Anton Verhoef

Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control

The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Missions (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs.

Validation of Two-Dimensional Variational Ambiguity Removal on SeaWinds Scatterometer Data

Abstract A two-dimensional variational ambiguity removal technique (2DVAR) is presented. It first makes an analysis based on the ambiguous scatterometer wind vector solutions and a model forecast, and next selects the ambiguity closest to the analysis as solution. 2DVAR is applied on SeaWinds scatterometer data and its merits for nowcasting applications are shown in a general statistical comparison with model forecasts and buoy observations, and in a number of case studies. The sensitivity of 2DVAR to changes in the parameters of its underlying error model is studied. It is shown that observational noise in the nadir swath of SeaWinds is effectively suppressed by application of 2DVAR in combination with the multisolution scheme (MSS). MSS retains the local wind vector probability density function after inversion, rather than only a limited number of ambiguous solutions. As a consequence, the influence of the background increases, but this can be mitigated by switching off variational quality control. A ca...

ASCAT Wind Quality Control Near Rain

In this paper, anomalous spatial gradients are investigated by an image processing method, known as singularity analysis, which is proposed to complement the current Advanced Scatterometer (ASCAT) quality control (QC) by using the singularity exponent (SE). The quality of ASCAT winds is known to be generally degraded, with increasing values of the inversion residual or maximum-likelihood estimator (MLE). In the current ASCAT Wind Data Processor (AWDP), an MLE-based QC is adopted to filter poor-quality winds, which has proven to be effective in screening artifacts in the ASCAT winds, associated with increased subcell wind variability and other phenomena such as confused sea state. However, some poorly verifying winds, which appear in areas with moist convection, are not screened by the operational QC. The extension of the QC procedure with SEs is investigated, based on a comprehensive analysis of quality-sensitive parameters, using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Missions (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference, taking into account their spatial and temporal representation. The validation results show that the proposed method indeed effectively removes ASCAT winds in spatially variable conditions. It filters three times as many wind vectors as the operational QC, while preserving verification statistics with local buoys. We find that not the rain itself, but the extreme local wind variability associated with rain appears to generally decrease the consistency between ASCAT, buoy, and ECMWF winds.

Rain Identification in ASCAT Winds Using Singularity Analysis

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

Improved ASCAT Wind Retrieval Using NWP Ocean Calibration

The Advanced Scatterometer (ASCAT) wind data processor (AWDP) currently uses the so called CMOD5n geophysical model function (GMF), which was originally derived for the European Remote Sensing (ERS) scatterometers. In order to deliver a high-quality ASCAT wind product, the operational AWDP uses backscatter measurement corrections that are estimated visually (VOC) for each wind vector cell. We propose an alternative and previously established method for estimating correction tables based on numerical weather prediction ocean calibration residuals (NOC). It embodies a smooth incidence-angle dependent part that could serve as an appropriate ASCAT GMF correction, and a radar-beam-dependent residual. The incidence-angle-dependent part of these correction tables is due to differences in calibration procedure of the ERS and ASCAT scatterometers. For the high ASCAT incidence angles for which the GMF has not been assessed by ERS data, the modification is quite large, almost 1 dB. The incidence angle-dependent part is derived by fitting the OC residuals of all beams obtained over one year of data. It is subsequently used to adapt the GMF (yielding CMOD5na). The remaining radar-beam-dependent residual (NOCa) shows a wiggle pattern as function of incidence angle that is very persistent over time, apart from a seasonally varying offset. Both the effects of the GMF modification and the beam-dependent residual on the wind retrieval quality are investigated in this paper. Overall, the performance of NOC is better than that obtained with the previously used VOC calibration method, and the wind statistics show a much better symmetry of the left and right swath for NOC. The beam-dependent corrections improve the quality of the retrieved winds. NOC may thus be used for the intercalibration of the ERS and ASCAT scatterometers.

A New Method for Improving Scatterometer Wind Quality Control

An important part of the scatterometer wind data processing is the quality control (QC). This letter shows the implementation of a new scatterometer QC procedure, based on a comprehensive analysis of the wind inversion residual, which significantly improves the effectiveness of the wind data QC. The method is applied on the Advanced Scatterometer onboard the EUMETSAT Polar System (EPS) Metop-A satellite but is generic and can therefore be applied to any scatterometer system.

