Abstract
In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.
Highlights
More accurate forecasts of rainfall occurrence, as well as estimates of expected rainfall amount have become increasingly demandable by the society
It is necessary to assess the quality of forecast accurately with appropriate verification methods, so that the forecasting model’s virtue can be better understood
As the rain-producing systems become smaller in scale with large rainfall variations both in space and time, the traditional measures based on point-to-point verification become ineffective and not efficient enough to meet present day needs, due to issues such as the “double penalty” [2]
Summary
More accurate forecasts of rainfall occurrence, as well as estimates of expected rainfall amount have become increasingly demandable by the society. The traditional measure-based statistical approaches of continuous verification (like the mean error and correlation coefficient) and categorical objective methods (like the threat score) have been used for a long time to understand the strengths and weaknesses of the Quantitative Precipitation Forecasting (QPFs), and the direction to improve numerical models. These methods often rely heavily on point-to-point correspondence or variations between the observation and model forecasts, and regular and routine verifications are often done. At the mesoscale, to which most heavy-rain producing systems belong, how to produce proper and reliable routine verification on a regular basis, for models with increasing resolution as well as for periods with significant or frequent heavy rainfall, has become a more and more pressing problem
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