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An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials [Journal of Atmospheric and Oceanic Technology](Journal of Atmospheric and Oceanic Technology Via Acquire Media NewsEdge) ABSTRACT For a number of maritime tasks there is a short time period, typically only a few tens of seconds, where a critical event occurs that defines a limiting wave height for the whole operation. Examples are the recovery of fixed and rotary winged aircraft, cargo transfers, final pipe mating in fluid transfer operations, and launch/recovery of small craft. The recovery of a 30-t rescue submersible onto a mother ship in the North Atlantic Treaty Organization (NATO) Submarine Rescue System is a prime example. In such applications short-term deterministic sea wave prediction (DSWP) can play a vital role in extending the sea states under which the system can be safely deployed. DSWP also has great potential in conducting experimental sea wave research at full scale. This report explores the feasibility of using data from an experimental wave profiling radar in achieving DSWP. The report includes theory, simulation, and field testing. Two forms of DSWP are employed: a fixed point system based upon a restricted set of wave directions from which some success is obtained and the other a fully two-dimensional technique that requires further development. The main finding is that using wave profiling radar for DSWP offers promise but requires improvements both to the spatial reliability and the resolution of the wave profiling radar and to the temporal resolution of its sweep before the technique can be considered to be viable as a usable tool. (ProQuest: ... denotes formulae omitted.) 1. Introduction The aim of this study is to explore the feasibility of using a wave profiling radar system called the Wave and Surface Current Monitoring System II (WaMoS II; Nieto Borge et al. 2004) as a data source for multidi- rectional extensions of linear deterministic sea wave prediction (DSWP) (Morris et al. 1992, 1998; Edgar et al. 2000; Belmont et al. 2003, 2006; Abusedra and Belmont 2011). The paper covers theory, potential sources of error and their mitigation, simulation testing, and results from field work. DSWP uses measurements of the past motion of the sea's surface to predict the actual profile of the sea surface for a short period into the future. In contrast to the mature discipline of the statistical prediction of waves (Pierson et al. 1955; Kinsman 1984; Tucker and Pitt 2001), this is a relatively new area with a very modest-sized literature (Morris et al. 1992, 1998; Prislin et al. 1997; Zhang et al. 1999; Belmont et al. 2003, 2006; Wu et al. 2000; Edgar et al. 2000; Janssen et al. 2002; Wu 2004; Blondel et al. 2008; Naaijen and Huijsmams 2008; Naaijen et al. 2009; Abusedra and Belmont 2011) that is only beginning to make the move from a research topic into practical applications. It has been known for some time that a number of maritime operations can benefit from short-term de- terministic knowledge of the sea surface shape. These range from various vessel-based applications (Belmont et al. 1995) to improvements in the performance of wave energy converters (Falnes 2002; Belmont 2009, 2010). For many of these activities there is a short time period, typically only a few tens of seconds, where a critical event occurs that defines a limiting wave height for safely conducting the whole operation. Examples of vessel applications are the recovery of fixed and rotary winged aircraft, cargo transfers, final pipe mating in fluid transfer operations, and launch/retrieval of small craft. The recovery of a 30-t rescue submersible onto a mother ship in the North Atlantic Treaty Organization (NATO) Submarine Rescue System is a prime example. In such applications, short-term DSWP can play a vital role in extending the sea states under which the system can be safely deployed (B. Ferrier et al. 2012, meeting pre- sentation). In addition to practical applications, DSWP provides a low-cost approach to performing experi- mental research on sea wave dynamics at full scale. Unlike using DSWP for research work, almost all of its practical maritime applications require real-time prediction. The maximum predict-ahead time available is set by the propagation time for waves to travel from the region where they are measured to the prediction site. Given that each set of wave measurements repre- sents only a very partial window of the sea surface, it is necessary to treat each batch of wave data used to build a prediction model as independent. This means that all computations needed to create a prediction model must be completed in times much shorter than the predic- tion time because all such calculations deduct directly from that prediction time. Moving vessels operating independently must typically use some