Ensemble precipitation nowcasting: limits to prediction, localization and seamless blending

Nowcasting precipitation, that is, accurately predicting its location and intensity minutes to a few hours ahead, is a difficult task. Extreme rainfall events can be a threat to the population and challenge the limits of weather monitoring and prediction systems. For practical use, the forecasting techniques at our disposal range from physically-based numerical models to heuristic extrapolation procedures based on weather radars. Radar-based nowcasting models rely on the high spatial and temporal resolution of radar measurements and thus benefit from the best possible initial conditions, but the assumption of persistence leads to a rapid decay of predictive skill with increasing lead time and decreasing spatial scale. Numerical models provide physically consistent precipitation forecasts, but their practical relevance for nowcasting can be undermined by uncertain initial conditions, spinup issues, model approximations, or simply by their computational limits. Analyses presented in this study show that, on average, radar-based nowcasting outperforms numerical simulations during the first three hours and that it is particularly useful for forecasting precipitation patterns with a horizontal dimension below 60 kilometers, as state-of-the-art numerical models cannot provide useful skill at those small spatial scales. An interesting finding is that the predictive uncertainty of numerical predictions relatively to radar nowcasting improves during warm convective days, which is explained by the combined effect of shorter precipitation lifetimes and more effective model assimilation of locally triggered air mass convection. After a lead time of 4.5 hours, we observed that precipitation on all scales below 150 kilometers is poorly predictable by all forecasting means. Such serious limits to predictability determine the need to represent forecast uncertainty as accurately as possible. In radar nowcasting, ensemble methods use stochastic simulations to perturb a deterministic extrapolation and thus quantify forecast errors. We found that a more precise representation of the statistical properties of the precipitation field through localization can have a positive impact on the realism of the simulations as well as in terms of probabilistic forecast skill. We showed that localized nowcasts perform better in terms of ensemble reliability and resolution, as well as conditional bias. However, we also found that a too strong a localization can lead to lower skill as it implicitly relies on a stricter assumption of persistence. The quantitative estimation of forecast uncertainty provided by ensembles was finally used to design a seamless blending procedure that integrates all available sources of predictive skill. Implemented using a recursive formulation of the Bayesian update equations, the blending scheme involves a prediction step through a stochastic radar extrapolation, while a subsequent correction step updates the extrapolation using information from the most recent numerical model run. It is found that such an approach is able to capture the flow dependence of both the numerical forecast and the radar nowcast ensemble spreads resulting in an adaptive blending scheme that depends on the relative uncertainty of the individual forecasts. Despite the non-Gaussian nature of rainfall data, we were able to produce blended precipitation forecasts that are at least as skillful as the radar-only or the numerical model-only forecasts at any lead time