Variational Assimilation of Cloud Liquid/ice Water Path and Its Impact On NWP

Abstract

Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this study, analysis of hydrometeors for the Weather Research and Forecasting (WRF) model and its impact on short-term regional numerical weather prediction are presented. Variational data assimilation system for WRF (WRFDA) is further developed to produce cloud liquid water and ice water analysis by directly assimilating cloud liquid water and ice water path products. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from the Global Geostationary Gridded Cloud Products at NASA. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 hPa and 150 hPa, and significantly reduces forecast errors in temperature and wind at model levels between 300hPa and 150hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun-up by the WRF model.