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The downward shortwave radiation (DSR) is an important part of the Earth's energy balance, driving Earth's system's energy, water, and carbon cycles. Due to the harsh Antarctic environment, the accuracy of DSR derived from satellite and reanalysis has not been systematically evaluated over the transect of Zhongshan station to Dome A, East Antarctica. Therefore, this study aims to evaluate DSR reanalysis products (ERA5-Land, ERA5, MERRA-2) and satellite products (CERES and ICDR) in this area. The results indicate that DSR exhibits obvious monthly and seasonal variations, with higher values in summer than in winter. The ERA5-Land (ICDR) DSR product demonstrated the highest (lowest) accuracy, as evidenced by a correlation coefficient of 0.988 (0.918), a root-mean-square error of 23.919 (69.383) W m−2, a mean bias of −1.667 (−28.223) W m−2 and a mean absolute error of 13.37 (58.99) W m−2. The RMSE values for the ERA5-Land reanalysis product at seven stations, namely Zhongshan, Panda 100, Panda 300, Panda 400, Taishan, Panda 1100, and Kunlun, were 30.938, 29.447, 34.507, 29.110, 20.339, 17.267, and 14.700 W m−2, respectively; with corresponding bias values of 9.887, −12.159, −19.181, −15.519, −8.118, 6.297, and 3.482 W m−2. Regarding seasonality, ERA5-Land, ERA5, and MERRA-2 reanalysis products demonstrate higher accuracies during spring and summer, while ICDR products are least accurate in autumn. Cloud cover, water vapor, total ozone, and severe weather are the main factors affecting DSR. The error of DSR products is greatest in coastal areas (particularly at the Zhongshan station) and decreases towards the inland areas of Antarctica. [ABSTRACT FROM AUTHOR]

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau's thermal state. In order to gain a more comprehensive understanding of the Earth's radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation.

Surface incident shortwave radiation (Rs) can promote the circulation of substance and energy, and the accuracy of its estimation is of great significance for climate studies. The Rs can be acquired from satellite retrievals, reanalysis predictions and general circulation model (GCM) simulations. Although Rs estimates have been evaluated and compared in previous studies, most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations. Therefore, it is essential to comprehensively validate Rs estimates from multiple data sources. In this study, ground measurements of 690 stations from BSRN, GEBA, CMA, GC-NET and buoys were employed to validate the Rs estimates from seven representative products (GLASS, GEWEX-SRB, CERES-EBAF, ERA5, MERRA2, CFSR and CMIP6). The validation results indicated that the selected products overestimated Rs globally, with biases ranged from 0.48 to 21.27 W/m2. The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations. Moreover, the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m2. The long-term variation trends were also analyzed in this study.

Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth’s surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, their performances in different time scales have not been well-documented, particularly in China. This study intended to evaluate several DSR products across multiple time scales (i.e. instantaneous, 1-hourly, daily, and monthly average) and ecosystems in China. Six DSR products, including GLASS, BESS, CLARA-A2, MCD18A1, ERA5 and MERRA-2, were evaluated against ground measurements at Chinese Ecosystem Research Network (CERN) and integrated land-atmosphere interaction observation (TPDC) sites from 2009 to 2012. The instantaneous DSR of MCD18 showed a root mean square error (RMSE) of 146.02 W/m2. The hourly RMSE of ERA5 (155.52 W/m2) was largely smaller than MERRA-2 (188.53 W/m2). On the daily and monthly scale, BESS had the most optimized accuracy among the six products (RMSE of 36.82 W/m2). For the satellite-derived DSR products, the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization (WMO) for Global Numerical Weather Prediction (20 W/m2).

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau’s thermal state. In order to gain a more comprehensive understanding of the Earth’s radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors.

Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth's surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, their performances in different time scales have not been well-documented, particularly in China. This study intended to evaluate several DSR products across multiple time scales (i.e. instantaneous, 1-hourly, daily, and monthly average) and ecosystems in China. Six DSR products, including GLASS, BESS, CLARA-A2, MCD18A1, ERA5 and MERRA-2, were evaluated against ground measurements at Chinese Ecosystem Research Network (CERN) and integrated land-atmosphere interaction observation (TPDC) sites from 2009 to 2012. The instantaneous DSR of MCD18 showed a root mean square error (RMSE) of 146.02 W/m2. The hourly RMSE of ERA5 (155.52 W/m2) was largely smaller than MERRA-2 (188.53 W/m2). On the daily and monthly scale, BESS had the most optimized accuracy among the six products (RMSE of 36.82 W/m2). For the satellite-derived DSR products, the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization (WMO) for Global Numerical Weather Prediction (20 W/m2). [ABSTRACT FROM AUTHOR]

Surface incident shortwave radiation (Rs) can promote the circulation of substance and energy, and the accuracy of its estimation is of great significance for climate studies. The Rs can be acquired from satellite retrievals, reanalysis predictions and general circulation model (GCM) simulations. Although Rs estimates have been evaluated and compared in previous studies, most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations. Therefore, it is essential to comprehensively validate Rs estimates from multiple data sources. In this study, ground measurements of 690 stations from BSRN, GEBA, CMA, GC-NET and buoys were employed to validate the Rs estimates from seven representative products (GLASS, GEWEX-SRB, CERES-EBAF, ERA5, MERRA2, CFSR and CMIP6). The validation results indicated that the selected products overestimated Rs globally, with biases ranged from 0.48 to 21.27 W/m2. The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations. Moreover, the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m2. The long-term variation trends were also analyzed in this study. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation. [ABSTRACT FROM AUTHOR]

Abstract: There are significant uncertainties in the retrieval accuracy of multilayer clouds with different phase states, leading to bias in the subsequent estimation of the surface downward shortwave radiation (DSR). Single-layer clouds are generally assumed for the retrieval of cloud optical and microphysical properties from satellite measurements, although multilayer clouds often occur in reality. In this article, the impact of multilayer clouds (thin ice clouds overlying lower-level water clouds) on the retrieval of cloud microphysical properties is simulated with the radiative transfer model RSTAR. The simulated results demonstrate the impact of double-layer clouds on the accuracy of retrieval of the cloud parameters and estimation of DSR. To understand the uncertainties of the input parameters, thorough sensitivity tests are simulated by RSTAR in the Results section. As compared with the retrieval results of single-layer clouds when the ice particle model of the upper-layer cloud is assumed to be ellipsoidal, the maximum relative bias in DSR is 0.63% when the COT for the ice cloud is 1.2 and for water cloud is 32.45. When the upper-layer ice cloud is assumed to be a hexagonal column, the maximum relative bias in DSR is 55.34% when the COT for the ice cloud is 2 and for the water cloud is 58.4. In addition, relative bias in DSR tends to increase both with radiance and ice cloud COT for a given radiance. This finding can provide a basis of reference for the estimation accuracy of radiative forcing in the IPCC report and the subsequent enhancement and improvement of retrieval algorithms.

