Conference Agenda

It is presented in this paper some recent progresses of ESA-MOST China Dragon Cooperation Program “Synergistic Monitoring of Ocean Dynamic Environment from Multi-Sensors (ID. 58009)” including: (1) Assessment of ocean swell height observations from Sentinel-1A/B Wave Mode against buoy in situ and modeling hindcasts; (2) Quantifying uncertainties in the partitioned swell heights observed from CFOSAT SWIM and Sentinel-1 SAR via triple collocation; (3) Up-to-Downwave asymmetry of the CFOSAT SWIM fluctuation spectrum for wave direction ambiguity removal; (4) Validation of wave spectral partitions from SWIM instrument on-board CFOSAT against in situ data; (5) Quality assessment of CFOSAT SCAT wind products using in situ measurements from buoys and research vessels; (6) Direct ocean surface velocity measurements from space in tropical cyclones; and (7) Deep learning-based model for reconstructing inner-core high winds in tropical cyclones using satellite remote sensing.

Polar lows are small and intense high latitude maritime cyclones and frequently induce typical ocean disasters such as strong winds, high waves and heavy rainfall. They remain difficult to observe and forecast due to their short lifetime (<48 hours) and small horizontal scales (200~1000 km). Satellite remote sensing is an important manner to monitor polar lows because of sparse synoptic observing network existing in subarctic and Arctic oceans. Previous studies subjectively identified polar lows by visual inspection of satellite thermal infrared imagery. However, this subjective visual analysis method is time-consuming and inevitably involves error in polar low detections. In this study, we present an automatic procedure to objectively detect and track a polar low occurring in Greenland Sea using spaceborne synthetic aperture radar (SAR) and passive microwave radiometer data. Based on the marker-controlled watershed segmentation method and the morphological image processing algorithm, Sentine-1A and RADARSAT-2 high-resolution SAR images and successive total atmospheric water vapor content field observations from multiple radiometers (e.g., AMSR2, SSM/I, GMI) are used to fix the center location of polar low. The track of this polar low is further determined from detected centers. The polar low detections are confirmed by the presence of cloud vortex signatures visible on the AVHRR and MODIS thermal infrared imagery, and the SAR-retrieved ocean surface high wind speeds. The results show that the proposed method has potential to efficiently detect and track polar low from multi-sensor data.