ATMOS — AI Climate Downscaling System

AI-powered 4x super-resolution downscaling of ERA5 temperature reanalysis data over India. Increases spatial resolution from 0.25° (28 km/px) to 0.0625° (7 km/px) using a Swin2SR ensemble with physics-aware improvements.

Model

Component	Detail
Architecture	Swin2SR Transformer (ECCV 2022)
Ensemble	realworld (BSRGAN-PSNR) + classical (bicubic), averaged
Parameters	12.1M × 2 = 24.2M total, both INT8 quantized
Input	ERA5 0.25° (129×121 grid, India)
Output	0.0625° (516×484 grid, 4× SR)
Source	caidas/swin2SR on HuggingFace

Four Improvements Over Baseline

1. Ensemble — Two Swin2SR variants averaged pixel-by-pixel. The realworld model handles texture; the classical model handles clean bicubic degradation (closer to ERA5). Their average cancels each model's noise while preserving genuine fine-scale structure. Result: sharpness gain doubled from +10.8% to +21.3%.

2. Physics anomaly pre-processing — Subtract the ERA5 temporal mean field before inference, add it back after. The model sees temperature anomalies (±2K) rather than absolute values (250-310K). This frees the model's attention capacity from reproducing the large-scale India temperature gradient and focuses it on fine structure.

3. Elevation lapse rate correction — Apply a 6.5K/1000m lapse rate correction post-inference using a DEM proxy derived from the ERA5 mean field Laplacian. Fixes the known cold bias at Himalayan and Western Ghats mountain edges. Correction range: -1.09K to +1.67K.

4. Land-sea mask — AI enhancement applied only to land pixels. Ocean pixels (Bay of Bengal, Arabian Sea, Indian Ocean) are passed through directly from ERA5. Swin2SR has no ocean knowledge and would hallucinate fine-scale SST structure that doesn't exist physically. Land: 7651/15609 pixels (49%) in the India bounding box.

Performance

Metric	Value
Live inference (first call)	~12,000ms (ensemble × 2 models)
Cached inference	~28ms
Sharpness gain vs bilinear	+21.3%
PSD gain @ 27km wavelength	+4.58 dB
RAM at runtime	~13-14GB (79-85% of 16.5GB)
CPU threads	12 logical (Ryzen 5 5600H)

Data

• Source: ERA5 Reanalysis 2020, 2m Temperature (t2m)
• Region: India (6°N–38°N, 68°E–98°E)
• Period: 2020-01-01 to 2020-12-31, hourly (8784 timesteps)
• File: data/raw/era5_real_2020-01-01_2020-12-31.nc

Note: this NetCDF file isn't bundled in this repo. To run ATMOS, bring your own — see below.

Bringing Your Own Data

This repo ships code only, not the data file. To test it, you need a NetCDF file matching what the pipeline expects:

• Source: ERA5 hourly reanalysis, 2m temperature (t2m) — downloadable free via the Copernicus Climate Data Store
• Region: India, 6°N to 38°N, 68°E to 98°E
• Resolution: 0.25° native ERA5 grid (129×121 points)
• Path: save it as data/raw/era5_real_2020-01-01_2020-12-31.nc — that exact filename is currently hardcoded in dashboard_backend/main.py

Using a different region or variable will also need matching changes in config/default.yaml (region bounds) and dashboard_frontend/index.html (the BOUNDS/CENTER constants), since the land-sea mask and elevation correction are both built against this specific India grid.

Usage

.\run.bat

Opens http://127.0.0.1:8080 automatically.

To pre-compute all 8784 frames (optional, makes every frame instant): Click the Build Cache button in the dashboard bottom bar. Estimated time: ~2 hrs (2 workers) or ~1 hr (4 workers). RAM during build: ~85%. CPU: ~90%.

Project Structure

project/
├── dashboard_backend/main.py     FastAPI backend, all 4 improvements
├── dashboard_frontend/index.html ATMOS dashboard (Leaflet, Canvas)
├── src/models/downscaler.py      Swin2SR ensemble + physics + elevation
├── src/models/land_mask.py       Land-sea mask builder
├── src/data/netcdf_loader.py     ERA5 NetCDF loader
├── src/data/preprocessor.py      Z-score normalisation
├── checkpoints/swin2sr/          HuggingFace cached model weights (downloaded on first run, not bundled)
├── data/raw/                     ERA5 NetCDF file
├── serve.py                      Local (Windows) entry point
└── run.bat                       Windows launcher

Hardware Requirements

• RAM: 14GB+ recommended (16GB ideal)
• CPU: 6+ cores (12 logical threads used)
• GPU: Not required (CPU-only, INT8 quantized)
• Python: 3.11+, PyTorch 2.0+