Meteorology, Volume 3, Issue 4 (December 2024) – 5 articles

This article describes the main features of the impacts of global observations on the reduction of errors in the data assimilation (DA) cycle carried out in the Brazilian Global Atmospheric Model (BAM) at Center for Weather Forecast and Climate Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)]. These results show the importance of studying and evaluating the contribution of each observation to the DA system, therefore, two experiments (exp1/exp2) were performed with different configurations of the BAM model, with exp2 presenting the best fit between the Gridpoint Statistical Interpolation (GSI) and BAM systems. The BAM model was validated by the statistical metrics of root mean-square error and correlation anomaly, but this validation is not explored in this paper. A metric was applied that does not depend on the adjoint-based method, but only on the residuals that are made available in the GSI system for the observation space, given by the total impact, the fractional impact and the fractional beneficial impact. In general, the average daily showed that the observations of the global system that contribute most to the reduction of errors in the DA cycle are from the pilot balloon data (−3.54/−3.45 J kg⁻¹)and the profilers (−2.13/−1.97 J kg⁻¹), and the smallest contributions came from the land (−0.28/−0.29 J kg⁻¹) and sea (−0.44/−0.44 J kg⁻¹) surfaces. The same pattern was observed for the synoptic times presented. However, when verifying the fraction of the impact by each type of observation, it was found that the radiance data (64.88/30.30%), followed by radiosondes (14.85/27.42%) and satellite winds (11.03/22.70%), are the most important fractions for both experiments. These results show that the DA system is working to generate the best analyses at the research center and that the deficiencies found in some observations can be adjusted to improve the development of the GSI and the BAM model, since together, the entire database used is evaluated, as well as the forecast model itself, indicating the relationship between the assertiveness of the atmospheric model and the DA system used at the research center. Full article

On the evening of 31 March 2019, Parsa and Bara Districts in central Nepal were severely hit by a wind storm which was the first documented tornadic incidence in Nepal.In this paper, we investigate the background of the tornado formation via numerical simulations with the WRF-ARW model. The results show that: (1) a flow situation favorable to the generation of mesocyclones was formed by a combination of local plain-to-mountain winds consisting of warm and humid southwesterly wind in the lower atmosphere and synoptic northwesterly wind aloft over the southern foothills of the Himalayan Mountain range, leading to significant vertical wind shear and strong buoyancy; (2) the generated mesocyclone continuously shed rain-cooled outflow with 600∼800 m depth above the ground into the Chitwan valley while moving southeastward along the Mahabharat Range at the northeastern rim of the Chitwan valley; (3) the cold outflow propagated in the valley, forming a front; and (4) the tornado was generated when this cold outflow passed over the Siwalik Hills bordering the southern rim of the Chitwan valley. At this point, descending flow around a high mountain generated positive vertical vorticity near the ground; blocking by this high mountain and channeling through a mountain pass enhanced updrafts at the front by forming a hydraulic jump. These updrafts amplified the positive vertical vorticity via stretching, and this interaction of the cold outflow with the Siwalik Hills contributed to tornadogenesis. The simulated location and time of the disaster showed generally good agreement with the reported location and time. Full article

This study investigates the synoptic conditions associated with lake-effect snow (LES) over northwestern Pennsylvania with a focus on classifying cases based on the tracks of cyclones influencing the region, including Nor’easters (NEs), Alberta Clippers (ACs), Colorado Lows (COs), and Great Lakes Lows (GLs). Synoptic composites were constructed using the North American Regional Reanalysis (NARR) for all cases, as well as each cyclone group, using an LES repository spanning from 2006–2020. Additionally, 95 percent bootstrapped confidence intervals were created for each cyclone track to compare the initial mesoscale environmental properties (i.e., surface lake/air temperature and wind direction/speed) and LES impact (i.e., duration, maximum snowfall, and property damage). Synoptic composites of all LES cases exhibited an archetypal LES synoptic pattern consisting of an upper-level low geopotential height anomaly over the Hudson Bay and surface dipole structure centered across the Great Lakes basin. Regarding the different tracks, NEs and COs featured dynamic support in the form of enhanced turbulent mixing and synoptic vertical forcing, while ACs and GLs had greater thermodynamic support in the form of higher lapse rates and heightened heat and moisture fluxes. However, the bootstrapping analysis revealed minimal differences in LES impact between the cyclone types. Full article

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