Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China

Tuanjie Hou,Fanyou Kong,Xunlai Chen,Hengchi Lei

doi:10.1155/2013/129642

Tuanjie Hou, Fanyou Kong + Show 2 more

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https://doi.org/10.1155/2013/129642

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Abstract

This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events.

Highlights

Convective storms accompanied with heavy precipitation, hail, and damaging wind occur frequently in summer season in Southern China
Several studies have demonstrated that the Advanced Regional Prediction System (ARPS) three-dimensional variational (3DVAR) system is capable of analyzing different data types, by using multiple analysis passes [4,5,6,7]
Since the positive impact of radar data assimilation has been demonstrated in previous studies, this study is emphasized on evaluating additional surface Automatic Weather Station (AWS) and radiosonde data assimilation impacts on short-term quantitative precipitation forecast (QPF) skill

Summary

Introduction

Convective storms accompanied with heavy precipitation, hail, and damaging wind occur frequently in summer season in Southern China. To reduce damage from such severe weather, more accurate short-term forecast from convectivescale numerical weather prediction (NWP) models incorporated with robust data assimilation systems have been paid more attention [1,2,3]. Based on the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the ARPS 3DVAR/. Cloud Analysis module, a real-time hourly updated stormscale forecasting system has been developed collaboratively by the Center for Analysis and Prediction of Storms (CAPS). Bureau (SZMB) of China and the Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences. The forecasting system, called Hourly Assimilation and Prediction System, or HAPS, has been in daily real-time forecast runs since March 2010. The system was initialized from Global Forecast System (GFS) data and characterized by assimilating reflectivity and radial wind from local Weather

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