Inferring Marine Atmospheric Boundary Layer Properties from Spectral Characteristics of Satellite-Borne SAR Imagery

Young, George S.; Sikora, Todd D.; Winstead, Nathaniel S.

doi:10.1175/1520-0493(2000)128<1506:imablp>2.0.co;2

Cited by 18 publications

(27 citation statements)

References 50 publications

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“…Unlike the Young et al (2000) study, the work reported in Sikora et al (2000) did not include spectral filtering of the neutral wind speed imagery prior to the generation of turbulence statistics. However, Sikora et al (2000) smoothed the pixel size of the SAR imagery to 300 m. This choice of pixel size and smoothing arose from the study of Mourad et al (2000), who found that doing so resulted in a close fit between the wind spectrum resulting from a SAR image and that developed using low-level turbulence measurements gathered from an aircraft flying over the imaged area.…”

Section: Approachmentioning

confidence: 99%

“…The demonstration of the SAR method by Young et al (2000) was accomplished using in situ turbulence data gathered during the second High Resolution Remote Sensing project as ground truth. The comparisons presented in Young et al (2000) were for data collected during rather quiescent synoptic scale and statically unstable microscale atmospheric conditions (i.e., light winds and negative air-sea temperature differences).…”

Section: Approachmentioning

confidence: 99%

“…The comparisons presented in Young et al (2000) were for data collected during rather quiescent synoptic scale and statically unstable microscale atmospheric conditions (i.e., light winds and negative air-sea temperature differences). Sikora et al (2000) advanced the testing of the SAR method by expanding the environmental conditions to more baroclinic regimes.…”

Section: Approachmentioning

confidence: 99%

“…These imagery are from off the east coast of the United States and the Gulf of Alaska. Young et al (2000) proposed and demonstrated a method to calculate diabatic wind speed and turbulence statistics from neutral wind speed imagery (Thompson and Beal, 2000) generated from synthetic aperture radar (SAR) data. Their SAR method is based on Monin-Obukhov and mixed layer similarity theory (Panofsky and Dutton, 1984;Stull, 1988).…”

mentioning

confidence: 99%

“…The scaling parameters arising from this relationship provide a stability correction to the neutral wind speed imagery in an iterative fashion. See Young et al (2000) for an indepth review of their method.…”

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confidence: 99%

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Air-Sea Fluxes from Synthetic Aperture Radar

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Section: Approachmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Approachmentioning

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confidence: 99%

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Flower trade‐wind clouds are shallow mesoscale convective systems

Dauhut

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et al. 2022

Quart J Royal Meteoro Soc

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Flower clouds are trade‐wind shallow cumuli, with tops reaching 3 km altitude, organised into 100‐km wide clusters. They are widespread over the subtropics and associated with the strongest cloud radiative effect among trade‐wind cumuli mesoscale organisations. In the context of large uncertainty in climate projections due to the representation of shallow clouds, major knowledge gaps remain about the global impact of mesoscale organisations and the local processes driving them. Here, the processes governing the flower organisation are investigated based on the case study of February 2, 2020 from the Elucidate the Couplings Between Clouds, Convection, and Circulation (EUREC4$$ {}^4 $$A) campaign, east of Barbados. One flower cloud is simulated with a large‐eddy simulation (LES), using the Meso‐NH model at 100‐m horizontal grid spacing, and validated extensively with high‐resolution observations from the High Altitude and Long‐range Research Aircraft (HALO), dropsondes, and satellite measurements. The cloud‐top altitudes exhibit a trimodal distribution. The processes shaping flower clouds are wide cold pools and cloudy updrafts organised in one large arc at the western edge. These updrafts are responsible for the highest cloud tops and drive most of the vertical turbulent fluxes of sensible heat, humidity, and momentum. A mesoscale circulation takes place at the scale of the flower clouds and makes them very similar to deep mesoscale convective systems.

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Synergy between synthetic aperture radar and other sensors for the remote sensing of the ocean

2001

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Over the last decades, satellite remote sensing has proved to be a valuable and effective tool for monitoring physical and biological ocean processes. However there are cases where data from one remote sensor alone cannot be interpreted unambiguously. In these situations the combination of data from different sensors can help to understand the observed processes due to the combined benefits of the various strengths and advantages of individual instruments. This paper illustrates the potential of synergy between synthetic aperture radar data and data from thermal and optical satellite sensors. Different aspects of oceanic and atmospheric fronts, eddies, upwelling, internal waves and surface films are imaged by the sensors and combined data give a broader picture of the physical processes involved. While the strengths of synergy are demonstrated in several examples, more frequent coincidence of data from existing and future sensors will be necessary before the benefits of synergy occur on an operational basis.

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Inferring Marine Atmospheric Boundary Layer Properties from Spectral Characteristics of Satellite-Borne SAR Imagery

Cited by 18 publications

References 50 publications

Air-Sea Fluxes from Synthetic Aperture Radar

Air-Sea Fluxes from Synthetic Aperture Radar

Flower trade‐wind clouds are shallow mesoscale convective systems

Synergy between synthetic aperture radar and other sensors for the remote sensing of the ocean

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