Jur Vogelzang

Current measurements by SAR along-track interferometry from a Space Shuttle

We present one of the first studies on ocean current retrievals from interferometric synthetic aperture radar (InSAR) data acquired during the Shuttle Radar Topography Mission (SRTM) in February 2000. The InSAR system of SRTM was designed for high-resolution topographic mapping of the Earths land surfaces, using two SAR antennas on a Space Shuttle with a cross-track separation of 60 m. An additional along-track antenna separation of 7 m resulted in an effective time lag of about 0.5 ms between the two images, which could theoretically be exploited for target velocity retrievals. However, the feasibility of ocean current measurements with SRTM has been questionable, since the time lag was much shorter than the theoretical optimum (about 3 ms at X-band) and the signal-to-noise ratio over water was quite low. Nevertheless, some X-band InSAR images of coastal areas exhibit clear signatures of tidal flow patterns. As an example, we discuss an image of the Dutch Wadden Sea. We convert the InSAR data into a line-of-sight current field, which is then compared with results of the numerical circulation model KUSTWAD. For tidal phases close to the conditions at the time of the SRTM overpass; we obtain correlation coefficients of up to 0.6 and rms differences on the order of 0.2 m/s. Furthermore we find that SRTM resolves current variations down to spatial scales on the order of 1 km. This is consistent with predictions of a numerical InSAR imaging model. Remaining differences between SRTM- and KUSTWAD-derived currents can be attributed mainly to residual motion errors in the SRTM data as well as to a limited representation of the conditions at the time of the SRTM overpass in the available KUSTWAD results.

Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target

Calibration and validation of geophysical measurement systems typically require knowledge of the true value of the target variable. However, the data considered to represent the true values often include their own measurement errors, biasing calibration, and validation results. Triple collocation (TC) can be used to estimate the root-mean-square-error (RMSE), using observations from three mutually independent, error-prone measurement systems. Here, we introduce Extended Triple Collocation (ETC): using exactly the same assumptions as TC, we derive an additional performance metric, the correlation coefficient of the measurement system with respect to the unknown target, rho(t,Xi). We demonstrate that rho(2)(t,Xi) is the scaled, unbiased signal-to-noise ratio and provides a complementary perspective compared to the RMSE. We apply it to three collocated wind data sets. Since ETC is as easy to implement as TC, requires no additional assumptions, and provides an extra performance metric, it may be of interest in a wide range of geophysical disciplines.

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 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.

Spatial variances of wind fields and their relation to second-order structure functions and spectra

Kinetic energy variance as a function of spatial scale for wind fields is commonly estimated either using second-order structure functions (in the spatial domain) or by spectral analysis (in the frequency domain). Both techniques give an order-of-magnitude estimate. More accurate estimates are given by a statistic called spatial variance. Spatial variances have a clear interpretation and are tolerant for missing data. They can be related to second-order structure functions, both for discrete and continuous data. Spatial variances can also be Fourier transformed to yield a relation with spectra. The flexibility of spatial variances is used to study various sampling strategies, and to compare them with second-order structure functions and spectral variances. It is shown that the spectral sampling strategy is not seriously biased to calm conditions for scatterometer ocean surface vector winds. When the second-order structure function behaves like rp, its ratio with the spatial variance equals (p+1)(p+2). Ocean surface winds in the tropics have p between 2/3 and 1, so one-sixth to one-fifth of the second-order structure function value is a good proxy for the cumulative variance.

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.

Upscale and downscale energy transfer over the tropical Pacific revealed by scatterometer winds

The direction of the energy cascade in the mesoscales of atmospheric turbulence is investigated using near-surface winds over the tropical Pacific measured by satellite scatterometers SeaWinds (QuikSCAT) and ASCAT (MetOp-A). The tropical Pacific was subdivided into nine regions, classified as rainy or dry. Longitudinal third-order along-track structure functions DLLLa and skewness SLa were calculated as a function of separation r for each region and month during the period November 2008 to October 2009. We find that the results support both downscale and upscale interpretations, depending on region and month. The results indicate that normally energy cascades downscale, but cascades upscale over the cold tongue in the cold season and over the west Pacific in summer months. An explanation is offered based on the heating or cooling of the air by the underlying sea surface temperature. It is also found that the signature of intermittent small-scale (<100 km) events could be identified in graphs of SLa, implying that this diagnostic may be useful in the studies of tropical disturbances.