form of remote sensing for wave measurements, such as the experi- mental WaMoS II (OceanWaves GMBH 2013) wave profiling radar system, which is based upon work by Nieto Borge (1998), Nieto Borge et al. (1999, 2004), Alpers and Hasselmann (1982), Ziemer and Gueuronther (1994), Plant and Zurk (1997), and Hessner et al. (2002), or shallow-angle wave profiling lidar (Belmont et al. 2007). Given that the measurement horizons for these techniques are of kilometer scale, this produces maxi- mum predict-ahead times of the order of 1 min, meaning that all data processing and prediction model building must be completed in a few seconds. Thus, while work has been undertaken on nonlinear DSWP (Prislin et al. 1997; Zhang et al. 1999; Wu et al. 2000; Wu 2004; Blondel et al. 2008), the need for short computational time scales means that generating nonlinear multi- direction prediction models is unrealistic without su- percomputer resources. So, for most practical maritime operations, real-time DSWP is restricted to using linear sea models (Morris et al. 1992, 1998; Edgar et al. 2000; Belmont et al. 2003, 2006; Abusedra and Belmont 2011). This limitation to linear models may seem very re- strictive because all wave systems are at least weakly nonlinear. However, in most practical vessel-based op- erations the use of DSWP is in so-called quiescent pe- riod prediction (QPP) (B. Ferrier et al. 2012, meeting presentation) QPP exploits the well-known phenome- non that, under most sea conditions, intervals of large waves alternate with smaller ones; the aim is then to predict the occurrence of the more quiescent intervals. In such cases it is only necessary to determine that the wave height is less than some value; the precise ampli- tude and profile are not significant and thus predicting the detailed wave shape is not critical. Only in highly cluttered, fully two-dimensional, highly nonlinear seas driven by local wind waves would this not be sufficient. For such seas to be large enough to inhibit most mari- time tasks of interest, the prevailing wind conditions would be so severe as to cause the operations to be suspended. Such wave-limited, vessel-based applica- tions are normally constrained by large swells created by a very limited number of remote large storm sys- tems, with modest directional spreading, and are gen- erally accepted to be well described by linear wave models (Kinsman 1984; Tucker and Pitt 2001). Clearly an affordable, convenient remote sensing system is a key requirement in DSWP. Satellite tech- niques at present cannot provide either the required spatial resolution or the coverage. Shallow angle wave profiling lidar (Belmont et al. 2007) is very much a re- search technique, and wave buoys are of no value in moving vessel applications. This leaves wave radar as the remaining candidate. Traditionally, wave radars re- turn wave statistics; however, a commercial product called WaMoS II (Nieto Borge et al. 2004) is being further developed to provide deterministic wave data over a two-dimensional region using standard vessel naviga- tion radars. The aim of the present study is to explore the feasibility of multidirectional extensions of linear DSWP (Morris et al. 1992, 1998; Edgar et al. 2000; Belmont et al. 2003, 2006; Abusedra and Belmont 2011) using this system as a data source. This paper describes two approaches to linear DSWP using wave profiling radar. The first extends the so-called fixed point method introduced for one-dimensional seas by Morris et al. (1992, 1998) to multidirectional seas, using wave height time series data obtained at a modest number of fixed locations that are equivalent to the outputs from an array of heave-only wave sensor buoys. The second method involves a fully two-dimensional sea model that uses all of the available wave data ob- tained from an area scan of the sea surface. In section 2 we introduce the theory behind fixed point linear DSWP. There are a number of potential sources of error in this model; these are described in section 3, along with methods for mitigating against them, or at least measuring their effect. In section 4 we describe the results from simulations using synthetic data and in section 5 real results from a sea trial. The fully two-dimensional DSWP model is described in section 6, with some results from both synthetic and real data. Section 7 concludes. 