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Abstract
Evidence for the radiometric determination of air–water interface temperature gradients is presented. Inherent radiometric characteristics in the water molecule cause variations in the absorption coefficient that allow radiation at near-infrared frequencies (2000–5000 wavenumbers, 2.0–5.0 μm) to carry information about subsurface water temperatures. This radiation leaving the surface is predominantly sensitive to water temperature in the layer between the surface and the “effective optical depth” (inverse of the absorption coefficient). Where atmospheric transmittance is high and/or the instrument is near the liquid, the radiance variations with frequency record temperature variations with depth. To measure the small radiance variations with frequency, an instrument must be radiometrically stable in suitable frequency bands with low instrument noise.
A simulation of this technique's use for airborne beat flux measurement indicated feasibility from low altitudes at night. Laboratory experiments produced radiometric signals that strongly indicated that the thermal structures in an air–water interface can be studied in detail. Corrected for variations of emissivity and reflectivity with frequency, the water spectra showed multiple correlations with those gradients inferred from bulk temperature measurements that assumed conductive heat loss. The use of high spectral resolution increased the vertical resolution of the interface thermal structures. Although high spectral resolution is not required for a field application, problems of system noise, atmospheric absorption, and solar reflection are more tractable with its use.
This technique may be useful in laboratory studies of thermal structures relevant to heat and gas flow that reside in the air–water interface.
Abstract
Evidence for the radiometric determination of air–water interface temperature gradients is presented. Inherent radiometric characteristics in the water molecule cause variations in the absorption coefficient that allow radiation at near-infrared frequencies (2000–5000 wavenumbers, 2.0–5.0 μm) to carry information about subsurface water temperatures. This radiation leaving the surface is predominantly sensitive to water temperature in the layer between the surface and the “effective optical depth” (inverse of the absorption coefficient). Where atmospheric transmittance is high and/or the instrument is near the liquid, the radiance variations with frequency record temperature variations with depth. To measure the small radiance variations with frequency, an instrument must be radiometrically stable in suitable frequency bands with low instrument noise.
A simulation of this technique's use for airborne beat flux measurement indicated feasibility from low altitudes at night. Laboratory experiments produced radiometric signals that strongly indicated that the thermal structures in an air–water interface can be studied in detail. Corrected for variations of emissivity and reflectivity with frequency, the water spectra showed multiple correlations with those gradients inferred from bulk temperature measurements that assumed conductive heat loss. The use of high spectral resolution increased the vertical resolution of the interface thermal structures. Although high spectral resolution is not required for a field application, problems of system noise, atmospheric absorption, and solar reflection are more tractable with its use.
This technique may be useful in laboratory studies of thermal structures relevant to heat and gas flow that reside in the air–water interface.
Improved Severe Weather Forecasts Using LEO and GEO Satellite SoundingsAbstract
It is shown here that improvements in numerical weather prediction (NWP) model forecasts of hazardous weather can be obtained by assimilating profile retrievals obtained in real time from combined direct broadcast system (DBS) polar satellite hyperspectral and geostationary satellite multispectral radiance data. Results of NWP model forecasts are shown for two recent tornado outbreak cases: 1) the 3 March 2019 tornado outbreak over the southeast United States and 2) the tornado outbreak that occurred across Illinois, Indiana, and Ohio during the night of 27 May and the morning of 28 May 2019, and 3) the 4 March 2019 severe precipitation event that occurred in southeast China. Improvements in both quantitative precipitation forecasts (QPFs) and predictions of the location of tornado occurrence are obtained. It is also shown that geostationary satellite hyperspectral soundings [i.e., Fengyun-4A (FY-4A) Geosynchronous Interferometric Infrared Sounder (GIIRS)] further improve hazardous precipitation forecasts when used, in addition to the combined polar hyperspectral and geostationary multispectral satellite profile data, to initialize the numerical forecast model. The lowest false alarm rate (FAR) and the highest probability of detection (POD) and critical success index (CSI) scores are achieved when assimilating atmospheric profile retrievals obtained by combining all the available satellite high-vertical-resolution hyperspectral radiance measurements with geostationary satellite high-spatial-resolution and high-temporal-resolution multispectral radiance measurements.
