Boost Satellite Rainfall Accuracy: A Comparison of Quantile, Linear, and Random Forest Corrections

Accurate precipitation data is vital for climate research, agriculture, and water modeling. Satellite rainfall estimates, like those from CHIRPS, provide extensive coverage for regions with low density of stations but may require adjustments to match ground measurements. In our next tutorial, we explore three correction methods: Quantile, Linear, and Random Forest to refine these satellite estimates using ground station data. The Quantile and Linear approaches adjust rainfall values based on statistical distributions, while the Random Forest method incorporates additional factors, such as elevation, to improve accuracy. Choosing the right correction method can greatly influence the reliability of rainfall data, ensuring better inputs for climate research or water modeling.

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