2. Theory: MFP linear DSWP As stated the multiple fixed point (MFP) method of linear DSWP uses wave height time series data obtained at a modest number of fixed locations that are equivalent to the outputs from an array of heave-only wave sensor buoys. Each wave time history (one from each sensor lo- cation) makes it possible to model a separate, poly- chromatic wave system propagating in a different direction. This approach assumes that the wave system is created by a modest number of remote storm systems, each with a relatively small angular spread in wave di- rections. Given that the wave radar system makes data available ideally as a sequence of ''snapshots'' over a finely spatially sampled area, this means that only a small frac- tion of the available wave data is used in this technique. MFP DSWP for multidirectional seas We employ the standard linear oceanographic wave model (Kinsman 1984; Tucker and Pitt 2001): the wave height, h(x, y, t), at the spatial coordinates x, y, and temporal coordinate t is given by ... (1) where N is the number of frequencies employed, R is the number of significant storm directions, A(vn, ur)is the directional magnitude spectrum, ur is the propa- gation direction of an individual wave component, vn is the angular frequency, kn is its wave number, and F(vn, ur) is the phase. In this case R is modest, typically R , 10. For convenience Eq. (1) is recast into complex form as ... (2) where ... are the complex Fourier coefficients. The sgn(n) function, which returns the sign of n, is re- quired because the deep-water form of the dispersion relationship typically used means that the real and imagi- nary parts of the right-hand side of Eq. (2) would other- wise not satisfy the necessary symmetry relationships needed to ensure that h(x, y, t) is a real function. Using sea surfaces obtained from overlapping wave radar scans mean that at each measurement location, designated by xi, yi, a time series of wave height values, h(xi, yi, tl), where 1 # i # R and 0 # l # N 2 1, is available. This allows a discrete Fourier transform (DFT) of Eq. (2) to be taken at each measurement location, giving a system of R complex frequency domain equa- tions of the form ... (3) where H(xi, yi, m) is the DFT of the set h(xi, yi, tl). This system is conveniently written in matrix form as ... (4) where H is the R 3 1 spectral data vector, [Coeff] is an R 3 R coefficient matrix whose elements are the terms exp fj[sgn(m)km cos(ur)xi 1 sgn(m)km sin(ur)yi]g, and C is the R 3 1 vector of unknown Fourier co- efficients. Inverting the matrix Eq. (4), either explicitly or via a least squares fitting process, yields estimates of the vector C of Fourier coefficients that when substituted into Eq. (1) produce a prediction model for thewaveheighth(x, y, t) at the required spatial and temporal prediction coordinates of the prediction site. This assumes that the wave radar only delivers wave elevation at each point; hence, R data locations are equivalent to R heave-only wave buoys. If, however, the north-south, east-west motions can be extracted from the radar (e.g., via wave-induced surface velocities), then any two of the three equivalent buoy motions can be used rather than merely heave. This allows R wave measurement locations to model 2R wave directions. The R new equations are simply the appropriate pro- jections of p/2 phase-shifted versions of the heave equations (one-quarter of a particle rotation later than heave) of the form ... (5) The reason for 2R directions and not 3R is because any two motions of the three (heave, north-south, east-west) are sufficient to define the circular water particle mo- tion for each wave component; hence, only two of these datasets are linearly independent. WAVE DIRECTIONS The above-mentioned process requires estimates of the set of wave directions, ur,where1# r # R. Given the physical assumption that the sea of interest is created by a modest number of remote storm systems, it is reason- able to assume that these directions remain unchanged over periods of at least several minutes. This is in con- trast to the C(vm, ur) Fourier coefficients which, for reasons given earlier, must be updated with every new wave dataset. Consequently, standard statistical di- rectional spectral estimation methods can be employed to estimate the ur. In real-time DSWP, this statistical estimation is run as a background task, updated on a time scale of approximately 10 min. A particularly con- venient approach is an extension to multiple data gath- ering sites of the original Longuet-Higgins et al. (1963) cross-spectral method, using Borgman (1979). Because of problems such as the generation of nonphysical neg- ative power density spectra, caused by Gibbs phenom- ena, such methods are normally superseded by various forms of maximum likelihood estimators. However, as only the locations of the peaks in the directional spectra are needed for DSWP, the nonphysicality is not critical. Alternatively, for the reasons given below, it is compu- tationally realistic to employ a fast optimization process to estimate the wave directions: (i) The accuracy of the predictions can be measured at the prediction site ''after the event.'' (ii) The term R is small, R # 10. (iii) The ur changes slowly and hence angle estimation is a background task repeated at the fastest rate of every 10 min. Furthermore, rapid convergence will be helped by using very good initial guesses based upon the previous values. 