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

Statistical analysis of ocean wave and wind parameters retrieved with an empirical SAR algorithum

A global dataset of two years (September 1998 to December 2000) of ERS SAR data was reprocessed to more than one million SAR imagettes. Met ocean Parameters like significant ocean wave height (H s), wind speed (U 10) and mean wave period (T m-10) are derived from the SAR images using a new empirical algorithm CWAVE [1]. The results are compared to collocated ERS altimeter data and in Situ measurements from NOAA buoys and observations taken onboard the vessel Polarstern. It is shown that the SAR derived H s is comparable in quality to altimeter measurements and can thus be used for real time assimilation

Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar

The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world’s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance

Sea State from High Resolution Satellite-borne Synthetic Aperture Radar Imagery

The Sea Sate Processor (SSP) was developed for fully automatic processing of high-resolution Synthetic Aperture Radar (SAR) data from TerraSAR-X (TS-X) satellites and implemented into the processing chain for Near Real Time (NRT) services in the DLR Ground Station "Neustrelitz". The NRT chain was organised and tested to provide the processed data to the German Weather Service (DWD) in order to validate the new coastal forecast model CWAM (Coastal WAve Model) in the German Bight of the North Sea with 900 m horizontal resolution. The NRT test-runs, wherein the processed TS-X data were transferred to DWD and then incorporated into forecast products reach the best performance about 10 min for delivery of processed TS-X data to DWD server after scene acquisition. To do this, a new empirical algorithm XWAVE_C (C = coastal) for estimation of significant wave height from X-band satellite-borne SAR data has been designed for coastal applications. The algorithm is based on the spectral analysis of subscenes and the empirical model function yields an estimation of integrated sea state parameters directly from SAR image spectra without transformation into wave spectra. To provide the raster coverage analysis, the SSP intends three steps of recognising and removing the influence of non-sea-state-produced signals in the Wadden Sea areas such as ships, buoys, dry sandbars as well as nonlinear SAR image distortions produced by e.g. short and breaking waves. For the validation, more than 150 TS-X StripMap scene sequences with a coverage of ~30 km × 300 km across the German Bight since 2013 were analysed and compared with in situ Buoy measurements from 6 different locations. On this basis, the SSP autonomous processing of TS-X Stripmap images has been confirmed to have a high accuracy with an error RMSE = 25 cm for the total significant wave height

Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar

The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world's oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance

Assessing the Real-Time Lagrangian Predictability of the Operational Navy Coastal Ocean Model in the Gulf of Mexico

This study quantitatively assesses the drift predictive skill of Fleet Numerical Meteorology and Oceanography Center’s (FNMOC’s) operational ocean models which are used to support a wide range of military and civilian applications. Overall, the findings of this work support the recommendation of spatial filtering for regional-scale ocean model velocity fields used in deep-water drift applications. In conjunction with filtering, the use of a pure particle drift algorithm is suggested for short-term forecasts and a drift algorithm including a sub-grid scale, random flight, parameterization for predictions requiring extended forecast predictions. Drift prediction skill is quantified through metrics of in-cloud percentage, distance error, and cloud size, which are used to assess the impact of different drift algorithms and underlying ocean models on the drift prediction capability. Through an exploration of parameterization additions to the drift algorithm, spatial filtering of model velocity fields, and increases in model horizontal resolution, drift prediction skill is shown to be counter-balanced on the accuracy of the model\u27s dispersive characteristics along with the accuracy of the underlying model velocity field (i.e. data-constrained, predictable features). A regional scale model at a horizontal resolution typically employed by FNMOC (3-kilometers) is found to be grossly under dispersive, and derived drift predictions using a pure particle algorithm are not skillful in terms of in-cloud percentage beyond a 24-hour forecast. Parameterization additions (i.e. sub-grid scale velocity and Leeway), which enhance model dispersion, are shown to greatly improve the regional scale model\u27s ability to predict a drift cloud that encompasses an object of interest at longer forecast lengths (\u3e 24-hours) by increasing cloud size. Increasing the model’s horizontal resolution (500-meters) is likewise shown to improve in-cloud prediction performance at all forecast lengths, due to its more accurate representation of dispersion which results in much larger cloud size predictions compared to those from a regional scale model. Spatial filtering of regional scale velocity fields using a Gaussian filter removes uncertain, unpredictable features (i.e. submesocale) leaving behind a data-constrained velocity field. Even though spatial filtering suppresses dispersion further for an already under-dispersion regional scale model, filtering is shown to significantly improve drift prediction performance extending in-cloud skill farther into the forecast, reducing distance errors by 15-20%, and reducing cloud size predictions by 20-30%