Downward shortwave radiation (DSR) is the main energy source for the Earth's system and a dominant component of the radiation budget. Although many algorithms have been proposed for estimating DSR, most models only consider either the radiative effects of terrain or atmosphere, and no research has considered the conditions of clouds on the mountainside. In this paper, a high-resolution Mountain Radiative Transfer model with Clouds (MRTC) is proposed to characterize the cloud-terrain coupling effects on DSR under different surface positions relative to the clouds. To operate the MRTC, a look-up table is employed to obtain initial radiative parameters. The comparison with Monte-Carlo based radiative transfer indicates that MRTC can be applied to conditions of clouds on the mountainside. And the reliability of MRTC under conditions when clouds are above mountains is also validated against in-situ measurements, with an RMSE of 149.9 W / m 2 , a bias of 28.3 W / m 2 , and an R 2 of 0.74. By using more accurate cloud cover data extracted from All-Sky images, the accuracy of MRTC has improved, with the RMSE decreasing to 115.8 W / m 2 , and the R 2 increasing to 0.8. This suggests that the uncertainty in satellite cloud products significantly contributes to errors in DSR estimations. MRTC is expected to more accurately represent cloud-terrain coupling effects on DSR, and improve the accuracy of DSR estimation over rugged terrain under cloudy skies. • A high-resolution mountain radiative transfer model with clouds (MRTC) is proposed. • The coupling effects of clouds and terrain on DSR are fully considered. • The first time the conditions of clouds on the mountainside are handled in DSR estimation. • Validation against a 3D radiative transfer model and in-situ measurements shows good agreement. • Uncertainty in cloud products and the complex 3D cloud structures significantly affect the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Accurate downward shortwave radiation (DSR) data is essential for the utilization of solar energy resources. However, existing various DSR data have certain limitations in applications. Among these, ERA5-Land DSR data is considered a relatively high-quality dataset. Nevertheless, it is reported notable deviations under different sky conditions, resulted from deficiencies in cloud and aerosol simulations. Therefore, this study aims to generate high-quality DSR data based on ERA5-Land data. We propose a sky condition-guided model using deviation characteristics of ERA5-Land DSR across different sky conditions as guidance, incorporating reliable cloud and aerosol parameters to provide atmosphere information. Consequently, station-like DSR data with accuracy close to station data is generated. Our model achieves high accuracy under different sky conditions, with RMSE below 32.19 W/m2. At the individual-station scale, 86.04 % of validation sites exhibit R values exceeding 0.9. At the seasonal scale, R values consistently surpass 0.87. Moreover, our data exhibits better accuracy compared to mainstream DSR datasets. Furthermore, it depicts the spatiotemporal variation of DSR and further reveals the photovoltaic potential in China, with relatively high values in the Tibetan Plateau and northwest China. The station-like DSR data is promising to advance the utilization of solar energy and accelerate the energy transition. • A sky condition-guided model based on data features of ERA5-Land DSR is proposed. • Station-like DSR data with advantages of ERA5-Land and station data is generated. • High accuracy is achieved across different sky conditions, locations, and seasons. • Higher precision is shown when comparing with CERES, GLASS, and BESS DSR data. • Spatial variation of DSR and photovoltaic potential in China is depicted. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a critical parameter of the surface radiation budget. Several DSR satellite products have been developed in recent years. In this study, five updated global satellite daily DSR products were evaluated using in situ measurements from 142 global sites with a special focus on high latitudes in 2004. These five products are Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds (CERES), Clouds, Albedo and Radiation Edition 2 data (CLARA), Global Land Surface Satellite Downward Shortwave Radiation (GLASS), Breathing Earth System Simulator (BESS) shortwave radiation product, and Moderate Resolution Imaging Spectroradiometer land surface Downward Shortwave Radiation (MCD18) with a spatial resolution of 100, 25, 5, 5, and 1 km, respectively. The CERES, BESS, and MCD18 provide full global coverage throughout the year, whereas CLARA and GLASS present different levels of seasonal data loss over high-latitude areas. The products were aggregated and compared at various spatial resolutions over different subareas. The overall accuracy increased after the products were aggregated to 100 km. However, the highest accuracy was achieved at a resolution of 25 km over high-latitude areas for GLASS and MCD18. When all products were evaluated at a resolution of 100 km, the global root-mean-square error of CERES, CLARA, GLASS, BESS, and MCD18 was 27.6, 29.1, 30.3, 29.6, and 31.6 W/m2, respectively, and the mean bias difference was 2.2, −1.5, −1.8, −3.4, and −8.0 W/m2. The accuracies of most products are ~7 W/m2 lower over high-latitude areas. A seasonal variation of the accuracies was observed for all products. It is particularly pronounced over high-latitude areas. With respect to the long term, both in situ data, BESS, and CERES show insignificant trends, while CLARA and GLASS present dimming trend. Besides, CLARA and GLASS exhibit slight annual changes of −0.250 and −0.387 W/m2 in the bias and 0.357 and 0.310 Wm−2 in the RMSE in the past two decades. GLASS and MCD18 exhibit a superior performance over coastal regions but degrade over snow-covered areas. Potential refinements of current high-resolution DSR retrieval algorithms are suggested, which will improve the retrieval accuracy. Highly accurate products with a long-term stability, especially over high-latitude areas, are required for future climate change analyses.

