Abstract. We analyzed the spatial pattern of wave extremes in the South Atlantic Ocean
by using multiple altimeter platforms spanning the period 1993–2015. Unlike
the traditional approach adopted by previous studies, consisting of computing
the monthly mean, median or maximum values inside a bin of certain size, we
tackled the problem with a different procedure in order to capture more
information from short-term events. All satellite tracks occurring during
a 2-day temporal window were gathered in the whole area and then gridded data
were generated onto a mesh size of 2∘×2∘ through
optimal interpolation. The peaks over threshold (POT) method was applied,
along with the generalized Pareto distribution (GPD). The results showed a
spatial distribution comparable to previous studies and, additionally,
this method allowed for capturing more information on shorter timescales without
compromising spatial coverage. A comparison with buoy observations
demonstrated that this approach improves the representativeness of short-term
events in an extreme events analysis.
In this study, we estimated near-surface wind speed 100-year return values over the southwestern South Atlantic Ocean, using present-day (1979Ocean, using present-day ( -2018 simulations from the regional climate models (RCMs) WRF and RegCM4. Extreme wind events, associated with hazardous conditions over coastal and oceanic areas, must be well represented in numerical models for risk assessment, and few studies focused on the added value offered by RCMs to wind extremes. Events were selected with the peaks-over-threshold method and extremes were calculated by fitting peaks to a generalized Pareto distribution. For the assessment of model performance, we used the satellite-based dataset from the Cross-Calibrated Multi Platform (CCMP), which has great agreement with in situ observations. While modern reanalysis underestimated higher wind speed quantiles, the CCMP was able to represent these quantiles. Dynamical downscaling with the WRF (RegCM4) indicates an underestimation (overestimation) of wind speed for upper quantiles. To mitigate the effects of these differences in the extreme value estimate, we applied a linear adjustment in the simulated wind speed using the CCMP as reference. This application reduced the bias for higher wind speeds in simulations for all regions, except over the coastal area near Argentina and Uruguay, where downscaling already realistically represents extreme events. The spatial distribution
Several studies showed the advantages of using nudging techniques to enforce the evolution of a numerical model to approximate large-scale features to a reference. Spectral nudging applications are particularly useful since they also provide the ability to maintain higher temporal and spatial variability in the simulations results. In this study, different nudging configurations are tested for an intense South Atlantic Convergence Zone (SACZ) event. Large-scale features directly modulate the SACZ formation and persistence. Therefore, the employment of relaxation methods enhances the representation of continental-wide features from lateral boundary conditions over the interior of broad domains. Results show that Spectral Nudging is very effective at imposing the selected synoptic scales onto the solution, while allowing the limited-area model to incorporate finer-scale features. Wave numbers with associated length scales of 2000km are ideal to enforce large-scale fields to represent features important for the SACZ formation and persistence. Using larger wave numbers can improve the simulation performance, but at a cost of damping the model dynamic and physical contribution to the final solution.
Abstract. We analyzed the spatial pattern of wave extremes in the South Atlantic Ocean by using multiple altimeter platforms spanning the period 1993-2015. Unlike the traditional approach adopted by previous studies, consisting in computing the monthly mean, median or maximum values inside a bin of certain size, we tackled the problem with a different procedure in order to capture more information from short term events. All satellite tracks occurring during two-day temporal window were added in the whole area and then gridded data was generated onto a mesh size of 2• × 2 • through optimal interpolation. The
1Ocean Sci. Discuss., https://doi
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