Automatic detection and tracking polar lows from synthetic aperture radar and radiometer observations

ABSTRACT

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively.

Acknowledgements

The authors would like to thank the European Space Agency and the Canadian Space Agency for providing Sentinel-1A and RADARSAT-2 SAR images, respectively. This work was supported in part by each of the following: Joint Project between National Natural Science Foundation of China, Russian Science Foundation under Grant 42061134016, National Natural Science Foundation under Grant 42076181 and Grant U2106211, the ESA MAXSS project – Marine Atmospheric eXtreme Satellite Synergy, the Canadian Ocean Frontier Institute Module A, and SWOT – Surface Water and Ocean Topography.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://asf.alaska.edu.

2. https://www.eodms-sgdot.nrcan-rncan.gc.ca.

3. https://www.remss.com.

4. https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MYD021KM.

5. https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:AVHRRL.

6. https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/science-domain/viirs-L0-L1/.

7. https://cds.climate.copernicus.eu.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42061134016].