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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations with AMSR-E Slow Rotation Data

Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (Tb s). The regression equations are used to modify AMSR2 Tb s into AMSR-E equivalent Tb s that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed Tb s result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 Tb s. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from 200 000 km2 for the original unmodified AMSR2 Tb s to –700 and 4700 km2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change.

remote sensing↗
NASA NTRS2017

AMSR2 Soil Moisture Product Validation

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

microwave↗
NASA NTRS2017

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗
NASA NTRS2018

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear-Red Rock watershed, central Oklahoma between 2015-2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

AMSR2; passive microwave soil moisture; soil moist↗
NASA NTRS2018

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

WOD↗
NASA NTRS2017

An Integrated Examination of AMSR2 Products over Ocean

Integrated processing and analysis are presented for oceanic retrievals of precipitation, sea ice concentration, columnar water vapor and cloud water, sea surface temperature, and near surface wind from the advanced microwave scanning radiometer-2 (AMSR2) sensor. By developing a common algorithmic framework and permitting iterative interaction between historically separate science algorithms, ambiguous and contradictory retrieval results are minimized. The integration also serves to improve each algorithm individually, decreasing reliance on ancillary datasets. Case studies are presented that exemplify both ongoing challenges for these retrievals and potential research uses of such an integrated satellite product. Two cases are presented each for precipitation at high latitudes and marginal sea ice detection, with analysis supplemented by satellite data from CloudSat profiles and Himawari-8 imagery. Detection and retrieval of snowfall remains a challenge, while light rainfall detection can be aided by a variational algorithm. Sea ice concentrations below 20% cause disagreements, but the algorithms otherwise agree well on the sea ice edge. Potential mitigation strategies for ambiguous areas of light rainfall and marginal sea ice are discussed. The analysis demonstrates potential avenues for future algorithm development, but also some physical limitations of remote sensing with the AMSR2 frequencies.

variational methods↗
NASA NTRS2018

Assimilating GCOM-W AMSR2 Radiance Data in Future GEOS Reanalysis

Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.

Jin, Jianjun↗
NASA NTRS2020

Assimilating GCOM-W1 AMSR2 and TRMM TMI Radiance Data in GEOS Analysis and Reanalysis

The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) observed the Earth in lower latitudes between 1997 - 2015. Its conical-scan radiometer has nine channels and measured microwave radiances between 10 and 89 GHz. These data provide information on atmospheric temperature, humidity, clouds, precipitation, as well as sea surface temperature. Radiance data from other microwave radiometers such as Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) onboard various Defense Meteorological Satellite Program (DMSP) satellites are assimilated in clear-sky conditions in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. The GMAO's Hybrid 4D-EnVar-based Atmospheric Data Assimilation System (ADAS) is enhanced with an all-sky microwave radiance data assimilation capability in the real-time GEOS-Forward Processing (FP) system. Currently, the FP system assimilates Global Precipitation Measurement (GPM) microwave imager (GMI) radiance data utilizing this all-sky capability, and is being extended to use more all-sky data from other microwave radiometers. In this presentation, we will focus on impacts of all-sky TMI radiance data on GEOS analyses of atmospheric moisture, precipitation and other fields, and discuss their applications for future GEOS reanalyses.

Jin, Jianjun↗
NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗
NASA NTRS2016

Enhancements to NASA's Land Atmosphere Near Real-Time Capability for EOS (LANCE)

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) supports application users interested in monitoring a wide variety of natural and man-made phenomena. Near Real- Time (NRT) data and imagery from the AIRS, AMSR2, MISR, MLS, MODIS, OMPS, OMI and VIIRS instruments are available much quicker than routine processing allows. Most data products are available within 3 hours from satellite observation. NRT imagery are generally available 3-5 hours after observation. This article describes the LANCE and the enhancements made to the LANCE over the last year. These enhancements include the addition of NRT products from AMSR2, MISR, OMPS and VIIRS. In addition, the selection of LANCE NRT imagery that can be interactively viewed through Worldview and the Global Imagery Browse Services (GIBS) has been expanded. Next year, data from the MOPITT will be added to the LANCE.

data products↗
NASA NTRS2017

Information Theoretic Evaluation of Satellite Soil Moisture Retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surfacesoil moisture measurements over continental and global scales. A consistent assessment of theerrors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluationof soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAPinstruments using information theory-based metrics. These metrics rely on time series analysis ofsoil moisture retrievals for estimating the measurement error, level of randomness (entropy) andregularity (complexity) of the data. The results of the study indicate that the measurement errors inthe remote sensing retrievals are significantly larger than that of the ground soil moisture measurements.The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to Preprint submitted to Remote Sensing of Environment October 1, 2017those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levelsof complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity ofremotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT,SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content.

Information theory↗
NASA NTRS2023

Evaluation of Snowfall Retrieval Performance of Gpm Constellation Radiometers Relative to Spaceborne Radars

This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.

Yalei You↗
DOE OSTI2022

Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)

Abstract. Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

assimilation↗
NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗
NASA NTRS2018

NASA's Land, Atmosphere Near Real-Time Capability for EOS (LANCE): Delivering Data and Imagery to Meet the Needs of Near Real-Time Applications

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) is a virtual system that provides near real-time EOS data and imagery from the AIRS, AMSR2, LIS (ISS), MISR, MLS, MODIS, MOPITT, OMI, OMPS, and VIIRS instruments, to meet the needs of scientists and application users interested in monitoring a wide variety of natural and man-made phenomena. NRT imagery from LANCE are available through NASA's Global Imagery Browse Services (GIBS), Worldview, FIRMS and most recently through Worldview Snapshots – a low band width application that has replaced the Rapid Response Subsets. Over the past year: data and imagery from the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS), OMPS and VIIRS-Land have been added to LANCE. In the coming year LANCE will integrate the MODIS NRT Global Flood product, VIIRS Black Marble nighttime lights and Cloud Mask and Aerosol Dark Target from VIIRS Atmosphere. Here we provide a brief overview of LANCE, focusing on what's new and describing how these new data sets have been used to monitor lightning flashes, hurricanes and fires. For more information on LANCE visit: https://earthdata.nasa.gov/lance.

Davies, Diane↗