Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis

• Main Authors: Hamed, Mohammed Magdy, Nashwan, Mohamed Salem, Shahid, Shamsuddin
• Format: Article
• Published: John Wiley and Sons Ltd 2021
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/94057/; http://dx.doi.org/10.1002/joc.7286

Selection of suitable gridded precipitation data is deemed for hydroclimatic assessment and climate change impact analysis, especially in regions where long-term reliable precipitation data is unavailable. A novel approach based on fuzzy entropy similarity analysis (FESA) is proposed to evaluate the performance of four reanalysis gridded precipitation datasets (GPDs) for Egypt, namely European Reanalysis v.5. (ERA5), TerraClimate, Global Land Data Assimilation System (GLDAS)—Noah Land Surface Model L4 v.2 and Climatologies at high resolution for the Earth's land surface areas (CHELSA), against gauge records. The proposed method was verified using conventional statistics. Besides, the relative performance of different GPDs was verified according to their response to the influence of North Atlantic Oscillation (NAO) and Mediterranean Oscillation (MO) on winter precipitation. The performance of the best reanalysis GPD was also compared with the gauge-based global precipitation climatology centre (GPCC) dataset to show its reliability. The FESA revealed CHELSA as the best reanalysis GPD for Egypt. The performance assessment of GDPs based on conventional statistical metrics and visual presentation confirms the results obtained using FESA. CHELSA showed significant correlations with NAO (r = 0.3627) and MO (r = 0.624) like that obtained for gauge records. CHELSA also showed a better representation of precipitation in Egypt than GPCC at nearly half of the gauge locations. As CHELSA has a much higher spatial resolution than GPCC, it can be recommended as the proxy of gauge records in Egypt. The FESA can be used for performance analysis of gridded climate data by avoiding the complexities of using multiple statistical metrics.