Assessing the skill of gridded satellite and re-analysis precipitation products over altitudinal gradient in East and Southern Africa
Validation of gridded precipitation products (GPP’s) increase confidence of the users and highlights possible improvements of the algorithms to handle complex rainfall forming processes. We evaluated the skill of three gridded precipitation products (GPP’s) in estimating the gauge observations and c...
| Autores principales: | , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Nacional Autónoma de México
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/130094 |
| Sumario: | Validation of gridded precipitation products (GPP’s) increase confidence of the users and highlights possible improvements of the algorithms to handle complex rainfall forming processes. We evaluated the skill of three gridded precipitation products (GPP’s) in estimating the gauge observations and compared the precipitation trends derived from these products across the east and southern Africa region. Taylor diagrams and Kling-Gupta Efficiency (KGE) was used to assess the accuracy. A modified Mann-Kendal test and the Sen’s slope estimator were utilized to determine the significance and the magnitude of the trends respectively. The three GPP’s had varied performance over temporal and altitudinal ranges. The skill of the three GPP’s at monthly scale, was generally high but showed lower performance at elevations over 1500 m especially during the OND season. The three GPP’s performed equally well between 1001 – 1500 m elevation range. CHELSA-v2.1 was most accurate at 0-500m but had the lowest skill at 501 – 1000 m and above 1500 m elevations that caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and over large inland waterbodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validation of climate datasets to avoid error propagation in different models and applications. Our results demonstrates that new or higher resolution precipitation data is not always the most accurate since update of the algorithms can introduce artifacts or biases. |
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