Date

2023-1

Author

Aydın, Beril

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This study was carried out to determine whether convective and stratiform (large-scale) precipitation datasets obtained from 5 model forecast-based products would be an adequate alternative for regions where station-based observation networks are sparse. Verification of precipitation types (convective or stratified) from numerical weather forecast (NWP) models (ALARO, CFS, ECMWF HRES, GFS, WRF) is done using station-based observations. Statistical assessments between these precipitation types in different topographies (complex and non-complex) are available between 2015 and 2018. Reference data are obtained from 836 Automatic Observation Stations (OMGI) operated by the Turkish State Meteorological Service (TSMS). Accuracy assessments are reviewed separately for each precipitation type (total, convective, stratiform) and topography (entire area, complex regions, non-complex regions). The results show that all model products have an average correlation of 0.6 in total and large-scale precipitation for all areas. In contrast, the convective component of precipitation has an average correlation of 0.16 and 0.19 for complex and non-complex regions, respectively. In complex areas where the risk of flash flooding is high and the slope is more than 5% under the influence of convective precipitation, the low prediction performance of model products reduces the ability to take precautions for dangerous situations. Overall, the ECMWF HRES product has the highest accuracy in total precipitation for all areas, while the WRF product has the highest correlation coefficient and closest spatial distributions in all regions for convective precipitation. The findings of this study will assist the global literature examining regions with complex topography not only over the entire study area but for future hydrological and hydrometeorological studies.

Subject Keywords

Complex Topography, Numerical Weather Prediction, Convective Precipitation, Stratiform Precipitation, Accuracy Assessments

URI

https://hdl.handle.net/11511/102022

Collections

Graduate School of Natural and Applied Sciences, Thesis