3. Potential sources of error and their mitigation Linear DSWP can only approximately predict the sea surface profile, and clearly it is necessary to assess the level of confidence that can be placed in predictions and where possible how to mitigate the errors, or at least measure their effects. Potential errors The major factors affecting errors in linear DSWP are as follows: (i) the sea is only ever approximately linear; (ii) the dataset used for estimation is finite; (iii) the sea is not periodic, whereas the Fourier series sea wave model constituting Eq. (1) is strictly periodic; and (iv) the ac- curacy of estimating the Fourier coefficients in the matrix Eq. (4) is affected by the extent to which the system is mathematically well conditioned. ADDRESSING ERRORS For multidirectional seas a particularly important non- linear effect is the degree of energy transfer between modes (during wave propagation after measurements) stemming from directional wave-wave resonance (Phillips 1960; Longuet-Higgins and Phillips 1962). The inherent restriction of practical DSWP to wave propagation dis- tances of the order of 1 km naturally limits the effects of this source of error. The departure from linearity can be measured by us- ing standard bispectral methods (Kim and Powers 1979). This does not remove the effects of nonlinearity but does provide a metric for assessing the confidence that can be placed in predictions. The effect of using a finite dataset involves the well- known phenomenon of ''windowing leakage errors'' in the frequency domain. In signal processing applications, these errors are often mitigated by using smooth window envelopes such as Hanning functions. Unfortunately, these introduce global distortion. A more suitable tech- nique is so-called ''end matching'' (Morris et al. 1992, 1998; Abusedra and Belmont 2011), which searches for the most periodic subsets within the wave data used. This approach not only minimizes windowing errors (Brigham 1988) but also addresses the inappropriate periodicity problem, which is actually closely linked. Of all the sources of error, the most significant is con- cerned with the degree of mathematical ill conditioning of Eq. (4). Given an uncertainty, dH,inthevectorH (e.g., experimental measurement errors), it is well known that any form of inversion produces an uncertainty, dC, in C, which satisfies the inequality ... (6) where cond([Coeff]) is the condition number of the co- efficient matrix [Coeff] and the k^k denotes any suitable norm. Clearly for cond([Coeff]) . 1, any errors associ- ated with H are inflated. In general the condition number of a system rises with its rank and unfortunately [Coeff] is closely related to the class of Vandemonde matrices (Golub and Van Loan 1996) that are well known to be especially poorly conditioned. The strong effect of rank in this case is why it is necessary to restrict R to modest values. Now the elements expfj[sgn(m)km cos(ur)xi 1 sgn(m)km sin(ur)yi]g of [Coeff] depend upon the wave directions and the locations of the measurement points relative to the prediction site. Thus, for a given set of sea conditions, there will be a best set of measurement locations with respect to system conditioning and as with the wave directions these can be estimated via an optimization routine run as a background processing task. 4. Simulation testing Comprehensive simulation testing of DSWP imple- mentation algorithms would require exploration of the directional characteristics, number and location of the measurement locations, data window lengths, predic- tion bandwidth, etc. Even presenting the results of such an undertaking is totally unrealistic and thus only in- dividual illustrations can be shown. a. Tests solely on the prediction process Figure 1 shows results designed to test only the pre- diction technique, and hence the mean wave directions for each wave system were taken as known quantities. The goal was to assess the goodness of fit of the pre- dicted heave values at the prediction site estimated over an interval up to 30 s into the future. The results were obtained under ''blind trial'' conditions in which a third party generated the wave data from the sea model generator described. In total 82 different sea scenarios were tested, each of which was provided with 3600 observations, sampled at 1 Hz. For each scenario, 1000 s of data were used to predict at 1 Hz up to 30 s into the future. A sliding window approach was used, moving along the dataset by one observation each time, resulting in 2571 sets of 30-s predictions for each scenario. These predictions were measured against the actual values using Pearson's linear correlation (Pearson 1920). The boxes in Fig. 1 represent the 25th-75th percentile of the resulting 2571 correlation values for each scenario; the center