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Ralph Foster

Principal Physicist

Email

ralph@apl.washington.edu

Phone

206-685-5201

Research Interests

Boudary Layer Turbulence, Remote Sensing

Biosketch

Dr. Foster's primary research interest is the dynamics of atmospheric planetary boundary layer (PBL) turbulence with an emphasis on improving PBL parameterization in global and mesoscale models. Of particular interest is the role of coherent structures on fluxes in the PBL and their effect on air-sea fluxes. Previous work has been primarily on theoretical models and numerical simulations of coherent structures and their effects.

The majority of his current research involves analysis of satellite remote sensing data products, especially scatterometer surface wind data and synthetic aperture radar (SAR) imagery of the ocean surface. The current scatterometers provide nearly global daily retrievals of the surface wind vectors over the world's oceans on 25 km footprints. Often clear signatures of atmospheric PBL eddies and organized flow are imaged by SAR as a result of the wind stress acting on the sea surface. He is currently working towards a better understanding of the air-sea momentum transfer and how it manifests in SAR imagery. A long-term goal is to integrate theoretical analyses, numerical simulation, observational and remote sensing studies in order to improve understanding of coherent structures and to incorporate their non-local effects in operational PBL parameterizations.

Education

B.S. Physics, University of California - Berkeley, 1983

Ph.D. Atmospheric Sciences, University of Washington - Seattle, 1996

Publications

2000-present and while at APL-UW

Automated global classification of surface layer stratification using high-resolution sea surface roughness measurements by satellite synthetic aperture radar

Stopa, J.E., C. Wang, D. Vandemark, R. Foster, A. Mouche, and B. Chapron, "Automated global classification of surface layer stratification using high-resolution sea surface roughness measurements by satellite synthetic aperture radar," Geophys. Res. Lett., 49, doi:10.1029/2022GL098686, 2022.

More Info

28 Jun 2022

A three-state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel-1 synthetic aperture radar wave mode images at incidence angle ≈ 36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri < –0.012, near-neutral: –0.012 < Ri < +0.001, and stable: Ri > +0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016 to 2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite-based approach for detecting air-sea stratification has implications for weather modeling and air-sea flux products.

An assessment of marine atmospheric boundary layer roll detection using Sentinel-1 SAR data

Wang, C., D. Vandemark, A. Mouche, B. Chapron, H. Li, and R.C. Foster, "An assessment of marine atmospheric boundary layer roll detection using Sentinel-1 SAR data," Remote Sens. Environ., 250, 12031, doi:10.1016/j.rse.2020.112031, 2020.

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1 Dec 2020

Highlights
- First global statistics of SAR response to MABL rolls using new S-1 WV data.
- Data show 50% greater sensitivity to rolls for 36° vs. 23° SAR incidence angle.
- Crosswind (relative to SAR look direction) shows weakest sensitivity to roll imprints.
- Rolls observed at 23° and crosswind are more stationary, in more unstable conditions.
- SAR-estimated surface wind perturbations due to roll impacts are 8 ± 3.5%.

Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies

Wang, C., and 8 others including R.C. Foster, "Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies," Remote Sens. Environ., 234, doi:10.1016/j.rse.2019.111457, 2019.

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1 Dec 2019

Highlights

• First deep learning model to classify ten geophysical phenomena from S-1 WV SAR data.
• Model performance is evaluated using an independent eye-selected dataset.
• Classified rain cells and sea ice are compared with other satellite measurements.
• The global S-1 SAR data show great potential for sea surface processes studies.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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