Abstract
A hybrid ensemble adjustment Kalman filter—three-dimensional ensemble—variational (EAKF-En3DVar) system is developed to assimilate conventional and radar data, and is applied to a convective case in Colorado and Kansas, USA. The system is based on the framework of the Weather Research and Forecasting model’s three-dimensional variational (3DVar) and Data Assimilation Research Testbed. A two-step assimilation procedure with a shorter length scale and analysis cycle is used to reduce analysis noise in radar data assimilation. Results show that the hybrid experiment assimilating only conventional data improves the quantitative precipitation forecast (QPF) and quantitative reflectivity forecast over those of a 3DVar experiment, and the improvements are also evident after assimilating radar data. The assimilation of radar data substantially improves the QPF up to seven hours, with either the 3DVar or hybrid method. The hybrid experiment assimilating both conventional and radar data forecasts a more accurate convective system in terms of structure, spatial extent and intensity and produces increased low-level cooling and mid-level warming in the convective region. These improvements are attributable to an improved forecast background field of wind, temperature and water vapor mixing ratio, with maximum root mean square error reduction at the tropopause and near the surface.
Highlights
Three-dimensional variational (3DVar) data assimilation (DA) has been a widely used technique in operational centers and the research communities
The radar radial velocity assimilation followed the procedure in Xiao and Sun [21], while radar reflectivity DA followed the indirect DA of Wang et al [5]
The results of fractions skill score (FSS), equitable threat score (ETS) and Probability of Detection (POD) from Exp3DVC, ExpHYBC, Exp3DVCR and ExpHYBCR (Table 1) for hourly accumulated precipitation are compared in Figure 6 for thresholds 1, 2.5 and 5 mm, respectively
Summary
Three-dimensional variational (3DVar) data assimilation (DA) has been a widely used technique in operational centers and the research communities. Gao and Stensrud [4] developed another radar reflectivity forward operator that included an ice microphysical scheme using temperature-based hydrometeor classification to distinguish liquid water, snow and hail. The differences in in forecast precipitation and 14 reflectivity of theRMSEs four experiments be attributed to differences the background. The differences in forecast precipitation and reflectivity of the four experiments can be attributed attributed to differences in the background. RMSEs of horizontal wind components, temperature, and water vapor forecast background against all 10 radiosondes in the to 3-km differences inshown the background. RMSEs of(orange horizontal wind components, components, and4a. Water vapor forecast background against all curve). RMSEs of horizontal wind components, temperature, and water vapor forecast background against all 10 radiosondes in the to 3-km differences inshown the background. 14 shows average
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