Abstract
Weather forecasting, particularly in short timeframes has been a longstanding challenge in meteorology, addressed in part by nowcasting methods. Leveraging radar data and innovative methodologies, nowcasting tools have evolved significantly, with open-source python platforms like Pysteps making is accessible to researchers to try advanced techniques. This review focus on Pysteps, a modular and user-friendly framework, offering optical flow based deterministic nowcasts and Short-Term Ensemble Prediction System (STEPS) ensemble nowcasts. Recent studies highlights its efficacy, including blending with Numerical Weather Prediction (NWP) models for improved performance beyond the nowcasting timeframe. Pysteps emerges as a versatile solution, facilitating both research innovation and operational forecasting needs, with wide range of input data and modularity.
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