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Y. Quilfen, B. Chapron, and D. Vandemark

Abstract

A validation of European Space Agency (ESA) remote sensing satellite (ERS) scatterometer ocean wind measurements is performed using a formalism recently proposed for and applied to NASA scatterometer (NSCAT) and Special Sensor Microwave Imager (SSM/I) measurements. This simple analytical model relates scatterometer measurements to true winds, taking into account errors in the satellite winds as well as errors in the data used for reference. In this study, National Data Buoy Center (NDBC) buoy winds are the chosen reference. In addition, ECMWF analysis winds are used as a third data source to completely determine the errors via a triple collocation analysis. According to this development, the resulting wind speed error analysis indicates that ERS scatterometer estimates are negatively biased at light winds. This result differs from recent results determined using standard regression analysis. It is also shown that ERS and NSCAT measurement accuracies are comparable in an overall sense.

This error model provides a more certain measure of both random and systematic terms and the authors use this tool to look at possible systematic scatterometer wind speed biases in two separate long-term (1992–98) ERS datasets. The chosen approach examines temporal and spatial variation between ocean buoy and ERS-derived winds to identify both seasonal and regional ERS wind error signatures. First, data indicate a time-dependent bias between NDBC and ERS winds that is strongly correlated with the seasonal cycle. Buoy-derived long-wave and atmospheric stability parameter averages exhibit similar cycles and are the likely geophysical links to this scatterometer error. An illustration of regional or spatially varying error sources is further provided using ERS data collocated with Tropical Atmosphere and Ocean (TAO) buoy array measurements. In this case the long-term average wind speed bias between TAO and ERS exhibits well-defined spatial structures within the equatorial belt (10°N, 10°S). Bias variations show qualitative agreement with a near-surface current climatology map for this Pacific region and also with the limited available buoy current measurements. Overall results indicate small but systematic nonwind sea surface effects on scatterometer products. It is concluded that there cannot be one set of values for ERS scatterometer wind validation parameters. Accounting for surface effects on scatterometer measurements may need consideration to ensure proper assimilation of scatterometer data into weather forecasting and climate prediction models.

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Douglas Vandemark, James B. Edson, and Bertrand Chapron

Abstract

Aircraft altimeter and in situ measurements are used to examine relationships between altimeter backscatter and the magnitude of near-surface wind and friction velocities. Comparison of altimeter radar cross section with wind speed is made through the modified Chelton–Wentz algorithm. Improved agreement is found after correcting 10-m winds for both surface current and atmospheric stability. An altimeter friction velocity algorithm is derived based on the wind speed model and an open-ocean drag coefficient. Close agreement between altimeter- and in situ–derived friction velocities is found. For this dataset, quality of the altimeter inversion to surface friction velocity is comparable to that for adjusted winds and clearly better than the inversion to true 10-m wind speed.

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Y. Quilfen, B. Chapron, F. Collard, and D. Vandemark

Abstract

Potential effects of environmental parameters such as sea state or atmospheric boundary layer stability on the normalized radar cross section (NRCS) measured by spaceborne sensors have been investigated for a long time. Using neural networks and large high quality collocated datasets, the relation between the European Remote Sensing Satellite (ERS) C-band scatterometer NRCS measurement and integrated sea state parameters (i.e., the mean wave period and significant wave height) measured by buoys is studied. As anticipated, NRCS measurements correlate well with an empirically derived parameter H α/T β, revealing the mean bulk relationship between a mean 10-m wind speed and the corresponding sea state development. The correlation and exponents exhibit dependency on the scatterometer incidence angles. A neural model that relates the scatterometer NRCS measurements to these wave spectral integrated parameters and wind speed is also developed. As obtained, the retrieval skill is significantly improved, by comparison with operational empirical models such as CMOD-IFR2 or CMOD4, when including wave effects. As illustrated, systematic biases occur under particular environmental conditions when using the operational scatterometer backscatter model functions.

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

Abstract

Combination of laser and radar aboard an aircraft is used to directly measure long gravity wave surface tilting simultaneously with nadir-viewing microwave backscatter from the sea surface. The presented dataset is extensive, encompassing varied wind conditions over coastal and open-ocean wave regimes. Laser-derived slope statistics and Ka-band (36 GHz) radar backscatter are detailed separately to document their respective variations versus near-surface wind speed. The slope statistics, measured for λ > 1–2 m, show good agreement with Cox and Munk's oil-slickened sea measurements. A notable exception is elevated distribution peakedness and an observed wind dependence in this likely proxy for nonlinear wave–wave interactions. Aircraft Ka-band radar data nearly mimic Ku-band satellite altimeter observations in their mean wind dependence. The present calibrated radar data, along with relevant observational and theoretical studies, suggest a large (−5 dB) bias in previous Ka-band results. Next, wave-diverse inland, coastal, and open-ocean observations are contrasted to show wind-independent long-wave slope variance changes of a factor of 2–3, always increasing as one heads to sea. Combined long-wave and radar data demonstrate that this long-wave tilt field variability is largely responsible for radar backscatter variations observed at a given wind speed, particularly at wind speeds below 5–7 m s−1. Results are consistent with, and provide quantititative support for, recent satellite altimeter studies eliciting signatures of long-wave impacts resident in the radar backscatter. Under a quasi-optical scattering assumption, the results illustrate long-wave control on the variance of the total mean square slope parameter due to changes in the directional long-wave spectrum, with high-wavenumbers being relatively unaffected in a mean sense. However, further analysis suggests that for winds above 7 m s−1 the high-wavenumber subrange also varies with change in the longer wave field slope and/or energy, the short gravity wave roughness being measurably greater for smoother seas.

