Verification of Physics-Based NWP for Hazardous Rainfall Forecasting in Piura, Peru: Parametrization Sensitivity of WRF and Eta Using Spatial and Extreme-Event Metrics

Heavy rainfall events in northern Peru, primarily associated with the Coastal El Niño phenomenon, are a major cause of devastating floods and severe socioeconomic impacts. To address a deficiency in operational forecasting, this study presents the first systematic verification-based assessment of the predictive skill of two regional numerical weather prediction (NWP) models used by SENAMHI: the WRF and Eta models. The analysis focuses on the most intense precipitation events in the Piura region during February–March 2023, corresponding to the most severe Coastal El Niño since 2017. Predictive skill of 24-hour and 48-hour forecasts is evaluated for each episode.

Both models were forced with identical initial and boundary conditions from the GFS global model. Sensitivity experiments with WRF identified an optimal configuration combining Thompson microphysics, New Tiedtke cumulus, and YSU planetary boundary layer schemes, which exhibited the lowest bias and RMSE in preliminary analysis.

Beyond traditional metrics, we employ spatial verification using the Fractions Skill Score (FSS) to assess precipitation pattern accuracy, and extreme event metrics including the Symmetric Extremal Dependence Index (SEDI) and Extreme Dependency Score (EDS). Results show that the optimized WRF configuration consistently outperforms the operational Eta setup, with lower errors validated against satellite estimates and observations from 27 weather stations, more accurately reproducing the spatial distribution and intensity of precipitation.

However, both models exhibit systematic limitations. WRF tends to overestimate precipitation over mid-to-high Andean slopes and slightly underestimate it in coastal areas. The Eta model underestimates precipitation at high-elevation stations and produces more widespread overestimation across the coastal plain. These persistent biases are quantified spatially, providing a benchmark for future model development. These findings identify a more robust configuration and diagnose regional biases, offering a basis for improving operational verification, extreme rainfall forecasting, and early warning systems in the Piura region.