Short-range (0-12 h) PQPFs from time-lagged multimodel ensembles using LAPS

NCUIR > College of Earth Sciences > Department of Atmospheric Sciences > journal & Dissertation > Item 987654321/99789

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/99789

Title:	Short-range (0-12 h) PQPFs from time-lagged multimodel ensembles using LAPS
Authors:	林沛練;Chang, Hui-Ling;Yuan, Huiling;Lin, Pay-Liam
Contributors:	地球科學學院大氣科學學系
Keywords:	Accuracy;Atmospheric Science;Bias;Calibration;Climatology;Data collection;Earth, ocean, space;Exact sciences and technology;External geophysics;Meteorology;Precipitation;Rain;Samples;Statistical analysis;Statistical methods;Training;Weather;Weather forecasting
Date:	2012-05-01
Issue Date:	2026-04-21 13:35:35 (UTC+8)
Publisher:	American Meteorological Society;Boston, MA: American Meteorological Society
Abstract:	摘要： This study pioneers the development of short-range (0–12 h) probabilistic quantitative precipitation forecasts (PQPFs) in Taiwan and aims to produce the PQPFs from time-lagged multimodel ensembles using the Local Analysis and Prediction System (LAPS). By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. Since LAPS adopts diabatic data assimilation, it is utilized to mitigate the “spinup” problem and produce more accurate precipitation forecasts during the early prediction stage (0–6 h).The LAPS ensemble prediction system (EPS) has a good spread–skill relationship and good discriminating ability. Therefore, though it is obviously wet biased, the forecast biases can be corrected to improve the skill of PQPFs through a linear regression (LR) calibration procedure. Sensitivity experiments for two important factors affecting calibration results are also conducted: the experiments on different training samples and the experiments on the accuracy of observation data. The first point reveals that the calibration results vary with training samples. Based on the statistical viewpoint, there should be enough samples for an effective calibration. Nevertheless, adopting more training samples does not necessarily produce better calibration results. It is essential to adopt training samples with similar forecast biases as validation samples to achieve better calibration results. The second factor indicates that as a result of the inconsistency of observation data accuracy in the sea and land areas, only separate calibration for these two areas can ensure better calibration results of the PQPFs. 出版者： Boston, MA: American Meteorological Society 出版日期： 2012-05-01 出處： Monthly Weather Review, 2012-05, Vol.140 (5), p.1496-1516 資源來源： EBSCOhost OmniFile Full Text Select 版權： 2015 INIST-CNRS 版權： Copyright American Meteorological Society May 2012 識別號： ISSN: 0027-0644 識別號： EISSN: 1520-0493 識別號： DOI: 10.1175/mwr-d-11-00085.1 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	10	View/Open

社群 sharing

Loading...