of the box marks the median and the whiskers the full range of values. The sea models used were produced over a range of sea states from various standard forms (Tucker and Pitt 2001). To avoid any individual wave systems being too long crested their directional magnitude spectra were very finely resolved, with 360 directions modeled for each storm system over a typical angular spread of 208. The phases for each separate direction in the spectra were uniformly randomly distributed over the range 2p to 1p. Three measurement points were used in the pre- diction process, specified over the half plane known to contain the wave directions at distances greater than 200 m from the prediction site. This allowed only three wave directions to be modeled in contrast to the 360 used in the sea model. Given the coarse nature of the directional modeling by the DSWP as compared to the finely resolved sea model and the deliberately nonoptimum distribution of measurement locations, the overall performance of the prediction process is deemed satisfactory, especially recalling the requirements of quiescent period predic- tion. What is clear is that the distribution of goodness of fit is bimodal; the correlations for the various sea conditions are either good or poor. This reinforces the point that in practical DSWP applications, it is impor- tant to provide feedback from the actual predicted quantity of interest, typically wave-induced vessel mo- tion, for under certain sea conditions the DSWP process fails. OVERLAPPING ESTIMATES (FORWARD MARCHING PREDICTION) Given a continuous stream of wave data from each measurement site, it is possible to update the DSWP prediction model on a regular basis, up to a maximum rate of once each time step. This produces a common forward marching prediction window within which mul- tiple estimates of the prediction at a given future instant are available. The possibility of such multiple estimates allows for considerable increases in the confidence level in the predictions. An illustration of this for a well- predicted scenario is shown in Fig. 2 in which the common interval is 30 samples, and hence 30 separate estimates are available at each predicted instant. In the figure the black line is the actual heave, each of the predictions is represented by a gray line, and the for- ward marching process moves forward a total of 200 samples. b. Condition number effects Taken together, cond([Coeff]) and kdHk are influ- enced by all the relevant physical parameters defining both the sea conditions and the DSWP process. Thus, again an exhaustive exploration of condition number behavior over the whole parameter space is unrealistic. Furthermore, inequality [Eq. (6)] provides an upper bound and not an actual value for error. What can be said anecdotally, based upon very partial parameter explorations, is that under the general conditions of the simulations, large condition numbers-for example, cond([Coeff]) . 100-ensure that predictions were very poor, while conditions producing substantially lower values did not necessarily guarantee optimum predic- tion results. As with the wave directions, the best set of measure- ment locations can be determined by fast optimization run as a background task, again using ''after the event'' measured prediction errors as the cost function. To il- lustrate this process, a search based on a genetic algo- rithm (GA) (Goldberg 1989) was undertaken to identify the best measurement positions. In the simulation the wave system was set up to be centered upon a north- northwesterly direction, and hence the search was re- strictedtotheregionshown.Giventhewavenatureof the problem, it was expected that there would be many near-optimal solutions and this was found in practice. Five almost equivalent clusters of locations are illus- trated in Fig. 3. c. Measures of nonlinearity As discussed in section 1, practical constraints mean that only linear DSWP can be used for real-time DSWP; thus, there are almost certain to be situations when the linear DWSP predictions will be very poor. It is thus necessary to provide a measure of confidence in the pre- diction accuracy and hence indicate when the predictions can be safely used in advising operational decisions. The natural approach to this is to simply compare the predicted wave-induced vessel motion (using DSWP as input to a vessel motion model) with those mea- sured and assume that the results are representative. The problem with solely relying on this approach is that it is an ''after the fact'' test and that there may be conditions when one good prediction does not abso- lutely guarantee that the next prediction will be equally good. These circumstances might occur when there is a significant level of nonlinearity present, where in ad- dition to the obvious direct nonlinear effects even the zeroth-order linear aspects of the wave system