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  • Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms
Title: Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms
Abstract

The differential reflectivity (ZDR) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor (qυ) observations are retrieved from observedZDRcolumns under the assumption that humidity is saturated within the convection whereZDRcolumns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo-qυobservations fromZDRcolumns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that theZDRcolumns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo-qυobservations leads to an enhancement ofqυwithin the convection, while concurrently reducing humidity in no-rain areas. Sensitivity experiments indicate that a tuned smaller observation error and a shorter horizontal decorrelation scale are optimal for a better assimilation of pseudo-qυfromZDRcolumns, resulting in more stable rain rates during short-term forecasts. Additionally, a 15-min cycling assimilation frequency yields the best performance, providing the most accurate reflectivity forecast in terms of both location and intensity. Analysis of thermodynamic fields reveal that assimilatingZDRcolumns provides more favorable initial conditions for sustaining convection, including sustainable moisture condition, a strong cold pool, and divergent winds near the surface, consequently enhancing reflectivity and precipitation. With the optimal configuration determined from the sensitivity tests, a quantitative evaluation further demonstrates that assimilating the pseudo-qυobservations fromZDRcolumns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.

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PAR ID:
10497280
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
American Meteorological Society
Date Published:
Journal Name:
Monthly Weather Review
Volume:
152
Issue:
4
ISSN:
0027-0644
Format(s):
Medium: X Size: p. 1077-1095
Size(s):
p. 1077-1095
Sponsoring Org:
National Science Foundation
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