ASCAT wind quality under high subcell wind variability conditions

The assessment and validation of the quality of satellite scatterometer vector winds is challenging under increased subcell wind variability conditions, since reference wind sources such as buoy winds or model output represent very different spatial scales from those resolved by scatterometers (i.e., increased representativeness error). In this paper, moored buoy wind time series are used to assess the correlation between subcell wind variability and several Advanced Scatterometer (ASCAT)-derived parameters, such as the wind-inversion residual, the backscatter measurement variability factor, and the singularity exponents derived from an image processing technique, called singularity analysis. It is proven that all three ASCAT parameters are sensitive to the subcell wind variability and complementary in flagging the most variable winds, which is useful for further application. A triple collocation (TC) analysis of ASCAT, buoy, and the European Centre for Medium-range Weather Forecasting (ECMWF) model output is then performed to assess the quality of each wind data source under different variability conditions. A novel approach is used to compute the representativeness errors, a key ingredient for the TC analysis. The experimental results show that the estimated errors of each wind source increase as the subcell wind variability increases. When temporally averaged buoy winds are used instead of 10 min buoy winds, the TC analysis results in smaller buoy wind errors (notably at increased wind variability conditions) while ASCAT and ECMWF errors do not significantly change, further validating the proposed TC approach. It is concluded that at 25 km resolution, ASCAT provides the best quality winds in general.

The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals

A new geophysical model function (GMF), called CMOD7, has been developed for intercalibrated ERS (ESCAT) and ASCAT C-band scatterometers. It is valid for their combined incidence angle range and being used to generate wind climate data records. CMOD7 has been developed in several steps as a successor of CMOD5.n. First, CMOD5.n has been adapted to the ASCAT transponder calibration, which is considered more accurate than any ESCAT gain calibration. This results in a linear scaling of the backscatter values. Second, for low winds, there is a clear mismatch between CMOD5.n and the measurements. An independently developed ASCAT C-band GMF, C2013, which performs particularly well for low winds was adopted to improve low winds for the ASCAT incidence angle range. Third, retrievals with CMOD5.n show wind speed probability distribution functions (pdf) that undesirably depend on wind vector cell (WVC) position across the swath. To overcome this effect, a higher order calibration is applied, which matches the wind speed pdfs for all WVCs of ASCAT and ESCAT. The resulting CMOD7 GMF indeed shows overall improved performance on all relevant quality parameters compared to CMOD5.n. It is found that the standard deviations of error for wind speed and wind direction of ASCAT are improved. The same holds for the maximum-likelihood estimates, showing an 8% improved consistency with the local triplet of backscatter measurements. As a consequence, triple collocation with moored buoy and numerical weather prediction winds results in smaller wind vector components and wind direction retrieval errorsim.

Bayesian Sea Ice Detection With the Advanced Scatterometer ASCAT

This paper details the construction of a Bayesian sea ice detection algorithm for the C-band Advanced Scatterometer ASCAT onboard MetOp based on probabilistic distances to ocean wind and sea ice geophysical model functions. The performance of the algorithm is validated against coincident active and passive microwave sea ice extents on a global scale across the seasons. The comparison between the ASCAT, QuikSCAT, and AMSR-E records during 2008 is satisfactory during the winter seasons, but reveals systematic biases between active and passive microwave methods during the summer months. These differences arise from their different sensitivities to mixed sea ice and open water conditions, scatterometers being more inclusive regarding the detection of lower concentration and summer ice. The sea ice normalized backscatter observed at C-band shows some loss of contrast between thin and thick ice types relative to the Ku-band QuikSCAT, but offers a better sensitivity to prominent surface features, such as fragmentation and rafting of marginal sea ice.

Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds

Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind measurements, both in statistical results and in local geographical regions. It is possible to rescale these reference winds to remove certain aspects of these systematic differences. Space-borne ocean surface winds actually measure ocean surface roughness, which is related more directly to stress. Air mass density is relevant in the air–sea momentum transfer as captured in the stress vector. Therefore, apart from the already common “neutral wind correction” for atmospheric stratification, also a “mass density wind correction” is investigated here to obtain a better correspondence between satellite stress measurements and buoy or NWP winds. The bicorrected winds are called stress-equivalent winds. Stress-equivalent winds do not strongly depend on the drag formulation used and provide a rather direct standard for comparison and assimilation in user applications. This paper presents details on how this correction is performed and first results that show the benefits of this correction mainly in the extratropical regions.