Evaluation of 2024 Meteorological Drought Forecasts for Türkiye: Using ECMWF SEAS5 Data and the SPEI Index
This study aims to evaluate meteorological drought predictions for Türkiye in 2024 using the SEAS5 seasonal forecast system developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The motivation behind this research is to assess the applicability of SEAS5 for drought forecastin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MO.RI Publishing
2025-06-01
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| Series: | Indonesian Journal of Earth Sciences |
| Subjects: | |
| Online Access: | https://www.journal.moripublishing.com/index.php/injoes/article/view/1559 |
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| Summary: | This study aims to evaluate meteorological drought predictions for Türkiye in 2024 using the SEAS5 seasonal forecast system developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The motivation behind this research is to assess the applicability of SEAS5 for drought forecasting and its potential contribution to drought management and early warning systems. Drought analysis was performed using the Standardized Precipitation-Evapotranspiration Index (SPEI) on a 3-month timescale, while the model’s predictive performance was evaluated through categorical verification metrics. The forecast data used were the monthly anomaly outputs of the SEAS5 system for 2024, combined with station-based reference normals from the 1991-2020 period to produce station-specific forecast series. Long-term precipitation and temperature data from 190 meteorological observation stations, with records starting from 1969, were employed to identify climate trends using the Mann-Kendall rank correlation method. The study presents spatial and temporal distributions of drought conditions and assesses the SEAS5 model’s success in predicting droughts across different months and seasons. The findings suggest that SEAS5 can be effectively used for drought forecasting in Türkiye and can contribute significantly to improving drought management and early warning systems.
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| ISSN: | 2798-1134 2797-3549 |