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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Causal Evaluations for Identifying Differences between Observations and Earth System Models

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Regional Climate Simulation and Data Assimilation with Variable-Resolution GCMs

Variable resolution GCMs using a global stretched grid (SG) with enhanced regional resolution over one or multiple areas of interest represents a viable new approach to regional climateklimate change and data assimilation studies and applications. The multiple areas of interest, at least one within each global quadrant, include the major global mountains and major global monsoonal circulations over North America, South America, India-China, and Australia. They also can include the polar domains, and the European and African regions. The SG-approach provides an efficient regional downscaling to mesoscales, and it is an ideal tool for representing consistent interactions of globaYlarge- and regionallmeso- scales while preserving the high quality of global circulation. Basically, the SG-GCM simulations are no different from those of the traditional uniform-grid GCM simulations besides using a variable-resolution grid. Several existing SG-GCMs developed by major centers and groups are briefly described. The major discussion is based on the GEOS (Goddard Earth Observing System) SG-GCM regional climate simulations.

Fox-Rabinovitz, Michael S.↗
NASA NTRS1976

Zonal average earth radiation budget measurements from satellites for climate studies

Data from 29 months of satellite radiation budget measurements, taken intermittently over the period 1964 through 1971, are composited into mean month, season and annual zonally averaged meridional profiles. Individual months, which comprise the 29 month set, were selected as representing the best available total flux data for compositing into large scale statistics for climate studies. A discussion of spatial resolution of the measurements along with an error analysis, including both the uncertainty and standard error of the mean, are presented.

Ellis, J. S.↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
NASA NTRS1981

A study of model parameters associated with the urban climate using HCMM data

The use of infrared and visible data from the Heat Capacity Mapping Mission (HCMM) and in situ data to study the intensity of the urban heat island of Saint Louis is described. Analysis of HCMM data shows that an urban heat island exists day and night in all seasons when clear skies prevail. The lower albedo value of the urban region during the day suggests that the higher temperatures are due to more absorption of solar radiation. Preliminary analysis of in situ meteorological data was performed after merging with HCMM data, and surface roughness, the exchange coefficient, and the soil moisture were calculated.

Source record↗
NASA NTRS1996

Quality and Control of Water Vapor Winds

Water vapor imagery from the geostationary satellites such as GOES, Meteosat, and GMS provides synoptic views of dynamical events on a continual basis. Because the imagery represents a non-linear combination of mid- and upper-tropospheric thermodynamic parameters (three-dimensional variations in temperature and humidity), video loops of these image products provide enlightening views of regional flow fields, the movement of tropical and extratropical storm systems, the transfer of moisture between hemispheres and from the tropics to the mid- latitudes, and the dominance of high pressure systems over particular regions of the Earth. Despite the obvious larger scale features, the water vapor imagery contains significant image variability down to the single 8 km GOES pixel. These features can be quantitatively identified and tracked from one time to the next using various image processing techniques. Merrill et al. (1991), Hayden and Schmidt (1992), and Laurent (1993) have documented the operational procedures and capabilities of NOAA and ESOC to produce cloud and water vapor winds. These techniques employ standard correlation and template matching approaches to wind tracking and use qualitative and quantitative procedures to eliminate bad wind vectors from the wind data set. Techniques have also been developed to improve the quality of the operational winds though robust editing procedures (Hayden and Veldon 1991). These quality and control approaches have limitations, are often subjective, and constrain wind variability to be consistent with model derived wind fields. This paper describes research focused on the refinement of objective quality and control parameters for water vapor wind vector data sets. New quality and control measures are developed and employed to provide a more robust wind data set for climate analysis, data assimilation studies, as well as operational weather forecasting. The parameters are applicable to cloud-tracked winds as well with minor modifications. The improvement in winds through use of these new quality and control parameters is measured without the use of rawinsonde or modeled wind field data and compared with other approaches.

Jedlovec, Gary J.↗
NASA NTRS2019

Applications of GLM Data for National Climate Assessment (NCA) Studies

The Geostationary Lightning Mapper (GLM) offers a new and exciting way to probe lightning/climate inter-relationships, and therefore supports the National Climate Assessment (NCA) program. In particular, there is a desire to use GLM to estimate lightning nitrogen oxides (LNOx) since trace quantities of NOx affect greenhouse gas concentrations (e.g., ozone).

Koshak, W. J.↗
NASA NTRS2016

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

knowledge base↗
NASA NTRS2008

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗
NASA NTRS2010

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Wolff, David B.↗
NASA NTRS2011

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗
NASA NTRS1999

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

Rokke, L.↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