| --- |
| 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. |
|
|
