Mini-review of Pysteps: An open-source python library for precipitation nowcasting
Nanjing University of Information Science and Technology, Nanjing, China.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 289–295
Article DOI: 10.30574/wjaets.2024.11.2.0114
Publication history:
Received on 23 February 2024; revised on 01 April 2024; accepted on 04 April 2024
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.
Keywords:
Pysteps; Blending; Optical Flow; Nowcasting; Open-source; Python package
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0