College of Engineering | Apply to CSU | Disclaimer | Equal Opportunity Statement | Privacy | Search CSU

Impacts of Assimilating Vertical Velocity, Latent Heat, or Hydrometer Water Contents Retrieved from a Single Reflectivity Data Set

You must be on the CSU network—either physically or using VPN—to watch this or any of the videos on this site.

December 7, 2016
Yoonjin Lee
Hosted by Chris Kummerow (advisor), Milija Zupanski (CIRA; co-advisor), Sue van den Heever, Steven Reising (Electrical and Computer Engineering)

Abstract

Assimilation of observation data in cloudy regions has been challenging due to the unknown distribution of cloud drop sizes. Attempts to assimilate data in cloudy regions generally assume a drop size distribution, but most assimilation system fail to maintain consistency between models and the observation data, as each has its own set of assumption. This study tries to retain the consistency between the forecast model and the retrieved data by developing a Bayesian retrieval scheme that uses the forecast model itself for the a-priori database. Through the retrieval algorithm, vertical profiles of three variables related to the development of tropical cyclones, including vertical velocity, latent heat, and hydrometeor water contents are derived from the same reflectivity observation. Vertical velocity and latent heat are variables related to dynamical processes of tropical cyclones whereas hydrometeors are byproducts of those processes. Each retrieved variable is assimilated in the data assimilation system using a flow dependent forecast error covariance matrix. The simulations are compared to evaluate which variable has the greatest positive impact in the assimilation system.

In this study, the three assimilation experiments were conducted for two hurricane cases: Hurricane Pali and Hurricane Jimena. Analyses from two hurricane cases suggest that assimilating latent heat and hydrometeor water contents have similar impacts on the assimilation system while vertical velocity has less impact than the other two variables. Using these analyses as an initial condition for the forecast model revealed that the assimilations of retrieved latent heat and hydrometeor water contents were also able to improve the track forecast of Hurricane Jimena.