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Process-Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America
Katherine M Kowal1, Louise J Slater1, Sihan Li2
1Department of Geography and the Environment University of Oxford Oxford UK.
Geophysical Research Letters
|July 12, 2024
Summary
Improving monthly rainfall forecasts in Central America, this study uses a process-informed evaluation. By subsampling climate model members based on wind and sea surface temperature (SST) predictors, forecast skill for rainfall extremes is enhanced.
Area of Science:
- Climate Science
- Meteorology
- Hydrology
Background:
- Subseasonal rainfall forecast skill is vital for managing hydrometeorological extremes.
- Accurate monthly rainfall predictions are crucial for preparedness in Central America.
Purpose of the Study:
- To assess if a process-informed evaluation can enhance monthly rainfall forecasts in Central America.
- To improve the skill of rainfall forecasts by subsampling climate model members based on key predictors.
Main Methods:
- A constrained ensemble mean was generated by subsampling 130 members from five dynamic forecasting models (C3S multimodel ensemble).
- Subsampling was based on the models' representation of zonal wind direction and Pacific/Atlantic sea surface temperatures (SSTs) at initialization.
- The evaluation focused on improving forecasts for Costa Rica and Guatemala.
Main Results:
- Increased mean squared error skill by 0.4 was observed in multiple months and locations.
- Improved detection rates for rainfall extremes were achieved.
- The method demonstrated success in identifying model members that accurately represent rainfall distributions.
Conclusions:
- Process-informed subsampling significantly enhances monthly rainfall forecast skill in Central America.
- This method is transferable to other regions influenced by slowly-changing climate processes.
- The approach effectively refines forecasts by accounting for errors in wind and SST predictors.
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