© 2015 Elsevier Inc. Hyperspectral remote sensing provides unique and abundant spectral information for quantification of the land surface shortwave radiation budget, which can be used to calibrate climate models and to estimate surface energy budget for monitoring agriculture and urban environment. However, only single broadband or multispectral data have been used in previous studies. In the present study, two methods are proposed to estimate the instantaneous land surface net shortwave radiation (NSR) with high spatial resolutions using hyperspectral remote sensing observations from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Method A calculates the NSR based on separate estimation of downward radiation and surface broadband albedo, which requires ancillary information for aerosol optical depth; and Method B directly estimates the NSR from the observed radiance. Results based on radiative transfer simulations showed that the use of hyperspectral data can significantly improve NSR estimation compared with the multispectral data method. Atmospheric water vapor correction was applied to adjust the surface radiation estimation. Validation of AVIRIS NSR estimates against ground measurements from two flux networks for the period of 2006-2014 showed that the two methods were similar and had consistent accuracy in the all-sky instantaneous NSR estimation with root-mean-square-errors (RMSEs) of approximately 28-56W/m2. The pixel-based water vapor content estimation from AVIRIS data provided slightly different results than those obtained using coarse resolution remote sensing data. A simplified topographic correction algorithm was found to be able to improve the results generated from Method A; however, the degree of improvement provided by Method B was unclear, possibly because of the lack of consideration of horizontal atmospheric scattering effects from adjacent pixels. In general, hyperspectral remote sensing data have been shown to improve the NSR estimation accuracies compared with results obtained in previous studies. Additional efforts are needed to refine the NSR estimation for application to future satellite hyperspectral data.

© 2015 Elsevier Inc. Hyperspectral remote sensing provides unique and abundant spectral information for quantification of the land surface shortwave radiation budget, which can be used to calibrate climate models and to estimate surface energy budget for monitoring agriculture and urban environment. However, only single broadband or multispectral data have been used in previous studies. In the present study, two methods are proposed to estimate the instantaneous land surface net shortwave radiation (NSR) with high spatial resolutions using hyperspectral remote sensing observations from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Method A calculates the NSR based on separate estimation of downward radiation and surface broadband albedo, which requires ancillary information for aerosol optical depth; and Method B directly estimates the NSR from the observed radiance. Results based on radiative transfer simulations showed that the use of hyperspectral data can significantly improve NSR estimation compared with the multispectral data method. Atmospheric water vapor correction was applied to adjust the surface radiation estimation. Validation of AVIRIS NSR estimates against ground measurements from two flux networks for the period of 2006-2014 showed that the two methods were similar and had consistent accuracy in the all-sky instantaneous NSR estimation with root-mean-square-errors (RMSEs) of approximately 28-56W/m2. The pixel-based water vapor content estimation from AVIRIS data provided slightly different results than those obtained using coarse resolution remote sensing data. A simplified topographic correction algorithm was found to be able to improve the results generated from Method A; however, the degree of improvement provided by Method B was unclear, possibly because of the lack of consideration of horizontal atmospheric scattering effects from adjacent pixels. In general, hyperspectral remote sensing data have been shown to improve the NSR estimation accuracies compared with results obtained in previous studies. Additional efforts are needed to refine the NSR estimation for application to future satellite hyperspectral data.

