Validation and Calibration of ASCAT Using CMOD5.n

Verspeek, Jeroen; Stoffelen, Ad; Portabella, Marcos; Bonekamp, Hans; Anderson, Craig; Saldana, Julia Figa

doi:10.1109/tgrs.2009.2027896

Cited by 109 publications

(56 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…This implies that DW averaged over the whole range of azimuth and wind speed does not depend on the ASCAT incidence angle. Indeed, previous studies [e.g., Bentamy et al, 2008;Verspeek et al, 2010] have indicated that ASCAT wind speed distributions are not incidence angle dependent. Although it does not verify that the dependence of DW on q can be ignored at any specific W AS and 8, we assume that incidence angle may be eliminated from fitting parameters of the correction function dW.…”

Section: Effect Of Geophysical Model Functionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Matching ASCAT and QuikSCAT winds

et al. 2012

View full text Add to dashboard Cite

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.

show abstract

Section: Datamentioning

confidence: 83%

Section: Effect Of Geophysical Model Functionmentioning

confidence: 99%

See 1 more Smart Citation

Matching ASCAT and QuikSCAT winds

et al. 2012

View full text Add to dashboard Cite

show abstract

“…The ERA-interim wind also has lower resolution than the wind speed retrievals from ASAR. Supposing correctness in the ERA-interim wind fields (commonly used as a reference for validation [32,33]), the mismatch between its resolution (0.75° in latitude and longitude) and the SAR wind cells present a source of error for wind directions with variations in scales smaller than the ERA-interim resolution. Nevertheless, although with low temporal resolution, the results achieved with the available ASAR data had a finer spatial delimitation and higher levels of significance than the corresponding results with ERA-interim wind speeds.…”

Section: Figure 12mentioning

confidence: 99%

Correlation between Synthetic Aperture Radar Surface Winds and Deep Water Velocity in the Amundsen Sea, Antarctica

et al. 2013

View full text Add to dashboard Cite

Abstract:The recent observed thinning of the glacier ice shelves in the Amundsen Sea (Antarctica) has been attributed to warm deep currents, possibly induced by along-coast winds in the vicinity of the glacial ice sheet. Here, high resolution maps of wind fields derived from Synthetic Aperture Radar (SAR) data have been studied and correlated with subsurface measurements of the deep water velocities in the Amundsen Sea area. Focus is on periods with low ice coverage in 2010 and 2011. In 2010, which had comparatively low ice coverage, the results indicate a more rapid response to wind forcing in the deep currents than in 2011. The SAR wind speed maps have better spatial resolution than available reanalysis data, and higher maximum correlation was obtained with SAR data than with reanalysis data despite the lower temporal resolution. The maximum correlation was R = 0.71, in a direction that is consistent with wind-driven Ekman theory. This is significantly larger than in previous studies. The larger correlation could be due to the better spatial resolution or the restriction to months with minimum ice coverage. The results indicate that SAR is a useful complement to infer the subsurface variability of the ocean circulation in remote areas in polar oceans. OPEN ACCESSRemote Sens. 2013, 5 4089

show abstract

“…AMI sensor onboard ERS-1 and ERS-2 satellites and the Advanced SCATterometer (ASCAT) onboard the MetOp-A satellite (Figa-Saldaña et al, 2002), were designed to provide retrievals of real (stability-dependent) winds (Offiler, 1994;Hersbach et al, 2007). However, the CMOD5.n GMF was developed more recently to provide equivalent neutral winds (Hersbach, 2010;Verspeek et al, 2010) and is now used to produce the operational ASCAT level 2 wind products at the Royal Netherlands Meteorological Institute (KNMI). The information on low-level atmospheric circulations provided by scatterometers can be used to improve numerical weather forecasts.…”

Section: Introductionmentioning

confidence: 99%

An equivalent neutral wind observation operator for variational assimilation of scatterometer ocean surface wind data

Tardif¹,

Laroche²

2012

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

This article describes a new observation operator developed for improved variational data assimilation of scatterometer ocean wind vectors. The forward operator is designed explicitly to take into account the equivalent neutral calibration of scatterometer wind retrievals. The corresponding tangent linear and adjoint (TL/AD) operators are developed for the calculation of near-surface increments based explicitly on parametrizations of surface-layer turbulent momentum transfer. Tangent linear and adjoint operators are formulated using the Jacobian of the forward operator estimated using the perturbation method. Various TL/AD configurations are investigated in more detail to assess the impact of sensitivities affecting the vertical gradient of surface-layer temperature during assimilation of ocean surface wind data. It is argued that these sensitivities should be neglected in the context of a stand-alone atmospheric prediction system. The complete form of the operator has, however, the potential to be a key component of a coupled atmosphere-ocean data assimilation system. Two data assimilation experiments were performed using distinct scatterometer observation operators, i.e. new equivalent neutral versus the simpler operational configuration using stability-dependent (real) background winds. Associated medium-range forecasts were run for a complete impact assessment. The impact of the new operator is tracked through the entire data assimilation and forecast process by comparing analyses and forecasts from both experiments. A significant sensitivity of the data assimilation system to the configuration of the observation operator has been found. Modest but beneficial impacts are obtained with the equivalent neutral wind observation operator with respect to short-range forecasts of ocean surface winds and medium-range forecasts of extratropical geopotential heights.

show abstract

Validation and Calibration of ASCAT Using CMOD5.n

Cited by 109 publications

References 13 publications

Matching ASCAT and QuikSCAT winds

Matching ASCAT and QuikSCAT winds

Correlation between Synthetic Aperture Radar Surface Winds and Deep Water Velocity in the Amundsen Sea, Antarctica

An equivalent neutral wind observation operator for variational assimilation of scatterometer ocean surface wind data

Contact Info

Product

Resources

About