Improved Method of Solar Radiation Simulation on Cloudy Days with LAPS-WRF Model System Based on Satellite Data Assimilation

Photovoltaic power is influenced by the temporal and spatial variation of cloud amounts. Therefore, to ensure safe operation of power grids on cloudy days, accuracy in simulating and forecasting temporal and spatial variations of solar radiation is critical. To reduce initial field errors in the mesoscale meteorological model and to improve the simulation accuracy of solar radiation on cloudy days, the three-dimensional cloud analysis assimilation method in the Local Analysis and Prediction System (LAPS is adopted in this study. The results are used to improve cloud simulation and are used as the initial field of the Weather Research and Forecasting (WRF model. The temporal and spatial distribution characteristics of the total cloud amount and global radiation in the Beijing area in January, June, July, and August and during the typical precipitation processes in June 2008 are simulated with the LAPS-WRF model system. This study focuses on the simulation results of global radiation with and without Fengyun satellite data assimilation and describes the reasons for the improvements on cloudy days and during the precipitation processes. The results showed that the temporal variation of simulated and observed values of total cloud amounts with and without satellite data assimilation were consistent. Without assimilation, the simulated values were significantly lower than observations in most cases. After assimilation, the simulated values of total cloud amounts were closer to observations. In addition, the correlation coefficients between simulation and observation values of global radiation before and after assimilation were higher and the differences of correlation coefficients with and without satellite data assimilation were smaller on clear and cloudy days in January and on clear days in summer. The simulation values of global radiation before and after assimilation were all lower than the measured values on sunny days. After assimilation, the error reduction of global radiation was not noticeable on cloudy days in January because the improvement of total cloud amount simulation was insignificant. Moreover, before and after assimilation, the correlation coefficients between simulation and observation values of global radiation on cloudy and rainy days in summer and during typical precipitation process in June were smaller than those on clear days. However, the correlation coefficients after assimilation were noticeably larger than those before assimilation, particularly during typical precipitation processes in June. Further, simulation errors in global radiation were significantly reduced. For example, the root mean square error and average relative errors during a typical precipitation process in June were reduced by 102.6 W m^-2 and 355.9%, respectively, and maximum relative error was reduced to a greater extent. Simulation errors in global radiation after assimilation in most cases were less than those before assimilation, with reduction ratios being 75%. The significant improvement in the simulation of global radiation after assimilation during cloudy days and precipitation processes is closely related to the improvement in total cloud amount. The results of this study have certain scientific and practical application values for the improvement of simulation and the forecasting of solar radiation and photovoltaic power on cloudy days and during precipitation process, and the objective assessments of solar energy resources.