Abstract
In flood warning systems, numerical weather predictions (NWP) are important complementary tools as they increase the forecast lead times required to provide timely warnings. However, these predictions may include non-predictable uncertainties that decrease the correlation with gauge observations and ascend the number of missed or false warnings. Investigation of the potential of such products that are generated one-day apart can reveal the success of lead times in hydrological applications. In this study, two periodically generated products of the Weather Research and Forecasting (WRF) model are assessed by considering 15 flood events experienced in the southwest Mediterranean region of Turkey with a network of 26 rain gauges. The general results show that the rainfall distribution of both WRF datasets is found to be similar with slight differences in magnitude. However, the rainfall product generated earlier shows greater agreement in the observational data for 1-hr interval time but the latter one shows less bias and less cumulative error as interval time increases. In season- and elevation-based analyses, both WRF datasets show the highest correlation value in the winter season. With this study, it is revealed that the rainfall correlations results are superior in the former data whereas the rainfall accumulations are better represented with the posterior data.
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All analyses presented here were performed and written by the author of this paper.
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The author is grateful to the anonymous reviewers for their valuable comments and suggestions to further improve this paper. The WRF and gauge rainfall data were provided by the Turkish State Meteorological Service (TSMS).
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Ozkaya, A. Assessing the numerical weather prediction (NWP) model in estimating extreme rainfall events: A case study for severe floods in the southwest Mediterranean region, Turkey. J Earth Syst Sci 132, 125 (2023). https://doi.org/10.1007/s12040-023-02137-7
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DOI: https://doi.org/10.1007/s12040-023-02137-7