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Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts.
Laura Harrison1, Martin Landsfeld2, Greg Husak2
1University of California Santa Barbara, Climate Hazards Center and Department of Geography, Santa Barbara, USA. harrison@geog.ucsb.edu.
Scientific Data
|June 30, 2022
Summary
The CHIRPS-GEFS dataset offers bias-corrected precipitation forecasts compatible with CHIRPS data. This enhances drought monitoring and early warning systems by improving forecast accuracy and local climatology representation.
Area of Science:
- Earth and Environmental Science
- Atmospheric Science
- Climate Science
Background:
- The Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) dataset is vital for drought monitoring and impact assessments.
- The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) provides precipitation forecasts.
- A need exists to bridge the gap between observational data and numerical weather prediction for improved climate analysis.
Purpose of the Study:
- To introduce CHIRPS-GEFS, an operational dataset providing bias-corrected precipitation forecasts.
- To enhance the compatibility of GEFS forecasts with CHIRPS data characteristics.
- To improve the accuracy and local relevance of medium-range precipitation forecasts.
Main Methods:
- Utilizing NCEP GEFS v12 precipitation forecasts.
- Applying a rank-based quantile matching procedure to align GEFS forecasts with CHIRPS data.
- Transforming GEFS reforecast and real-time ensemble means to match CHIRPS spatial-temporal attributes.
Main Results:
- CHIRPS-GEFS provides daily bias-corrected precipitation forecasts from 1 to 15 days at 0.05-degree resolution.
- The quantile matching procedure improves the reflection of local climatology in forecasts.
- Forecasts exhibit reduced moderate-to-large errors, enhancing reliability.
Conclusions:
- CHIRPS-GEFS effectively integrates observational data with weather predictions.
- The dataset enables rapid assessment of current forecasts within a local historical context.
- CHIRPS-GEFS increases the value of both monitoring resources and interoperable forecasts.
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