Statistical prediction of typhoon‐induced accumulated rainfall over the Korean Peninsula based on storm and rainfall data
Abstract A statistical model for typhoon‐induced accumulated rainfall (TAR) prediction over the Korean Peninsula using track, intensity and rainfall data of 91 typhoons affecting the peninsula during the period 1977–2014 is developed. The statistical estimation of the TAR consists of three steps: (1...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Meteorological Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/met.1853 |
| Summary: | Abstract A statistical model for typhoon‐induced accumulated rainfall (TAR) prediction over the Korean Peninsula using track, intensity and rainfall data of 91 typhoons affecting the peninsula during the period 1977–2014 is developed. The statistical estimation of the TAR consists of three steps: (1) estimating the TAR at 56 observational weather stations for the 91 typhoons; (2) selecting typhoons whose tracks are similar to that of the target typhoon within the area of 32–40 ° N and 120–138 ° E using a fuzzy C‐mean clustering method; and (3) calculating the mean TAR for the 16 selected typhoons based on track similarity after an intensity correction of the TAR using a linear regression between the TAR and intensity anomaly. To validate the model, real case predictions were performed on typhoons Chan‐hom and Goni in 2015 and compared with the observed TARs as well as with those from local and global operational models. The result showed that when the best‐track data are used, the present statistical model can predict the TAR with the accuracy of mean root mean square errors (RMSEs) of 33 mm (Chan‐hom) and 29 mm (Goni) at the 56 stations, which were much less than the results of the local model. With the predicted track and intensity data for the two typhoons, the present model also showed an overall good performance with RMSEs of 30–34 mm (Chan‐hom) and 29–49 mm (Goni), depending on the accuracy of the predicted track and intensity, which were generally less than the results of the global model. |
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| ISSN: | 1350-4827 1469-8080 |