Search NASA⌕ Search

SEARCH · Search NASA

Results for “Retrieval Algorithm”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗
NASA NTRS1992

A cloud and radiation model-based algorithm for rainfall retrieval from SSM/I multispectral microwave measurements

A hybrid statistical-physical retrieval scheme is explored which combines a statistical approach with an approach based on the development of cloud-radiation models designed to simulate precipitating atmospheres. The algorithm employs the detailed microphysical information from a cloud model as input to a radiative transfer model which generates a cloud-radiation model database. Statistical procedures are then invoked to objectively generate an initial guess composite profile data set from the database. The retrieval algorithm has been tested for a tropical typhoon case using Special Sensor Microwave/Imager (SSM/I) data and has shown satisfactory results.

Xiang, Xuwu↗
NASA NTRS1983

Remote assessment of ocean color for interpretation of satellite visible imagery: A review

An assessment is presented of the state-of-the-art of remote, (satellite-based) Coastal Zone Color (CZCS) Scanning of color variations in the ocean due to phytoplankton. Attention is given to physical problems associated with ocean color remote sensing, in-water algorithms for the correction of atmospheric effects, constituent retrieval algorithms and application of the algorithms to CZCS imagery. The applicability of CZCS to both near-coast and mid-ocean waters is considered, and it is concluded that while differences between the two environments are complex, universal algorithms can be used for the case of mid-ocean waters, and site-specific algorithms are adequate for CZCS imaging of the near-coast oceanic environment. A short description of CVCS and some sample photographs are provided in an appendix.

Gordon, H. R.↗
NASA NTRS2002

A Method to Retrieve Rainfall Rate over Land from TRMM Observations

Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations over mesoscale convective systems (MCSs) reveal that there are localized maxima in the rain rate with a scale of about 10 to 20 km that represent thunderstorms (Cbs). Some of these Cbs are developing or intense, while others are decaying or weak. These Cbs constitute only about 20 % of the rain area of a given MCS. Outside of Cbs, the average rain rate is much weaker than that within Cbs. From an analysis of the PR data, we find that the spatial distribution of rain and its character, convective or stratiform, is highly inhomogeneous. This complex nature of rain exists on a scale comparable to that of a Cb. The 85 GHz brightness temperature, T85, observations of the TRMM Microwave Imager (TMI) radiometer taken over an MCS reflect closely the PR rain rate pattern over land. Local maxima in rain rate shown by PR are observed as local minima in T85. Where there are no minima in T85, PR observations indicate there is light rain. However, the TMI brightness temperature measurements (Tbs) have poor ability to discriminate convective rain from stratiform rain. For this reason, a TMI rain retrieval procedure that depends primarily on the magnitude of Tbs performs poorly. In order to retrieve rain rate from TMI data on land one has to include the spatial distribution information deduced from the T85 data in the retrieval method. Then, quantitative estimation of rain rate can be accomplished. A TMI rain retrieval method developed along these lines can yield estimates of rain rate and its frequency distribution which agree closely with that given by PR. We find the current TRMM project TMI (Version 5) rain retrieval algorithm on land could be improved with the retrieval scheme developed here. To support the conceptual frame work of the rain retrieval method developed here, a theoretical analysis of the TMI brightness temperatures in convective and stratiform regions is presented.

Prabhakara, C.↗
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 NTRS2018

Observing the Full Spectrum of the Rain Drop Size Distribution

GPM DSD retrievals exhibit inconsistencies between GV, DPR and Combined algorithm retrievals. Development of positive bias in convective Dm rain DSD noted, and strongest in KuPR retrieval. Associated epsilons are too low and result in markedly reduced convective rain rates (a current issue in the retrievals). Source may be NUBF. Issues with the large end of the DSD not withstanding, on the small end of the DSD, combined MPS and 2DVD measurements fit with generalized gamma functions exhibit strong potential for representing the entire spectrum of the DSD and subsequently the whole rain rate spectrum.

Hydrology↗
NASA NTRS2019

SMAP Mission Status and Plan

The prime mission phase of National Aeronautics Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) project was successfully completed in June 2018. The extended phase has been approved by NASA for operation through 2021 (with option to 2023). SMAP data have been well calibrated and have enabled diverse scientific investigations in water, energy and carbon cycle research, terrestrial ecology and ocean science. This paper will provide the highlights of algorithm updates to radiometric calibration and soil moisture retrieval algorithms. A summary of extended phase activities, in particular the SMAPVEX19 campaign, for product enhancements will be provided.

