Improving the model convective storm quantitative precipitation nowcasting by assimilating state variables retrieved from multiple-Doppler radar observations

NCUIR > College of Earth Sciences > Department of Atmospheric Sciences > journal & Dissertation > Item 987654321/100147

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/100147

Title:	Improving the model convective storm quantitative precipitation nowcasting by assimilating state variables retrieved from multiple-Doppler radar observations
Authors:	廖宇慶;Liou, Yu-Chieng;Chiou, Jian-Luen;Chen, Wei-Hao;Yu, Hsin-Yu
Contributors:	地球科學學院大氣科學學系
Keywords:	Algorithms;Boundary conditions;Data assimilation;Data collection;Doppler effect;Doppler radar;Doppler radar observation;Doppler sonar;Information retrieval;Kalman filters;Mathematical models;Methods;Modelling;Moisture;Moisture effects;Nowcasting;Numerical models;Precipitation;Procedures;Radar;Radar data;Radiosondes;Rain;Rainfall;Rainfall forecasting;Retrieval;State variable;Storms;Studies;Thermodynamics;Velocity;Weather;Wind;Wind measurement
Date:	2014-01-01
Issue Date:	2026-04-21 13:51:11 (UTC+8)
Publisher:	American Meteorological Society;Washington: American Meteorological Society
Abstract:	摘要： AbstractThis research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions.It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model.Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as four-dimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods. 出版者： Washington: American Meteorological Society 出版日期： 2014-11-01 出處： Monthly Weather Review, 2014-11, Vol.142 (11), p.4017-4035 資源來源： EBSCOhost OmniFile Full Text Select 版權： Copyright American Meteorological Society Nov 2014 版權： Copyright American Meteorological Society 2014 識別號： ISSN: 0027-0644 識別號： EISSN: 1520-0493 識別號： DOI: 10.1175/mwr-d-13-00315.1 識別號： CODEN: MWREAB
Appears in Collections:	[Department of Atmospheric Sciences] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	38	View/Open

社群 sharing

Loading...