Meteorology

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included. Full article

Climatological variables undergo changes over time, and it is important to understand such dynamic changes at global, regional, and local levels. While global and regional studies are common in the study of climate, such studies at a local level are not as common. The aim of this article is to study temporal changes in precipitation, snowfall, and temperature variables at specific stations located on the rims of Lake Erie and Lake Michigan. The identification of changes is carried out by applying change-point analysis to precipitation, snowfall, and temperature data from Buffalo, Erie, and Cleveland stations located on the rim of Lake Erie and at Chicago, Milwaukee, and Green Bay stations located on the rim of Lake Michigan. We adopt mainly the Bayesian information criterion (BIC) method to identify the number and locations of change points, and then we apply the generalized likelihood ratio statistic to test for the statistical significance of the identified change points. We follow this up by finding 95% confidence intervals for those change points that were found to be statistically significant. The results from the analysis show that there are significant changes in precipitation, snowfall, and temperature variables at all six rim stations. Changes in precipitation show consistently significant increases, whereas there is no similar consistency in snowfall increases. Temperature increases are generally quite sharp, and they occur consistently around 1985. Overall, upon combining the amounts of changes from all six stations, the average amount of change in annual average temperature is found to be 0.96 °C, the average percentage of change in precipitation is 16%, and the average percentage of change in snowfall is 17%. The changing local climatic conditions identified in the study are important for local city planners, as well as residents, so that they can be well prepared for changing climatic scenarios. Full article

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings. Full article

Most damage to buildings across the contiguous United States of America (USA) is caused by gusts in convective events associated with thunderstorms. Design rules for structures to resist these events rely on the integrity of meteorological observations and the methods of assessment. These issues were addressed for the US Automated Surface Observation System (ASOS) in six preliminary studies published in 2022 and 2023, allowing this present study to focus on the analysis and reporting of gust events observed between 2000 and 2023 at 642 well-exposed ASOS stations distributed across the contiguous USA. It has been recently recognized that the response of buildings to convective gusts, which are non-stationary transient events, differs in character from the response to the locally stationary atmospheric boundary gusts, requiring gust events to be classified and assessed by type. This study sorts the mixture of all observed gust events exceeding 20 kn, but excluding contributions from hurricanes and tropical storms, into five classes of valid meteorological types and two classes of invalid artefacts. The valid classes are individually fitted to optimal sub-asymptotic models through extreme value analysis. Classes are recombined into a joint mixture model and compared with current design rules. Full article

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends. Full article

Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks and their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation of the climatological component in conventional models restricts the prediction of unusual TC tracks. The climatological component should be a forcing quantity, not a predictor in the numerical integration of all models. Anomaly-based variable models can overcome the bottleneck of forecast time length or the one-week forecasting barrier, which is limited to less than one week for conventional models. The challenge in extreme precipitation forecasting is how to physically get the vertical velocity. The anomalous moisture stress modulus (AMSM), as an indicator of heavy rainfall presented in this paper, considers the two conditions associated with vertical velocity and anomalous specific humidity in the lower troposphere. Vertical velocity is produced by the orthogonal collision of horizontal anomalous airflows. Full article

The twin problems of closure and dissipation have been barriers to the analytical solution of the Navier–Stokes equation for fluid flow by top-down methods for two centuries. Here, the statistical multifractal analysis of airborne observations is used to argue that bottom-up approaches based on the dynamic behaviour of the basic constituent particles are necessary. Contrasts among differing systems will yield scale invariant turbulence, but not with universal analytical solutions to the Navier–Stokes equation. The small number of publications regarding a molecular origin for turbulence are briefly considered. Research approaches using suitable observations are recommended. Full article

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein. Full article

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales. Full article

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m² are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration. Full article

