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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Precipitation Nowcasting
+- Probabilistic Forecasting
+- Radar
+- STEPS
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">pysteps</span></strong></p>
+
+# Model Introduction
+
+pysteps is an open-source framework for probabilistic precipitation nowcasting. This reproduction focuses on optical flow, cascade decomposition, AR(2), and STEPS ensemble generation.
+
+Paper: Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)  
+https://doi.org/10.5194/gmd-12-4185-2019
+
+# Model Description
+
+The method was proposed by teams from the Finnish Meteorological Institute, MeteoSwiss, ETH Zurich, Colorado State University, and collaborators. It estimates motion, cascade, and autoregressive parameters online from five-minute radar sequences collected in several countries. It supports one- to three-hour probabilistic precipitation nowcasting and ensemble uncertainty analysis.
+
+# Usage Instructions
+
+```bash
+hf download OneScience-Group/pysteps --local-dir ./pysteps
+cd pysteps
+```
+
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can be used for connectivity validation with the default small-sample configuration.
+- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.
+
+**DCU Environment**
+
+```bash
+# Activate DTK and Conda first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+**GPU Environment**
+
+```bash
+# Activate Conda first
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+```bash
+python scripts/fake_data.py
+python scripts/train.py
+torchrun --standalone --nproc_per_node=2 scripts/train.py
+python scripts/inference.py
+python scripts/result.py
+```
+
+STEPS has no offline gradient training. Inference generates a finite ensemble with shape `[24,12,128,128]`, and evaluation reports RMSE and spread.
+
+# Trained Weights
+
+No weights are bundled under `weight/`. pysteps estimates parameters online and does not use pretrained neural-network weights; the official software is available at https://github.com/pySTEPS/pysteps.
+
+# Citation and License
+
+This repository is an independent engineering reproduction of the public pysteps specifications.
+
+The original paper is licensed under CC BY 4.0 and the official pysteps software under BSD-3-Clause; the paper, software, and radar data retain their respective terms.