can be- come highly nonstationary due to phenomena such as directional wave-wave resonance (Phillips 1960; Longuet- Higgins and Phillips 1962). This strongly influences the estimates of wave direction that are assumed to remain unchanged for significant periods of time. Thus, in addition to the ''after the fact'' measures, what is also required is a metric that measures the de- parture of the present sea wave conditions from the linear behavior assumed by linear DSWP. In the present context linearity is equivalent to the wave system sta- tistics being Gaussian and nonlinearity induces higher- order statistics. A well-established technique that tests equivalently for departure from Gaussian and for non- linearity is the bispectral method (Kim and Powers 1979). This is based upon a third-order correlation func- tion and allows the derivation of a measure that can be shown to be zero for linear/Gaussian processes and in- creases with departure from these conditions. Figure 4 shows a frequency domain bicoherence plot (Kim and Powers 1979) for a sequence of real sea wave data as estimated by the WaMoS II wave radar system. The grayscale starts at white for linear/Gaussian with in- creasing lightness indicating the presence of increasing levels of nonlinearity. The two axes are frequencies, and the location of the lighter pixels indicate the spectral interaction regions. In effect such a plot shows the pres- ence of frequency domain mixing. Such two-dimensional plots are very data intensive, so in order to extract a simple single-value metric for nonlinearity, the magni- tude of the plots are averaged over the frequency domain. Given such an integrated bispectral metric (IBSM), if the time evolution of this number correlates significantly with the prediction quality, it is very probable that non- linearity is significantly affecting the prediction process and hence linear DSWP should not be relied upon. 5. Sea trial A sea trial was conducted as part of the NATO Sub- marine Rescue System (NSRS) program in order to begin the process of assessing the suitability of both a DSWP system and of deterministic wave profiling ra- dar (as a data input to the DSWP process). The eventual goal was to produce a quiescent period prediction sys- tem aimed at extending the sea-state operational enve- lope of NSRS. The wave profiling equipment used was the WaMoS II, which is signal processing/software technology, undergoing commercial development, that employs data from standard marine radars (Dittmer 1995) to estimate deterministic wave profiles as well as standard sea wave statistics. A fundamental challenge inherent in the trial was that very limited independent validation data were available for the wave radar, and hence considerable care was needed in interpreting the outcomes of the trial. The standard operating conditions for the NSRS de- ployments are that the mother ship, typically a vessel of approximately 3000-8000 tonnes equipped to launch and recover the 30-t rescue submersible, steams at ex- tremely slow speed in the direction of the prevailing sea wave system. This allowed multiple scans of the wave profiling radar, ahead of the vessel, to be overlaid pro- ducing a 1.2 km 3 1.2 km region common to a large number of the scans. Within this common region, fixed spatial points could be selected at which wave height time series could be obtained to serve as inputs for the MFP form of DSWP. The wave height data at an addi- tional point, ''down wave,'' within the common region was intended to check the accuracy of the predictions. The standard statistical processing mode of WaMoS II also provided one possible source of the directional wave spectral data required for assigning the wave di- rections needed by the prediction model. In this feasi- bility study, the DSWP system was not intended to operate in real time, with all calculations being performed ashore after completion of the trial. This made it possible to corroborate the WaMoS II wave directions with those produced by the hind casting MetOcean wave model operated by the Met Office (Stretch 2012). Experimental process 1) RADAR ESTIMATION OF SEA SURFACE ELEVATION The following provides a brief summary of the wave profile estimation process. Interested readers are re- ferred to the cited literature. It is known that under various conditions, signatures of the sea surface are visible in the near range (,3 n mi) of marine radar images. These signatures are known as sea clutter because they are undesirable for navigation purposes and generally suppressed by filter algorithms; the longer waves become visible in the radar images because they modulate the sea clutter signals (Hessner et al. 1999; Nieto Borge 1997; Seeman et al. 1997). This modulation is a nonlinear process and so the sea clutter radar image intensities do not have a one-to-one map- ping with the sea surface