Impact of simulated SWOT Observations in a Multi-Scale Assimilation

This research builds on an existing a multi-scale, data assimilative, high-resolution ocean forecasting system that has demonstrated successful assimilation of simulated Surface Water Ocean Topography (SWOT) observations using a two-step 3DVAR analysis procedure. For this presentation, we examine the impact of the second step and the combination of observations going into that second step, in the multi-scale, data assimilation system. The first analysis step seeks to correct the large-scale, while the second step seeks to correct for the smaller-scale features present in the SWOT observations. An Observing System Simulation Experiment (OSSE) is used to explicitly calculate errors produced by competing single- and multi-scale analysis procedures. The first analysis step is the standard exploitation of observations with a 5-day data analysis period, horizontal correlation scales, vertical structure, and background errors consistent with mesoscale corrections. Experimentation with the observation window length of the second analysis step shows that a shorter time window produces lower analysis errors. This is consistent with the larger mesoscale structures having a longer time period and submesoscale structures having short Eulerian times being advected by the mesoscale. Therefore, a 24-hour observation window with a first guess at appropriate time (FGAT) for the second step was selected and sequential analysis/forecast cycles were performed for an entire year. The multi-scale analysis with SWOT observation in both steps produces less overall error than any other combination when analyzing both area-averaged errors and wavenumber spectral analysis. Therefore, the multi-scale assimilation is essential for most effectively utilizing the forthcoming SWOT observations, which resolve features across a much wider spectrum of horizontal scales than are observed by the current nadir altimeters.