Ishak et al. (2026) Evaluating Rainfall Forecast Skill in Numerical Weather Prediction Models and the Effects of Bias Correction on the PCJ (Piracicaba-Capivari-Jundiaí) River Basins in Brazil

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Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-09-16
• Authors: Violet Ishak, Danieli Mara Ferreira, Maria Fernanda D. d. S. Lima, José Eduardo Gonçalves
• DOI: 10.3390/hydrology13090254

Research Groups

• University of São Paulo (USP)
• National Institute for Space Research (INPE)

Short Summary

This study evaluates the performance of two operational precipitation forecasting systems against observational reference datasets in three basins in Brazil and assesses statistical post-processing techniques to correct dry/wet occurrence and precipitation magnitude errors.

Objective

• Evaluate the accuracy of operational precipitation forecasting systems and their response to statistical post-processing for hydrological applications.

Study Configuration

• Spatial Scale: Three basins in São Paulo State, Brazil.
• Temporal Scale: Long-term (evaluation period not specified).

Methodology and Data

• Models used:
  • HBLR-AR1
  • LR-Seasonal
  • Logistic-regression-based methods for occurrence correction
• Data sources:
  • Two observational reference datasets

Main Results

• Occurrence correction modified categorical performance, with effects depending on forecast–observation pairing, basin, and lead time.
• HBLR-AR1 showed the most balanced overall performance.
• QDM improved RMSE skill score, particularly at longer lead times.

Contributions

• This study provides insights into the effectiveness of statistical post-processing techniques for correcting precipitation forecasting errors in hydrological applications.
• The findings highlight the importance of considering forecast–observation discrepancies when evaluating correction methods.

Funding

• Not specified.

Citation

@article{Ishak2026Evaluating,
  author = {Ishak, Violet and Ferreira, Danieli Mara and Lima, Maria Fernanda D. d. S. and Gonçalves, José Eduardo},
  title = {Evaluating Rainfall Forecast Skill in Numerical Weather Prediction Models and the Effects of Bias Correction on the PCJ (Piracicaba-Capivari-Jundiaí) River Basins in Brazil},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13090254},
  url = {https://doi.org/10.3390/hydrology13090254}
}