Based on European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation global atmospheric reanalysis （ERA5） every day from May to August during 1979-2020, three land-atmosphere indexes to investigate land-atmosphere coupling processes were calculated,characteristics of land-atmosphere coupling in climatology and their difference under different dry and wetsoil conditions were analyzed over eastern and southern Asia. The results show that Northeast and North China,the Tibetan plateau, India, Yunnan Province of China and Southeast Asia,the middle latitude arid zone were strong land-atmosphere coupling zones in climatology. In the middle latitude arid zone, land-atmosphere coupling had no significant difference under different soil conditions due to the low soil moisture and its little variability. In the other strong coupling zones, the coupling strength decreased with increasing soil moisture condition because of the bigger variability of soil moisture in these regions, and this law is applicable to the coupling processes between soil moisture（SM） and evapotranspiration （ET）, between ET and water vapor condition of boundary layer, between ET and instability condition of boundary layer. The land-atmosphere couplings over South China were weak in climatology, coupling between SM and ET was significant only under dry soil conditions, while the coupling between ET and atmospheric boundary layer were not significant under all soil moisture conditions.

Based on observed total solar radiation, air temperature, relative humidity and air pressure data at representative photovoltaic power stations of Gansu Province, total solar radiation data forecasted by WRF model, and total cloud cover products from FY satellite in 2019, the correlation between total solar radiation and meteorological factors was analyzed, and the prediction ability of WRF model was evaluated, firstly. And on this basis the errors of short-time solar radiation forecast were corrected. The results show that the atmospheric transmissivity was positively correlated with air temperature, and the correlation coefficient was 0.61, while it was negatively correlated with relative humidity, air pressure and total cloud cover, and the correlation coefficients were -0.44, -0.31 and -0.81 in turn. The contribution of total cloud cover to solar radiation attenuation was the most, followed by relative humidity. The deviation of solar radiation forecasted by WRF model was bigger, and the monthly distribution of forecast errors appeared ‘single peak’ pattern, the forecast errors was the biggest in June. The root mean square error (RMSE) of solar radiation forecast was the smallest in winter (45.63 W·m ^-2) and the biggest in summer (240.4 W·m^-2). The forecast ability of WRF model was better on sunny days or partly cloudy days, while it was worse on cloudy days. The forecast errors mainly came from phase bias and system bias. The correction effect of solar radiation forecast considering cloud cover was significant, the RMSE of solar radiation forecast after correction sharply decreased by 101-216.4 W·m^-2 on cloudy days, the average absolute error decreased by 59.5-173.07 W·m^-2, and the RMSE decreased by 1.92-64.23 W·m^-2 in summer with the maximum error.

Based on observation data and numerical forecast data at ZDLYFP photovoltaic power station from March 2017 to February 2019, the inclined plane total solar radiation algorithm was improved, firstly. And on this basis two forecast models of short-term photovoltaic output power were established by using multiple linear regression (MLR) and empirical formula methods, then the forecast results were tested and evaluated. The results are as follows: (1) The inclined plane total solar radiation and temperature were higher correlated with photovoltaic output power in each season, the total correlation coefficients were 0.896 and 0.386, respectively, so they were introduced to forecast model based on MLR method as the predictors of photovoltaic output power. (2) The forecast effect of short-term power improved after the improvement of inclined plane solar radiation algorithm, and the relative root mean square error (RRMSE) of photovoltaic output power forecasted by two models of MLR and empirical formula methods reduced by 0.066 and 0.040, respectively. (3) The total root mean square error (RMSE) of photovoltaic output power obtained by MLR and empirical formula methods were 940.917 kW and 1147.172 kW, respectively, and total RRMSEs were 0.188 and 0.229. In addition, RMSEs and RRMSEs based on MLR method were less than those based on empirical formula method in each month, and the correlation coefficient of the former was slightly higher than that of the latter, which indicated that the forecast effect of MLR method was better and more stable in practical application. (4) The effect of photovoltaic power prediction was obviously distinct under different weather conditions, RRMSEs of two methods increased in turn for sunny weather, cloudy weather, overcast weather, rainy weather, dust weather and snow weather. In general, the effect of photovoltaic power prediction based on MLR method was better under different weather conditions.