Published January 26, 2026
| Version v1
Dataset
Open
Data for publication 'Inferring the Controlling Factors of Ice Aggregation from Targeted Cloud Seeding Experiments'
Description
# Data for publication '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
## 📄 Overview
This repository contains post-processed cloud microphysics measurements from the **CLOUDLAB 2023** campaign.
The dataset focuses on ice crystal properties, aggregation processes, riming and irregularity diagnostics, and associated environmental and dynamical conditions.
All time-resolved statistics are recorded at **1-second resolution**, where each row represents an aggregated observation at a specific second within a given experiment (`time_label`).
---
## 📂 Repository Structure
```text
.
├── README.md
├── cloudlab2023_ice_properties_postprocessed.csv
├── merged_with_edr_postprocessed.csv
├── merged_with_edr_irregular_postprocessed.csv
└── merged_with_edr_rimed_postprocessed.csv
````
---
## 📑 File Descriptions
### 1️⃣ `cloudlab2023_ice_properties_postprocessed.csv`
**Particle-level ice crystal properties**
* Contains **individual ice crystal measurements** derived from image-based processing.
* Each row corresponds to **one detected ice particle**.
* Includes detailed geometric, morphological, and texture-related descriptors (e.g. size, aspect ratio, perimeter statistics, phase-related features).
* Intended for **particle-scale analysis** or custom re-aggregation.
⚠️ **Note**
This file is **not second-averaged** and is generally **not required** for bulk microphysical or aggregation-rate analysis unless particle-level diagnostics are needed.
**Key identifiers**
* `time_id`
* `time_label`
* `class` (e.g. `Column_aged`)
* `num_objects`
---
### 2️⃣ `merged_with_edr_postprocessed.csv`
**Core second-level microphysics dataset (recommended main file)**
* Second-averaged statistics aggregated from particle-level measurements
* Merged with turbulence (EDR) and environmental variables
* This is the **primary dataset** for most analyses
Each row represents **one second within one experiment**.
---
### 3️⃣ `merged_with_edr_irregular_postprocessed.csv`
**Second-level dataset with irregular ice diagnostics**
* Extension of `merged_with_edr_postprocessed.csv`
* Includes diagnostics related to **irregular ice monomers**
**Additional variables**
* `irregular_monomers`: Number of irregular monomers
* `irregular_ice_count`: Number of ice particles containing at least one irregular monomer
---
### 4️⃣ `merged_with_edr_rimed_postprocessed.csv`
**Second-level dataset with riming diagnostics**
* Extension of `merged_with_edr_postprocessed.csv`
* Includes statistics related to rimed ice particles
**Additional variables**
* `rimed_monomers`: Number of rimed monomers
* `rimed_ice_count`: Number of ice particles containing at least one rimed monomer
---
## 🔑 Key Variables (Second-Level Files)
### ⏱ Identifiers & Time
* `time`: Observation timestamp (1-second resolution)
* `time_label`: Experiment label (e.g. `SM048`)
* `Time_gps`: GPS time
---
### ❄️ Ice Crystal Properties
* `avg_ice`: Mean ice crystal number
* `num_ice_original`: Initial ice crystal count (at seeding time)
* `num_agg_events`: Number of ice aggregation events
* `asprat_mean`: Mean aspect ratio
* `majsiz_mean`: Mean major-axis length (m)
* `majsiz_std`: Standard deviation of major-axis length
* `majsiz_large_fraction`: Fraction of crystals larger than 200 μm
* `majsiz_cov`: Coefficient of variation of major-axis length
* `ice_volume`: Total ice volume (cm³)
* `ice_con`: Ice crystal number concentration (cm⁻³)
---
### 💧 Water-Related Properties
* `water_content`: Cloud water content (g m⁻³)
* `water_meanD`: Mean droplet diameter (m)
* `water_concentration`: Droplet number concentration (cm⁻³)
* `background_water_concentration`: Background droplet concentration (cm⁻³)
* `background_water_content`: Background liquid water content (g m⁻³)
---
### 🌡 Environmental & Dynamical Conditions
* `resi_time_remsens_s`: Residence time from seeding to measurement site (s)
* `seed_distance`: Distance between seeding and measurement sites (m)
* `seed_temp_degC`: Ambient temperature (°C)
* `ICNC_origin`: Initial ice crystal number concentration (cm⁻³)
* `R_agg`: Ice aggregation rate (cm⁻³ s⁻¹)
* `EDR (m^2 s^-3)`: Eddy Dissipation Rate (turbulence intensity)
---
## 📌 Usage Recommendations
* **General microphysics & aggregation analysis**
→ `merged_with_edr_postprocessed.csv`
* **Irregularity-related studies**
→ `merged_with_edr_irregular_postprocessed.csv`
* **Riming-related studies**
→ `merged_with_edr_rimed_postprocessed.csv`
* **Particle-level morphology analysis**
→ `cloudlab2023_ice_properties_postprocessed.csv`
---
## 📎 Notes
* All number concentrations are reported in **cm⁻³** unless otherwise stated.
* Aggregation rates are reported in **cm⁻³ s⁻¹**.
* Variable names are kept consistent across second-level files to facilitate comparison.
---
## 📬 Contact
For questions regarding data processing or usage, please contact the dataset author.
```
Files
data.zip
Files
(97.7 MB)
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Additional details
Related works
- Is supplement to
- Model: 10.5281/zenodo.18348241 (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.