30-70 K) in response to geophysical parameters other than liquid water and precipitation. Because of these complications, physical algorithms which attempt to directly invert observed brightness temperatures have typically relied on the iterative adjustment of detailed micro-physical profiles or cloud models, guided by explicit forward microwave radiative transfer calculations. In support of an effort to develop a significantly simpler and more efficient inversion-type rain rate algorithm, the physical information content of two linear transformations of single-frequency, dual-polarization brightness temperatures is studied: the normalized polarization difference P of Petty and Katsaros (1990, 1992), which is intended as a measure of footprint-averaged rain cloud transmittance for a given frequency; and a scattering index S (similar to the polarization corrected temperature of Spencer et al.,1989) which is sensitive almost exclusively to ice. A reverse Monte Carlo radiative transfer model is used to elucidate the qualitative response of these physically distinct single-frequency indices to idealized 3-dimensional rain clouds and to demonstrate their advantages over raw brightness temperatures both as stand-alone indices of precipitation activity and as primary variables in physical, multichannel rain rate retrieval schemes. As a byproduct of the present analysis, it is shown that conventional plane-parallel analyses of the well-known foot-print-filling problem for emission-based algorithms may in some cases give seriously misleading results.

Petty, G. W.↗