Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Winifred Ayinpogbilla Atiah; Robert Johnson; Francis Kamau Muthoni; Gizaw Tsidu Mengistu; Leonard Kofitse Amekudzi; Osei Kwabena; Fred Kizito

doi:10.1016/j.heliyon.2023.e17604

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Heliyon. 2023 Jun 28;9(7):e17604. doi: 10.1016/j.heliyon.2023.e17604. eCollection 2023 Jul.

Authors

Winifred Ayinpogbilla Atiah¹, Robert Johnson¹, Francis Kamau Muthoni², Gizaw Tsidu Mengistu³, Leonard Kofitse Amekudzi¹, Osei Kwabena¹, Fred Kizito⁴

Affiliations

¹ Kwame Nkrumah University of Science and Technology (KNUST), Department of Physics, Meteorology and Climate Science Unit, Kumasi, Ghana.
² International Institute of Tropical Agriculture (IITA), Duluti, Arusha, P.O. Box 10, Tanzania.
³ Botswana International University of Science and Technology (BIUST), Department of Earth and Environmental Science, Palapye, Botswana.
⁴ International Institute of Tropical Agriculture, Accra GA-184, Ghana.

Abstract

Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.

Keywords: BCSD; Bias-correction; Cessation; Onset; Rainfall; Satellite-based.