Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations

Bentamy, Abderrahim; Croizé-Fillon, Denis; Périgaud, C.

doi:10.5194/os-4-265-2008

Cited by 106 publications

(62 citation statements)

References 13 publications

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“…Here we use Level 2b ASCAT near realtime data (version 1.10) distributed by EUMETSAT and by KNMI at 25 Â 25 km 2 resolution. Comparisons to independent mooring and shipboard observations by Bentamy et al [2008] and Verspeek et al [2010] show that ASCAT wind speed and direction has accuracies similar to QuikSCAT.…”

Section: Datamentioning

confidence: 83%

“…[14] Both satellites are in quasi sun-synchronous orbits, with the QuikSCAT local equator crossing time at the ascending node (6:30 A.M.) leading the ASCAT local equator crossing time (9:30 A.M.) by 3 h. This difference implies that data precisely collocated spatially are available only with a few hours time difference at low latitudes ( Figure. 1) [Bentamy et al, 2008]. Here we accept for examination data pairs collocated in space and time where QuikSCAT WVC is collected within 0 < t < 4 h and 50 km of each valid ASCAT WVC.…”

Section: Collocated Observationsmentioning

confidence: 99%

“…However, systematic errors are also present. For example, Bentamy et al [2008] have shown that ASCAT has a systematic underestimation of wind speed that increases with wind speed, reaching 1 m s À1 for winds of 20 m s À1 .…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to such previous efforts, this current study focuses on directly matching scatterometer data. We illustrate the processes by matching the current ASCAT and the recent QuikSCAT winds (following Bentamy et al [2008]). This matching exploits the existence of a 13 month period November 2008 to November 2009 when both missions were collecting data and when ASCAT processing used the current CMOD5N GMF [Verspeek et al, 2010] to derive 10 m neutral wind.…”

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confidence: 99%

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Matching ASCAT and QuikSCAT winds

et al. 2012

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Surface winds from two scatterometers, the Advanced scatterometer (ASCAT), available since 2007, and QuikSCAT, which was available through November 2009, show persistent differences during their period of overlap. This study examines a set of collocated observations during a 13‐month period November 2008 through November 2009, to evaluate the causes of these differences. A difference in the operating frequency of the scatterometers leads to differences that this study argues depend on rain rate, wind velocity, and SST. The impact of rainfall on the higher frequency QuikSCAT introduces biases of up to 1 m s−1 in the tropical convergence zones and along the western boundary currents even after rain flagging is applied. This difference from ASCAT is reduced by some 30% to 40% when data for which the multidimensional rain probabilities > 0.05 is also removed. An additional component of the difference in wind speed seems to be the result of biases in the geophysical transfer functions used in processing the two data sets and is parameterized here as a function of ASCAT wind speed and direction relative to the mid‐beam azimuth. After applying the above two corrections, QuikSCAT wind speed remains systematically lower (by 0.5 m s−1) than ASCAT over regions of cold SST < 5°C. This difference appears to be the result of temperature‐dependence in the viscous damping of surface waves which has a greater impact on shorter waves and thus preferentially impacts QuikSCAT. The difference in wind retrievals also increases in the storm track corridors as well as in the coastal regions where the diurnal cycle of winds is aliased by the time lag between satellites.

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Section: Datamentioning

confidence: 83%

Section: Collocated Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Matching ASCAT and QuikSCAT winds

et al. 2012

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“…In contrast, at high wind speeds (>13 m¨s´1), the bias dramatically increased to more than 5 m¨s´1, and the STD was amplified up to 6 m¨s´1. These results are probably due to the obvious underestimation of ASCAT at wind speed exceeding 20 m¨s´1 [32]. The wind direction was negligibly biased by less than 2˝, but the STD was increased to 50˝-90˝at low wind speeds (<4 m¨s´1).…”

Section: Comparison With Ascat Retrieved Wind Vectorsmentioning

confidence: 95%

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

et al. 2016

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Abstract:The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m¨s´1, respectively. The corresponding RMSEs of the wind direction were 46.63˝, 43.11˝, 39.93˝, and 47.47˝, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m¨s´1 and 39.68˝for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

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Short‐term upwelling events at the western African coast related to synoptic atmospheric structures as derived from satellite observations

2014

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[1] Satellite scatterometers provide continuously valuable surface wind speed and direction estimates over the global ocean on a regular grid both in space and time. The Level 3 data derived from the Advanced Scatterometer (ASCAT), available at 1/4 spatial resolution (hereafter AS25), and Quick Scatterometer (QuikSCAT), available on 1/2 and 1/4 horizontal grids (QS50 and QS25, respectively), are studied at regional scales in both the Benguela and Canary upwelling systems. They are compared to the European Center for Medium-Range Weather Forecast surface wind analysis, with insight into their intrinsic and actual spatial resolutions. In the coastal band, the finest spatial patterns are found in the QS25 winds and are O(75 km). This demonstrates the sensitivity of the high-resolution satellite-derived winds to coastal processes related to sea surface temperature (SST) perturbations and land-sea transition. Next, short-lived upwelling episodes (SUEs) calculated from SST anomalies are defined consistently with the QS25 actual resolution. These cold events refer to local, short-lived perturbations that add to seasonal upwelling variability. We characterize concomitant atmospheric synoptic conditions for SUEs identified at chosen latitudes and highlight two subregions in both upwelling systems, with contrasted patterns for the alongshore wind stress component and curl. The complexity of the latter patterns is closely linked to local, short-term SST variability. Closer to the shore, numerical sensitivity experiments show that the imbalance between Ekman transport and Ekman pumping has an impact on ocean dynamics: wind reduction in the coastal QS25 forcing, partially induced by orography, tends to reduce coastal SST cooling.

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Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations

Cited by 106 publications

References 13 publications

Matching ASCAT and QuikSCAT winds

Matching ASCAT and QuikSCAT winds

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

Short‐term upwelling events at the western African coast related to synoptic atmospheric structures as derived from satellite observations

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