Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States

Abstract

Precipitation data assimilation has been developed in the Weather Research and Forecasting model data assimilation system (WRFDA) using four-dimensional variational (4D-Var) approach. Unlike other conventional data, precipitation is an integral quantity and it is not included as a control variable in WRFDA. A simplified Kessler scheme is used in tangent linear and adjoint model. Precipitation data are directly assimilated in WRFDA 4D-Var, and the assimilation of precipitation will have feedback to all the control variables via the constraint of the linearized physics package. Firstly, we present single observation experiments to exhibit how dynamic, thermodynamic and moisture fields are adjusted by assimilating rainfall information. Then, the National Centers for Environmental Prediction Stage IV precipitation data are assimilated for a heavy rainfall case on 9 June 2010 at a convection-permitting model setting (4-km). Finally, we conducted one-week experiments to further validate the robustness of the results for precipitation assimilation. Results show that precipitation assimilation has a positive impact on model fields, particularly on the low-level humidity. For the impact on precipitation forecasts, it indicates that precipitation assimilation reduces spin-up time efficiently, removes false alarms and produces model forecast precipitation closer to the observations through the changes in temperature, moisture and wind imposed in the analyses. The impact from precipitation assimilation persists up to three hours on average.