What question did this study set out to answer?

To assess the skill of operational numerical weather prediction precipitation forecasts and determine whether statistical bias correction improves dry/wet occurrence and rainfall magnitude estimates across river basins.

September 17, 2026HydrologyOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

VIViolet Ishak DFDanieli Mara Ferreira MLMaria Fernanda D. d. S. Lima

Discussion

Member takes

Overview

Comparative evaluation reveals variable impacts of statistical bias correction across river basins, highlighting that post-processing gains depend strongly on observational reference choices.

Key Points

To assess the skill of operational numerical weather prediction precipitation forecasts and determine whether statistical bias correction improves dry/wet occurrence and rainfall magnitude estimates across river basins.
Evaluated two operational precipitation forecasting systems against two observational reference datasets across three basins in São Paulo State, Brazil, covering seven lead times.
Applied four logistic-regression-based methods to adjust dry/wet occurrence and Quantile Delta Mapping (QDM) to correct precipitation amounts.
Assessed occurrence shifts using exact McNemar tests with Holm adjustment, evaluated metric changes via exact paired permutation tests and bootstrap 95% confidence intervals, and measured magnitude skill using RMSE skill score and KGE.
Occurrence correction significantly altered individual event classifications in some configurations per McNemar testing, but paired permutation tests revealed no systematic metric improvements across lead times.
HBLR-AR1 exhibited the most balanced overall performance among occurrence correction techniques, whereas LR-Seasonal was the least consistent.
Quantile Delta Mapping improved RMSE skill scores—particularly at extended forecast lead times—without delivering consistent improvements in KGE.

Cite This Study

Ishak et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7275f706d05830e612c https://doi.org/10.3390/hydrology13090254

View Full Paper

Ask AI

Bookmark

September 17, 2026HydrologyOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

VIViolet Ishak DFDanieli Mara Ferreira MLMaria Fernanda D. d. S. Lima

Discussion

Member takes

Overview

Comparative evaluation reveals variable impacts of statistical bias correction across river basins, highlighting that post-processing gains depend strongly on observational reference choices.

Key Points

To assess the skill of operational numerical weather prediction precipitation forecasts and determine whether statistical bias correction improves dry/wet occurrence and rainfall magnitude estimates across river basins.
Evaluated two operational precipitation forecasting systems against two observational reference datasets across three basins in São Paulo State, Brazil, covering seven lead times.
Applied four logistic-regression-based methods to adjust dry/wet occurrence and Quantile Delta Mapping (QDM) to correct precipitation amounts.
Assessed occurrence shifts using exact McNemar tests with Holm adjustment, evaluated metric changes via exact paired permutation tests and bootstrap 95% confidence intervals, and measured magnitude skill using RMSE skill score and KGE.
Occurrence correction significantly altered individual event classifications in some configurations per McNemar testing, but paired permutation tests revealed no systematic metric improvements across lead times.
HBLR-AR1 exhibited the most balanced overall performance among occurrence correction techniques, whereas LR-Seasonal was the least consistent.
Quantile Delta Mapping improved RMSE skill scores—particularly at extended forecast lead times—without delivering consistent improvements in KGE.

Cite This Study

Ishak et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7275f706d05830e612c https://doi.org/10.3390/hydrology13090254

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark