Abstract
An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.
Highlights
There have been higher requirements put forward for the prediction of convective systems precipitation and its related disasters in recent years
Compared with “NA_1km”, the rainfall forecasts at different assimilation schemes are improved significantly
This study explores the effect of radar reflectivity and Global Telecommunication System (GTS) data assimilation from assimilation frequency using Weather Research and Forecasting Model (WRF)-3DVar for rainfall forecasting
Summary
There have been higher requirements put forward for the prediction of convective systems precipitation and its related disasters in recent years. Improving the accuracy of precipitation forecast has long been a challenge for Numerical Weather Prediction (NWP). Kryza and Werner et al [3] forecasted several short and intensive rainfalls over the SW area of Poland using the Weather Research and Forecasting Model (WRF) with different parameterization and spatial resolution. Hamill and Thomas [4] applied ensemble prediction systems to describe the performance of the WRF precipitation forecasts. Ensemble forecast can reflect the predictability or reliability of real atmosphere to some extent, it cannot improve the physical mechanism of the model. Among many considerable causes that could lead to the inherent low predictability of convective precipitation forecasting
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