AI nowcasting of localized heavy precipitation from fast-scanning radar with probabilistic and 3D motion guided prediction

Accurate real-time prediction of heavy precipitation is essential for disaster prevention. It remains a challenge for operational meteorology, especially for sudden localized convective storms for which traditional radar and observation extrapolation methods struggle to capture their rapid vertical development, which typically originate at altitudes of 4--8 km before descending to the surface in about 10 minutes.  

In Japan, three Multi-Parameter Phased Array Weather Radars (MP-PAWR) generating 3D data every 30 seconds with high vertical resolution have been deployed. Leveraging these dense 4D observations, an AI-based model produces real-time nowcasts (very short-term forecasts) with high-resolution of 500 m and 10-minute lead time. Updated every 30 seconds, our nowcasts outperform traditional methods for predicting the onset and the dissipation of localized convective precipitation. However, performance is degraded during the mature phase of the storm when its structure becomes more complex (e.g., overlapping  convective cells in different lifecycle states, domination of horizontal motion in radar pattern changes) (Baron et al., 2025a).

Two major improvements are currently being investigated: 1) a Quantile Regression Neural Network (QRNN) technique has been integrated to assess the probability distribution of possible nowcasts and thus provide credible intervals (Baron et al., 2025b), and 2) a better representation of 3D motion is being implemented, as it plays a critical role during the mature phase of storms. The new version of the model will integrate two separate modules: one specialized for capturing 3D-motion vectors, while the second predicts rainfall intensity with motion guidance. Both modules use the current nowcast model architecture which has demonstrated solid performance. The motion module is trained using 3D motion vectors derived directly from the radar observations through a 3D Tracking Radar Echoes by Correlation (TREC) method originally designed for PAWR extrapolation (Otsuka et al., 2016).

This study will present these developments with a special focus on the motion guidance module that is being implemented. The limitations of our approach will also be discussed (e.g., QRNN vs diffusion model, TREC limitation for weak gradient cases, no information on rain precursors and mesoscale scales).

Baron et al., 2025a: “Real-time nowcasting of sudden heavy rainfall using artificial neural network and multi-parameter phased array radar”, SOLA, https://doi.org/10.2151/sola.2025-039

Baron et al., 2025b: “3D Precipitation Nowcasting from Phased Array Radar with Uncertainty Estimation Using a Quantile Regression Neural Network”, IEEE RadarConf25,  10.1109/RadarConf2559087.2025.11204931

Otsuka et al., 2016: Precipitation nowcasting with three-dimensional space–time extrapolation of dense and frequent phased-array weather radar observations. Wea. Forecasting, 31, 329–340.