Ocean winds from RADARSAT-1 ScanSAR

Horstmann, Jochen; Koch, Wolfgang; Lehner, Susanne; Tonboe, Rasmus

doi:10.5589/m02-043

Cited by 113 publications

(72 citation statements)

References 12 publications

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“…Wind directions are extracted from wind-induced phenomena that are aligned in wind direction. Within this process pixels associated to land, surface slicks, and strong rain, are masked and excluded from the analysis (Horstmann et al, 2002;Horstmann and Koch, 2005). To reduce the probability of outliers in the wind direction retrieval, CMRE's wind directions were merged with the results obtained from the projection method developed by GDAIS (Wackerman et al, 2003).…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

“…Wind speeds are retrieved utilizing a geophysical model function (GMF) that describes the dependency of the NRCS on the local near-surface wind and imaging geometry. For C-band VV-pol we utilize the most commonly used GMF CMOD5n (Hersbach, 2010) and in case of HH-pol a hybrid model function which consists of a C-band polarization ratio (PR) and the CMOD5n (Horstmann et al, 2002). For retrieval of wind speeds from SAR operating at X-band, CMRE has developed an empirical GMF for both VV and HH polarization .…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

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Estimating Winds from Synthetic Aperture Radar under Typhoon Conditions

Horstmann¹

2012

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LONG-TERM GOALSThe Impacts of Typhoons on the Ocean in the Pacific (ITOP) program is a multi-national field campaign that aims to study the ocean response to typhoons in the western Pacific Ocean. The main goals are to understand the formation and dissipation of cold wakes and how ocean eddies affect typhoons. CMRE's activities within ITOP are related to SAR wind retrieval and therefore will be helping to better understand air-sea fluxes under extreme wind speeds and improve the knowledge and forecast of surface wave field under typhoons as well as the Typhoon forcasting. OBJECTIVESCMRE (former NATO Undersea Research Center) is part of the ITOP SAR Team working on extracting wind and wave information from synthetic aperture radar (SAR) data. Their major objectives are the development and implementation of a SAR Typhoon Processing System (SARTyPS) for near real time processing of SAR data with respect to estimating wind and sea state under tropical cyclone conditions. It is anticipated to incorporate the SAR-retrieved information into numerical models in particular with respect to tropical cyclone forecasting.The main objectives for CMRE are the development of algorithms that are capable of extracting wind information from SAR images in near real time and fully automated. The SAR activity within ITOP is a close cooperation with the Center for Southeastern Tropical Advanced Remote Sensing (CSTARS) of the University of Miami, General Dynamics Advanced Information Systems (GDAIS) and the Applied Physics Laboratory (APL) of the University of Washington. APPROACHIn a first step prior to wind retrieval all SAR data have to be accurately calibrated for the normalized radar cross section (NRCS). In addition all acquisitions performed in the scanning mode also have to be corrected for scalloping as well as beams and nadir ambiguities. SAR data, which were acquired at cross polarization (pol) have to be corrected for the contribution of the noiseequivalent sigma naught (noise floor) of the system to the NRCS. Following these data calibration steps the wind retrieval can be undertaken. In case of SAR data collected at C-and X-band in co-pol, meaning vertical (V) or horizontal (H) polarization in transmit and receive, the detailed methodology

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Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

Estimating Winds from Synthetic Aperture Radar under Typhoon Conditions

Horstmann¹

2012

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“…A value of 1.0 for α was proposed by comparing RADARSAT-1 SAR-retrieved wind speeds with weather forecast model results [36]. Horstmann et al [20] suggested that different α values were partially due to the different calibrations of RADARSAT-1 SAR data at processing facilities. Mouche et al [39] showed that the PR model of Elfouhaily [3] produced generally the best agreement with their observations and developed two new PR model using airborne real aperture radar data acquired at the C-band with both vertical and horizontal polarizations for moderate incidence angles.…”

Section: Wind Speed Retrievalmentioning

confidence: 99%

“…Generally, there are three approaches to the derivation of wind directions. The first method directly extracts wind directions from wind-induced streaks visible in SAR images using fast Fourier transforms [20,25], local gradients [26][27][28] and wavelet analysis techniques [29]. However, the wind streak signature is sometimes weak, and other non-wind-streak features grow in SAR images, which can contaminate wind direction retrievals.…”

Section: Wind Direction Retrievalmentioning

confidence: 99%

“…Since the launch of the European Remote Sensing Satellite-1(ERS-1), European Remote Sensing Satellite-2(ERS-2), Envisat and the Canadian satellites, RADARSAT-1 and RADARSAT-2, SAR images have been acquired over the oceans continuously over the past two decades. Well-calibrated SAR images can be used to routinely provide high spatial resolution ocean surface wind fields over a specified domain [19][20][21]. The retrieved wind speeds from SAR images have been refined and improved with an accuracy up to approximately 2 m/s in the wind speed range of 0-35 m/s [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Reconstructed Wind Fields from Multi-Satellite Observations

Tang

Liu

et al. 2014

Remote Sensing

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Abstract:We present and validate a method of reconstructing high-resolution sea surface wind fields from multi-sensor satellite data over the Grand Banks of Newfoundland off Atlantic Canada. Six-hourly ocean wind fields from blended products (including multi-satellite measurements) with 0.25° spatial resolution and 226 RADARSAT-2 synthetic aperture radar (SAR) wind fields with 1-km spatial resolution have been used to reconstruct new six-hourly wind fields with a resolution of 10 km for the period from August 2008 to December 2010, except July 2009 to November 2009. The reconstruction process is based on the heapsort bucket method with topdown search and the modified Gauss-Markov theorem. The result shows that the mean difference between the reconstructed wind speed and buoy-estimated wind speed is smaller than 0.6 m/s, and the standard deviation is smaller than 2.5 m/s. The mean difference in wind direction between reconstructed and buoy estimates is 3.7°; the standard deviation is 40.2°. There is fair agreement between the reconstructed wind vectors and buoy-estimated ones.

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Detection of convective systems through surface wind gust estimation based on Sentinel‐1 images: A new approach

Messager

Honnorat

et al. 2018

Atmospheric Science Letters

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Convective system (CS) tracking and identification are mainly based on the analyses of brightness temperature and precipitation. Few studies reported about the detection of CSs through surface wind gust estimation due to CS downdraft reaching sea surface. Based on high-resolution Sentinel-1 images, this study proposes the detection of CS signatures through an enhancement of surface wind magnitude in the vicinity of CSs. The CMOD5.N and Sapp's model (CMOD-like function) are used for the retrieval of surface wind speed from VV-pol and HH-pol images, respectively. Based on numerous cases, the study presents the retrieved winds from two representative Sentinel-1 images acquired over the Gulf of Mexico, on which several types of CS effect on sea surface (i.e., round form, convection line) are detected. Wind gust produced by the downdraft associated to these CSs exceeds 20-25 m/s. In order to verify whether the detected wind gust is associated to CSs, moderate/heavy precipitation extracted from the Next-Generation Radar (NEXRAD) images is used as an additional assessment. K E Y W O R D Sconvective system detection, Sentinel-1, surface wind speed estimation

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Ocean winds from RADARSAT-1 ScanSAR

Cited by 113 publications

References 12 publications

Estimating Winds from Synthetic Aperture Radar under Typhoon Conditions

Estimating Winds from Synthetic Aperture Radar under Typhoon Conditions

Reconstructed Wind Fields from Multi-Satellite Observations

Detection of convective systems through surface wind gust estimation based on Sentinel‐1 images: A new approach

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