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Results for “surface energy balance”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Atmospheric correction of LANDSAT TM thermal band using surface energy balance

Thermal infrared data of LANDSAT Thematic Mapper (TM) are hardly used, probably due to the difficulties met when trying to correct them for atmospheric effects. A method for correcting these data was designed, based on surface energy balance estimation of known wet and dry targets included in the TM image to be corrected. This method, only using the image itself and local meteorological data was tested and validated on various surfaces: agricultural, forest and rangeland. The root mean square error on corrected temperatures is on the order of 1C.

Vidal, Alain↗
NASA NTRS1987

Estimating regional evapotranspiration from remotely sensed data by surface energy balance models

Spatial and temporal variations of surface radiative temperatures of the burned and unburned areas of the Konza tallgrass prairie were studied. The role of management practices, topographic conditions and the uncertainties associated with in situ or airborne surface temperature measurements were assessed. Evaluation of diurnal and seasonal spectral characteristics of the burned and unburned areas of the prairie was also made. This was accomplished based on the analysis of measured spectral reflectance of the grass canopies under field conditions, and modelling their spectral behavior using a one dimensional radiative transfer model.

Asrar, Ghassem↗
NASA NTRS2013

Tropical Ocean Surface Energy Balance Variability: Linking Weather to Climate Scales

Radiative and turbulent surface exchanges of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth s energy and water balance. Characterizing the spatiotemporal variability of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. These fluxes are integral components to tropical ocean-atmosphere variability; they can drive ocean mixed layer variations and modify the atmospheric boundary layer properties including moist static stability, thereby influencing larger-scale tropical dynamics. Non-parametric cluster-based classification of atmospheric and ocean surface properties has shown an ability to identify coherent weather regimes, each typically associated with similar properties and processes. Using satellite-based observational radiative and turbulent energy flux products, this study investigates the relationship between these weather states and surface energy processes within the context of tropical climate variability. Investigations of surface energy variations accompanying intraseasonal and interannual tropical variability often use composite-based analyses of the mean quantities of interest. Here, a similar compositing technique is employed, but the focus is on the distribution of the heat and moisture fluxes within their weather regimes. Are the observed changes in surface energy components dominated by changes in the frequency of the weather regimes or through changes in the associated fluxes within those regimes? It is this question that the presented work intends to address. The distribution of the surface heat and moisture fluxes is evaluated for both normal and non-normal states. By examining both phases of the climatic oscillations, the symmetry of energy and water cycle responses are considered.

Roberts, J. Brent↗
DOE OSTI2025

Surface Energy Balance Responses to Radiative Forcing in the Central Arctic From MOSAiC and Models

The Arctic surface energy budget (SEB) couples the atmosphere with the sea ice, making it useful for both studying surface processes as well as evaluating models. Improved understanding of atmosphere-ice interactions is required to improve models, requiring year-round observations to address seasonally dependent biases. This work uses novel observations from the MOSAiC expedition to quantify the responses of surface fluxes to radiative forcing over sea ice throughout a complete annual cycle. We identify two primary regimes of flux response: an ice growth regime in winter and an ice melt regime in summer. In the growth regime, changes in radiative forcing impact upwelling longwave, sensible heat, and subsurface heat fluxes, whereas in the melt regime changes in radiative forcing primarily alter the amount of melt and subsurface transmission because the surface temperature is fixed. These observed responses of surface fluxes to radiative forcing are used to evaluate seven weather forecast models during the ice growth regime. In most models, the responses of surface fluxes to radiative forcing do not match observations. Many models also have biased downwelling longwave. One model (the Coupled Arctic Forecast System; CAFS) adequately captures both the mean radiative forcing and the flux responses in winter. CAFS is further evaluated against observations spanning the full MOSAiC year, demonstrating sufficient agreement to provide a more generalized understanding of these SEB process relationships across the Arctic.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Satellite estimation of the surface energy balance, moisture availability and thermal inertia

A method for inferring the distribution of surface heat and evaporative fluxes and the ground moisture availability and thermal inertia (ground conductive capacity) is used to analyze two urbanized areas, Los Angeles and St. Louis. The technique employs infrared satellite temperature measurements in conjunction with a one-dimensional boundary-layer model. Results show that there is a marked reduction of evaporation and moisture availability and a corresponding elevation of sensible heat flux over urbanized areas and over cropped areas with low vegetative cover. Conversely, low heat flux and high evaporation characterize vegetated and, especially, forested areas. Warm urban centers appear directly related to a reduction in vegetation, which normally allows for a greater fraction of available radiant energy to be converted into latent heat flux. The distribution of thermal inertia was surprisingly ill-defined and its variation between urban and rural areas was quite small. Thus, the increased heat storage within the urban fabric, which has been proposed as the underlying cause of the nocturnal heat island, may be caused mainly by enhanced daytime surface heating which occurs because of surface dryness, rather than by large spatial variations in the conductivity of the surface.

Carlson, T. N.↗
NASA NTRS1992

Satellite remote sensing of surface energy balance - Success, failures, and unresolved issues in FIFE

Utilizing single-view-angle visible and NIR satellite observations, it was possible to make the necessary radiometric corrections to produce reliable estimates of the simple ratio and the normalized difference vegetation index as inputs to surface evaporation models. Agreement was achieved between helicopter and satellite measures of surface reflectance to within +/-1 percent absolute for both the visible and the NIR bands. A theoretical calculation utilizing radiative transfer models indicates that this translates into a +/-10 percent error in the estimation in Fpar.

