![]() These stochastic simulations allow for ensemble nowcasts from which both probabilistic and deterministic forecasts can be derived and are applicable and consistent at multiple spatial scales, from the kilometre scale to the size of a catchment area 13.Īpproaches based on deep learning have been developed that move beyond reliance on the advection equation 5, 6, 14, 15, 16, 17, 18, 19. In these models, motion fields are estimated by optical flow, smoothness penalties are used to approximate an advection forecast, and stochastic perturbations are added to the motion field and intensity model 3, 4, 12. Established probabilistic nowcasting methods, such as STEPS and PySTEPS 3, 4, follow the NWP approach of using ensembles to account for uncertainty, but model precipitation following the advection equation with a radar source term. As a result, alternative methods that make predictions using composite radar observations have been used radar data is now available (in the UK) every five minutes and at 1 km × 1 km grid resolution 11. For precipitation at zero to two hours lead time, NWPs tend to provide poor forecasts as this is less than the time needed for model spin-up and due to difficulties in non-Gaussian data assimilation 8, 9, 10. For nowcasting to be useful in these applications the forecast must provide accurate predictions across multiple spatial and temporal scales, account for uncertainty and be verified probabilistically, and perform well on heavier precipitation events that are rarer, but more critically affect human life and economy.Įnsemble numerical weather prediction (NWP) systems, which simulate coupled physical equations of the atmosphere to generate multiple realistic precipitation forecasts, are natural candidates for nowcasting as one can derive probabilistic forecasts and uncertainty estimates from the ensemble of future predictions 7. ![]() Nowcasting informs the operations of a wide variety of sectors, including emergency services, energy management, retail, flood early-warning systems, air traffic control and marine services 1, 2. The high-resolution forecasting of rainfall and hydrometeors zero to two hours into the future, known as precipitation nowcasting, is crucial for weather-dependent decision-making. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle. When verified quantitatively, these nowcasts are skillful without resorting to blurring. Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods. Our model produces realistic and spatiotemporally consistent predictions over regions up to 1,536 km × 1,280 km and with lead times from 5–90 min ahead. Using statistical, economic and cognitive measures, we show that our method provides improved forecast quality, forecast consistency and forecast value. Here we present a deep generative model for the probabilistic nowcasting of precipitation from radar that addresses these challenges. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints 5, 6. ![]() ![]() State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations 3, 4. Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making 1, 2.
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