Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

Abstract

<jats:p>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.</jats:p>

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-yu Season of 2008 in Taiwan. Part II: Development of an Object-Oriented Method

“…Thus, this apparent over-forecast by CReSS in rainfall intensity deserves to be explored further also using other verification methods. The results obtained with this further investigation are reported in Part I [24] and Part III [49]. The distribution of number of objects as a function of (a) total water production (mega ton), (b) characteristic length (km), (c) maximum rainfall (mm), (d) areal-mean rainfall (mm), (e) centroid longitude (° E), (f) centroid latitude (° N), (g) aspect ratio (dimensionless), (h) long-axis orientation (degree), and (i) object curvature (10 −2 , or radius per 100 km) in the observation and CReSS QPFs at the range of 0-48 h, initialized twice daily at 00:00 and 12:00 UTC during South-West Monsoon Experiment (SoWMEX).…”

“…Thus, this apparent over-forecast by CReSS in rainfall intensity deserves to be explored further also using other verification methods. The results obtained with this further investigation are reported in Part I [24] and Part III [49].…”

“…The potential for many further applications using such an object-oriented verification method is high. For instance, in Part III [49] of this paper series, the performance of three mesoscale models (including CReSS) are inter-compared. The method can be applied to other rainfall regimes such as typhoon rainfall or afternoon convection.…”

Evaluating Quantitative Precipitation Forecasts Using the 2.5 km CReSS Model for Typhoons in Taiwan: An Update through the 2015 Season

“…As model resolution increases to make QPFs for migratory mesoscale systems (such as squall lines or rainbands), hits are difficult to achieve, and issues such as "double penalty" become more serious, especially at longer lead times; thus, the TS may no longer be effective in evaluating model QPFs (e.g., [6,15,16]). Thus, various new methods that do not require hits, such as attribute-or object-oriented methods, have been developed and used for rainfall verification in recent years (e.g., [16][17][18][19][20]). However, if the model has the ability to produce some "hits", i.e., to predict rainfall at the correct amount, location, and time simultaneously, this would still be much desired in many important applications, such as hazard prevention and mitigation linked to flooding, landslide, or water reservoir management.…”