Abstract
It is shown here that improvements in numerical weather prediction (NWP) model forecasts of hazardous weather can be obtained by assimilating profile retrievals obtained in real time from combined direct broadcast system (DBS) polar satellite hyperspectral and geostationary satellite multispectral radiance data. Results of NWP model forecasts are shown for two recent tornado outbreak cases: 1) the 3 March 2019 tornado outbreak over the southeast United States and 2) the tornado outbreak that occurred across Illinois, Indiana, and Ohio during the night of 27 May and the morning of 28 May 2019, and 3) the 4 March 2019 severe precipitation event that occurred in southeast China. Improvements in both quantitative precipitation forecasts (QPFs) and predictions of the location of tornado occurrence are obtained. It is also shown that geostationary satellite hyperspectral soundings [i.e., Fengyun-4A (FY-4A) Geosynchronous Interferometric Infrared Sounder (GIIRS)] further improve hazardous precipitation forecasts when used, in addition to the combined polar hyperspectral and geostationary multispectral satellite profile data, to initialize the numerical forecast model. The lowest false alarm rate (FAR) and the highest probability of detection (POD) and critical success index (CSI) scores are achieved when assimilating atmospheric profile retrievals obtained by combining all the available satellite high-vertical-resolution hyperspectral radiance measurements with geostationary satellite high-spatial-resolution and high-temporal-resolution multispectral radiance measurements.
Abstract
The robust diffusion sensor provides a simple, inexpensive, accurate method to integrate temperature, relative humidity (or water activity), or salinity for time periods up to several years. It does not require power or maintenance. Depending on temperature, precision of about 0.1°C for temperature and a few percent for relative humidity or salinity can be achieved. Various combinations of water-filled and desiccant-filled cells can be used for simultaneously integrating temperature, relative humidity or salinity by simply weighing the cells before and after exposure to the environment.
Abstract
The robust diffusion sensor provides a simple, inexpensive, accurate method to integrate temperature, relative humidity (or water activity), or salinity for time periods up to several years. It does not require power or maintenance. Depending on temperature, precision of about 0.1°C for temperature and a few percent for relative humidity or salinity can be achieved. Various combinations of water-filled and desiccant-filled cells can be used for simultaneously integrating temperature, relative humidity or salinity by simply weighing the cells before and after exposure to the environment.
Abstract
A precipitation retrieval algorithm based on the application of a 3D radiative transfer model to a hybrid physical-stochastic 3D cloud model is described. The cloud model uses a statistical rainfall clustering scheme to generate 3D cloud structure while ensuring that the stochastically generated quantities remain physically plausible. The radiative transfer model is applied to the cloud structures to simulate satellite remotely sensed upwelling microwave brightness temperatures TB 's. Regression-derived relationships between model TB 's and surface rainfall rates for Special Sensor Microwave/Imager (SSM/I) frequencies are used as the foundation of the retrieval algorithm, which is valid over oceans. A case study calibrates the retrieval algorithm to the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model and applies the algorithm to SSM/I data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment. Comparisons between the satellite-derived precipitation amounts and radar-derived amounts, at a spatial resolution of approximately 55 km, give correlations of about 0.7 for instantaneous rain rates and 0.634 for monthly accumulations. Although the satellite-derived totals are reasonably well correlated with the radar totals, they also appear to contain a relatively large positive bias, which may in part be due to the ECMWF tuning. However, optical rain gauge measurements are lager than both the satellite- and radar-derived amounts, casting uncertainty into the level of bias of the satellite algorithm. Finally, an important aspect of 3D radiative transfer in precipitating systems is illustrated by demonstrating that satellite viewing angle effects realized in the simulation framework also appear to be present in empirical relations between SSM/I TB 's and radar-derived surface rainfall rates.
Abstract
A precipitation retrieval algorithm based on the application of a 3D radiative transfer model to a hybrid physical-stochastic 3D cloud model is described. The cloud model uses a statistical rainfall clustering scheme to generate 3D cloud structure while ensuring that the stochastically generated quantities remain physically plausible. The radiative transfer model is applied to the cloud structures to simulate satellite remotely sensed upwelling microwave brightness temperatures TB 's. Regression-derived relationships between model TB 's and surface rainfall rates for Special Sensor Microwave/Imager (SSM/I) frequencies are used as the foundation of the retrieval algorithm, which is valid over oceans. A case study calibrates the retrieval algorithm to the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model and applies the algorithm to SSM/I data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment. Comparisons between the satellite-derived precipitation amounts and radar-derived amounts, at a spatial resolution of approximately 55 km, give correlations of about 0.7 for instantaneous rain rates and 0.634 for monthly accumulations. Although the satellite-derived totals are reasonably well correlated with the radar totals, they also appear to contain a relatively large positive bias, which may in part be due to the ECMWF tuning. However, optical rain gauge measurements are lager than both the satellite- and radar-derived amounts, casting uncertainty into the level of bias of the satellite algorithm. Finally, an important aspect of 3D radiative transfer in precipitating systems is illustrated by demonstrating that satellite viewing angle effects realized in the simulation framework also appear to be present in empirical relations between SSM/I TB 's and radar-derived surface rainfall rates.