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Highlights • Four machine learning methods were used to estimate downward shortwave radiation. • GBRT is an efficient method to estimate downward shortwave radiation. • The DSR estimates based on GBRT has similar spatial distributions with CERES-EBAF. • The DSR estimates based on the GBRT does not show overestimation tendency. Abstract Downward shortwave radiation (DSR) is one of the major driving forces of climate system. Knowledge of the Earth's radiation budget is essential for improving our understanding of the Earth's climate. Therefore, accurate estimation of DSR has great significance. Satellite remote sensing is a practical way to derive DSR with high spatial resolution and coverage. In this study, four machine learning methods, including gradient boosting regression tree (GBRT), random forest (RF), multivariate adaptive regression spline (MARS), and artificial neural network (ANN), were applied to estimate DSR at a spatial resolution of 5 km and a temporal resolution of 1 day using Advanced Very High Resolution Radiometer (AVHRR) data. The DSR estimates based on four machine learning methods were evaluated using ground measurements at 96 sites over China. The measurements were collected from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) from 2001 to 2003. The evaluation results showed that the GBRT method performed best at both daily and monthly time scales under both clear and cloudy sky conditions. The validation results at the daily time scale showed an overall root mean square error (RMSE) of 30.34 W m−2 and an R value of 0.90 under clear sky conditions, whereas these values were 42.03 W m−2 and 0.86, respectively, under cloudy sky conditions. The DSR estimates had an overall RMSE value of 16.93 W m−2 and an R value of 0.97 at the monthly time scale. The Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data sets were also used for comparison with the DSR estimates based on the GBRT method. The DSR estimates based on the GBRT method exhibited similar spatial distributions with those of the CERES-EBAF DSR product. Moreover, the DSR estimates based on the GBRT method did not show a clear overestimation tendency, as the CERES-EBAF DSR product did, at the CDC/CMA sites. [ABSTRACT FROM AUTHOR]

Downward shortwave radiation (RS) drives many processes related to atmosphere−surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m2 and root mean square error (RMSE) of 101.0 W/m2, while the instantaneous PAR has a bias of −0.6 W/m2 and RMSE of 45.7 W/m2. RMSE of daily DSR is 32.3 W/m2, and that of the daily PAR is 13.1 W/m2. The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)). [ABSTRACT FROM AUTHOR]

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W⋅m-2 (38.38%). These values are 0.64 and 42.97 W⋅m-2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R2 value of 0.92 and an RMSE of 15.40 W⋅m-2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data. [ABSTRACT FROM AUTHOR]

Estimates of solar irradiance at the earth's surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75-90° under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 Wm-2) under all sky while relatively larger under clear-sky (-22.29 Wm-2) and cloudy-sky (+40.31 Wm-2) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area. [ABSTRACT FROM AUTHOR]

Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.

Downward shortwave radiation (DSR) is a highly variable solar source on spatiotemporal basis and essential for energy and agriculture systems, while its calculations are helpful in the environment-related studies, climatology, and monitoring fire risk. Statistical methods developed to extrapolate values of climatic variables and radiation could fail to generate reliable findings of DSR over a complex terrain without considering local topographic factors. In the present study, we proposed an integrative approach of MTCLIM-XL extrapolation with remote sensing (RS) and geographic information system (GIS) to estimate real-time DSR and its spatial potential over surfaces of contrasting elevated sites on a mountainous terrain of Quetta (Pakistan).Based on methodological approach, remote sensing data product of high-resolution DEM (SRTM 30m) was processed to extract topographic data, and meteorological data were obtained from a base site, Subsequently, MTCLIM-XL executed the simulation to calculate the daily-based DSR (W/m).Spatial distribution of DSR was generated by applying deterministic interpolation with complementing quantification of Hillshade analysis for spatially obstructive surfaces, and resultant spatial hotspot-based potential was assessed on basis of specified threshold level (above 250 W/m = 2 kW h/m) over the specified area. We observed usable potential of DSR at target sites and its spatial distribution during the study period of 2015 to April 2016. Using EUMETSAT CMSAF data as a standard, the validation demonstrates agreeable results of low RMSE and high correlation coefficient values for selected sites, except some sites with relatively high elevations and irregular gradients. Analysis of solar zenith angle to evaluate its inverse relation with increment in DSR values shows agreeable high inverse relation, while the negative trend for only some sites features relatively high rugged topography. In conclusion, MTCLIM-XL with RS and GIS integration manifests as a reliable approach for estimation and spatial potential assessment-based exploration of DSR over complex terrain having no ground data, while prospectively it will complement to the environment-related studies on local to mesoscale. [ABSTRACT FROM AUTHOR]

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)).

