Preprint Article Version 1 This version is not peer-reviewed

Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies

Version 1 : Received: 16 April 2018 / Approved: 17 April 2018 / Online: 17 April 2018 (11:29:28 CEST)

How to cite: Kimani, M.W.; Hoedjes, J.C.; Su, Z. Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies. Preprints 2018, 2018040225 (doi: 10.20944/preprints201804.0225.v1). Kimani, M.W.; Hoedjes, J.C.; Su, Z. Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies. Preprints 2018, 2018040225 (doi: 10.20944/preprints201804.0225.v1).

Abstract

Advances in remote sensing have led to use of satellite-derived rainfall products to complement the sparse rain gauge data. Although globally derived and some regional bias corrected, these products often show large discrepancies with ground measurements attributed to local and external factors that require systematic consideration. Decreasing rain gauge network however inhibits continuous validation of these products. We propose to deal with this problem by the use of Bayesian approach to merge the existing historical rain gauge information to create a consistent satellite rainfall data that can be used for climate studies. Monthly Bayesian bias correction is applied to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS v2) data to reduce systematic errors using a corresponding gridded (0.05°) rain gauge data over East Africa for a period of 33 (1981–2013) years of which 22 years are utilized to derive error fields which are then applied to an independent CHIRPS data for 11 years for validation. The bias correction is spatially and temporally assessed during the rainfall wet months of March-May (MAM), June-August (JJA) and October–December (OND) in East Africa. Results show significant reduction of systematic errors at both monthly and yearly scales and harmonization of their cumulative distributions. Monthly statistics showed a reduction of RMSD (29–56)% and MAE (28–60)% and an increase of correlations (2–32) %, while yearly ones showed reductions of RMSD (9-23)%, and MAE (7–27)% and increase of correlations (4–77)% for MAM months, reduction of RMSD (15–35)% and MAE (16–41)% and increase in correlations (5–16)% for JJA months, and reduction of RMSD (3–35)% and MAE (9–32)% and increase of correlations (3–65)% for OND months. Systematic errors of corrected data were influenced by local processes especially over Lake Victoria and high elevated areas. Large-scale circulations induced errors were mainly during JJA and OND rainfall seasons and were reduced by the separation of anomalous years during training. The proposed approach is recommended for generating long-term data for climate studies where consistencies of errors can be assumed.

Subject Areas

Bayesian bias correction; satellite rainfall; rain gauge; climate studies; East Africa

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