EnKF Assimilation of Satellite-retrieved Cloud Water Path to Improve Tropical Cyclone Rainfall Forecast

Abstract

Tropical cyclone(TC) rainfall forecast has remained a challenge. To create initial conditions with high quality for simulation, the present study implemented a data assimilation scheme based on the EnKF method to ingest the satellite-retrieved cloud water path(Cw) and tested it in WRF. The scheme uses the vertical integration of forecasted cloud water content to transform control variables to the observation space, and creates the correlations between Cw and control variables in the flow-dependent background error covariance based on all the ensemble members, so that the observed cloud information can affect the background temperature and humidity. For two typhoons in 2018(Yagi and Rumiba), assimilating Cw significantly increases the simulated rainfalls and TC intensities. In terms of the average equitable threat score of daily moderate to heavy rainfall(5-120 mm), the improvements are over 130%, and the dry biases are cut by about 30%. Such improvements are traced down to the fact that Cw assimilation increases the moisture content, especially that further away from the TC center, which provides more precipitable water for the rainfall, strengthens the TC and broadens the TC size via latent heat release and internal wind field adjustment.