Abstract
In the winter of 1986, two microwave radiometers were operated side by side at a high-altitude weather observation site in the central Sierra Nevada for the purpose of comparing measurements in a variety of ambient weather conditions. The instruments continuously recorded measurements of vertically integrated water vapor and liquid water during storms affecting the area. One radiometer was designed with a spinning reflector to shed precipitation particles while the other radiometer's reflector was fixed. Temporal records of the data show periods of wet weather contamination for the fixed reflector radiometer. The absence (presence) of these contaminated periods is mainly explained by the difference in the design of the radiometers. These contaminated periods led to larger standard deviation in the data from the fixed-reflector radiometer and lower correlation coefficients between the two instruments. Correlation coefficients of 0.83 for the liquid and 0.68 for the vapor values were found for the radiometer-radiometer comparisons. When some of the points suspected of contamination were removed, the correlation coefficients improved to 0.87 and 0.71 for the liquid and vapor channels, respectively. The standard deviations were 0.1 mm and 0.12 cm for the liquid and vapor channels, respectively, of the spinning reflector radiometer. For the fixed-reflector design radiometer, a standard deviation of 0.1 mm for the liquid and 0.26 cm for the vapor was found. Comparison of radiometer vapor and rawinsonde precipitable water resulted in a correlation coefficient of 0.97 for the spinning-reflector radiometer and 0.8 for the fixed-reflector radiometer.
Abstract
In the winter of 1986, two microwave radiometers were operated side by side at a high-altitude weather observation site in the central Sierra Nevada for the purpose of comparing measurements in a variety of ambient weather conditions. The instruments continuously recorded measurements of vertically integrated water vapor and liquid water during storms affecting the area. One radiometer was designed with a spinning reflector to shed precipitation particles while the other radiometer's reflector was fixed. Temporal records of the data show periods of wet weather contamination for the fixed reflector radiometer. The absence (presence) of these contaminated periods is mainly explained by the difference in the design of the radiometers. These contaminated periods led to larger standard deviation in the data from the fixed-reflector radiometer and lower correlation coefficients between the two instruments. Correlation coefficients of 0.83 for the liquid and 0.68 for the vapor values were found for the radiometer-radiometer comparisons. When some of the points suspected of contamination were removed, the correlation coefficients improved to 0.87 and 0.71 for the liquid and vapor channels, respectively. The standard deviations were 0.1 mm and 0.12 cm for the liquid and vapor channels, respectively, of the spinning reflector radiometer. For the fixed-reflector design radiometer, a standard deviation of 0.1 mm for the liquid and 0.26 cm for the vapor was found. Comparison of radiometer vapor and rawinsonde precipitable water resulted in a correlation coefficient of 0.97 for the spinning-reflector radiometer and 0.8 for the fixed-reflector radiometer.
Abstract
Knowledge of cirrus cloud optical depths is necessary to understand the earth’s current climate and to model the cloud radiation impact on future climate. Cirrus clouds, depending on the ratio of their shortwave “visible” to longwave “infrared” optical depth, can act to either cool or warm the planet. In this study, visible-to-infrared cirrus cloud optical depth ratios were measured using ground-based lidar and Fourier transform spectrometry. A radiosonde temperature profile combined with the 0.532-μm-high spectral resolution lidar vertical cloud optical depth profile provided an effective weighting to the cloud radiance measured by the interferometer. This allowed evaluation of cirrus cloud optical depths in 18 infrared microwindows between water vapor absorption lines within the 800–1200-cm−1 infrared atmospheric window. The data analysis was performed near the peak solar and terrestrial emission regions, which represent the effective radiative cloud forcing efficiency of the given cloud sample. Results are also presented that demonstrate the measurement of infrared optical depth using an assumed uniform cloud extinction cross section, which requires generic lidar cloud boundary data. The measured cloud extinction profile provided a more robust solution that would allow analysis of multiple-layer clouds and removed the uniform cloud extinction cross-section assumption. Mie calculations for ice particles were used to generate visible and infrared extinction coefficients; these were compared against the measured visible-to-infrared optical depth ratios. The results demonstrate strong particle size and shape sensitivity across the infrared atmospheric window.