Hall, Forrest G.↗
NASA NTRS1983

Satellite-derived surface energy balance estimates in the Alaskan sub-Arctic

The capabilities of the heat capacity mapping mission (HCMM) satellite are described, together with the numerical model used for data analysis. The HCMM carries the HCM radiometer for remote sensing in the visible and 0.55-1.1 micron wavelength regions, as well as in the 10.5-12.5 micron interval, with the swath being 720 km and each pixel being a square 500 m on a side. The HCMM is intended to aid in hydrological studies of soil moisture, runoff, and evapotranspiration estimates. Data are taken of the albedos and temperatures of vegetation, assuming that all reflection is diffuse. Corrections are made in the algorithm to account for sun angle and the spacecraft distance from the earth. Sample calculations are provided from scans of a coastal plain, mountains, and plateaus of Alaska.

Gurney, R. J.↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
NASA NTRS1986

Composition and stability of the condensate observed at the Viking Lander 2 site on Mars

Surface energy balance and near-surface temperature data from the Viking Lander 2 site taken during the first winter that condensated were observed and analyzed to determine the relative stability of CO2 and H2O frosts. The CO2 frost stability is calculated with an equilibrium surface energy balance model, i.e., the total energy incident on a frost surface is compared with the blackbody energy emitted by the surface. The energy sources considered were IR emission from the atmosphere, sunlight, and the sensible heat flux from the atmosphere. H2O stability was examined as a function of buoyant diffusion and turbulent mixing processes which could remove saturated near-surface gases. The CO2 frost is found to be sufficiently unstable at the time the condensate was observed on the ground, so all CO2 ice deposited at night would boil away in a few hours of sunlight. CO2 ice would not form during a dust storm. Water frost would be stable during the condensate observations, since sublimation would occur at a rate below 1 micron/day. A stable winter thickness of 10 microns is projected for the water ice.

Hart, H. M.↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2023

Regional and Teleconnected Impacts of Solar Radiation‐Topography Interaction Over the Tibetan Plateau

Abstract Solar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
NASA NTRS1986

The first International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment - FIFE

The International Satellite Land Surface Climatology Project (ISLSCP) will verify the use of satellite data for the estimation of land-surface properties through field experiments using point measurements on the ground and areal measurements from aircraft overflights. It will study approaches for obtaining areal averages of the radiation, moisture, and heat fluxes made using remotely sensed data, by combining the surface point measurements of the fluxes with the aircraft areal observations using a surface energy balance model. Surface parameters to be estimated from aircraft observations include: surface radiation temperature, albedo, land cover or vegetation index, and surface soil moisture. The latter will be obtained using passive and active microwave approaches. The area-averages of the surface properties will be compared with satellite data. The First ISLSCP Field Experiment is planned for a site having relatively uniform vegetation cover in the central great plains of the U.S.A.

Schmugge, T. J.↗
NASA NTRS2021

Distinct Surface Response to Black Carbon Aerosols

For the radiative impact of individual climate forcings, most previous studies focused on the global mean values at the top of the atmosphere (TOA), and less attention has been paid to surface processes, especially for black carbon (BC) aerosols. In this study, the surface radiative responses to five different forcing agents were analyzed by using idealized model simulations. Our analyses reveal that for greenhouse gases, solar irradiance, and scattering aerosols, the surface temperature changes are mainly dictated by the changes of surface radiative heating, but for BC, surface energy redistribution between different components plays a more crucial role. Globally, when a unit BC forcing is imposed at TOA, the net shortwave radiation at the surface decreases by −5.87±0.67 W m −2 (W m −2 ) −1 (averaged over global land without Antarctica), which is partially offset by increased downward longwave radiation (2.32±0.38 W m −2 (W m −2 ) −1 from the warmer atmosphere, causing a net decrease in the incoming downward surface radiation of −3.56±0.60 W m −2 (W m −2 ) −1 . Despite a reduction in the downward radiation energy, the surface air temperature still increases by 0.25±0.08 K because of less efficient energy dissipation, manifested by reduced surface sensible (−2.88±0.43 W m −2 (W m −2 ) −1 ) and latent heat flux (−1.54±0.27 W m −2 (W m −2 ) −1 ), as well as a decrease in Bowen ratio (−0.20±0.07 (W m −2 ) −1 ). Such reductions of turbulent fluxes can be largely explained by enhanced air stability (0.07±0.02 K (W m −2 ) −1 ), measured as the difference of the potential temperature between 925 hPa and surface, and reduced surface wind speed (−0.05±0.01 m s −1 (W m −2 ) −1 ). The enhanced stability is due to the faster atmospheric warming relative to the surface, whereas the reduced wind speed can be partially explained by enhanced stability and reduced Equator-to-pole atmospheric temperature gradient. These rapid adjustments under BC forcing occur in the lower atmosphere and propagate downward to influence the surface energy redistribution and thus surface temperature response, which is not observed under greenhouse gases or scattering aerosols. Our study provides new insights into the impact of absorbing aerosols on surface energy balance and surface temperature response.

radiative impact↗
NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗
DOE OSTI2023

Impact of Seasonal Snow-Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High-Altitude Mountain Valley

The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High-Resolution-Rapid-Refresh model and find large warm biases on clear-sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well-developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.

54 ENVIRONMENTAL SCIENCES↗