Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

Abstract

The coast of São Paulo, Brazil, is exposed to storm surges that can cause damage and floods. These storm surges are produced by slowly traveling cyclone–anticyclone systems. The motivation behind this work was the need to evaluate high-resolution forecasts of the mean sea-level pressure and 10 m winds, which are the major drivers of the wave model. This work is part of the activity in devising an early warning system for São Paulo coastal storm surges. For the evaluation, four case studies that had a major impact on the coast of São Paulo in 2020 were selected. Because storm surges that reach the coast may cause coastal flooding, precipitation forecasts were also evaluated. The mesoscale Eta model produces forecasts with a 5 km resolution for up to an 84 h lead time. The model was set up in a region that covers part of southeast and south Brazil. The ERA5 reanalysis was used to describe the large-scale synoptic conditions and to evaluate the weather forecasts. The cases showed a region in common between 35° S, 40° S and 35° W, 45° W where the low-pressure center deepened rapidly on the day before the highest waves reached the coast of São Paulo, with a mostly eastward, rather than northeastward, displacement of the associated surface cyclone and minimal or no tilt with height. The winds on the coast were the strongest on the day before the surge reached the coast of São Paulo, and then the winds weakened on the day of the maximum wave height. The pattern of the mean sea-level pressure and 10 m wind in the 36 h, 60 h, and 84 h forecasts agreed with the ERA5 reanalysis, but the pressure was slightly underestimated. In contrast, the winds along the coast were slightly overestimated. The 24 h accumulated precipitation pattern was also captured by the forecast, but was overestimated, especially at high precipitation rates. The 36 h forecasts showed the smallest error, but the growth in the error for longer lead times was small, which made the 84 h forecasts useful for driving wave models and other local applications, such as an early warning system.

Keywords

• Eta model
• weather forecasts
• forecast evaluation
• coastal disasters
• early warning
• high coastal waves