Hello — let’s explore cyclone detection

This short informative site shows how Sentinel satellites can help spot, track, and characterise tropical cyclones. The tone is friendly and the visuals are soothing — think radar glow and calm maps.

Data source: ESA Sentinel-1 (radar) and Sentinel-3 (ocean/infrared) — free, global swaths.
Key idea: use synthetic-aperture radar (SAR) and infrared signatures to detect rotation, wind speed proxies, and cloud patterns.
Goal: fast detection, clear visuals, and easy-to-understand explanations.

What is Sentinel data? (quick)

The Sentinel satellites are part of the European Copernicus programme. For cyclone work we mainly use:

  • Sentinel-1: Active radar (SAR) — works through clouds and at night, great for wind/wave signatures.
  • Sentinel-3: Infrared and altimetry — useful for sea-surface temperature and wave height.
  • Sentinel-2 (optional): Optical imagery when clouds allow.

Why radar helps

SAR measures microwave reflectivity of the sea surface. Wind roughens the ocean, changing backscatter — with careful analysis we can estimate wind speed and look for the characteristic circular patterns and eye signatures of cyclones.

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Typical detection pipeline

1
Acquire — pull Sentinel swath (SAR product) for the region and timeframe.
2
Preprocess — radiometric calibration, thermal noise removal, georeference and terrain correction.
3
Feature extraction — compute wind proxies, vorticity maps, and texture descriptors.
4
Detect — thresholding, pattern matching, and optionally ML models to flag vortex candidates.
5
Validate & visualize — overlay results on map, human review, and produce alerts.

Interactive visualiser (demo)

This small demo paints a calm "map" and simulates a Sentinel swath and a detected cyclone. It is not real data — it's for illustration and teaching.

Data: simulated

Further learning

If you'd like to try this for real, two practical next steps are:

  1. Download Sentinel-1 GRD products from the Copernicus Open Access Hub or AWS Public Datasets.
  2. Use open-source tools like SNAP (ESA) or Python libraries (rasterio, sentinelsat, xarray) for processing.

Quick tips

  • Radar is robust in cloudy storms — great for cyclones.
  • Combine SAR with IR/SST to improve intensity estimates.
  • Keep workflows reproducible: use notebooks and store processing steps.