Water Cycle↗
NASA NTRS2023

PCRTM-RA Enhancements for Improving CO Retrievals Using NAST-I Measurements From the FIREX-AQ Field Campaign

The Principal Component-based Radiative Transfer Model Retrieval Algorithm (PCRTM-RA) for carbon monoxide (CO) retrieval has been improved for better use of National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. One of the explicit purposes of the campaign was to characterize wildfire-induced atmospheric changes. Coincidental measurements from various airborne instruments including NAST-I infrared hyperspectral measurements from the NASA ER-2 aircraft provided us an opportunity to test and improve the PCRTM-RA CO retrieval. By relaxing the vertical correlation of CO profiles in the a-priori covariance constraint, a significant improvement in the vertical structure of the CO retrieval has been confirmed. The methodology is validated using a synthetic testing dataset that covers observations associated with various CO vertical profiles including that of an extremely polluted atmosphere. The methodology is also applied to real NAST-I measurements, and the results have successfully demonstrated the capability of using PCRTM-RA retrieval results for CO plume evolution and transport monitoring.

Carbon monoxide (CO)↗
NASA NTRS2023

A Performance Analysis on Soil Dielectric Models Over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM < 15%) and organic soils (SOM ≥ 15%) would be the preferred option.

Soil Moisture↗
NASA NTRS1999

Aerosol Retrievals over the Ocean using Channel 1 and 2 AVHRR Data: A Sensitivity Analysis and Preliminary Results

This paper outlines the methodology of interpreting channel 1 and 2 AVHRR radiance data over the oceans and describes a detailed analysis of the sensitivity of monthly averages of retrieved aerosol parameters to the assumptions made in different retrieval algorithms. The analysis is based on using real AVHRR data and exploiting accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmosphere-ocean system. We show that two-channel algorithms can be expected to provide significantly more accurate and less biased retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening and calibration uncertainties are by far the largest sources of errors in the retrieved aerosol parameters. Both underestimating and overestimating aerosol absorption as well as the potentially strong variability of the real part of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.

Mishchenko, Michael I.↗
NASA NTRS2024

Synergistic Retrievals of Ice in High Clouds From Elastic Backscatter Lidar, Ku-band Radar and Submillimeter Wave Radiometer Observations

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and sub-millimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of IceWater Content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments e.g. frequencies, sensitivities, etc. are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, out of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-Means clustering algorithm. In the second step, the IWC profile is estimated using an Ensemble Kalman Smoother (EKS) algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

Mircea Grecu↗
NASA NTRS1985

Pattern recognition in the satellite temperature retrieval problem

Pattern recognition procedures have been developed in order to improve the first-guess fields for satellite temperature retrievals. The first procedure is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The second procedure is used to organize a priori data into shape-coherent pattern libraries using structural information inherent in the data itself. On the basis of independent tests of about 800 temperature retrievals, it was found that: (1) the pattern recognition techniques reduced first-guess profile errors by nearly 50 percent in comparison with traditional partitioning schemes; and (2) with regression and physical-iterative retrieval algorithms, however, the effect of pattern recognition on temperature retrieval error was insignificant. Analysis of individual retrieval errors showed that poor retrievals may outweigh the potential benefits of both pattern recognition techniques.

Thompson, O. E.↗
NASA NTRS1995

Nonsphericity of dust-like tropospheric aerosols: Implications for aerosol remote sensing and climate modeling

T-matrix computations of light scattering by polydispersions of randomly oriented nonspherical aerosols and Mie computations for equivalent spheres are compared. Findings show that even moderate nonsphericity results in suubstantial errors in the retrieved aerosol optical thickness if satellite reflectance measurements are analyzed using Mie theory. On the other hand, the use of Mie theory for nonspherical aerosols produces negligible errors in the computation of albedo and flux related quantities, provided that the aerosol size distribution and optical thickness are known beforehand. The first result can be explained by large nonspherical-spherical differences in scattering phase function, while the second result follows from small nonspherical-spherical differences in single-scattering albedo and asymmetry parameter. No cancellation of errors occurs if one consistently uses Mie theory in the retrieval algorithm and then in computing the albedo for the retrieved aerosol optical thickness.

