A bias correction method for precipitation through recognizing mesoscale precipitation systems corresponding to weather conditions

Abstract

Accurate estimations of local precipitation are necessary for assessing water resources and water-related disaster risks. Numerical models are typically used to estimate precipitation, but biases can result from insufficient resolution and incomplete physical processes. To correct these biases, various bias correction methods have been developed. Recently, bias correction methods using machine learning have been developed for improved performance. However, estimating local hourly precipitation characteristics remains difficult due to the nonlinearity of precipitation. Here, we focused on precipitation systems that could be reproduced by numerical models, and estimated the spatial distribution of local precipitation by recognizing the relationship between simulated and observed precipitation with a resolution of 0.06 degrees using a machine learning method. We subsequently applied a quantile mapping method to modify the precipitation amounts. Validation showed that our method could significantly reduce bias in numerical simulations, especially the spatial distribution of hourly precipitation frequency. However, the bias in the temporal distribution of hourly precipitation did not improve. Spatial autocorrelation analysis showed that this method can predict precipitation systems with spatial scales of 2500 to 40000 km2, which are associated with large-scale disturbances (e.g., cold fronts, warm fronts, and low-pressure systems). The high accuracy of these estimates indicates that the spatial distribution of hourly precipitation frequency is strongly dependent on precipitation systems with these spatial scales. Accordingly, our method shows that the relationship between the spatial distribution of precipitation systems and local precipitation is strong, and by recognizing this relationship, the spatial distribution of local hourly precipitation can be accurately estimated.