Spatial–temporal distribution and forecasting model of precipitation using dynamic-statistical information fusion

Abstract

Abstract In order to explore the evolvement mechanism of hydrometeorological elements, spatial–temporal distribution of precipitation in the Huai river basin is studied by statistical drawing and empirical orthogonal function decomposition. How to make an objective combination for the predictive results of precipitation? Information fusion in data assimilation is introduced to merge the improved National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) with the multilinear regression model. Firstly, in terms of time, the annual precipitation is apt to decline at most stations within 30 years, and precipitation mainly concentrates in the flood season. The characteristics of spatial distribution are similar to topographic features. It can also be found that precipitation gradually decreases from south to north. Secondly, from statistical forecasting, the relationship between precipitation and global sea surface temperature (SST) is explored. Prediction equation is established with SST and the average precipitation. Thirdly, from dynamic model forecasting, the CFSv2 original model and the CFSv2 statistical downscaling model are used to analyze the influence of model deviation on fusion prediction. The optimum interpolation assimilation method is applied for realizing the optimal integration of statistical and dynamic model prediction. Finally, the standardized precipitation index (SPI) is calculated by the combined forecasting of annual precipitation to evaluate drought conditions. The results show that SST is an important factor affecting precipitation, which may be applied as a forecasting direction with other factors. The merged precipitation prediction skill by the CFSv2 original model and the statistical model do not have the great promotion, which is still lower than the prediction skill only by the statistical model. However, the merged precipitation prediction skill by the CFSv2 statistical downscaling model and the statistical model is better than the prediction skills by the two models mentioned above, respectively. These indicate that when the prediction difference between the models is large, the merged prediction error cannot be minimized. When the prediction skill levels are equal, there is an improvement in the merged result. So, it is necessary to revise the climate dynamic model by downscaling. What is more, the obtained drought levels match the actual disaster conditions, providing theoretical support of hydrology and meteorology for the prevention of natural disasters.