Four-dimensional variational assimilation of AWS precipitation data

Abstract

Four-dimensional variation data assimilation (4D-VAR) is a logical and rigorous mathematical method to obtain the 'best' estimate of the model initial conditions from observations and a priori knowledge of the atmospheric state. It is one of the most advanced data assimilation methods today. Automation weather station (AWS) precipitation data is assimilated by 4D-VAR in experiments. Experiment results show that, due to addition of information of AWS precipitation data, the initial field of test is enhanced in meso-scale information, and it matches the model better in thermo-dynamical mechanism. After assimilation, the simulation is improved. The precipitation during the start period in simulation is increased, and the situation of simulating precipitation matches real situation better. The 'spin-up' problem of the model is weakened. Experiment results also show that temporal information of AWS precipitation data is very important for assimilation.