The use of a cloud resolving model in the development and evaluation of a probabilistic forecasting algorithm for convective gusts
A Cloud Resolving Model (CRM) is used to simulate the showery convective activity that occurred over the United Kingdom on 13 April 1999, which was notable for observations of strong gusts. Simulations based on radiosonde profiles from both Herstmonceux and Hemsby are shown to give good agreement wi...
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| Published in: | Meteorological applications Vol. 10; no. 3; pp. 239 - 252 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cambridge, UK
Cambridge University Press
01.09.2003
John Wiley & Sons, Ltd |
| Subjects: | |
| ISSN: | 1350-4827, 1469-8080 |
| Online Access: | Get full text |
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| Summary: | A Cloud Resolving Model (CRM) is used to simulate the showery convective activity that occurred over the United Kingdom on 13 April 1999, which was notable for observations of strong gusts. Simulations based on radiosonde profiles from both Herstmonceux and Hemsby are shown to give good agreement with available observations for this case. In particular, there is good agreement with the observed cloud top heights, precipitation amount and type, and strength of convective gusts. An algorithm to predict the maximum convective gust strength is shown to be consistent with values measured from the model. Further analysis of the model gust strengths shows that they could be approximated to a Gaussian distribution and that the mean and standard deviation of the Gaussian can be related to the maximum gust strength and the mean wind. A simple algorithm for predicting the probability distribution of the gust strengths measured in the model gives reasonable qualitative agreement with the measured probabilities and it is suggested that this demonstrates the utility of the CRM in developing and evaluating probabilistic forecasting tools for convective weather. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1350-4827 1469-8080 |
| DOI: | 10.1017/S1350482703003049 |