The research draws on data from ATDnet, the Met Office’s very low frequency lightning location system, which detects the electromagnetic sferics produced mainly by cloud-to-ground return strokes as they propagate through the Earth-ionosphere waveguide. Rather than counting flashes, ATDnet records individual strokes, the discrete current pulses that make up a complete flash, and this distinction matters throughout the analysis. The team mined a dataset covering the region between 13 degrees West and 5 degrees East and 49 to 61 degrees North from 2008 to 2016, and selected two days of exceptional lightning activity as case studies: 28 June 2012, when three supercell thunderstorms swept across the English Midlands, and 1 July 2015, when three structurally distinct severe storms crossed northern England.

Assigning individual strokes to individual storms is far from trivial, and the researchers leaned heavily on published synoptic analyses of both days. For the 2012 event, they used Doppler radar snapshots from the Clee Hill and Ingham stations, along with observations of a tornado near Sleaford and accompanying hail, to carve out the strokes belonging to each of the three supercells. The 2015 case relied on radar stations at Hameldon Hill, High Moorsley and Ingham. The result was six distinct stroke datasets: the 2012 storms produced 7,955, 11,988 and 5,655 strokes respectively, while the 2015 storms yielded 4,218, 455 and 1,926. Five of the six storms were classified as supercells, with the remaining one a single-cell thunderstorm, giving the team a rare opportunity to compare lightning behaviour across storm types.

With the strokes assigned, the researchers characterised three physical variables for each storm. The first is the inter-event time, the gap between successive strokes, which ranged across four orders of magnitude, from below 0.01 seconds to more than 100 seconds. Crucially, the distributions turned out to be bimodal, with a shorter peak around 0.2 seconds and a longer peak between 1 and 10 seconds. The team interprets this structure physically: the short peak reflects inter-stroke intervals, the time between successive current pulses within a single flash, while the longer peak corresponds to inter-flash periods tied to the charge-replenishment processes inside the storm. The short-peak values align well with high-speed camera studies from Brazil, the eastern Mediterranean and the United States, which found mean inter-stroke intervals of roughly 83 to 98 milliseconds.

The second variable is the movement speed of the stroke cluster, which serves as a proxy for the speed of the thunderstorm itself. Here the researchers applied two complementary least-squares techniques. The first fits a plane directly to the spatio-temporal coordinates of the strokes, with the slope of that plane encoding the storm’s velocity. The second transforms the data into spatio-temporal lag space, where the spatial and temporal separations between every pair of strokes are compared, and fits a line through the origin. Both methods agreed closely: the 2012 supercells moved at 13 to 17 metres per second, while the faster 2015 storms reached 18 to 31 metres per second, equivalent to up to about 108 kilometres per hour. These values sit comfortably within the ranges reported by radar-based thunderstorm climatologies over Central Europe and North Dakota.

The lag-space analysis also exposed hidden complexity within the storms. Bandings of pixels in the two-dimensional count plots revealed sub-clusters of strokes moving at similar speeds but offset from the main cluster, and other bandings moving at different speeds altogether, evidence that a single thunderstorm can behave as a collection of smaller cells travelling together. The authors treat this internal structure as a limitation on the precision of their bulk speed estimates rather than a flaw in the method, noting that the plane-fit deliberately returns a single representative speed for the storm’s dominant motion, which is exactly what their downstream model requires.

The third variable is spatial spread: how far strokes land from the storm’s track. To estimate the track, the team applied a uniform linear filter to the easting and northing coordinates of the strokes ordered by occurrence, then calculated the distances of each stroke from that smoothed path. Typical spreads reached up to 80 kilometres in either the easting or northing dimension, with one outlier at 226 kilometres. The distribution of these distances proved non-trivial, elongated diagonally in a way that reflects the orientation of each storm’s track relative to the geographic coordinate axes. Fitting a bivariate Gaussian distribution to the data, using the median and interquartile range to blunt the influence of outliers, captured the overall extent well, though it smoothed away the finer structure of the empirical distribution.

