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 High resolution grid of potential incoming solar radiation for Serbia

2015 ◽  
Vol 19 (suppl. 2) ◽  
pp. 427-435 ◽  
Author(s):  
Jelena Lukovic ◽  
Branislav Bajat ◽  
Milan Kilibarda ◽  
Dejan Filipovic
Keyword(s):  
Solar Radiation ◽  
Driving Force ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Natural Processes ◽  
Web Map ◽  
Complex Interactions ◽  
The Earth ◽  
Digital Elevation ◽  
Elevation Model

Solar radiation is a key driving force for many natural processes. At the Earth?s surface solar radiation is the result of complex interactions between the atmosphere and Earth?s surface. Our study highlights the development and evaluation of a data base of potential solar radiation that is based on a digital elevation model (DEM) with a resolution of 90 m over Serbia. The main aim of this paper is to map solar radiation in Serbia using DEM. This is so far the finest resolution being applied and presented using DEM. The final results of the potential direct, diffuse and total solar radiation as well as duration of insolation databases of Serbia are portrayed as thematic maps that can be communicated and shared easily through the cartographic web map-based service.

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).

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>

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 Gains and losses in surface solar radiation with dynamic aerosols in regional climate simulations for Europe

2020 ◽  
Author(s):  
Sonia Jerez ◽  
Laura Palacios-Peña ◽  
Claudia Gutiérrez ◽  
Pedro Jiménez-Guerrero ◽  
Jose María López-Romero ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Atmospheric Aerosol ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Surface Solar Radiation ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Gains And Losses

Abstract. The solar resource can be highly influenced by clouds and atmospheric aerosol, which has been named by the IPCC as the most uncertainty climate forcing agent. Nonetheless, Regional Climate Models (RCMs) hardly ever model dynamically atmospheric aerosol concentration and their interaction with radiation and clouds, in contrast to Global Circulation Models (GCMs). The objective of this work is to evince the role of the interactively modeling of aerosol concentrations and their interactions with radiation and clouds in Weather Research and Forecast (WRF) model simulations with a focus on summer mean surface downward solar radiation (RSDS) and over Europe. The results show that the response of RSDS is mainly led by the aerosol effects on cloudiness, which explain well the differences between the experiments in which aerosol-radiation and aerosol-radiation-cloud interactions are taken into account or not. Under present climate, a reduction about 5% in RSDS was found when aerosols are dynamically solved by the RCM, which is larger when only aerosol-radiation interactions are considered. However, for future projections, the inclusion of aerosol-radiation-cloud interactions results in the most negative RSDS change pattern (while with slight values), showing noticeable differences with the projections from either the other RCM experiments or from their driving GCM (which do hold some significant positive signals). Differences in RSDS among experiments are much more softer under clear-sky conditions.

scholarly journals Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Correlation Coefficient ◽  
Numerical Models ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

Abstract Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

scholarly journals Trends in Daily Solar Radiation and Precipitation Coefficients of Variation since 1984

2012 ◽  
Vol 25 (4) ◽  
pp. 1330-1339 ◽  
Author(s):  
David Medvigy ◽  
Claudie Beaulieu
Keyword(s):  
Solar Radiation ◽  
Climate Variability ◽  
High Frequency ◽  
Terrestrial Ecosystem ◽  
Cloud Amount ◽  
Convective Cloud ◽  
Global Precipitation Climatology Project ◽  
Precipitation Variability ◽  
Surface Solar Radiation ◽  
Coefficients Of Variation

Abstract This study investigates the possibility of changes in daily scale solar radiation and precipitation variability. Coefficients of variation (CVs) were computed for the daily downward surface solar radiation product from the International Satellite Cloud Climatology Project and the daily precipitation product from the Global Precipitation Climatology Project. Regression analysis was used to identify trends in CVs. Statistically significant changes in solar radiation variability were found for 35% of the globe, and particularly large increases were found for tropical Africa and the Maritime Continent. These increases in solar radiation variability were correlated with increases in precipitation variability and increases in deep convective cloud amount. The changes in high-frequency climate variability identified here have consequences for any process depending nonlinearly on climate, including solar energy production and terrestrial ecosystem photosynthesis. To assess these consequences, additional work is needed to understand how high-frequency climate variability will change in the coming decades.

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