Impacts of Assimilating Vertical Velocity, Latent Heating, or Hydrometeor Water Contents Retrieved from a Single Reflectivity Dataset

Data assimilation in cloudy condition has been challenging due to significant errors coming from unknown cloud properties. Nevertheless, use of cloud-affected data has become popular especially in the hurricane forecast as it contains valuable information. In addition to direct assimilation of all-sky satellite radiances, assimilation of retrieved cloud data is also a valuable option.

In this study we focus on assimilating retrieved cloud products that are directly related to precipitation and dynamical variables controlling the precipitation process. A retrieval algorithm that produces vertical profiles of three variables including hydrometeor water contents, latent heating, and vertical velocities from the same reflectivity profile has been developed. The retrieval algorithm uses a Bayesian approach with a-priori information derived from the same forecast that is consistent with model physics. Each of the three retrieved variables is assimilated in the data assimilation system using a flow dependent forecast error covariance matrix and their results are compared to examine the respective impact of each variable in the assimilation system.

The three assimilation experiments were conducted for two hurricane cases captured by the Global Precipitation Measurement (GPM) satellite: Hurricane Pali and Hurricane Jimena. Analyses from these two hurricane cases suggest that assimilating latent heating and hydrometeor water contents have similar impacts on the assimilation system while vertical velocity has less of a positive impact than the other two variables. Using these analyses as the initial conditions for the forecast model reveals that the assimilation of the three retrieved variables was able to improve the track forecast as well.