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m−2 (38.38%). These values are 0.64 and 42.97 W·m−2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R2 value of 0.92 and an RMSE of 15.40 W·m−2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

• Quantifying the DEM uncertainty on shortwave radiation estimation in mountains at diverse spatial and temporal scales. • Fine resolution shortwave radiation accuracy was largely limited by DEM uncertainty. • Different DEM datasets could generate a relative difference of 2% – 17% in shortwave radiation estimation. • Shortwave radiation uncertainty decreased more than 50% when upscaled from 30 m to 120 m. Accurate estimation of shortwave radiation in mountains will advance our knowledge of climate change effects, especially on mountain ecosystems. Recently, some approaches have been developed to estimate shortwave radiation parameters in mountains with satellite data, but few attempts were made to understand the impacts of digital elevation model (DEM) uncertainty on estimates. Our study investigates such impacts quantitatively in clear-sky conditions at multiple spatial and temporal scales (30–3000 m, instantaneous to daily). We employed a retrieval algorithm to estimate instantaneous and daily mean clear-sky downward shortwave radiation (DSR) and net shortwave radiation (NSR), as a proxy for our evaluation. The accuracy of our method based on accurate terrain data was verified against in-situ measurements with root-mean-square errors (RMSEs) of 65.9 W / m 2 and 65.1 W / m 2 for instantaneous DSR and NSR, and 21.2 W / m 2 and 22.5 W / m 2 for daily mean values, respectively. When using satellite DEM products, the DSR estimation uncertainty could increase by 64.0% for instantaneous values and 46.2% for daily mean values. Using AW3D30 and SRTM DEMs for DSR estimation led to a maximum difference of 16.8% (103.6 W / m 2) and 13.0% (25.8 W / m 2) on instantaneous and daily mean values, respectively. That estimation difference of shortwave radiation decreased with an increase in spatial scale, with RMS deviation lower than 2% for spatial resolution beyond 3000 m. In addition, the evaluation of introducing random errors into AW3D30 DEM showed that the shortwave radiation uncertainty caused by DEM may exceed the algorithm uncertainty itself with DEM mean absolute error (MAE) equaling about 5.0 m. Considering the current DEM accuracy, the impacts of DEM errors on shortwave radiation in mountains cannot be ignored. This study emphasizes the potential impacts of DEM uncertainty on surface shortwave radiation estimation, which is crucial in using satellite-derived datasets for energy balance calculation and climate change applications in mountains. [ABSTRACT FROM AUTHOR]

Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature ( T NARR ) and downward shortwave radiation ( R NARR ) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPP VPM ) at seven AmeriFlux crop sites, and investigated the uncertainties in GPP VPM from climate inputs as compared with eddy covariance-based GPP (GPP EC ). Results showed that T NARR agreed well with in-situ measurements; R NARR , however, was positively biased. An empirical linear correction was applied to R NARR , and significantly reduced the relative error of R NARR by ∼25% for crop site-years. Overall, GPP VPM calculated from the in-situ (GPP VPM(EC) ), original (GPP VPM(NARR) ) and adjusted NARR (GPP VPM(adjNARR) ) climate data tracked the seasonality of GPP EC well, albeit with different degrees of biases. GPP VPM(EC) showed a good match with GPP EC for maize ( Zea mays L.), but was slightly underestimated for soybean ( Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPP VPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean) . GPP VPM(adjNARR) showed a good agreement with GPP VPM(EC) for both crops due to the reduction in the bias of R NARR . The results imply that the bias of R NARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales. [ABSTRACT FROM AUTHOR]

