ACSAF is a short-term forecasting tool designed to predict the occurrence and intensity of vibrant sunset and sunrise cloud afterglows for the current and following day.
The tool computes a normalized Afterglow Index (0–100):
- 0: Dull, short-lived, or unappreciable cloud afterglow display.
- 100: Vivid, long-lasting, and highly visible cloud afterglow display.
The ACSAF index is physically rooted in Beer-Lambert's Law, tracking light attenuation through the atmosphere. However, the final index is normalized and weighted alongside empirical factors for human perception.
The engine processes data across 13 pressure levels at a 3-hour temporal resolution and 0.4-degree horizontal resolution, utilizing:
- ECMWF IFS: Cloud and Ice Water Content forecast data.
- CAMS: Atmospheric Total Aerosol Optical Depth (AOD) forecast data.
The easiest way to view the forecast is to visit the live app: 👉 afterglow.top
To generate the raw forecast JSON schema locally, clone the repository, install your required dependencies, and run the calculation script:
git clone [https://github.com/Her0n24/ACSAF.git](https://github.com/Her0n24/ACSAF.git)
cd ACSAF
# Install your dependencies here (e.g., environment.yml)
python calc_afterglow_realistic_path_lwc_global.py
Why such information is useful?
The principle of cloud afterglow forecasting
Index and details of the events in the legacy ASCAF Dashboard
Legacy supplementary figures displaying raw model output cloud coves for the selected region