Comparison of the Surface Solar Radiation Budget Derived from Satellite Data with that Simulated by the NCAR CCM2

1995 ◽  
Vol 8 (11) ◽  
pp. 2824-2842 ◽  
Author(s):  
Dale M. Ward
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Radiation Budget ◽  
Surface Solar Radiation

scholarly journals A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation

2019 ◽  
Vol 11 (4) ◽  
pp. 1905-1915 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Bias Error ◽  
Surface Solar Radiation ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at  https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

scholarly journals Assessing the Surface Solar Radiation Budget in the WRF Model: A Spatiotemporal Analysis of the Bias and Its Causes

2016 ◽  
Vol 144 (2) ◽  
pp. 703-711 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
Clara Arbizu-Barrena ◽  
Francisco J. Santos-Alamillos ◽  
Joaquín Tovar-Pescador ◽  
David Pozo-Vázquez
Keyword(s):  
Solar Radiation ◽  
Wrf Model ◽  
Cloud Amount ◽  
Principal Component ◽  
Spatiotemporal Analysis ◽  
Radiation Budget ◽  
Weather Research And Forecasting ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Solar Radiation Flux

Abstract Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m−2, while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m−2. An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.

scholarly journals Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

2019 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Power Plants ◽  
Numerical Models ◽  
Regional Climate ◽  
Diffuse Radiation ◽  
Terrestrial Ecosystems ◽  
Global Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).

Solar radiation in the cloud-free atmosphere

2021 ◽  
Author(s):  
Martin Wild
Keyword(s):  
Solar Radiation ◽  
Climate Models ◽  
Climate Model ◽  
Temporal Changes ◽  
Climate System ◽  
Radiation Budget ◽  
Radiative Effects ◽  
Free Atmosphere ◽  
Surface Solar Radiation ◽  
Radiative Fluxes

<p>The quantification of Earth’s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of  temporal changes in the solar radiative components of the climate system.</p> <p>We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm<sup>-2</sup> globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model  generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).</p> <p>With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.</p>

Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data

2020 ◽  
Vol 270 ◽  
pp. 115178 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Guanghui Huang ◽  
Ling Yao ◽  
Jun Qin ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Spatial Scale ◽  
Satellite Data ◽  
Scale Effects ◽  
Surface Solar Radiation ◽  
Retrieval Accuracy

scholarly journals A 16-year dataset (2000–2015) of high-resolution (3 hour, 10 km) global surface solar radiation

2019 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset with an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products, the water vapor, surface pressure and ozone from ERA5 reanalysis data, and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data was evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimate at 10 km scale were −11.5 W m−2, 113.5 W m−2, and 0.92, respectively. The error was clearly reduced when the data were upscaled to 90 km; RMSE decreased to 93.4 W m−2 and R increased to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The data set is available at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

scholarly journals An Improved Model for the Estimation of Solar Radiation from Satellite Data for Thailand

1970 ◽  
Vol 8 (3) ◽  
pp. 130-139
Author(s):  
Serm Janjai ◽  
Itsara Masiri ◽  
Somjet Pattarapanitchai ◽  
Jarungsaeng Laksanaboonsong
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Global Solar Radiation ◽  
Original Model ◽  
Digital Data ◽  
Multiple Reflections ◽  
Surface Solar Radiation ◽  
Main Input ◽  
Mean Square Difference ◽  
Improved Model

This paper presents an improved model for estimating surface solar radiation from satellite data for Thailand. Digital data from the visible channel of the GOES9 and MTSAT-1R satellites were used as the main input data of the model. This model accounted for the scattering of solar radiation by clouds, absorption of solar radiation by water vapour, ozone and gases and solar radiation depletion by aerosols. Additionally, the multiple reflections between the atmosphere and the ground in satellite band, which were ignored in the original model, were included in the improved model. For testing its validity, the model was employed to calculate monthly average daily global solar radiation at 38 solar monitoring stations in Thailand. It was found that the solar radiation calculated from the model and that obtained from the measurements were in good agreement, with a root mean square difference (RMSD) of 6.1% and mean bias difference (MBD) of 0.3%. The performance of the improved model was better than that of the original model. DOI: http://dx.doi.org/10.3126/jie.v8i3.5939 JIE 2011; 8(3): 130-139

scholarly journals Merging of satellite and ground measurements of hourly surface solar radiation variables in Germany

2021 ◽  
Author(s):  
Sven Brinckmann ◽  
Anna Klameth ◽  
Jörg Trentmann
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Sunshine Duration ◽  
Data Sources ◽  
Visible Radiation ◽  
High Quality ◽  
Surface Solar Radiation ◽  
Station Data ◽  
First Results ◽  
Radiation Measurements

<p>Measurements of the surface solar radiation have a high importance for the fields of meteorology, climatology, solar energy, agriculture, forestry and other applications. Radiation measurements at ground stations using high quality instruments such as pyranometers began in the second half of the nineteenth century. From the 1980s onward, satellite imagery in the visible radiation spectrum has been used to calculate gridded data of cloud information and, subsequently, of solar radiation at the Earth's surface. Compared to station data, satellite data have the advantage of spatial continuity, but have disadvantages in temporal resolution and data accuracy.</p><p>As part of a restructuring of the radiation measurement network, the German Meteorological Service (DWD) is pursuing the goal of expanding its high-quality surface measurements using pyranometers (to 42 stations) and largely discontinuing other radiation measurements, such as direct measurements of sunshine duration. At the same time, surface solar radiation products from satellite data are progressively improving in quality and can be used to compensate for the reduction of ground measurements and increase the spatial coverage of radiation information over Germany. For this purpose, the project DUETT aims at a merging between solar radiation data from the 42 pyranometer stations and near-real-time data based on measurements from METEOSAT-SEVIRI. As products, hourly values of the parameters global horizontal irradiance (GHI) and sunshine duration (SDU) will be provided on a 1x1km grid for Germany with a time delay of 15 minutes after each full hour.</p><p>Merging is performed in three main steps, which are described in the following for the parameter GHI. First, the hourly mean values of both data sources are calculated. In the case of the satellite data, this step involves the use of an 'optical flow' technique to generate intermediate images to increase the original time resolution from 15 minutes to virtually 1 minute. Using this technique, the displacement of fast-moving clouds is better reflected. In the second step, systematic deviations between the two data sources are determined and corrected for by using predictors. Preliminary research suggests that cloudiness (or clearness index) is one such appropriate predictor. In the final step, the local differences between the corrected satellite data and the station data are interpolated to the target grid using Universal Kriging and the results are added to the corrected satellite data.</p><p>We present the first results of the merging procedure to be developed for both radiation parameters GHI and SDU. Analyses of the systematically occurring radiation differences between the two data sources are shown as well as the related correction functions. Furthermore, first results of the validation of the combined radiation products will be presented. This includes comparisons with measurements at validation stations as well as analyses based on cross-validation.</p>

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