elevation. The standard WaMoS II wave analysis is derived from a Cartesian subset of the radar images, typically 0.25- 2.25 km2. The digitized electromagnetic (EM) intensity, that is, ''sea clutter,'' J(x, y, t), is Fourier transformed in space and time to yield the three-dimensional image spectrum x(kx, ky, v): ... (7) where F(?) is the Fourier transform operator, k 5 (kx, ky)T is the wave vector, jkj 5 k 5 2p/l is the wavenumber, with l as the wavelength, and v 5 2pT is the angular frequency with the period T. The energy related to the surface waves is localized in the image spectrum follow- ing the dispersion relation for linear gravity waves: ... (8) where g is the acceleration of gravity, H is the local water depth, and U 5 (Ux, Uy)T is the surface current. By de- termining the current and filtering x (kx, ky, v), the wave image spectrum is obtained: ... (9) As the digitized EM intensity is not linearly related to sea surface elevation, a modulation transfer func- tion, MTF(^), (Plant 1989; Ziemer and Gueuro nther 1994; Nieto Borge et al. 2004)isrequiredtoderivethe wave spectrum. By integrating over all positive fre- quencies the directional wavenumber spectrum is obtained: ... (10) The directional wavenumber spectrum is then inverse Fourier transformed to yield the sea surface elevation: ... (11) 2) EXPERIMENTAL DATA Figure 5 shows the arrangement of the vessel and the common measured region, approximately 1.2 km 3 1.2 km, used for the experiment. The measurement locations used were jointly optimized for the condi- tion number of the coefficient matrix and for pre- diction quality. The three black spots mark the chosen measurement locations, while the small black square is the prediction site. For these tests, 200 observations were used to predict 20 observations ahead, with the observations spaced at 2.52-s intervals. 3) RESULTS The black line in Fig. 6 is a plot of the time evolution of the prediction correlation coefficient at the predic- tion site. It clearly shows the presence of quasi-periodic variations from reasonable prediction quality (certainly good enough for QPP) to antiphase predictions. Figure 7 provides an illustration of wave profile fits for a short period around test 30. 4) CONSIDERATION OF ERRORS Given that the measurement locations had been op- timized as described above, it is unlikely that condition number was the cause of the cyclic errors. To deter- mine the effect of nonlinearity, the time evolution of the IBSM measure was plotted against prediction quality, as illustrated in Fig. 6; the gray lines are the bicoherence measure at the prediction site (solid line) and at each of the measurement sites (dashed and dotted lines). The bicoherence was measured over the 200 observation training interval, which was split up into four seg- ments of 50 observations each in order to perform the calculation. No evident relationship was found, so non- linearity was ruled out as a case of the quasi-periodic error. There are no reasons to expect that windowing or discretization effects would induced the observed long- period quasi periodicity. The only remaining source was some form of time-dependent resolution error in the radar-estimated wave profiles that was further sup- ported by anecdotal reports of the need to locally spa- tially realign wave height estimates in previous wave radar trials. 6. Two-dimensional linear DSWP (TWD) As stated in section 4, the general findings from the field work using WaMoS II data as input to MFP indicate that there are almost certainly some spatial referencing issues with the wave profile estimation that require resolving. Furthermore, MFP inherently involves optimizing the measurement locations used. Thus, pro- viding the remote storm-generated wave systems have reasonably narrow angular spreads, this approach is likely to lead to best-case results. In contrast the fully two-dimensional approach to linear DSWP requires the use of all the available data across the measurement region and in no sense is it optimized with respect to data locations. Thus, it would be expected that under the present circumstances, the two-dimensional prediction might perform significantly worse than MFP. This was found to be the case for the field data and so the work on the two-dimensional reported here has been re- stricted to merely demonstrating the basic approach; no efforts were made to refine the technique (as had been undertaken in the MFP case). a. The two-dimensional prediction process Given an appropriately sampled spatial array of data, it is possible to employ a two-dimensional Fourier trans- form approach to linear DSWP. The starting point is a continuum version of Eq. (2),thatis, ... (12) Again, the sgn(v) functions return the sign of v and are included to force the appropriate symme- tries that guarantee that h (x , y, t) remains a real function. By employing a ''Fourier