Skillful Precipitation Nowcasting using Deep Generative Models of Radar (2104.00954v1)

Abstract: Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-dependent decision-making. 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. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints. 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 more rare medium-to-heavy rain events. To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar. Our model produces realistic and spatio-temporally consistent predictions over regions up to 1536 km x 1280 km and with lead times from 5-90 min ahead. In a systematic evaluation by more than fifty expert forecasters from the Met Office, our generative model ranked first for its accuracy and usefulness in 88% of cases against two competitive methods, demonstrating its decision-making value and ability to provide physical insight to real-world experts. When verified quantitatively, these nowcasts are skillful without resorting to blurring. 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.

• The paper presents a novel deep generative model that creates probabilistic precipitation nowcasts from radar data to enhance short-term forecasting accuracy.
• It overcomes the issue of blurry predictions by generating spatially and temporally consistent forecasts across large regions, validated by over fifty expert forecasters.
• Quantitative evaluations using metrics like CSI and CRPS demonstrate significant improvements over conventional methods, highlighting its operational potential.

Overview of "Skillful Precipitation Nowcasting using Deep Generative Models of Radar"

This paper presents a sophisticated approach to precipitation nowcasting using Deep Generative Models (DGMs) to address challenges present in existing methodologies. Precipitation nowcasting, which involves predicting rainfall up to two hours in advance, is crucial for many sectors, yet current methods often fall short when it comes to capturing non-linear weather events.

Methodology

The authors propose a novel DGM approach that produces probabilistic nowcasts of precipitation derived from radar data, aiming to overcome limitations of state-of-the-art models, such as blurry predictions at longer lead times. The DGM achieves spatially and temporally consistent precipitation forecasts over large regions (1536 km × 1280 km) and varying lead times up to 90 minutes.

Key Results

The model's effectiveness was validated through systematic evaluation by over fifty expert forecasters from the Met Office, demonstrating superior accuracy and usefulness in 88% of cases compared to existing methods. Importantly, this approach of probabilistic nowcasting allows for realistic, fine-grained predictions without relying on overly simplistic physical constraints.

Implications

This research illustrates the operational utility of DGMs in weather prediction, specifically their ability to provide valuable decision-making insights for nowcasting. The generative framework excels at predicting complex precipitation phenomena by learning directly from observational data rather than predefined assumptions.

Quantitative Evaluation

The paper provides a comprehensive quantitative analysis, employing metrics such as the Critical Success Index (CSI), Continuous Ranked Probability Score (CRPS), and power spectral density (PSD). The authors report significant improvements over existing models, particularly at medium and small-scale precipitation variability.

Future Directions

While the DGM approach shows promising skill in providing realistic nowcasts, challenges remain, notably in predicting high precipitation at extended lead times. The paper suggests potential for further integration of machine learning with environmental science, paving the way for advancements in not only nowcasting but broader forecasting applications.

In summary, this paper advances the field of precipitation prediction by introducing a data-driven approach that enhances current methodologies' skill and utility, particularly benefiting weather-dependent decision-making processes in operational settings.