Second-order structure function analysis of scatterometer winds over the Tropical Pacific

Kolmogorov second-order structure functions are used to quantify and compare the small-scale information contained in near-surface ocean wind products derived from measurements by ASCAT on MetOp-A and SeaWinds on QuikSCAT. Two ASCAT and three SeaWinds products are compared in nine regions (classified as rainy or dry) in the tropical Pacific between 10°S and 10°N and 140° and 260°E for the period November 2008 to October 2009. Monthly and regionally averaged longitudinal and transverse structure functions are calculated using along-track samples. To ease the analysis, the following quantities were estimated for the scale range 50 to 300 km and used to intercompare the wind products: (i) structure function slopes, (ii) turbulent kinetic energies ( TKE), and (iii) vorticity-to-divergence ratios. All wind products are in good qualitative agreement, but also have important differences. Structure function slopes and TKE differ per wind product, but also show a common variation over time and space. Independent of wind product, longitudinal slopes decrease when sea surface temperature exceeds the threshold for onset of deep convection (about 28°C). In rainy areas and in dry regions during rainy periods, ASCAT has larger divergent TKE than SeaWinds, while SeaWinds has larger vortical TKE than ASCAT. Differences between SeaWinds and ASCAT vortical TKE and vorticity-to-divergence ratios for the convectively active months of each region are large.

NWP Model Error Structure Functions Obtained From Scatterometer Winds

Wind vectors derived from scatterometer measurements are spatially detailed as compared to global numerical weather prediction (NWP) model fields. Since the Advanced Scatterometer (ASCAT)s wind vector ambiguities are, in general, well defined, ambiguity removal results in accurate wind fields. The dense and regular spatial sampling of ASCAT winds represents a unique resource to study the NWP model field spatial error structure. The current level 2 ASCAT data processor employs 2-D variational ambiguity removal (2DVAR), in which an analysis is made from the ambiguous wind solutions and a prior NWP wind field using a variational technique, and, subsequently, the ambiguity closest to the analysis is selected as best wind. 2DVAR will yield an optimal analysis when the structure functions (background error correlations in the potential domain) are well specified. In this paper, a new method is presented to calculate structure functions from autocorrelations of observed scatterometer wind components minus NWP model predictions (O-B). It is based on direct integration of the differential equations relating structure functions and observed autocorrelations. Reprocessing ASCAT data at 12.5-km grid size with structure functions obtained this way shows a considerable increase in the spectral density of the analysis for scales from about 800 to about 100 km, with the largest effect at scales of around 250 km. In line with this finding, it is shown in a case study that a more detailed analysis leads to fewer ambiguity removal errors for ASCAT data recorded over a frontal zone with rapidly varying wind direction.

The ASCAT 6.25-km Wind Product

The advanced scatterometer (ASCAT) wind data processor (AWDP) produces ocean surface vector winds from radar measurements by the ASCAT on board the Metop satellites. So far, the ASCAT-coastal product with a grid size of 12.5 km has been the one with the highest resolution. Version 2.4 of AWDP, released May 2016, offers the possibility to process wind data on a 6.25 km grid. In this paper, the true spatial resolution and accuracy of that product is assessed using various methods. The crucial parameter is the radius of the area used to aggregate individual backscatter observations to a wind vector cell (WVC) level. A value of 7.5 km, half of that for ASCAT-coastal, appears to be the best compromise between resolution and accuracy. Spatial responses from multiple radar cross-section measurements are combined to cumulative responses, and show that the ASCAT-6.25 product has a spatial resolution of about 17 km, better than the 28 km found for the ASCAT-coastal product. The accuracy of the ASCAT-6.25 product is estimated using comparison with collocated buoys, triple collocation analysis, and a new method based on spatial variances. These methods show consistently that the ASCAT-6.25 product contains about