School of Earth Sciences - Theses
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ItemThe effect of statistical wind corrections on global wave forecasts( 2011)The ability to forecast ocean waves relies to a large extent on numerical models. Current third generation wave models have been found by many studies to produce highly accurate forecasts several days in advance. The skill of these models is such that the quality of the wave forecast is highly dependent on errors in the forcing wind field. On global scales, a lack of wind and wave observations has historically hampered efforts to separate large scale systematic error due to inherent wave model deficiencies from that imparted by the forcing winds. The advent of remotely sensed observations from altimetry, and more recently scatterometry, provide high quality observations on the open ocean, allowing the spatial structure of the systematic error in both modelled fields to be quantified. In this study, surface winds from the Australian Community Climate Earth System Simulator (ACCESS), the recently implemented operational atmospheric model at the Australian Bureau of Meteorology, are used as forcing for the WAVE-WATCH III® wave model. A number of global wave hindcasts are performed over a four month period from July to October 2008. The geographical variation of systematic error in the surface winds and resulting modelled Significant Wave Height (Hs) are then assessed using QuikSCAT scatterometer data and Jason-1 and Envisat altimeter data respectively. A negative bias in the modelled Hs is identified over most of the globe. The cause of this bias is determined to be largely due to a negative bias in the ACCESS winds. Subsequent to this finding, a number of means of statistically correcting the winds are explored. A simple correction over the entire domain is found to inadequately account for geographical variation in the wind bias. This is addressed by considering corrections that vary in space. Finally, these spatially varying corrections are extended to vary in time. In an operational environment, the error characteristics of the wind forcing can be expecting to change over time with the evolution of the atmospheric model. This in turn requires any applied correction to be monitored and maintained. Motivated by a desire to avoid this manual maintenance, a self learning correction method is proposed whereby spatially and temporally varying corrections are calculated in real time from a moving window of historical comparisons between observations and preceding forecasts. This technique is shown to effectively remove both global, and regionally varying wind speed biases. Finally, the effect of these wind corrections on the modelled wave field is assessed. Large improvement is demonstrated in the Northern Hemisphere Hs, however, the applied corrections produce a positive bias in the Southern Hemisphere. Overall, it is clear that by correcting the winds, their contribution to the modelled Hs error is reduced, allowing inherent wave model deficiencies to be more confidently isolated.