Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Meteorology Hydrology and Water Management. Research and Operational Applications

1 2025

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: ensemble simulations

Sortuj według:

Ogranicz wyniki do:

Assessment of bias correction techniques for historical precipitation simulations in Sudan

Hilal Khalid, Salieva Kamar

Meteorology Hydrology and Water Management. Research and Operational Applications

2025

Vol. 13, Iss. 2

53--70

This study evaluates the performance of several bias correction techniques applied to CMIP6 precipitation simulations over Sudan for the period 1991-2014, using the high-resolution CHIRPS observational dataset as a reference. Four widely used bias correction methods: Empirical Quantile Mapping (EQM), Gamma Quantile Mapping (Gamma-QM), Local Intensity Scaling (LOCI), and the Delta Method were applied to ten CMIP6 models to assess their ability to reduce systematic biases and improve consistency with observed climatology. The raw simulations reveal pronounced seasonal biases, characterized by overestimation during the pre-monsoon season (MAM) and underestimation during the monsoon season (JJAS), whereas annual biases are moderate but exhibit notable spatial heterogeneity. Among the tested techniques, EQM and Gamma-QM consistently yield the most effective corrections, achieving median bias reductions of 94-100% across both annual and seasonal timescales, and markedly enhancing Kling–Gupta Efficiency (KGE) values. Among the evaluated models, EC-Earth3, GFDL-ESM4, and INMCM4-8 demonstrate the best performance annually and during June-September, whereas NESM3 performs better during MarchMay, highlighting model-specific strengths in simulating seasonal precipitation variability. Spatial analyses further confirm that bias corrections effectively align precipitation variability with observations, with statistically significant improvements across most regions of Sudan. These findings highlight the critical role of quantile-based correction methods in producing reliable CMIP6 precipitation outputs over Sudan and establish a robust framework for assessing both model skill and bias correction performance in regions characterized by complex, seasonally varying rainfall regimes.