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Journal ArticleDOI

Sea State Impacts on Wind Speed Retrievals From C-Band Radars

TLDR
Analysis of data from advanced scatterometer (ASCAT) aboard MetOp-A and the advanced synthetic aperture radar (ASAR) aboard Envisat against the in situ buoy wind speeds finds that the sea state dominates the wind speed errors and these trends increase with the significant wave height.
Abstract
Scatterometers, a proven technology, provide ocean wind speeds and directions that are essential in operational forecasts, monitoring of the climate, and scientific applications. While the missions and geophysical model functions are performing well, challenges remain. We analyze data from advanced scatterometer (ASCAT) aboard MetOp-A and the advanced synthetic aperture radar (ASAR) aboard Envisat, both of which operate in the C-band, against the in situ buoy wind speeds. We observe large variability in the wind speed residuals. Through analysis of these residuals, we find that they are related to sea state effects and atmospheric stability. The sea state dependence created by low-frequency swells is more pronounced for the lower incidence angles in ASCAT. In ASAR with a fixed angle of $23^{\circ }$ , the sea state dominates the wind speed errors and these trends increase with the significant wave height. We observe that wind speeds from ASAR and ASCAT have a close resemblance, which helps us to extrapolate our findings. The synergy between the two technologies can be further exploited to improve wind speed retrievals. Future scatterometer missions, such as the next MetOp, will operate with the wider range of incidence angles (including lower angles) to increase their coverage together, have higher spatial resolution, and obtain measurements closer to the coasts. In these cases, high-resolution SAR data can aide in the understanding of the radar response.

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Citations
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Journal ArticleDOI

Calibration of the Normalized Radar Cross Section for Sentinel-1 Wave Mode

TL;DR: Based on the global data set acquired by S-1A WV, assessment of normalized radar cross section (NRCS) is carried out through comparison with CMOD5.N predictions over open ocean and the necessity for recalibration and comparable correction factors for WV1 and WV2 is shown.
Journal ArticleDOI

Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

TL;DR: In this article, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016-2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights.
Journal ArticleDOI

Impact of Sea State on Wind Retrieval From Sentinel-1 Wave Mode Data

TL;DR: The possibility to compensate the sea state impact by including azimuth cutoff in the geophysical model function of the normalized radar cross section of Sentinel-1 wave mode is demonstrated.
Journal ArticleDOI

Characterizing Buoy Wind Speed Error in High Winds and Varying Sea State with ASCAT and ERA5

TL;DR: In this article, a triple collocation (TC) exercise comparing buoy winds to winds from ASCAT and ERA5 was conducted, where the residuals by anemometer height and testing under high wind-wave and swell conditions were analyzed.
Journal ArticleDOI

Evaluation of CFOSAT Scatterometer Wind Data in Global Oceans

TL;DR: In this paper, the authors evaluated the performance of wind retrievals under the passage of tropical cyclones and showed that the results satisfy the accuracy requirements of scientific research, although some improvements are needed to enhance the performance.
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