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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 NTRS1988

Analysis of algorithms for the retrieval of rain-rate profiles from a spaceborne dual-wavelength radar

The ability to retrieve rain-rate profiles from a dual-wavelength spaceborne radar system operating at 13.6 and 35 GHz is analyzed. The fundamental problem of extracting either the attenuation and/or the reflectivity from the backscatter echo, which contains both contributions, is addressed. Three algorithms, the backscatter, the attenuation coefficient, and the dual-wavelength methods, are examined. These algorithms are tested using four rain-rate profiles derived from radar measurements. In particular, measured (true) values are compared with calculated (retrieved) rain rates applying the algorithms with superimposed uncertainties assuming a suggested spaceborne dual-wavelength radar system. Error values of rain rates are determined where these values reflect failure of the assumptions utilized in the derivation of the algorithms, rain backscatter noise, and instrument noise. It is concluded that no single technique gives rise to a panacea in the making of accurate rain measurements and that difficulties exist with each method.

Goldhirsh, Julius↗
NASA NTRS2019

Multi-Band Atmospheric Correction Algorithm for Ocean Color Retrievals

NASA's current Atmospheric Correction (AC) algorithm for ocean color utilizes two bands and their ratio in the Near Infrared (NIR) to estimate aerosol reflectance and aerosol type. The algorithm then extrapolates the spectral dependence of aerosol reflectance to the visible wavelengths based on modeled spectral dependence of the identified aerosol type. Future advanced ocean color sensors, such as the Ocean Color Instrument (OCI) that will be carried on the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite, will be capable of measuring the hyperspectral radiance from 340 to 890 nm at 5-nm spectral resolution and at 7 discrete Short-wave Infrared (SWIR) channels: 940, 1038,1250, 1378, 1615, 2130, and 2260 nm. To optimally employ this unprecedented instrument capability, we propose an improved AC algorithm that utilizes all atmospheric-window channels in the NIR to SWIR spectral range to reduce the uncertainty in the AC process. A theoretical uncertainty analysis of this, namely Multi-Band AC (MBAC), indicates that the algorithm can reduce the uncertainty in remote sensing reflectance (Rrs) retrievals of the ocean caused by sensor random noise. Furthermore, in optically complex waters, where the NIR signal is affected by contributions from highly-reflective turbid waters, the MBAC algorithm can be adaptively weighted to the strongly-absorbing SWIR channels to enable improved ocean color retrievals in coastal waters. We provide here a description of the algorithm and demonstrate the improved performance in ocean color retrievals, relative to the current NASA standard AC algorithm, through comparison with field measurements and assessment of propagated uncertainties in applying the MBAC algorithm to MODIS and simulated PACE OCI data.

PACE↗
NASA NTRS2005

Measuring Low-Order Aberrations in a Segmented Telescope

The in-focus PSF optimizer (IPO) is an algorithm for use in monitoring and controlling the alignment of the segments of a segmented-mirror astronomical telescope. IPO is so named because it computes wave-front aberrations of the telescope from digitized pointspread functions (PSFs) measured in infocus images. Inasmuch as distant astronomical objects that behave optically as point sources can typically be seen in almost any astronomical image, the main benefit afforded by IPO may be to enable maintenance of mirror-segment alignments without detracting from valuable scientific-observation time. IPO evolved from prescription-retrieval type algorithms. Prescription retrieval uses in-focus and out-of-focus PSFs to infer the state of an imaging optical system. The state, in this context, refers to the positions, orientations, and low-order figure errors of the optical elements in the system. Both prescription- retrieval and IPO use an iterative, nonlinear, least-squares optimizer to compute the optimal state parameters such that a digital computer-generated model image matches the digitized image acquired from the real system. The difference between IPO and prescription- retrieval algorithms is that IPO is specifically designed to utilize infocus images only. Although the restriction to in-focus images limits IPO to calculating only the lowest-order wave front aberrations, it also causes the resulting computation to take much less time because fewer degrees of freedom are included in the optimization process. In the prescription retrieval software developed at JPL, the model images are generated using the ray-trace/physical optics program, MACOS. IPO, on the other hand, uses a linear sensitivity matrix to compute the exit-pupil wave front from the system parameters; the wave front is then converted into a complex pupil field, which is then propagated to the image plane via a fast Fourier transform. This approach is computationally faster and requires less computer memory than is needed for prescription retrieval.