These characterisations feed directly into the study’s centrepiece: a synthetic lightning stroke model. The model treats a thunderstorm as a moving point source and generates point events along its path. A user sets an initiation point, a constant movement speed and a direction, then samples inter-event times from an empirical cumulative distribution and spatial offsets from the fitted bivariate Gaussian. A sorting and re-indexing step, validated by autocorrelation analysis, ensures the synthetic sequence reproduces the temporal structure of real storms, which the study showed to be significantly autocorrelated across all six cases, from short to long range. Demonstrations include a single run mimicking one 2012 supercell and a full synthetic three-storm system replicating the entire first case study, with parameters adjustable to explore how spatial spread alone reshapes the pattern of strikes.

The framework is deliberately simple, and the authors are candid about its limits. Real thunderstorms accelerate, decelerate and change direction, while the model assumes straight-line propagation at constant speed, an assumption that introduces growing positional error over long runs. The inter-event time data convolve two physically distinct processes, and the combined supercell sampling distribution is dominated by the larger 2012 dataset, giving synthetic storms a heavier tail that lets them persist longer than their real-world counterparts. Even so, the model’s transparency and controllability are its strengths: every parameter has a clear empirical basis and a clear procedure, making the framework replicable and extensible.

The implications reach beyond lightning. The authors frame their approach as a template for any natural hazard that can be characterised as point events produced by a moving source, from tornado touchdown locations to wildfire ignitions, and they identify storm-relative coordinate systems, non-linear track modelling and additional storm categories as priorities for future work. For hazard assessment, the ability to generate statistically faithful synthetic lightning datasets offers something observational records cannot: unlimited, fully controlled scenarios for testing forecasting algorithms, validating clustering methods and stress-testing infrastructure risk models. In an era when severe convective storms are a growing concern across Europe, turning the chaotic flicker of lightning into rigorous statistics may prove one of the most practical ways to see a storm coming.

Subject of Research: Statistical characterisation and spatio-temporal modelling of lightning strokes from individual UK thunderstorms

Article Title: Characterisation and modelling of lightning strokes as point events in time and space

Article References: Zandovskis, U., Pigoli, D., & Malamud, B. D. (2026). Characterisation and modelling of lightning strokes as point events in time and space. Natural Hazards and Earth System Sciences, 26(10), 4997-5033. https://doi.org/10.5194/nhess-26-4997-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4997-2026

Keywords: lightning, thunderstorms, supercells, ATDnet, spatio-temporal point processes, inter-event times, storm movement speed, spatial spread, synthetic modelling, natural hazards, UK Met Office, severe weather

The institute’s home is the National Weather Center in Norman, Oklahoma, a building whose very architecture embodies the partnership’s philosophy. Inside, University of Oklahoma researchers work side by side with scientists from NOAA’s National Severe Storms Laboratory, forecasters from the Storm Prediction Center, and staff from National Weather Service field offices, alongside additional state and private partners. This co-location is not a matter of convenience; it is the mechanism by which the institute has built its reputation. When a research team develops a new algorithm for interpreting radar data, the forecasters who would use it are down the hall, able to test it, critique it, and shape it into a tool that works under the pressure of an approaching squall line. The result is a weather research hub that has repeatedly compressed the traditional gap between scientific discovery and operational deployment, moving products from the research bench into National Weather Service operations in years rather than decades.

Greg McFarquhar, director of CIWRO, described the effect of the cooperative agreement in transforming the university’s standing in the field. With the funding, he said, OU has become a global leader in severe and high-impact weather research, and by turning scientific understanding into operational products now used by the National Weather Service, the Storm Prediction Center and others, the work delivers innovative, life-saving forecasts that reduce the impacts of extreme weather on communities. That translation function is the institute’s defining characteristic. Basic research into storm dynamics, radar meteorology, and numerical modeling is pursued not for its own sake but with an explicit eye toward the forecaster’s desk. Matt Hulver, OU’s vice president for research and partnerships, emphasized the same point, noting that few research partnerships have done more to address weather prediction, mitigation and resilience, and that the research does not sit on a shelf but goes straight into the forecasts that keep Oklahomans and communities across the country safe.