Instead of using the traditional space-time Fourier analysis of filtered specific atmospheric fields, a normal-mode decomposition method was used to analyze South American intraseasonal variability (ISV). Intraseasonal variability was examined separately in the 30–90-day band, 20–30-day band, and 10–20-day band. The most characteristic structure in the intraseasonal time-scale, in the three bands, was the dipole-like convection between the South Atlantic Convergence Zone (SACZ) and the central-east South America (CESA) region. In the 30–90-day band, the convective and circulation patterns were modulated by the large-scale Madden–Julian oscillation (MJO). In the 20–30-day and 10–20-day bands, the convection structures were primarily controlled by extratropical Rossby wave trains. The normal-mode decomposition of reanalysis data based on 30–90-day, 20–30-day, and 10–20-day ISV showed that the tropospheric circulation and CESA–SACZ convective structure observed over South America were dominated by rotational modes (i.e., Rossby waves, mixed Rossby-gravity waves). A considerable portion of the 30–90-day ISV was also associated with the inertio-gravity (IGW) modes (e.g., Kelvin waves), mainly prevailing during the austral rainy season. The proposed decomposition methodology demonstrated that a realistic circulation can be reproduced, giving a powerful tool for diagnosing and studying the dynamics of waves and the interactions between them in terms of their ability to provide causal accounts of the features seen in observations. Full article

Accurate atmospheric 3D wind observations are one of the top priorities for the global scientific community. To address this requirement, and to support researchers’ needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses the following: a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019–August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. The recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC’s capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture. Full article

Idealized simulations with a cloud-resolving model are conducted to examine the impact of a simplified city on the structure of a supercell thunderstorm. The simplified city is created by enhancing the surface roughness length and/or surface temperature relative to the surroundings. When the simplified city is both warmer and has larger surface roughness relative to its surroundings, the supercell that passes over it has a larger updraft helicity (at both midlevels and the surface) and enhanced precipitation and hail downwind of the city, all relative to the control simulation. The storm environment within the city has larger convective available potential energy which helps stimulate stronger low-level updrafts. Storm relative helicity (SRH) is actually reduced over the city, but enhanced in a narrow band on the northern edge of the city. This band of larger SRH is ingested by the primary updraft just prior to passing over the city, corresponding with enhancement to the near-surface mesocyclone. Additional simulations in which the simplified city is altered by removing either the heat island or surface roughness length gradient reveal that the presence of a heat island is most closely associated with enhancements in updraft helicity and low-level updrafts relative to the control simulation. Full article

We characterized the thermal load of a person walking and/or standing in the fog by analyzing the thermal resistance of clothing, r_cl, and operative temperature, T_o. The r_cl–T_o model applies to individuals using weather data. The body mass index and basal metabolic flux density values of the person analyzed in this study are 25 kg m⁻² and 40 W m⁻², respectively. Weather data are taken from the nearest automatic weather station. We observed 146 fog events in the period 2017–2024 in Martonvásár (Hungary’s Great Plain region, Central Europe). The main results are as follows: (1) The r_cl and T_o values were mostly between 2 and 0.5 clo and −4 and 16 °C during fog events, respectively. (2) The largest and smallest r_cl and T_o values were around 2.5 and 0 clo and −7 and 22 °C, respectively. (3) The r_cl differences resulting from interpersonal and wind speed variability are comparable, with a maximum value of around 0.5–0.7 clo. (4) Finally, r_cl values are significantly different for standing and walking persons. At the very end, we can emphasize that the thermal load of the fog depends noticeably on the person’s activity and anthropometric characteristics. Full article

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output. Full article

Tropical sea surface temperature (SST) variability, mainly driven by the El Niño–Southern Oscillation (ENSO), influences the atmospheric circulation and hence the transport of heat and chemical species in both the troposphere and stratosphere. This paper uses Met Office, ERA5 and MERRA2 reanalysis data to examine the impact of SST variability on the dynamics of the polar stratosphere and ozone layer over the period from 1980 to 2020. Particular attention is paid to studying the differences in the influence of different types of ENSO (East Pacific (EP) and Central Pacific (CP)) for the El Niño and La Niña phases. It is shown that during the CP El Niño, the zonal wind weakens more strongly and changes direction more often than during the EP El Niño, and the CP El Niño leads to a more rapid decay of the polar vortex (PV), an increase in stratospheric air temperature and an increase in the concentration and total column ozone than during EP El Niño. For the CP La Niña, the PV is more stable, which often leads to a significant decrease in Arctic ozone. During EP La Niña, powerful sudden stratospheric warming events are often observed, which lead to the destruction of PV and an increase in column ozone. Full article

Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: $\Lambda$, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate $\Lambda$ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized $\Lambda \left(t\right)$, the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics. Full article

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall’s spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network’s response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks. Full article