Ohara, Catherine↗
NASA NTRS2016

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations: 2. Retrieval Evaluation

An infrared-based optimal estimation (OE-IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A-Train) with Aqua. The results show that OE-IR cloud optical thickness (tau) and effective radius (r(sub eff)) retrievals perform best for ice clouds having 0.5 < tau< 7 and r(sub eff) < 50microns. For global ice clouds, the averaged retrieval uncertainties of tau and r(sub eff) are 19% and 33%, respectively. For optically thick ice clouds with tau larger than 10, however, the tau and r(sub eff) retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height (h), the averaged global uncertainty is 0.48km. Relatively large h uncertainty (e.g., > 1km) occurs for tau < 0.5. Analysis of 1month of the OE-IR retrievals shows large tau and r(sub eff) uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high-latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE-IR and the operational products show consistent tau and h retrievals. However, obvious differences between the OE-IR and the MODIS Collection 6 r(sub eff) are found.

cloud optics↗
NASA NTRS2013

Classification of Aerosol Retrievals from Spaceborne Polarimetry Using a Multiparameter Algorithm

In this presentation, we demonstrate application of a new aerosol classification algorithm to retrievals from the POLDER-3 polarimter on the PARASOL spacecraft. Motivation and method: Since the development of global aerosol measurements by satellites and AERONET, classification of observed aerosols into several types (e.g., urban-industrial, biomass burning, mineral dust, maritime, and various subtypes or mixtures of these) has proven useful to: understanding aerosol sources, transformations, effects, and feedback mechanisms; improving accuracy of satellite retrievals and quantifying assessments of aerosol radiative impacts on climate.

Atmospheric Processes Clouds & Aerosols↗
NASA NTRS2011

An Algorithm for the Retrieval of 30-m Snow-Free Albedo from Landsat Surface Reflectance and MODIS BRDF

We present a new methodology to generate 30-m resolution land surface albedo using Landsat surface reflectance and anisotropy information from concurrent MODIS 500-m observations. Albedo information at fine spatial resolution is particularly useful for quantifying climate impacts associated with land use change and ecosystem disturbance. The derived white-sky and black-sky spectral albedos maybe used to estimate actual spectral albedos by taking into account the proportion of direct and diffuse solar radiation arriving at the ground. A further spectral-to-broadband conversion based on extensive radiative transfer simulations is applied to produce the broadband albedos at visible, near infrared, and shortwave regimes. The accuracy of this approach has been evaluated using 270 Landsat scenes covering six field stations supported by the SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) network. Comparison with field measurements shows that Landsat 30-m snow-free shortwave albedos from all seasons generally achieve an absolute accuracy of +/-0.02 - 0.05 for these validation sites during available clear days in 2003-2005,with a root mean square error less than 0.03 and a bias less than 0.02. This level of accuracy has been regarded as sufficient for driving global and regional climate models. The Landsat-based retrievals have also been compared to the operational 16-day MODIS albedo produced every 8-days from MODIS on Terra and Aqua (MCD43A). The Landsat albedo provides more detailed landscape texture, and achieves better agreement (correlation and dynamic range) with in-situ data at the validation stations, particularly when the stations include a heterogeneous mix of surface covers.