Among the institute’s most technically significant recent achievements is its work on adaptive scanning with the Advanced Technology Demonstrator, a phased array radar research platform operated with the National Severe Storms Laboratory. Conventional weather radars sweep mechanically through a full volume of the sky in a fixed pattern, spending as much time staring at clear air as at the storm that matters. Phased array radar instead steers its beam electronically, and the adaptive scanning techniques demonstrated on the ATD allow the radar to automatically focus its attention on important storms while skipping regions of clear skies. The payoff is dramatic: scan times are slashed by up to 90 percent, which means forecasters receive updates on fast-moving tornadoes and severe thunderstorms far more frequently than legacy scanning strategies allow. In a tornado situation, where a storm can traverse a county in minutes and evolve in seconds, the difference between a radar update every few minutes and one nearly continuous can translate directly into additional warning lead time for people in the storm’s path.

Equally consequential is CIWRO’s contribution to the Warn-on-Forecast System, developed in collaboration with the National Severe Storms Laboratory. Traditional severe weather warnings have historically been built on observations: a radar detects rotation, and a warning follows. Warn-on-Forecast inverts that logic by running rapidly updating, high-resolution numerical weather models that blend the latest radar and other observations into short-term forecasts of individual storms, designed to give the National Weather Service earlier watches and warnings before hazards fully materialize. The system represents a shift toward probabilistic, convection-allowing prediction at the scale of the thunderstorm itself, a computational challenge that requires assimilating enormous volumes of radar data into models running at horizontal grid spacings fine enough to resolve storm-scale structure. The significance of this work was recognized at the federal level with a U.S. Department of Commerce Gold Medal, one of the department’s highest honors.

The institute’s record of moving research into practice is measurable. Since 2021, CIWRO teams have developed more than 55 algorithms and products that NOAA has transitioned into operational use. That figure captures a broad sweep of activity across the institute’s portfolio, from tools that help forecasters interpret dual-polarization radar signatures to decision aids that structure how warnings are issued and communicated. Each transitioned product represents the endpoint of a deliberate process: a scientific insight, validated through field campaigns and retrospective case studies, is hardened into software, tested against operational constraints, and handed to forecasters with training and documentation. In an era when the volume of meteorological data far exceeds what any human can absorb unaided, such tools have become essential intermediaries between the raw observation stream and the warning decision.

The renewal also comes with a forward-looking research agenda. Over the next five years, McFarquhar said, CIWRO will focus on weather radar and observations, storm-scale and mesoscale modeling, forecast improvements, and subseasonal to seasonal forecasting. That last area extends the institute’s reach beyond the minutes-to-days horizon of severe weather warning into the weeks-to-months range, where forecast information can inform agriculture, water management, and long-range planning. Determining the societal impacts of research will also be key to the institute’s work, an acknowledgment that a forecast only saves lives if people receive it, understand it, and act on it. As will increasing the use of machine learning tools to quickly pull data from historical weather accounts, tapping archives of past storms that are too vast for manual analysis but rich with patterns that modern algorithms can extract to improve future prediction.

Beyond its research mission, CIWRO functions as one of the nation’s most important training grounds for the atmospheric science workforce. Since 2021, the institute has supported 32 postdoctoral researchers, 47 graduate students and 130 undergraduates, embedding young scientists in an environment where the distance between a research question and an operational consequence is measured in footsteps rather than flight miles. The institute also delivers in-person and online training to National Weather Service forecasters through the NWS Human Capital Management Division, ensuring that the operational workforce stays current with the latest science as new tools and techniques enter service. This dual role, producing both new science and the people equipped to apply it, multiplies the return on the federal investment and helps explain why the partnership has survived and deepened across five decades and multiple generations of technology, from the earliest Doppler radar experiments to today’s phased array and machine learning systems.

Robert Palmer, dean of OU’s College of Atmospheric and Geographic Sciences and director of the National Weather Center, framed the renewal as a strengthening of that longstanding relationship, saying the institute is a cornerstone of the university’s leadership in weather research and of the unique research and operational community at the National Weather Center, and that the five-year renewal ensures OU will continue advancing weather science, improving operations and helping protect lives and property. As extreme weather events grow costlier and the demand for precise, actionable forecasts intensifies across the country, the extension of the OU-NOAA partnership through 2031 secures the institutional machinery on which much of the nation’s severe weather warning capability depends. For the researchers, forecasters, and students of Norman, Oklahoma, the work now continues with a mandate that is both familiar and renewed: to understand the atmosphere’s most dangerous behavior and to make sure that understanding reaches the public in time to matter.