Land cover change affects surface radiation budget and energy balance by changing surface albedo and further impacts the regional and global climate. In this article, high spatial and temporal resolution satellite products were used to analyze the driving mechanism for surface albedo change caused by land cover change during 1990-2010. In addition, the annual-scale radiative forcing caused by surface albedo changes in China's 50 ecological regions were calculated to reveal the biophysical mechanisms of land cover change affecting climate change at regional scale. Our results showed that the national land cover changes were mainly caused by land reclamation, grassland desertification and urbanization in past 20 years, which were almost induced by anthropogenic activities. Grassland and forest area decreased by 0.60% and 0.11%, respectively. The area of urban and farmland increased by 0.60% and 0.19%, respectively. The mean radiative forcing caused by land cover changes during 1990-2010 was 0.062 W/m in China, indicating a warming climate effect. However, spatial heterogeneity of radiative forcing was huge among different ecological regions. Farmland conversing to urban construction land, the main type of land cover change for the urban and suburban agricultural ecological region in Beijing-Tianjin-Tangshan region, caused an albedo reduction by 0.00456 and a maximum positive radiative forcing of 0.863 W/m, which was presented as warming climate effects. Grassland and forest conversing to farmland, the main type of land cover change for the temperate humid agricultural and wetland ecological region in Sanjiang Plain, caused an albedo increase by 0.00152 and a maximum negative radiative forcing of 0.184 W/m, implying cooling climate effects. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has been successfully used in estimating downward shortwave radiation (DSR). To achieve global estimations, traditional ML models need sufficient ground measurements covering various atmospheric and surface conditions globally, which is difficult to accomplish. Training on the simulated data of a radiative transfer model (RTM) is a possible solution, but widely used RTMs ignore some complex cloud conditions which brings bias to simulations. In this study, a neural network applied with the transfer-learning (TL) concept is introduced to utilize both radiative transfer simulations and ground measurement data, achieving global DSR estimation with only top-of-atmosphere and surface albedo at local solar noon as inputs. The proposed method estimates both instantaneous and daily DSR from Visible Infrared Imaging Radiometer Suite (VIIRS) data at 750-m resolution, and both the estimates are validated by 40 independent stations globally. The root mean-square error and relative root mean square error of instantaneous DSR validation over 25 Baseline Surface Radiation Network, seven Surface Radiation Network, and eight Greenland Climate Network stations in 2013 were 91.2 (16.1%), 106.3 (18.3%), 75.0 (24.2%) W / m 2, respectively, and the daily validation achieved 30.8 (15.5%), 33.5 (17.6%), and 31.3 (14.4) W / m 2, respectively. The proposed method presents significant high accuracy over polar regions and similar performances over other areas compared with traditional ML models, physics models (e.g., look-up tables and direct estimations), and existing DSR products. The algorithm is also applied to VIIRS swath data to test its global efficacy. Instantaneous mapping captures the spatial pattern of the cloud-mask product, and daily mapping shows spatial patterns similar to the Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds product, but with more detail. Further analysis indicates that model performance is less sensitive to the quantity of training data after TL has been incorporated. This study demonstrates the advantages of TL on boosting both the generality and accuracy of DSR estimation, which can potentially be applied to other variable retrievals. • Transfer-learning used to estimate surface downward shortwave radiation from VIIRS data. • The method leverages the advantage of radiative transfer simulation and ground-based measurement. • Estimation of downward shortwave radiation over polar area was significantly improved. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological, ecological and biogeochemical processes. The new Global LAnd Surface Satellite (GLASS) DSR datasets have been generated recently using multiple satellite data in China. This study investigates the performances of direct comparison approach, which is mostly used for validation of surface insolation retrieved from satellite data over the plain area, and indirect comparison approach, which needs a fine resolution map of DSR as reference, for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface. Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain, especially at Mt. Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m in time-steps of 1 and 3 hours, respectively. Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface. The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m over three Chinese Ecosystem Research Network sites, although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation. [ABSTRACT FROM AUTHOR]

Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth's climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Since the main attenuation of solar irradiance reaching the earth's surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites. [ABSTRACT FROM AUTHOR]