Published January 23, 2026
| Version v1
Model
Open
Model and Code for publication 'Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments'
Authors/Creators
-
1.
ETH Zürich
-
2.
ETH Zurich
Description
# Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments
> **Authors:** Huiying Zhang , Fabiola Ramelli, Christopher Fuchs , Nadja Omanovic , Anna J. Miller , Robert Spirig, Zhaolong Wu , Yunpei Chu , Xia Li, Ulrike Lohmann , and Jan Henneberger
> **Affiliation:** ETH Zurich
> **Contact:** huiying.zhang@env.ethz.ch
## 📖 Introduction
This repository contains the source code, machine learning models, and datasets associated with the research project/paper: **"Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments"**.
It includes:
1. **Machine Learning**: aggregation rate prediction models (XGBoost, AdaBoost, etc.)
2. **Visualization**: Correlation plots and Probability Density Function (PDF) analysis etc.
---
## 📂 Repository Structure
The project is organized as follows:
```text
.
├── README.md
├── casual_analysis
│ └── casual.ipynb
├── correlation_plots
│ ├── EDR_temp.ipynb
│ ├── ICNCt0_IWC.ipynb
│ ├── ICNCt0_Temp.ipynb
│ └── residenceTime_Temp.ipynb
├── correlation_subplots
│ └── agg_rate
│ └── ICNCt0_AggRate.ipynb
├── environment.yml
├── ml_models
│ ├── AdaBoost.pkl
│ ├── BayesianRidge.pkl
│ ├── CatBoost.pkl
│ ├── DecisionTree.pkl
│ ├── ExtraTrees.pkl
│ ├── GradientBoosting.pkl
│ ├── KNN.pkl
│ ├── LightGBM.pkl
│ ├── LinearRegression.pkl
│ ├── RandomForest.pkl
│ ├── Ridge.pkl
│ ├── XGBoost.pkl
│ └── models_performance.csv
├── models_training
│ ├── model_training.ipynb
│ └── physical_ml_models_2.0_withoutEDR_groupBased.ipynb
└── pdf
├── icnc_pdf.ipynb
├── irre_rim_pdf.ipynb
└── majsiz_pdf.ipynb
```
---
## 🚀 Getting Started
### 1. Prerequisites
The code relies on Python and several data science libraries. We recommend using **Conda** to manage the environment.
### 2. Installation
To set up the environment and reproduce the results, please follow these steps:
```bash
# 1.Create the environment from the provided file
conda env create -f environment.yml
# 2. Activate the environment
conda activate ice
```
---
## 📊 Data Usage
The data is located in `casual_analysis/data/`.
* **Main Dataset:** `merged_with_edr_postprocessed.csv`
* **Metadata:** Please refer to `data_description.md` in the data folder for a detailed explanation of column names, physical units, and measurement techniques.
---
## 🤖 Model Usage
Models are stored in `ml_models/`. You can load them using Python's `joblib` or `pickle` library to make predictions on new data.
**Example (Python):**
```python
import joblib
import pandas as pd
# 1. Load a trained model (e.g., XGBoost)
model_path = 'ml_models/XGBoost.pkl'
model = joblib.load(model_path)
# 2. Load your data (ensure feature columns match the training data)
# data = pd.read_csv('your_new_data.csv')
# 3. Predict
# predictions = model.predict(data)
```
To retrain the models or view the training process, run the notebook:
`models_training/model_training.ipynb`
---
## 📈 Visualizations (Notebooks)
To view the analysis plots (PDFs and Correlations), start the Jupyter Notebook server:
```bash
jupyter notebook
```
* **Correlation Analysis:** Go to `correlation_plots/` (e.g., `EDR_temp.ipynb`).
* **PDF Analysis:** Go to `pdf/` (e.g., `icnc_pdf.ipynb`).
---
## 📝 Citation
If you use this code or data in your research, please cite our paper:
```bibtex
@article{zhang2025inferring,
title={Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments},
author={Zhang, Huiying and Ramelli, Fabiola and Fuchs, Christopher and Omanovic, Nadja and Miller, Anna J and Spirig, Robert and Wu, Zhaolong and Chu, Yunpei and Li, Xia and Lohmann, Ulrike and others},
journal={EGUsphere},
volume={2025},
pages={1--31},
year={2025},
publisher={Copernicus Publications G{\"o}ttingen, Germany}
}
```
## 📄 License
This project is licensed under the [CC-BY](https://www.google.com/search?q=LICENSE) - see the https://www.google.com/search?q=LICENSE file for details.
```
```
Files
models_and_code.zip
Files
(23.1 MB)
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Additional details
Related works
- Is supplement to
- Dataset: 10.5281/zenodo.18348125 (DOI)
Funding
References
- Zhang H, Ramelli F, Fuchs C, et al. Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments[J]. EGUsphere, 2025, 2025: 1-31.