Predictability of Precipitation in Complex Terrain using the WRF Model with Varying Physics Parameterizations

Canada’s west coast topography plays a crucial role for the local precipitation patterns, which are often shaped by orographic lifting on one side of the mountains, and rain shadows on the other side. The hydroelectric infrastructure in southwest British Columbia (BC) relies heavily on the abundant rainfall of the wet season, but long lasting and heavy precipitation can cause local flooding and make reliable precipitation forecasts crucial for resource management, risk assessment, and disaster mitigation.

This research evaluates hourly precipitation forecasts from the Weather Research and Forecasting (WRF) model over the complex terrain of southwest BC. The model data includes a full year of daily runs across three nested domains (27-9-3 km). A selection of different parameterizations is systematically varied, including microphysics, cumulus, turbulence, and land-surface parameterizations. The resulting over 100 model configurations are evaluated with observations from ground-based quality-controlled precipitation gauges. The individual model skill of the precipitation forecasts is assessed with respect to different accumulation windows, forecast horizons, grid resolutions, and precipitation intensities. Furthermore, the ensemble mean and spread provide insight to the general error growth for precipitation forecasts in WRF.

Cumulus and microphysics parameterizations together determine the total precipitation in numerical weather prediction models and this study confirms the expectation that the combination of those physics parameterizations is most decisive for the precipitation forecasts. However, the boundary-layer and land-surface parameterizations have a secondary effect on precipitation skill. The verification shows that the WSM5 microphysics parameterization yields surprisingly competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although, the scale-aware Grell-Freitas cumulus parameterization performs better for summer-time convective precipitation, the conventional Kain-Fritsch parameterization performs better for winter-time frontal precipitation, which contributes to the majority of the annual rainfall in southwest BC.

Throughout a 3-day forecast horizon mean absolute errors are observed to grow by ~5% per forecast day. Furthermore, this study indicates that coarser resolutions suffer from larger total biases and larger random error components, however, they have slightly higher correlation coefficients. The mid-size 9-km domain yields the highest relative hit rate for significant and extreme precipitation. Verification metrics improve exponentially with longer accumulation windows: On one side, hourly precipitation values are highly prone to double-penalty issues (where a timing error can, for example, result in an over-forecast error in one hour and an under-forecast in a subsequent hour); on the other side, extended accumulation windows can compensate for timing errors, but lose information about short-term rain intensities.