Shuai, Yanmin↗
NASA NTRS2019

Extending XBAER Algorithm to Aerosol and Cloud Condition

The retrieval of cloud optical properties for aerosol contaminated water cloud is challenging because of the complexity of physical processes in such situations. Conventionally, cloud optical data products are typically derived, ignoring the aerosol impacts on radiative transfer in the retrieval process. This is potentially a significant source of error. In this paper, the eXtensible Bremen Aerosol Retrieval (XBAER) algorithm has been optimized for the retrieval of aerosol/cloud properties for the aerosol contaminated cloud (ACC) scenarios. This version of XBAER delivers cloud optical thickness (COT) and cloud effective radius (CER) for ACCs and simultaneously retrieves aerosol optical thickness (AOT). The surface parameterization and aerosol types used in the standard XBAER algorithm have been adapted in this retrieval to account for the ACC conditions. Aerosol types in XBAER for the retrieval of ACC scenarios have been parameterized to comprise weak and strong absorptions. The comparisons of COT, CER, and AOT retrieved using this adapted XBAER algorithm and two new NASA algorithms show good agreements, especially for biomass burning aerosol. The correlation coefficients are >0.9 for AOT, ~0.8 for CER, and ~0.7 for COT. There is also a good agreement for dust plume contaminated cloud scene. The XBAER derived AOT values for dust aerosols are systematically smaller than the NASA retrieval, but both products have the same spatial distribution patterns. The comparison of COT retrieved using the adapted XBAER algorithm and that retrieved from ground-based microwave radiometer (MWR) measurements shows much better agreement for ACC conditions with high AOT.

satellite↗
NASA NTRS1995

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Key, Jeff↗
NASA NTRS2025

Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm

The diurnal variability of aerosol optical depth retrieved from L1 point satellite radiance measurements closely matches surface observations. Seasonal variability of retrieved single scattering albedo is in good qualitative agreement with equivalent ground-based observations. EPIC aerosol products can be used in the qualitative and quantitative monitoring of extraordinary aerosol events resulting from wildfires and desert dust storms.

Aerosols, Absorption, Satellite retrieval↗
NASA NTRS1996

SSM/I Rain Retrievals Within a Unified All-Weather Ocean Algorithm

A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented and compared to two other rainfall climatologies derived from satellites. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T(sub U) for the upwelling radiation. The performance of the algorithm in the absence of rain is discussed in Wentz, and this paper focuses on the rain component of the algorithm. A particular strength of the unified algorithm is its ability to 'orthogonalize' the retrievals so that there is minimum cross-talk between the retrieved parameters. For example, comparisons of the retrieved water vapor with radiosonde observations show that there is very little correlation between the water vapor retrieval error and rain rate. For rain rates from 1 to 15 mm/h, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 GHz and 19.35 GHz. This ratio decreases by about 40% when heavy and light rain co-exist within the SSM/I footprint as compared to the case of uniform rain. This correction has the effect of increasing the rain rate when the spectral ratio of the absorption coefficients is small. Even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the tropics (approx. 5 km) is used to specify the rain layer thickness. We restore realism by reducing the assumed averaged tropical rain layer thickness to 3 km, thereby accounting for the existence of warm rain processes in which the rain layer does not extend to the freezing level. Global rain rates are produced for the 1991 through 1994 period from observations taken by microwave radiometers (SSM/I) that are aboard two polar-orbiting satellites. We find that approximately 6% of the SSM/I observations detect measurable rain rates (R greater than 0.2 mm/h). Zonal averages of the rain rates show the peak at the intertropical convergence zone (ITCZ) is quite narrow in meridional extent and varies from about 7 mm/day in the winter to a maximum 11 mm/day in the summer. Very low precipitation rates (less than 0.3 mm/day) are observed in those areas of subsidence influenced by the large semipermanent anticyclones. In general, these features are similar to those reported in previously published rain climatologies. However, significant differences do exists between our rain rates and those produced by Spencer. These differences seem to be related to non-precipitating cloud water.

Wentz, Frank J.↗
NASA NTRS2022

Time-Series Ratio Algorithm for Nisar Soil Moisture Retrieval

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

Jeonghwan Park↗
NASA NTRS2013

Retrieval of Aerosol Optical Depth Under Thin Cirrus from MODIS: Application to an Ocean Algorithm

