Convective DA I

14-4 January 24 12:00-12:20

Development of Optimized Radar Data Assimilation Capability within the Fully Coupled EnKF-EnVar Hybrid System for Convective-Permitting Ensemble Forecasting and Testing via NOAA Hazardous Weather Testbed Spring Forecasting Experiments

C.Liu (OU/CAPS), M. Xue (OU/CAPS), J. Youngsun(OU/CAPS), R. Kong (OU/CAPS) and L. Chen (CMA)

Abstract

OU/CAPS has been producing a real-time EnKF-based storm-scale ensemble forecasts each spring as a part of the NOAA Hazardous Weather Testbed (HWT) Spring Forecast Experiment since 2013. One unique feature of the CAPS ensemble is the assimilation of full-volume operational WSR-88D radar radial wind and reflectivity data at the native model grid resolution (4 km initially, 3 km since 2016) with a complex multi-moment microphysics scheme. In 2019, CAPS is planning to run a fully coupled EnKF-EnVar hybrid system directly assimilating radar radial velocity and reflectivity within both EnKF and hybrid EnVar systems. To support this, the capability to directly assimilate reflectivity within 3DVar and En3Dvar frameworks with some special treatments to deal with issues associated with the non-linear reflectivity operator has been implemented in the NCEP’s operational DA system. The new capability is being evaluated through several storm cases from the 2018 HWT in collaboration with NOAA/NSSL. The system is planned to run in real-time during the 2019 NOAA HWT. Preliminary evaluation of GSI-based EnKF, 3DVar and pure/hybrid En3DVar systems suggests that the hybrid En3DVar with 20% static background error covariance matrix produces better forecasts than other systems in terms of storm structure and intensity of MCSs.

Contact information

Dr. Chengsi Liu University of Oklahoma, USA