Precipitation Forecasting Using Doppler Radar Data, a Cloud Model with Adjoint, and the Weather Research and Forecasting Model: Real Case Studies during SoWMEX in Taiwan

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50392

Title:	Precipitation Forecasting Using Doppler Radar Data, a Cloud Model with Adjoint, and the Weather Research and Forecasting Model: Real Case Studies during SoWMEX in Taiwan
Authors:	Tai,SL;Liou,YC;Sun,JZ;Chang,SF;Kuo,MC
Contributors:	大氣物理研究所
Keywords:	ENSEMBLE KALMAN FILTER;PART I;DATA ASSIMILATION;CONVECTIVE STORM;DEMONSTRATION PROJECT;MESOSCALE MODEL;PREDICTION;IMPACT;RAINFALL;SYSTEM
Date:	2011
Issue Date:	2012-03-27 17:30:42 (UTC+8)
Publisher:	國立中央大學
Abstract:	The quantitative precipitation forecast (QPF) capability of the Variational Doppler Radar Analysis System (VDRAS) is investigated in the Taiwan area, where the complex topography and surrounding oceans pose great challenges to accurate rainfall prediction. Two real cases observed during intensive operation periods (IOPs) 4 and 8 of the 2008 Southwest Monsoon Experiment (SoWMEX) are selected for this study. Experiments are first carried out to explore the sensitivity of the retrieved fields and model forecasts with respect to different background fields. All results after assimilation of the Doppler radar data indicate that the principal kinematic and thermodynamic features recovered by the VDRAS four-dimensional variational data assimilation (4DVAR) technique are rather reasonable. Starting from a background field generated by blending ground-based in situ measurements (radiosonde and surface mesonet station) and reanalysis data over the oceans, VDRAS is capable of capturing the evolution of the major precipitation systems after 2 h of simulation. The model QPF capability is generally comparable to or better than that obtained using only in situ observations or reanalysis data to prepare the background fields. In a second set of experiments, it is proposed to merge the VDRAS analysis field with the Weather Research and Forecasting Model (WRF), and let the latter continue with the following model integration. The results indicate that through this combination, the performance of the model QPF can be further improved. The accuracy of the predicted 2-h accumulated rainfall turns out to be significantly higher than that generated by using VDRAS or WRF alone. This can be attributed to the assimilation of meso- and convective-scale information, embedded in the radar data. into VDRAS, and to better treatment of the topographic effects by the WRF simulation. The results illustrated in this study demonstrate a feasible extension for the application of VDRAS in other regions with similar geographic conditions and observational limitations.
Relation:	WEATHER AND FORECASTING
Appears in Collections:	[Department of Atmospheric Sciences and Graduate Institute of Atmospheric Physics ] journal & Dissertation

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