The study, published in Theoretical and Applied Climatology, pitted the Weather Research and Forecasting model, known universally as WRF, against its younger rival, the Model for Prediction Across Scales–Atmosphere, or MPAS-A. Both were developed at the National Center for Atmospheric Research in collaboration with the U.S. Department of Energy, but they represent fundamentally different philosophies of how to chop the atmosphere into computable pieces. WRF, the long-reigning workhorse of operational forecasting in Brazil and worldwide, divides space into a regular grid of quadrilateral cells using finite-difference mathematics. MPAS-A instead wraps the planet in an unstructured Voronoi mesh — a honeycomb of hexagonal cells solved with finite-volume methods — that can transition smoothly from coarse global coverage to razor-sharp regional detail without the awkward seams of nested grids.

That architectural difference matters because the São Paulo storm was a textbook case of scale interaction. Brazilian Navy synoptic charts from the day show a high-pressure system of 1026 hPa parked over the South Atlantic, east of the state, while a deepening low-pressure area associated with a trough near southern Brazil and Paraguay intensified rapidly. The clash between warm pre-frontal air and cold post-frontal air, supercharged by daytime heating and oceanic moisture, ignited a cluster of cumulonimbus clouds. GOES-16 satellite imagery shows isolated convective cores forming between 5:30 and 6:30 in the evening, organizing into a full-blown convective system by 8:30, and then sweeping out to sea after 9:00. A pre-frontal low-level jet, channeled by valleys and hills toward the coast, added fuel to the fire.

To see which model could capture this chaos, the team, led by Rosales Aylas Georgynio Yossimar of SENAI CIMATEC’s Supercomputing Center in Salvador, ran both models at 3-kilometer resolution over a domain covering southeastern Brazil and a generous slice of the South Atlantic. WRF used its classic two-way nested setup at 9 and 3 kilometers, while MPAS-A employed a quasi-uniform mesh at roughly 3 kilometers across the entire domain. Both simulations shared 41 vertical levels and equivalent physics suites — the tropical package in WRF and the mesoscale reference package in MPAS-A — and both were initialized and driven by the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts. Three separate experiments, initialized at different times before the event, tested how forecast skill decayed with lead time.

The verdict on the big picture was encouraging: both models successfully reproduced the synoptic skeleton of the event, including the trough, the frontal organization, and the intensification of low-level winds. At the 850 hPa level, roughly one to two kilometers above the surface, both simulations captured wind speeds exceeding 25 meters per second — Beaufort force 10, a strong gale — sweeping over the central and northern parts of São Paulo state near Bauru before the wind corridor shifted southeastward and weakened after 8:00 in the evening. WRF painted broader wind fields, while MPAS-A drew more compact, concentrated cores of extreme wind, a signature the authors link to its finite-volume formulation on the Voronoi mesh.

Beneath that broad agreement, however, the models diverged in revealing ways. Against ERA5, MPAS-A showed stronger spatial correlation over most of the analyzed period, particularly over the ocean, suggesting its unstructured grid handles mesoscale gradients and frontal features with more numerical grace. Yet higher spatial correlation did not translate into smaller errors: during the storm’s intensification phase, MPAS-A’s mean absolute error and root mean square error actually grew, hinting that the model may have over-intensified wind maxima or displaced the storm’s core. WRF, meanwhile, showed a more heterogeneous bias pattern, overestimating winds over land while underestimating them over the southern ocean sector. The team is careful to note that these differences cannot be pinned on mesh geometry alone — dynamics, physics coupling, and land-surface representation all conspire.

One unexpected confound emerged from the ground up. Because of limitations in MPAS-A’s preprocessing tools, the two models were fed different land-use datasets: WRF received the current 2019 MapBiomas classification of Brazil, while MPAS-A relied on the older MODIS-IGBP dataset from the early 2000s. Grasslands covered about 35 percent of WRF’s domain but only 7 percent of MPAS-A’s, with corresponding shifts in savanna and cropland fractions. Since vegetation categories control roughness length, albedo, and evapotranspiration — and therefore the surface energy balance that drives boundary-layer turbulence — this discrepancy adds a layer of ambiguity to any model-versus-model comparison, and the authors flag it as a caveat that future studies must untangle.

Comparisons against Brazil’s INMET weather stations exposed the models’ Achilles’ heel: complex topography. At Campos do Jordão, perched at 1,628 meters in the Serra da Mantiqueira, WRF produced its worst errors, with root mean square errors and mean biases exceeding 6 meters per second. The reason is almost cartographic: WRF’s nearest grid point sat closer to the mountain slope, where terrain-induced flow acceleration inflated its wind estimates, while MPAS-A’s representative point fell in the calmer urban core. At Cachoeira Paulista, tucked into the Paraíba Valley at the foot of the same range, similar problems appeared. Near the storm center around Bauru, by contrast, both models performed respectably, with errors under 3 meters per second and biases below 2.

Temperature told a story of mirror-image biases. WRF tended to overestimate 2-meter air temperature, sometimes by more than 1.5 degrees Celsius, while MPAS-A generally underestimated it, especially during daytime heating. Yet both models faithfully reproduced the diurnal cycle — the pre-storm warming on October 10, the abrupt frontal cooling on the 11th, and the recovery on the 12th — though they sharpened the storm-driven temperature drop more than observations showed. The most troubling statistic came from hourly wind correlations: no model exceeded 0.78 at any station, roughly 63 percent of stations fell between 0.2 and 0.6, and some correlations were near zero or negative, exposing a fundamental struggle to capture hour-by-hour wind variability during the event.

The authors attribute these timing failures to a familiar culprit: the sparse observational network of the Southern Hemisphere, which degrades the initial conditions fed into every model, compounded by uncertainties in turbulence parameterization and the use of hourly mean winds rather than true gust measurements. Their prescription is clear — better physics, denser observations, and complementary techniques such as data assimilation and statistical post-processing. As forecasting agencies worldwide weigh a transition from WRF’s rigid squares to MPAS-A’s flexible hexagons, this storm-soaked Brazilian case study offers a sobering reminder: the grid may be getting smarter, but the atmosphere still keeps some of its wildest moments just beyond the model’s grasp.

Subject of Research: Comparative simulation of an extreme wind gust event in São Paulo using the WRF and MPAS-A atmospheric models

Article Title: Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models

Article References: Yossimar, R. A. G., da Silveira, P. W. M., dos, S. T. S., Duarte, J. W., de, L. F. J. L., Cotta, W. A. L., Cavalcante, A. A., Rodrigues, S. A., da Silva, R. D. N., & Martins, M. D. (2026). Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models. Theoretical and Applied Climatology, 157(10), Article 644. https://doi.org/10.1007/s00704-026-06537-9

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06537-9

Keywords: WRF, MPAS-A, wind gusts, extratropical cyclone, São Paulo, numerical weather prediction, mesoscale modeling, Voronoi mesh, ERA5 reanalysis, boundary layer, severe weather, Brazil