Ambiguous Wind Research Articles

First‐order ocean surface cross‐section for shipborne HFSWR incorporating a horizontal oscillation motion model

To investigate the characteristic of ocean surface backscatter echo, the first-order ocean surface cross-section for shipborne high-frequency surface wave radar (HFSWR) incorporating a horizontal oscillation motion model is mathematically derived. The horizontal oscillation motion includes yaw, sway and surge. Simulation results show that the horizontal oscillation motion can induce more additional peaks in Doppler spectrum due to combined oscillation motions, and the amplitudes as well as frequency locations of these motion-induced peaks are determined by the amplitude and frequency of the oscillation motion. Furthermore, the Bragg and motion-induced peaks are spread due to the forward movement of the ship. These spreading peaks overlap each other as the ship speed increases, which may severely influence moving target detection and ocean remote sensing. However, different characteristics of the first-order cross-sections for the true and ambiguous wind directions provide a new idea to measure ocean surface wind direction by shipborne HFSWR with a single receiving antenna.

Assimilation of HY-2A scatterometer ambiguous winds based on feature thinning

This paper focuses on the data assimilation methods for sea surface winds, based on the level-2B HY-2A satellite microwave scatterometer wind products. We propose a new feature thinning method, which is herein used to screen scatterometer winds while maintaining the key structure of the wind field in the process of data thinning for highresolution satellite observations. We also accomplish feeding the ambiguous wind solutions directly into the data assimilation system, thus making better use of the retrieved information while simplifying the assimilation process of the scatterometer products. A numerical simulation experiment involving Typhoon Danas shows that our method gives better results than the traditional approach. This method may be a valuable alternative for operational satellite data assimilation.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC), root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Measurement of Sea Surface Wind Direction Using Bistatic High-Frequency Radar

A method for extracting sea surface wind direction information from bistatic high-frequency (HF) radar Doppler spectra is presented. By analogy to the monostatic case, the ratio of the intensities of the positive and negative bistatic Bragg peaks is used to derive the (ambiguous) wind direction. For bistatic operation, the reference is taken with respect to the scattering ellipse normal rather than the radar beam direction. The method is shown to be valid based on simulated bistatic HF radar Doppler spectra. Wind direction is also extracted from the bistatic radar data collected on the Southern China coast. Comparison between the radar-measured wind directions and those obtained from the Advanced Scatterometer shows good agreement.

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.

A probabilistic approach for SeaWinds data assimilation

AbstractScatterometer sea surface wind observations are being successfully assimilated into numerical weather prediction models. The quality of the winds retrieved from the new SeaWinds scatterometer (onboard the QuikSCAT satellite) depends on the subsatellite cross‐track location. In particular, the poor azimuth separation or diversity between views in the nadir region results in poor quality winds. In the QuikSCAT nadir region, where the local cost‐function minima are broad, the use of the standard procedure results in arbitrary and inaccurate winds. A new scheme, which accounts for broad cost‐function minima by allowing more ambiguous wind solutions, i.e. a multiple solution scheme (MSS), is proposed as an alternative to the standard procedure. The probability of every ambiguous solution being the ‘true’ wind is empirically derived, and used in the ambiguity‐removal procedure to make the scheme flexible enough to accept many wind solutions.A comparison between the standard wind retrieval and the MSS procedures at 100 km resolution is then performed, using independent model winds for validation. The MSS turns out to be more in agreement with the model reference than the standard procedure, especially at nadir. Moreover, it shows more spatially consistent and realistic winds by more effectively exploiting the information content of the observations. Copyright © 2004 Royal Meteorological Society

A median-filter-based ambiguity removal algorithm for NSCAT

A description is given of the baseline NSCAT (the NASA scatterometer) ambiguity removal algorithm and the method used to select the set of optimum parameter values. An extensive simulation of the NSCAT instrument and ground data processor provides a means of testing the resulting tuned algorithm. This simulation generates the ambiguous wind-field vectors expected from the instrument as it orbits over a set of realistic mesoscale wind fields. The ambiguous wind field is then de-aliased using the median-filter-based ambiguity removal algorithm. Performance is measured by comparison of the selected wind fields with the true wind fields. Results have shown that this median-filter-based ambiguity removal algorithm satisfies NSCAT mission requirements, and it therefore has been incorporated into the baseline geophysical data-processing system for NSCAT.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A circular median filter approach for resolving directional ambiguities in wind fields retrieved from spaceborne scatterometer data

A common problem associated with spaceborne scatterometry, including SASS, NSCAT and ERS‐1, is that retrieval algorithms often compute multiple wind vectors per retrieval cell. Only one wind vector per retrieval cell corresponds to the true solution while the others are aliases that arise from symmetries in the response of small scale surface waves to wind forcing. The NSCAT retrieval algorithm ranks each ambiguous wind vector within a retrieval cell according to the likelihood that the wind vector is the true solution. Although the most likely wind vectors are usually correct, in approximately 40% of the retrieval cells an alias is ranked first. Based on the mathematical properties of these likelihood assignment errors, an ambiguity removal algorithm is derived that utilizes a nonlinear circular median filter (CMF) to select the true solution in each retrieval cell. The CMF algorithm was tested by analyzing twelve simulated wind fields with an average likelihood assignment error rate of 389 per 1000 retrievals. After processing by the CMF algorithm, the error rate was significantly reduced to an average of only 36 per 1000. To better understand the performance of the CMF algorithm under operational conditions, the analysis was extended by adding spatially correlated likelihood assignment errors to one of the simulated wind fields. A re‐analysis of this wind field showed that the performance of the CMF algorithm may decrease significantly if the likelihood assignment errors are correlated over several retrieval cells. In addition to deriving an ambiguity removal algorithm, a wind field smoothing technique that utilizes a circular median filter is derived and tested on one of the resolved wind fields. The results showed that CMF smoothing technique increases the spatial coherency between the true and resolved wind fields. Based on these analyses, specific recommendations are made for monitoring the performance of the wind retrieval process and for improving the accuracy of the retrieved wind fields.

Removal of Ambiguous Wind Directions for a Ku-Band Wind Scatterometer Using Three Different Azimuth Angles

The Seasat-A satellite scatterometer (SASS) sensor demonstrated very successfully that Ku-band scatterometers can make accurate synoptic measurements of surface wind speed over the ocean. Because SASS provided normalized radar cross section (NRCS) measurements from only two azimuths, however, the harmonic relationship of NRCS with azimuth results in up to four ambiguous wind directions. The primary improvement to be incorporated in a next-generation scatterometer design such as Navy Remote Ocean Sensing System (NROSS) is the addition of a third azimuth look at each sampled cell. With this and other instrument improvements, preliminary studies indicate that wind-direction ambiguities (aliases) could successfully be removed in at least 80 percent of the cases. Furthermore, these studies show that in over 90 percent of the wind solutions, the two most probable solutions correctly identify the wind streamlines. Methods were studied which could examine typical streamline patterns derived from scatterometers using continuity or pattern-recognition techniques to determine which of the possible two wind directions was correct. In addition, unambiguous solutions were sought for cases where streamlines were not correctly defined. This paper describes several approaches for such alias-removal algorithms. These algorithms were developed with the aid of simulated three-beam scatterometer ambiguous wind-solution data (based on NOSS conditions) over a known windfield. The resulting algorithms were evaluated using a different set of simulated orbital data, but withholding the true winds.