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Short-range (0–12 H) Pqpfs From Time-lagged Multimodel Ensembles Using LAPS


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.

Article / Publication Data
Print 0027-0644/Online 1520-0493
Available Metadata
Accepted On
April 03, 2011
Fiscal Year
Publication Name
Mon. Wea. Rev.
Published On
January 01, 2012
Final Online Publication On
May 01, 2012
Publisher Name
Amer Meteorological Soc
Print Volume
Print Number
Page Range


Not available


Authors who have authored or contributed to this publication.

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