A strategy for retrieving aerosol optical depth (AOD) under conditions of thin cirrus coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. We adopt an empirical method that derives the cirrus contribution to measured reflectance in seven bands from the visible to shortwave infrared (0.47, 0.55, 0.65, 0.86, 1.24, 1.63, and 2.12 μm, commonly used for AOD retrievals) by using the correlations between the top-of-atmosphere (TOA) reflectance at 1.38 micron and these bands. The 1.38 micron band is used due to its strong absorption by water vapor and allows us to extract the contribution of cirrus clouds to TOA reflectance and create cirrus-corrected TOA reflectances in the seven bands of interest. These cirrus-corrected TOA reflectances are then used in the aerosol retrieval algorithm to determine cirrus-corrected AOD. The cirrus correction algorithm reduces the cirrus contamination in the AOD data as shown by a decrease in both magnitude and spatial variability of AOD over areas contaminated by thin cirrus. Comparisons of retrieved AOD against Aerosol Robotic Network observations at Nauru in the equatorial Pacific reveal that the cirrus correction procedure improves the data quality: the percentage of data within the expected error +/-(0.03 + 0.05 ×AOD) increases from 40% to 80% for cirrus-corrected points only and from 80% to 86% for all points (i.e., both corrected and uncorrected retrievals). Statistical comparisons with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals are also carried out. A high correlation (R = 0.89) between the CALIOP cirrus optical depth and AOD correction magnitude suggests potential applicability of the cirrus correction procedure to other MODIS-like sensors.

cirrus correction↗
NASA NTRS2016

Highlights from the 11-Year Record of Tropospheric Ozone from OMI/MLS and Continuation of that Long Record Using OMPS Measurements

Since October 2004 the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) onboard the Aura satellite have provided over 11 years of continuous tropospheric ozone measurements. These OMI/MLS measurements have been used in many studies to evaluate dynamical and photochemical effects caused by ENSO, the Madden-Julian Oscillation (MJO) and shorter timescales, as well as long-term trends and the effects of deep convection on tropospheric ozone. Given that the OMI and MLS instruments have now extended well beyond their expected lifetimes, our goal is to continue their long record of tropospheric ozone using recent Ozone Mapping Profiler Suite (OMPS) measurements. The OMPS onboard the Suomi National Polar-orbiting Partnership NPP satellite was launched on October 28, 2011 and is comprised of three instruments: the nadir mapper, the nadir profiler, and the limb profiler. Our study combines total column ozone from the OMPS nadir mapper with stratospheric column ozone from the OMPS limb profiler to measure tropospheric ozone residual. The time period for the OMPS measurements is March 2012 present. For the OMPS limb profiler retrievals, the OMPS v2 algorithm from Goddard is tested against the University of Saskatchewan (USask) Algorithm. The retrieved ozone profiles from each of these algorithms are evaluated with ozone profiles from both ozonesondes and the Aura Microwave Limb Sounder (MLS). Effects on derived OMPS tropospheric ozone caused by the 2015-2016 El Nino event are highlighted. This recent El Nino produced anomalies in tropospheric ozone throughout the tropical Pacific involving increases of approximately 10 DU over Indonesia and decreases approximately 5-10 DU in the eastern Pacific. These changes in ozone due to El Nino were predominantly dynamically-induced, caused by the eastward shift in sea-surface temperature and convection from the western to the eastern Pacific.

MJO↗
NASA NTRS2008

Precipitating Snow Retrievals from Combined Airborne Cloud Radar and Millimeter-Wave Radiometer Observations

An algorithm for retrieving snow over oceans from combined cloud radar and millimeter-wave radiometer observations is developed. The algorithm involves the use of physical models to simulate cloud radar and millimeter-wave radiometer observations from basic atmospheric variables such as hydrometeor content, temperature, and relative humidity profiles and is based on an optimal estimation technique to retrieve these variables from actual observations. A high-resolution simulation of a lake-effect snowstorm by a cloud-resolving model is used to test the algorithm. That is, synthetic observations are generated from the output of the cloud numerical model, and the retrieval algorithm is applied to the synthetic data. The algorithm performance is assessed by comparing the retrievals with the reference variables used in synthesizing the observations. The synthetic observation experiment indicates good performance of the retrieval algorithm. The algorithm is also applied to real observations from the Wakasa Bay field experiment that took place over the Sea of Japan in January and February 2003. The application of the retrieval algorithm to data from the field experiment yields snow estimates that are consistent with both the cloud radar and radiometer observations.

Grecu, Mircea↗