Abstract
Knowledge of cirrus cloud optical depths is necessary to understand the earth’s current climate and to model the cloud radiation impact on future climate. Cirrus clouds, depending on the ratio of their shortwave “visible” to longwave “infrared” optical depth, can act to either cool or warm the planet. In this study, visible-to-infrared cirrus cloud optical depth ratios were measured using ground-based lidar and Fourier transform spectrometry. A radiosonde temperature profile combined with the 0.532-μm-high spectral resolution lidar vertical cloud optical depth profile provided an effective weighting to the cloud radiance measured by the interferometer. This allowed evaluation of cirrus cloud optical depths in 18 infrared microwindows between water vapor absorption lines within the 800–1200-cm−1 infrared atmospheric window. The data analysis was performed near the peak solar and terrestrial emission regions, which represent the effective radiative cloud forcing efficiency of the given cloud sample. Results are also presented that demonstrate the measurement of infrared optical depth using an assumed uniform cloud extinction cross section, which requires generic lidar cloud boundary data. The measured cloud extinction profile provided a more robust solution that would allow analysis of multiple-layer clouds and removed the uniform cloud extinction cross-section assumption. Mie calculations for ice particles were used to generate visible and infrared extinction coefficients; these were compared against the measured visible-to-infrared optical depth ratios. The results demonstrate strong particle size and shape sensitivity across the infrared atmospheric window.
Abstract
Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA’s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation’s (ISRO’s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)’s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results.
Abstract
Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA’s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation’s (ISRO’s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)’s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results.
Abstract
An Advanced Microwave Precipitation Radiometer (AMPR) has been developed and flown in the NASA ER-2 high-altitude aircraft for imaging various atmospheric and surface processes, primarily the internal structure of rain clouds. The AMPR is a scanning four-frequency total power microwave radiometer that is externally calibrated with high-emissivity warm and cold loads. Separate antenna systems allow the sampling of the 10.7- and 19.35-GHz channels at the same spatial resolution, while the 37.1- and 85.5-GHz channels utilize the same multifrequency feedhorn as the 19.35-GHz channel. Spatial resolutions from an aircraft altitude of 20-km range from 0.6 km at 85.5 GHz to 2.8 km at 19.35 and 10.7 GHz. All channels are sampled every 0.6 km in both along-track and cross-track directions, leading to a contiguous sampling pattern ofthe 85.5-GHz 3-dB beamwidth footprints, 2.3 × oversampling of the 37.1-GHz data, and 4.4 × oversampling of the 19.35- and 10.7-GHz data. Radiometer temperature sensitivities range from 0.2° to 0.5°C. Details of the system are described, including two different calibration systems and their effect on the data collected. Examples of oceanic rain systems are presented from Florida and the tropical west Pacific that illustrate the wide variety of cloud water, rainwater, and precipitation-size ice combinations that are observable from aircraft altitudes.
Abstract
An Advanced Microwave Precipitation Radiometer (AMPR) has been developed and flown in the NASA ER-2 high-altitude aircraft for imaging various atmospheric and surface processes, primarily the internal structure of rain clouds. The AMPR is a scanning four-frequency total power microwave radiometer that is externally calibrated with high-emissivity warm and cold loads. Separate antenna systems allow the sampling of the 10.7- and 19.35-GHz channels at the same spatial resolution, while the 37.1- and 85.5-GHz channels utilize the same multifrequency feedhorn as the 19.35-GHz channel. Spatial resolutions from an aircraft altitude of 20-km range from 0.6 km at 85.5 GHz to 2.8 km at 19.35 and 10.7 GHz. All channels are sampled every 0.6 km in both along-track and cross-track directions, leading to a contiguous sampling pattern ofthe 85.5-GHz 3-dB beamwidth footprints, 2.3 × oversampling of the 37.1-GHz data, and 4.4 × oversampling of the 19.35- and 10.7-GHz data. Radiometer temperature sensitivities range from 0.2° to 0.5°C. Details of the system are described, including two different calibration systems and their effect on the data collected. Examples of oceanic rain systems are presented from Florida and the tropical west Pacific that illustrate the wide variety of cloud water, rainwater, and precipitation-size ice combinations that are observable from aircraft altitudes.
Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus CloudsAbstract
Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.
Abstract
Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.
Clouds and the Earth’s Radiant Energy System (CERES) Cloud Radiative Swath (CRS) Edition 4 Data ProductAbstract
Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.
Abstract
Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.
