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- Author or Editor: Nicolas Reul x
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Abstract
This paper presents a new method to analyze high-resolution altimeter waveforms in terms of surface backscatter. Over the ocean, a basic assumption of modeling altimeter echo waveforms is to consider a homogeneous sea surface within the altimeter footprint that can be described by a mean backscatter coefficient. When the surface backscatter varies strongly at scales smaller than the altimeter footprint size, such as in the presence of surface slicks, rain, small islands, and altimeter echoes can be interpreted as high-resolution images of the surface whose geometry is annular and not rectangular. A method based on the computation of the imaging matrix and its pseudoinverse to infer the surface backscatter at high resolution (~300 m) from the measured waveforms is presented. The method is tested using synthetic waveforms for different surface backscatter fields and is shown to be unbiased and accurate. Several applications can be foreseen to refine the analysis of rain patterns, surface slicks, and lake surfaces. The authors choose here to focus on the small-scale variability of backscatter induced by a submerged reef smaller than the altimeter footprint as the function of tide, significant wave height, and wind.
Abstract
This paper presents a new method to analyze high-resolution altimeter waveforms in terms of surface backscatter. Over the ocean, a basic assumption of modeling altimeter echo waveforms is to consider a homogeneous sea surface within the altimeter footprint that can be described by a mean backscatter coefficient. When the surface backscatter varies strongly at scales smaller than the altimeter footprint size, such as in the presence of surface slicks, rain, small islands, and altimeter echoes can be interpreted as high-resolution images of the surface whose geometry is annular and not rectangular. A method based on the computation of the imaging matrix and its pseudoinverse to infer the surface backscatter at high resolution (~300 m) from the measured waveforms is presented. The method is tested using synthetic waveforms for different surface backscatter fields and is shown to be unbiased and accurate. Several applications can be foreseen to refine the analysis of rain patterns, surface slicks, and lake surfaces. The authors choose here to focus on the small-scale variability of backscatter induced by a submerged reef smaller than the altimeter footprint as the function of tide, significant wave height, and wind.
Abstract
Whitecaps manifest surface wave breaking that impacts many ocean processes, of which surface wind stress is the driving force. For close to a half century of quantitative whitecap reporting, only a small number of observations are obtained under conditions with wind speed exceeding 25 m s−1. Whitecap contribution is a critical component of ocean surface microwave thermal emission. In the forward solution of microwave thermal emission, the input forcing parameter is wind speed, which is used to generate the modeled surface wind stress, surface wave spectrum, and whitecap coverage necessary for the subsequent electromagnetic (EM) computation. In this respect, microwave radiometer data can be used to evaluate various formulations of the drag coefficient, whitecap coverage, and surface wave spectrum. In reverse, whitecap coverage and surface wind stress can be retrieved from microwave radiometer data by employing precalculated solutions of an analytical microwave thermal emission model that yields good agreement with field measurements. There are many published microwave radiometer datasets covering a wide range of frequency, incidence angle, and both vertical and horizontal polarizations, with maximum wind speed exceeding 90 m s−1. These datasets provide information of whitecap coverage and surface wind stress from global oceans and in extreme wind conditions. Breaking wave energy dissipation rate per unit surface area can be estimated also by making use of its linear relationship with whitecap coverage derived from earlier studies.
Abstract
Whitecaps manifest surface wave breaking that impacts many ocean processes, of which surface wind stress is the driving force. For close to a half century of quantitative whitecap reporting, only a small number of observations are obtained under conditions with wind speed exceeding 25 m s−1. Whitecap contribution is a critical component of ocean surface microwave thermal emission. In the forward solution of microwave thermal emission, the input forcing parameter is wind speed, which is used to generate the modeled surface wind stress, surface wave spectrum, and whitecap coverage necessary for the subsequent electromagnetic (EM) computation. In this respect, microwave radiometer data can be used to evaluate various formulations of the drag coefficient, whitecap coverage, and surface wave spectrum. In reverse, whitecap coverage and surface wind stress can be retrieved from microwave radiometer data by employing precalculated solutions of an analytical microwave thermal emission model that yields good agreement with field measurements. There are many published microwave radiometer datasets covering a wide range of frequency, incidence angle, and both vertical and horizontal polarizations, with maximum wind speed exceeding 90 m s−1. These datasets provide information of whitecap coverage and surface wind stress from global oceans and in extreme wind conditions. Breaking wave energy dissipation rate per unit surface area can be estimated also by making use of its linear relationship with whitecap coverage derived from earlier studies.
Objective Analysis of SMOS and SMAP Sea Surface Salinity to Reduce Large-Scale and Time-Dependent Biases from Low to High LatitudesAbstract
Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.
Abstract
Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.
Abstract
Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.
Abstract
Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.