Subject of Research: Renewal of the University of Oklahoma and NOAA cooperative institute partnership for severe and high-impact weather research

Article Title: University of Oklahoma, NOAA partnership continues life-saving research with five-year renewal

Article References: University of Oklahoma, NOAA partnership continues life-saving research with five-year renewal. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: University of Oklahoma, NOAA, CIWRO, severe weather, phased array radar, Warn-on-Forecast, National Weather Service, weather forecasting, Storm Prediction Center, machine learning, tornado warnings, research funding

Melioidosis is caused by Burkholderia pseudomallei, a gram-negative bacterium that lives in soil and freshwater. Humans typically acquire it through direct contact with contaminated soil or water, particularly through cuts in the skin, inhalation of aerosolized particles, or, less commonly, ingestion. The disease is notoriously difficult to diagnose because it can present in almost any organ and with an incubation period ranging from days to years. In endemic regions such as northeast Thailand and northern Australia, melioidosis is a leading cause of community-acquired sepsis, with mortality rates that can exceed 40 percent even with treatment. Globally, estimates suggest the bacterium may be responsible for tens of thousands of deaths annually, a burden that many researchers believe is substantially undercounted because of limited diagnostic capacity in the regions where the disease is most common.

For decades, the conventional wisdom held that melioidosis in the United States occurred almost exclusively in travelers returning from endemic areas. That assumption began to erode when environmental surveys detected B. pseudomallei in soil and water samples along the Gulf Coast of Mississippi and Texas, and when cases began appearing in residents of those states who had never left the country. The Centers for Disease Control and Prevention has confirmed sporadic locally acquired cases in recent years, including a cluster in Georgia and cases in Kansas and Minnesota linked to contaminated products or uncertain exposure routes. The Florida case described in the new report fits this emerging pattern: a patient with no recent international travel who nonetheless developed a classic pulmonary presentation of the disease, suggesting that the bacterium had been acquired locally, likely through inhalation of contaminated aerosols stirred up by severe weather.

The patient in the report arrived with a constellation of symptoms that would challenge any emergency department. Fever and sharp chest pain worsened by breathing, combined with measurable hypoxia, pointed initially toward community-acquired pneumonia. But computed tomography of the chest told a more complicated story. Instead of the lobar consolidation typical of bacterial pneumonia, the scan revealed multiple solid pulmonary nodules surrounded by ground-glass opacities, along with enlarged mediastinal lymph nodes. This radiographic pattern is a recognized hallmark of melioidosis, which can seed the lungs hematogenously and produce discrete nodular lesions that resemble metastatic disease. The differential diagnosis for such findings is broad and includes fungal infections like histoplasmosis, mycobacterial disease, vasculitis, and malignancy, and clinicians who do not routinely consider melioidosis may not order the specialized cultures needed to identify the organism.

Laboratory confirmation of B. pseudomallei requires awareness at the microbiology bench as well as at the bedside. The bacterium can be misidentified by automated systems as a less dangerous relative, such as Burkholderia cepacia or even Pseudomonas species, and handling it in the laboratory poses a biosafety risk because it can infect laboratory workers through inhalation. Accurate identification typically depends on matrix-assisted laser desorption ionization mass spectrometry with updated databases, molecular methods, or specific biochemical testing. Serological assays exist but have limited utility in regions where background exposure is low and in patients whose disease is localized rather than disseminated. The delay between presentation and definitive diagnosis is a recurring theme in non-endemic settings, and it matters clinically because early, appropriate antibiotic therapy is one of the strongest predictors of survival in melioidosis.

Treatment of melioidosis follows a two-phase strategy refined over decades of clinical trials in Thailand and Australia. The intensive phase uses intravenous ceftazidime or a carbapenem such as meropenem for at least two weeks to bring the acute infection under control. This is followed by an eradication phase, traditionally oral trimethoprim-sulfamethoxazole for three to six months, designed to eliminate the bacterium from intracellular sanctuaries where it can persist and relapse. The Florida patient’s management illustrates how comorbidities complicate this standard approach. Because he had stage 3b chronic kidney disease, his physicians were reluctant to prescribe trimethoprim-sulfamethoxazole, which requires renal dosing and carries a risk of further kidney stress and hyperkalemia. Instead, after completing intravenous ceftazidime, he received oral doxycycline monotherapy as an alternative eradication regimen.

