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Results for “satellite-derived irradiance”

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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29 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗
DOE OSTI2022

Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint

Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.

cloud mask forecast↗
NASA NTRS2018

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

Bio-optics↗
DOE OSTI2021

Measured and satellite-derived albedo data for estimating bifacial photovoltaic system performance

The albedo of the ground surface is an important factor in the cost-effectiveness of a bifacial photovoltaic (PV) system. To improve the availability of reliable albedo data, datasets of ground albedo and associated meteorological data were developed by using existing measurement network data and data measured by the PV industry. The measured datasets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Satellite-derived values of albedo are available from the National Solar Radiation Data Base (NSRDB). The NSRDB albedos were compared to the measured albedos for Surface Radiation budget (SURFRAD) network locations for the period 2001–2017, and the mean bias difference results were from -0.044 to +0.056. Overall, these differences are greater than the albedo measurement uncertainty of ±0.02; consequently, the NSRDB albedos should be used with caution for estimating the performance of bifacial PV systems. Differences between SURFRAD and NSRDB albedos are attributed to the NSRDB method for determining albedo and to the ground surfaces within the NSRDB 4 km spatial resolution pixel consisting of a mixture of surface types rather than just the single surface types viewed by the albedometers at the SURFRAD stations.

14 SOLAR ENERGY↗
DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗
DOE OSTI2024

The influence of cloud cover on the reliability of satellite-based solar resource data

Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2022

Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes

Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm –2 and underestimates the upwelling shortwave flux by –15.70 Wm –2 and downwelling longwave fluxes by –12.58 Wm –2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm –2 ) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm –2 ). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Banse, Karl↗
NASA NTRS2000

OCTS And Seawifs Bio-Optical Algorithm and Product Vaildattion and Intercomparison in US Coastal Waters

Monitoring the health of U.S. coastal waters is an important goal of the National Oceanic and Atmospheric Administration (NOAA). Satellite sensors are capable of providing daily synoptic data of large expanses of the U.S. coast. Ocean color sensor, in particular, can be used to monitor the water quality of coastal waters on an operational basis. To appraise the validity of satellite-derived measurements, such as chlorophyll concentration, the bio-optical algorithms used to derive them must be evaluated in coastal environments. Towards this purpose, over 21 cruises in diverse U.S. coastal waters have been conducted. Of these 21 cruises, 12 have been performed in conjunction with and under the auspices of the NASA/SIMBIOS Project. The primary goal of these cruises has been to obtain in-situ measurements of downwelling irradiance, upwelling radiance, and chlorophyll concentrations in order to evaluate bio-optical algorithms that estimate chlorophyll concentration. In this Technical Memorandum, we evaluate the ability of five bio-optical algorithms, including the current SeaWiFS algorithm, to estimate chlorophyll concentration in surface waters of the South Atlantic Bight (SAB). The SAB consists of a variety of environments including coastal and continental shelf regimes, Gulf Stream waters, and the Sargasso Sea. The biological and optical characteristics of the region is complicated by temporal and spatial variability in phytoplankton composition, primary productivity, and the concentrations of colored dissolved organic matter (CDOM) and suspended sediment. As such, the SAB is an ideal location to test the robustness of algorithms for coastal use.

Brow, Chirstopher↗
NASA NTRS2001

Sun and Sky Radiance Measurements and Data Analysis Protocols

This chapter is concerned with two types of radiometric measurements essential to verify atmospheric correction algorithms and to calibrate vicariously satellite ocean color sensors. The first type is a photometric measurement of the direct solar beam to determine the optical thickness of the atmosphere. The intensity of the solar beam can be measured directly, or obtained indirectly from measurements of diffuse global upper hemispheric irradiance. The second type is a measurement of the solar aureole and sky radiance distribution using a CCD camera, or a scanning radiometer viewing in and perpendicular to the solar principal plane. From the two types of measurements, the optical properties of aerosols, highly variable in space and time, can be derived. Because of the high variability, the aerosol properties should be known at the time of satellite overpass. Atmospheric optics measurements, however, are not easy to perform at sea, from a ship or any platform. This complicates the measurement protocols and data analysis. Some instrumentation cannot be deployed at sea, and is limited to island and coastal sites. In the following, measurement protocols are described for radiometers commonly used to measure direct atmospheric transmittance and sky radiance, namely standard sun photometers, fast-rotating shadow-band radiometers, automated sky scanning systems, and CCD cameras. Methods and procedures to analyze and quality control the data are discussed, as well as proper measurement strategies for evaluation of atmospheric correction algorithms and satellite-derived ocean color.

Frouin, Robert↗
NASA NTRS2001

OCTS and SeaWiFS Bio-Optical Algorithm and Product Validation and Intercomparison in US Coastal Waters

Monitoring the health of US coastal waters is an important goal of the National Oceanic and Atmospheric Administration (NOAA). Satellite sensors are capable of providing daily synoptic data of large expanses of the US coast. Ocean color sensors, in particular, can be used to monitor the water quality of coastal waters on an operational basis. To appraise the validity of satellite-derived measurements, such as chlorophyll concentration, the bio-optical algorithms used to derive them must be evaluated in coastal environments. Towards this purpose, over 21 cruises in diverse US coastal waters have been conducted. Of these 21 cruises, 12 have been performed in conjunction with and under the auspices of the NASA/Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project. The primary goal of these cruises has been to obtain in-situ measurements of downwelling irradiance, upwelling radiance, and chlorophyll concentrations in order to evaluate bio-optical algorithms that estimate chlorophyll concentration. In this Technical Memorandum, we evaluate the ability of five bio-optical algorithms, including the current Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) algorithm, to estimate chlorophyll concentration in surface waters of the South Atlantic Bight (SAB). The SAB consists of a variety of environments including coastal and continental shelf regimes, Gulf Stream waters, and the Sargasso Sea. The biological and optical characteristics of the region is complicated by temporal and spatial variability in phytoplankton composition, primary productivity, and the concentrations of colored dissolved organic matter (CDOM) and suspended sediment. As such, the SAB is an ideal location to test the robustness of algorithms for coastal use.

Brown, Christopher W.↗