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N. Tran, O-Z. Zanife, B. Chapron, D. Vandemark, and P. Vincent

Abstract

One year of collocated, rain-free nadir Ku-band backscatter cross-section measurements from the Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) and both Jason-1 and Envisat RA-2 altimeter measurements have been compiled to compare these three sources of Ku-band radar cross section. With the exception of a +1.46 dB relative offset between Jason-1 and PR measurements and a −1.40 dB offset between Envisat and PR ones, all three Ku-band measurements compare very well in terms of dependencies upon model wind speed estimates and significant wave height measurements. The altimeter radars and the rain radar thus provide consistent measurements, and observed biases can be rationalized as differences in the radar calibration. The precipitation radar, which also covers off-nadir measurements, has been absolutely calibrated using an active radar calibrator. Consequently, the observed relative offsets can be used to indirectly calibrate both Jason-1 and Envisat altimeter Ku-band radar cross sections in an absolute sense.

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E. J. Walsh, C. W. Wright, M. L. Banner, D. C. Vandemark, B. Chapron, J. Jensen, and S. Lee

Abstract

During the Southern Ocean Waves Experiment (SOWEX), registered ocean wave topography and backscattered power data at Ka band (36 GHz) were collected with the NASA Scanning Radar Altimeter (SRA) off the coast of Tasmania under a wide range of wind and sea conditions, from quiescent to gale-force winds with 9-m significant wave height. Collection altitude varied from 35 m to over 1 km, allowing determination of the sea surface mean square slope (mss), the directional wave spectrum, and the detailed variation of backscattered power with incidence angle, which deviated from a simple Gaussian scattering model. The non-Gaussian characteristics of the backscatter increased systematically with the mss, suggesting that a global model to characterize Ka-band radar backscatter from the sea surface within 25° of nadir might be possible.

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J. Gourrion, D. Vandemark, S. Bailey, B. Chapron, G. P. Gommenginger, P. G. Challenor, and M. A. Srokosz

Abstract

Globally distributed crossovers of altimeter and scatterometer observations clearly demonstrate that ocean altimeter backscatter correlates with both the near-surface wind speed and the sea state. Satellite data from TOPEX/Poseidon and NSCAT are used to develop an empirical altimeter wind speed model that attenuates the sea-state signature and improves upon the present operational altimeter wind model. The inversion is defined using a multilayer perceptron neural network with altimeter-derived backscatter and significant wave height as inputs. Comparisons between this new model and past single input routines indicates that the rms wind error is reduced by 10%–15% in tandem with the lowering of wind error residuals dependent on the sea state. Both model intercomparison and validation of the new routine are detailed, including the use of large independent data compilations that include the SeaWinds and ERS scatterometers, ECMWF wind fields, and buoy measurements. The model provides consistent improvement against these varied sources with a wind-independent bias below 0.3 m s−1. The continuous form of the defined function, along with the global data used in its derivation, suggest an algorithm suitable for operational application to Ku-band altimeters. Further model improvement through wave height inclusion is limited due to an inherent multivaluedness between any single realization of the altimeter measurement pair [σ o, H S] and observed near-surface winds. This ambiguity indicates that H S is a limited proxy for variable gravity wave properties that impact upon altimeter backscatter.

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N. Reul, B. Chapron, E. Zabolotskikh, C. Donlon, A. Mouche, J. Tenerelli, F. Collard, J. F. Piolle, A. Fore, S. Yueh, J. Cotton, P. Francis, Y. Quilfen, and V. Kudryavtsev

Abstract

Wind radii estimates in tropical cyclones (TCs) are crucial to helping determine the TC wind structure for the production of effective warnings and to constrain initial conditions for a number of applications. In that context, we report on the capabilities of a new generation of satellite microwave radiometers operating at L-band frequency (∼1.4 GHz) and dual C band (∼6.9 and 7.3 GHz). These radiometers provide wide-swath (>1,000 km) coverage at a spatial resolution of ∼40 km and revisit of ∼3 days. The L-band measurements are almost unaffected by rain and atmospheric effects, while dual C-band data offer an efficient way to significantly minimize these impacts. During storm conditions, increasing foam coverage and thickness at the ocean surface sufficiently modify the surface emissivity at these frequencies and, in turn, the brightness temperature (Tb) measurements. Based on aircraft measurements, new geophysical model functions have been derived to infer reliable ocean surface wind speeds from measured Tb variations. Data from these sensors collected over 2010–15 are shown to provide reliable estimates of the gale-force (34 kt), damaging (50 kt), and destructive winds (64 kt) within the best track wind radii uncertainty. Combined, and further associated with other available observations, these measurements can now provide regular quantitative and complementary surface wind information of interest for operational TC forecasting operations.

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Jennifer A. Hanafin, Yves Quilfen, Fabrice Ardhuin, Joseph Sienkiewicz, Pierre Queffeulou, Mathias Obrebski, Bertrand Chapron, Nicolas Reul, Fabrice Collard, David Corman, Eduardo B. de Azevedo, Doug Vandemark, and Eleonore Stutzmann
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