Development of a satellite SAR image spectra and altimeter wave height data assimilation system for ERS-1

The applicability of ERS-1 wind and wave data for wave models was studied using the WAM third generation wave model and SEASAT altimeter, scatterometer and SAR data. A series of global wave hindcasts is made for the surface stress and surface wind fields by assimilation of scatterometer data for the full 96-day SEASAT and also for two wind field analyses for shorter periods by assimilation with the higher resolution ECMWF T63 model and by subjective analysis methods. It is found that wave models respond very sensitively to inconsistencies in wind field analyses and therefore provide a valuable data validation tool. Comparisons between SEASAT SAR image spectra and theoretical SAR spectra derived from the hindcast wave spectra by Monte Carlo simulations yield good overall agreement for 32 cases representing a wide variety of wave conditions. It is concluded that SAR wave imaging is sufficiently well understood to apply SAR image spectra with confidence for wave studies if supported by realistic wave models and theoretical computations of the strongly nonlinear mapping of the wave spectrum into the SAR image spectrum. A closed nonlinear integral expression for this spectral mapping relation is derived which avoids the inherent statistical errors of Monte Carlo computations and may prove to be more efficient numerically

The International Workshop on Wave Hindcasting and Forecasting and the Coastal Hazards Symposium

Following the 13th International Workshop on Wave Hindcasting and Forecasting and 4th Coastal Hazards Symposium in October 2013 in Banff, Canada, a topical collection has appeared in recent issues of Ocean Dynamics. Here we give a brief overview of the history of the conference since its inception in 1986 and of the progress made in the fields of wind-generated ocean waves and the modelling of coastal hazards before we summarize the main results of the papers that have appeared in the topical collection

Validation and assimilation of Seasat altimeter wave heights using the WAM wave model

The mutual consistency of the Seasat global data sets of scatterometer winds and altimeter wave heights is investigated for the complete Seasat period using the third-generation wave model WAM. The wave model was driven by surface (1000 hPa) wind and surface stress fields constructed by the Goddard Laboratory for Atmospheres (GLA) by assimilation of the scatterometer winds in an atmospheric model. For the 10-day period September 7?17 the intercomparison was extended to two further scatterometer wind fields: a 1000-hPa assimilated wind field from the European Centre for Medium-Range Weather Forecasts and a subjectively analyzed 19.5-m-height surface wind field from the Jet Propulsion Laboratory. On the global average, the modeled and observed wave heights agree reasonably well. Regional differences, however, can be large and sometimes exceed 40%. The errors are attributed mainly to deficiencies in the forcing wind fields. Low wind speeds are found to be overestimated and high wind speeds underestimated by the Seasat scatterometer algorithm. The friction velocities of the GLA model are found to be significantly underestimated in the high-wind belt of the southern hemisphere. The analysis demonstrates the diagnostic advantages of applying a wave model for the quality assessment of satellite wind and wave data. A preliminary wave data assimilation scheme is presented in which the wave field is updated without changing the forcing wind field. A considerable improvement of the computed wave field is achieved, particularly in regions in which the wave energy is dominated by swell. However, a more general assimilation scheme including modifications of the wind field is needed to upgrade wind sea forecasts

Assimilation of wave data into the wave model WAM using an impulse response function method

A new method for the assimilation of wave data into a third-generation wave model is presented, Deviations between observed and modeled wave spectra are used to derive corrections of the wind field which drives the wave model, The wave field can then be subsequently corrected by a new integration of the wave model with the improved wind field, A basic difficulty of such dynamically consistent wave data assimilations schemes which correct both wind and wave data is the nonsynchronous and nonlocal nature of the wind field corrections: errors observed in the wave spectrum at a given measurement time and location can be produced by errors in the wind field at much earlier times and far distant locations, Formally, these problems can be rigorously resolved by the adjoint modeling method, However, in practice, the adjoint technique requires an order of magnitude more computer time than the integration of the wave model itself, Here an alternative method is developed, The linearized wave model equation which relates small wind to wave spectrum changes is inverted, The central assumption of the inversion is that the wind impact functions representing the impulse response (Green's) function of the wave evolution can be approximated by a S-function, Physically, this implies that the wind field perturbations responsible for observed perturbations in the wave spectrum can be regarded as strongly localized in space and time for any given component of the spectrum, To obtain stable estimates, the corrections for different wave components are averaged over wavenumber clusters representing different wave systems, For cases in which the linear approximation is inadequate, the method can be applied iteratively, Tests of the concept and application of the method for a number of synthetic wind field cases are encouraging

Statistical analysis and intercomparison of WAM model data with global ERS-1 SAR wave mode spectral retrievals over 3 years

Ocean wave spectra were retrieved from a set of ERS-1 synthetic aperture radar (SAR) wave mode (SWM) spectra between January 1993 and December 1995. An assessment is given of the SWM data quality and the retrieval performance as well as the operational feasibility of the retrieval algorithm. Sensitivity studies are performed to demonstrate the weak residual dependence of the retrieval on the first-guess input spectrum. The mean spectral parameters of the SWM retrievals are compared with spectral parameters from collocated wave model (WAM) spectra. The time series of SWM-retrieved and WAM-derived monthly mean significant wave heights H-s in various ocean basins show good overall agreement but with a small systematic underestimation of H-s by the WAM. A decomposition of the wave spectra into wind sea and swell reveals an average 10% overprediction of the wind sea by the WAM while swell is underpredicted by 20-30%. The positive wind-sea bias exhibits no clear wave height dependence, while the negative swell bias decreases with swell wave height. This could be due to a too strong damping in the WAM at low frequencies. Detailed regional investigations point to the existence of smaller-scale phenomena, which may not be adequately reproduced by the WAM at the present resolution of the wind forcing. Finally, an intercomparison is made of the observed and modeled azimuthal cutoff length scales, and global distributions are investigated. Ratios of the observed azimuthal cutoff wavenumber to the mean azimuthal wavenumber component indicate that about 75% of the swell can be directly resolved by the SAR, while about 70% of the wind sea lies at least partially beyond the cutoff

Wind and Wave Extremes over the World Oceans from Very Large Ensembles

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the dataset. The period (9 yrs) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for non-parametric 100-yr return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.Comment: 28 pages, 16 figure