Presenter: Justin Stopa (University of Hawai`i at Manoa)
Description:
A marine atmospheric boundary layer state indicator (MABL-SI) is proposed and used to document globally the thermal stability from Synthetic Aperture Radar (SAR) observations. The MABL-SI can be stable, near stable, and unstable MABL states, depending on the SAR image texture and spectral information. MABL state discrimination is possible because near-neutral and unstable stratification regimes induce well-defined perturbation wind patterns that leave a recognizable signature in the surface wave field that can be detected by SAR. Furthermore, stable stratification inhibits the formation of MABL perturbation winds and this lack of surface wave variability in the length scales associated with MABL eddies can also be identified by SAR. The relationship between MABL-SI and SAR observation is derived using both a large catalog of Sentinel-1 SAR images (20x20 km) acquired globally in the open ocean at high resolution (5 m) and the bulk Richardson number (Ri) derived from collocated ERA5 surface analyses. Then, the use of image texture classification scores from a machine learning algorithm (convolutional neural network) and image two-dimensional fast Fourier Transform statistics allows indicating the three MABL states for the SAR observation. These three classes are roughly defined as images that lack any signature of surface wind variability, cases of evident wind streaks, and 3D variation attributed to microscale convection. They correspond to stable, near neural, and unstable MABL states respectively. Using both S-1 satellites for years 2016-2019, we discriminate between unstable (Ri<-0.012), near neutral (-0.012<Ri<-0.002), and stable (Ri>-0.002) relative to ERA5. The regional patterns of the features between ERA5 and independent observations from SAR are strikingly similar. The S-1 MABL-SI is a new satellite-based indicator/metrics to document globally the MABL stability from observations with implications for weather modeling and gridded air-sea flux products.
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Full list of Authors
- Douglas Vandemark (Ocean Processes Analysis Laboratory, University of New Hampshire)
- Ralph Foster (Applied Physics Laboratory, University of Washington)
- Chen Wang (School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing, China)
- Alexis Mouche (IFREMER, Univ. Brest, CNRS, IRD, Laboratoire d'Oceanographie Physique et Spatiale (LOPS))
- Jonathan Chapman (University of Hawai`i at Manoa, Department of Ocean and Resources Engineering)
- Bertrand Chapron (IFREMER, Univ. Brest, CNRS, IRD, Laboratoire d'Oceanographie Physique et Spatiale (LOPS))
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Synthetic Aperture Radar Marine Atmospheric Boundary Layer State Estimator
Category
Scientific Session > AI - Air-Sea Interaction > AI02 Under the Weather: Using Active and Passive Microwave Observations to Study Atmosphere-Ocean Interactions
Description
Presentation Preference: Oral
Supporting Program: None
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