易受災地區之梅雨季定量降雨預報技術之建立與改進-子計畫:發展應用LETKF及雷達資料以改進定量降水預報之同化系統(III);The Development and Application of LETKF Assimilation System and Radar Data to Improve the QPF (III)

NCUIR > College of Earth Sciences > Department of Atmospheric Sciences > Research Project > Item 987654321/62253

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/62253

Title:	易受災地區之梅雨季定量降雨預報技術之建立與改進-子計畫:發展應用LETKF及雷達資料以改進定量降水預報之同化系統(III);The Development and Application of LETKF Assimilation System and Radar Data to Improve the QPF (III)
Authors:	廖宇慶;楊舒芝
Contributors:	國立中央大學大氣科學系
Keywords:	大氣科學
Date:	2012-12-01
Issue Date:	2014-03-17 11:24:20 (UTC+8)
Publisher:	行政院國家科學委員會
Abstract:	研究期間：10108~10207;This research project uses the WRF (Weather Research and Forecasting) model and develops a Doppler radar data assimilation system based on the LETKF (local ensemble transform Kalman filter) method. The goal is to evaluate the capability of this system to improve short-term QPF (quantitative precipitation forecasting). This system has been established and able to assimilate radial velocity and reflectivity. For the case of Typhoon Morakot (2009), several OSSE-type sensitivity tests of assimilating radial velocity have been accomplished. It is found that this system benefits differently the analysis quality of different model variables and improves significantly the short-term QPF. This research project is designed for three years and currently entering the third year. Upcoming research goals are listed as below: (1)Accomplish the OSSE-type sensitivity tests of assimilating reflectivity in addition. (2)Assimilate more radar data in Taiwan’s radar network, to see if this further improves the analysis of large-scale environment indirectly and the short-term QPF. (3) Investigate the ensemble information. Find multivariate correlations at the convective scale by the error covariance structure, which helps to optimize the tuning parameters of this system (e.g. localization parameters, covariance inflation factor, etc.). (4) Develop the observation operators of other radar products (e.g. ice-phase reflectivity, dual-polarimetric observations). (5)Assimilate real observations. Pre-processing work includes radial velocity de-aliasing, reflectivity attenuation correction, superobservation, etc.. The QPF after data assimilation will be compared with in situ rainfall measurement to evaluate the performance of this system in real cases.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Department of Atmospheric Sciences] Research Project

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