The choice of doxycycline is noteworthy because it departs from the conventional first-line recommendation, and the authors of the report suggest it may serve as a viable option for patients in whom trimethoprim-sulfamethoxazole is contraindicated. Evidence for doxycycline monotherapy in the eradication phase is thinner than for the standard regimen, drawing largely from older trials and in vitro susceptibility data, but the drug offers practical advantages: it does not require renal dose adjustment, is inexpensive, and is well tolerated. The patient’s outcome supports its cautious use in selected cases. At three-month follow-up, repeat imaging demonstrated complete resolution of the pulmonary nodules, and he had achieved a full clinical recovery, an encouraging result in a disease where relapse rates of 10 to 20 percent are reported even after apparently successful therapy.

Underlying conditions play a decisive role in determining who develops symptomatic melioidosis after exposure. Diabetes mellitus is by far the most important risk factor, increasing susceptibility several-fold and accounting for a large proportion of cases in endemic regions. Chronic kidney disease, chronic lung disease, alcohol use disorder, and immunosuppression also elevate risk. The Florida patient’s chronic kidney disease likely placed him in a vulnerable category, and the report’s authors emphasize that clinicians in the southern United States should view diabetes and recent severe weather events as key clinical clues when evaluating unexplained pneumonia or pulmonary nodules. Hurricanes, tropical storms, and prolonged flooding create precisely the conditions under which soil bacteria become aerosolized and inhaled, and climate models suggest that the warm, humid Gulf Coast environment is increasingly hospitable to the environmental persistence of B. pseudomallei.

The broader implications of the case extend beyond a single patient. If B. pseudomallei has established ecological niches along the Gulf Coast and potentially beyond, melioidosis will join the growing list of tropical infections becoming domestically relevant in a warming climate. Physicians who trained with the assumption that melioidosis requires a travel history will need to update that reflex, particularly when evaluating patients with non-resolving community-acquired pneumonia, cavitary or nodular lung disease, or sepsis of unclear origin in the southern states. Public health surveillance will likewise need to adapt, since melioidosis is not uniformly reportable and environmental mapping of the bacterium remains incomplete. Each confirmed locally acquired case is a data point in an unfinished map of where the organism lives and how far it has spread.

For now, the practical lessons of the Florida case are straightforward. Melioidosis belongs on the differential diagnosis for pulmonary nodules and non-resolving pneumonia in the southern United States, even in patients who have never traveled abroad. Severe weather and diabetes should sharpen clinical suspicion. When trimethoprim-sulfamethoxazole cannot be used because of renal impairment or drug interactions, doxycycline may offer an alternative path to eradication, though it should be weighed carefully against the stronger evidence base for standard therapy. And as the environment shifts, so too must the clinical imagination of the physicians who treat the infections it harbors. A bacterium once described as brought with the wind from distant tropics may, in fact, already be home.

Subject of Research: A locally acquired case of pulmonary melioidosis in Florida caused by Burkholderia pseudomallei

Article Title: Brought with the wind: A case of melioidosis acquired in the United States presenting with multiple pulmonary nodules

Article References: Garcia-Santisteban, R., Kaufman, M., Jacqueline Chung, H.-T., Khella, R., Pousa, A., Kujundzic, W., Mbaga, I., & Voin, V. (2026). Brought with the wind: A case of melioidosis acquired in the United States presenting with multiple pulmonary nodules. PLOS Neglected Tropical Diseases, 20(10), e0014790. https://doi.org/10.1371/journal.pntd.0014790

Image Credits: AI Generated

DOI: 10.1371/journal.pntd.0014790

Keywords: melioidosis, Burkholderia pseudomallei, Florida, Gulf Coast, pulmonary nodules, ceftazidime, doxycycline, chronic kidney disease, severe weather, emerging infectious disease, PLOS Neglected Tropical Diseases, diabetes

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