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Paul A. Hwang, Nicolas Reul, Thomas Meissner, and Simon H. Yueh

aircraft in the scene to make photographic or video observations, and tower-based operations are suspended during inclement weather. Microwave radiometer data represent another source of whitecap information. As in ocean surface optical images, microwave brightness temperature T bp increases sharply in the presence of whitecaps (surface foams); subscript p is polarization and is either vertical (V) or horizontal (H) in this paper. Several investigations of whitecap retrieval from T bp data have

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Graham D. Quartly and Meric A. Srokosz

rings ∼200 km in diameter being shed at the retroflection and large eddies occurring along the ARC ( Olson and Evans 1986 ; Gründlingh 1983 ; Lutjeharms and Valentine 1988 ). The AC is fed by the Mozambique Current and the East Madagascar Current (EMC), but there is debate about their relative contributions ( de Ruijter et al. 1999a ). Indeed, recent in situ observations by de Ruijter et al. (2001) suggest that the Mozambique Current is not itself a permanent feature, but only present

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David A. Honegger, Merrick C. Haller, W. Rockwell Geyer, and Gordon Farquharson

. Conclusions Two oblique, internal hydraulic jumps were identified and analyzed using a suite of remote sensing and in situ observations at the Columbia River Mouth. The internal jumps were detected by the presence of locally enhanced microwave backscatter in temporally filtered X-band marine radar imagery and verified by the presence of an internal critical transition from supercritical to subcritical in the cross-jump direction. The jumps were also detected via a sharp deflection of the surface current

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Paul A. Hwang

Black 2003 ; Uhlhorn et al. 2007 ; Klotz and Uhlhorn 2014 , hereafter K14 ). The results of the drag coefficient and whitecap coverage derived from the SFMR observations are given in section 3 . Section 4 presents discussions on the frequency dependence, foam and roughness contributions, and the drag coefficient and whitecap observations. Section 5 is a summary. 2. Roughness and whitecap contributions to microwave brightness temperature a. Formulation The brightness temperature T B of

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Yanmin Zhang, Yunhua Wang, and Qiaohui Xu

Abstract

A new nonlinear transformation relation is derived to describe the mapping of a two-dimensional ocean wave spectrum into a new polarimetric synthetic aperture radar (SAR) image spectrum. It is a further expansion and improvement of Hasselmann’s work. First, the nonlinear mapping relation proposed is derived on the basis of a new polarimetric SAR image instead of the conventional single-polarization SAR image. Second, the nonlinear mapping relation no longer includes the complex hydrodynamic modulation transfer function (MTF). Third, the traditional tilt MTF, which is not accurate enough for the retrieval of sea wave spectrum, is replaced by an empirical tilt MTF derived on the basis of the C-band geophysical model function [i.e., C-band synthetic aperture radar (CSAR) normalized radar cross section (NRCS) model]. A sea wave spectrum retrieval algorithm is then proposed that is based on the new nonlinear mapping and the empirical tilt MTF. The retrieved spectra from C-band polarized RADARSAT-2 SAR images are compared with the results obtained by the ECMWF Ocean Wave Model (ECWAM) and buoy measurements.

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Jiayi Pan, Xiao-Hai Yan, Young-Heon Jo, Quanan Zheng, and W. Timothy Liu

satellite observations to estimate the sensible heat flux, we need to know the sea surface and air temperatures. The sea surface temperature can be retrieved from observations with the Advanced Very High Resolution Radiometers (AVHRR) on the National Oceanic and Atmospheric Administration (NOAA) satellites. However, it is difficult to remotely observe the sea level air temperature. We have to resort to in situ measurements of the air temperature. It is expensive and time-consuming to make direct

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D. Vandemark, B. Chapron, J. Sun, G. H. Crescenti, and H. C. Graber

1. Introduction The mean relationship between sea wave slope statistics and near-surface ocean wind speed has long been established ( Cox and Munk 1956 ), as has the tie between these optically derived wave slope variance measurements and the ocean radar backscatter measured by a microwave altimeter ( Barrick 1968b ; Jackson et al. 1992 ). However, it has also been supposed for decades that factors such as wind gustiness, currents, fetch, and long gravity wave dynamics will serve to

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Matthew R. Mazloff, Patrick Heimbach, and Carl Wunsch

and an atmospheric state that, when prescribed to this ocean model, gives a solution consistent with the majority of 2005–06 observations. In this section, the consistency of the model solution, the state estimate, with the observations is assessed. The magnitude of the misfit to both SST (here representing 5-m temperatures) infrared and microwave radiometer products is quite small, with 60% of the area having a mean misfit of less than 0.3°C ( Figs. 3a,b ). The expected uncertainty averages 0.4°C

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Changming Dong and Lie-Yauw Oey

agreement with observations than that forced by the coarse-grid ECMWF wind only. In this paper, we extend the work of Oey et al. and examine more closely the sensitivity of the ocean currents to forcing by different wind datasets. Apart from the ECMWF, two higher-resolution wind datasets are used in this work. Dong et al. (2003) produced a new wind dataset in SMB and SBC by combining three wind datasets: the Special Sensor Microwave Imager (SSM/I) wind data from satellite remote sensing, ECMWF wind

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to have different effects of stabilityon freshwater and saltwater whitecapping.c. Whitecap coverage and remote sensing The possibility of determining marine wind velocity from satellite observations of whitecaps with amicrowave radiometer was discussed by Williams(1969). He reported that the microwave radiometercould be used to obtain the percentage of foam andwhitecap coverage on the ocean surface. If a functional relationship such as those discussed in theprevious section can be established

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