Mishchenko, M. I.↗
NASA NTRS2019

Characteristics of Total Column CO2 Retrievals from the OCO Missions: Biases, Information Content and Implications for Flux Inversions

The Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3, launched in 2015 and 2019, respectively, are intended to collect and deliver high-resolution observations of CO2 with unprecedented space and time coverage. Observations of CO2 from these remote-sensing missions (also known as XCO2, or column-based average, dry air mole fraction of CO2) are then used by the global carbon cycle community to answer a wide range of science questions, from the distribution and quantification of global and regional CO2 source-sink patterns to quantification of anthropogenic sources at urban scales. Even though we have had the OCO-2 mission flying for a few years now, the retrieval algorithms are continuously evolving and improving to deliver XCO2 retrievals with very high precision and high accuracy (or low biases). In this presentation, we will discuss a simple yet effective quantitative framework that has been developed by the OCO-2 flux team to evaluate the information content of these XCO2 retrievals as soon as they are released, i.e., with lower latency than full-scale flux inversions. This framework serves as a precursor to advanced inverse modeling frameworks and is intended to provide an early but accurate assessment of the signal present in the satellite retrievals, the robustness of that signal, and the ability of these retrievals to resolve patterns in CO2 surface fluxes that cannot be resolved by our current network of surface sites. Specific results will tackle a tiered set of questions that are being addressed using this framework: (a) what are the distribution of retrievals in the different modes of operation and how do they vary in space and time? (b) what is the information that is being given to the inverse modeling frameworks from the space-based data, information above and beyond what is provided by the in-situ data? and (c) how do these factors influence our choices for doing flux inversions with the satellite retrievals? While the primary focus of the results will be on application of this technique to mature OCO-2 retrievals, we will show early results for a couple of months of OCO-3 retrievals. For the time-period that the retrievals from the two missions overlap, we will highlight how this framework allows us to effortlessly put the information from OCO-3 and OCO-2 on an equal footing, thus enabling easy comparison between the two pioneering missions.

Chatterjee, Abhishek↗
NASA NTRS2021

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers.

Dominique Carrer↗
NASA NTRS2014

Cloud Retrieval Intercomparisons Between SEVIRI, MODIS and VIIRS with CHIMAERA PGE06 Data Collection 6 Products

The Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms (CHIMAERA) system allows us to perform MODIS-like cloud top, optical and microphysical properties retrievals on any sensor that possesses a minimum set of common spectral channels. The CHIMAERA system uses a shared-core architecture that takes retrieval method out of the equation when intercomparisons are made. Here we show an example of such retrieval and a comparison of simultaneous retrievals done using SEVIRI, MODIS and VIIRS sensors. All sensor retrievals are performed using CLAVR-x (or CLAVR-x based) cloud top properties algorithm. SEVIRI uses the SAF_NWC cloud mask. MODIS and VIIRS use the IFF-based cloud mask that is a shared algorithm between MODIS and VIIRS. The MODIS and VIIRS retrievals are performed using a VIIRS branch of CHIMAERA that limits available MODIS channel set. Even though in that mode certain MODIS products such as multilayer cloud map are not available, the cloud retrieval remains fully equivalent to operational Data Collection 6.

cloud retrieval↗
NASA NTRS1989

Satellite cloud retrieval - Past, present, future

Satellite cloud observation is reviewed, including past accomplishments, current problems, and possible future developments. Past and current cloud retrieval algorithms are discussed, noting efforts to overcome various problems associated with cloud retrieval. The Hybrid Bispectral Threshold Method (Minnis and Harrison, 1984), the CO2 slicing algorithm (Chahine, 1974) and the ISCCEP algorithm (Rossow et al., 1985) are considered. An error analysis is presented for the determination of reference cloud fraction from Landsat TM data. The implications of the preliminary results from the First ISCCP Regional Experiment are examined and plans for future research projects are given.

Wielicki, Bruce A.↗
NASA NTRS2015

Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = −0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Soil MOisture↗