like'' orthogonal integral transform, Eq. (12) canbeinvertedto produce ... (13) Substituting for C(v, u) into Eq. (12) provides a two- dimensional frequency-domain-based prediction model. For sampled data the above equation discretizes in an obvious manner to ... (14) where ... satisfy both the spatial form of the Nyquist sampling theorem and the domain size re- quirements for the lowest value of wavelength em- ployed. Thus, a direct approach to the two-dimensional linear DSWP involves the following steps: (i) Acquire the sampled spatial data, h( p.dx, q.dy, t0), over the region 0 # x # P.dx,0# y # Q.dy at a given time t0, and transform the spatial data according to Eq. (13), which in practice can be recast into a standard base two fast Fourier transform. (ii) Substitute the resulting estimate for the complex vector C(vn, m.du) into Eq. (2), which can be used to predict the time wave elevation at the desired location and time. b. Space domain wave filters By substituting for C(v, u) from Eq. (13) into Eq. (12), it can be shown that the prediction problem can be re- cast into a spatial convolution: ... (15) where Y(x, y, t, g) is the spatial impulse response function: ... (16) Alternatively, the problem can be recast into the wavenumber domain, producing this time a spatial impulse re- sponse function of the form ... (17) This is an infinite impulse response wave-filter approach to prediction as opposed to the frequency domain phase- shifting approach employed so far. The filter technique offers some advantages and some disadvantages. The advantages are the avoidance of explicit periodicity and that asymptotic analytic forms can be developed for the impulse responses to reduce computational costs. How- ever, two new problems raised by the methodology are (i) the impulse responses are noncausal and (ii) they have unbounded derivatives in the limit of large x, y, t. Resolving these two issues requires conjugate domain truncation, which is explored in a one-dimensional treat- ment of wave filtering (Belmont et al. 2006). c. Simulation testing of two-dimensional DSWP Simulation of the two-dimensional frequency domain prediction method is illustrated for the case of a single- frequency sinusoid with a 10-s period propagating at an angle of p/4 rad. The results are shown in Fig. 8. The modest errors are primarily a consequence of a com- bination of discretization and finite data windowing effects. d. Sea trial using two-dimensional DSWP Using the prediction code employed in the simulation shown in section 6c, the sea model data were replaced with WaMoS II wave profile estimates from the sea trial to estimate wave height time series at a single point in the measured region. The same dataset was used as in the MFP method, although clearly unlike in MFP, all the spatial data (for each radar scan) were employed in the two-dimensional predictions. The results, presented in Fig. 9, are very poor compared to those in Fig. 6, pro- duced using MFP. Figure 10 illustrates wave profile fits for the best case obtained. However, for the seas ob- served the ratio of spatial resolution to wavelength was much larger than expected under NSRS operating conditions when significantly better performance was expected. 7. Conclusions concerning the sea trials results The general finding from the sea trials is that using the multiple fixed point (MFP) method, there are sig- nificant intervals where the prediction is good enough for many practical marine tasks, such as quiescent period prediction for the NSRS application. How- ever, these alternate, on a reasonable predictable quasi- periodic basis, with intervals of increasingly poor prediction. We also find that even under the observed conditions, which are at the threshold of viability for the wave profiling radar system, for substantial time periods the wave prediction technology is sufficiently successful to already be of practical value in certain quiescent period prediction applications. The sponsor of this work, the U.K. Ministry of Defence, was sufficiently encouraged by the results presented here to support further work on this technology with the practical goal of developing an operational sea-going system. 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CHRISTMAS University of Exeter, Exeter, United Kingdom J. DANNENBERG AND T. HILMER OceanWaves GmbH, Lueuroneburg, Germany J. DUNCAN AND J. M. DUNCAN Defence Equipment and Support, Ministry of Defence, Bristol, United Kingdom B. FERRIER Dynamic Interface Laboratory, Hoffman Engineering Corporation, Stamford, Connecticut (Manuscript received 19 August 2013, in final form 28 January 2014) Corresponding author address: Jacqueline Christmas, University of Exeter, Harrison Building, North Park Road, Exeter, Devon EX4 4QF, United Kingdom. E-mail: [email protected] DOI: 10.1175/JTECH-D-13-00170.1 (c) 2014 American Meteorological Society |