Search Results (92)

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar coast. It caused heavy rainfall (~90 mm) and gusty winds (~115 knots) over the coastal regions of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, such as the coasts of Bangladesh and Myanmar. The factors responsible for the RI of the cyclone in lower latitudes, such as sea surface temperature (SST), tropical cyclone heat potential (TCHP), vertical wind shear (VWS), and mid-tropospheric moisture content, are studied using the National Ocean and Atmospheric Administration (NOAA) SST and National Center for Medium-Range Weather Forecasting (NCMRWF) Unified Model (NCUM) global analysis. The results show that SST and TCHP values of 30 °C and 100 (KJ cm⁻²) over the Bay of Bengal (BoB) favored cyclogenesis. However, a VWS (ms⁻¹) and relative humidity (RH; %) within the range of 10 ms⁻¹ and >70% also provided a conducive environment for the low-pressure system to transform into the ESCS category. The physical mechanism of RI and recurvature of the Mocha cyclone have been investigated using forecast products and compared with Cooperative Institute for Research in the Atmosphere (CIRA) and Indian Meteorological Department (IMD) satellite observations. The key results indicate that a dry air intrusion associated with a series of troughs and ridges at a 500 hPa level due to the western disturbance (WD) during that time was very active over the northern part of India and adjoining Pakistan, which brought north-westerlies at the 200 hPa level. The existence of troughs at 500 and 200 hPa levels are significantly associated with a Rossby wave pattern over the mid-latitude that creates the baroclinic zone and favorable for the recurvature and RI of Mocha cyclone clearly represented in the NCUM analysis. Moreover the Q-vector analysis and steering flow (SF) emphasize the vertical motion and recurvature of the Mocha cyclone so as to move in a northeast direction, and this has been reasonably well represented by the NCUM model analysis and the 24, 7-, and 120 h forecasts. Additionally, a quantitative assessment of the system indicates that the model forecasts of TC tracks have an error of 50, 70, and 100 km in 24, 72, and 120 h lead times. Thus, this case study underscores the capability of the NCUM model in representing the physical mechanisms behind the recurving and RI over the BoB. Full article

Arabian Sea (AS) and Bay of Bengal (BoB) cyclones around the Indian subcontinent cause widespread floods and other natural hazards. There is no single convincing answer to this puzzle in the era of global warming. The warming of the western and central Indian Ocean is one of the few prominent features of local warming. The availability of moisture in the atmosphere in the last decade is an important factor in the rapid intensification and strengthening of tropical cyclones (TCs) before landfall. Essentially, the AS basin has shown an upward trend in the number and intensity of very severe cyclones during the period of 2009–2019. The decadal variation (1991–2001, 2002–2011, and 2012–2021) in SST, vorticity, wind shear, and moisture is primarily responsible for the genesis and intensification of cyclones during the post-monsoon season (October–November–December) over the AS. The results showed that slight changes in wind conditions, such as increased wind shear and the northward shift of the Asian Jet Stream over the region, facilitate TC formation. Full article

Low-level jets (LLJs) are important features in the Arctic atmospheric boundary layer (ABL). In the present paper, a LLJ event during winter 2014/15 is investigated, which was observed at the Tiksi observatory (71.586° N, 128.918° E, 7 m asl) in the Laptev Sea region. Besides the routine synoptic observations, data from a meteorological tower and SODAR/RASS (sound detection and ranging/radio acoustic sounding system) were available. The latter yielded vertical profiles of wind and temperature in the ABL with a vertical resolution of 10 m and a temporal resolution of 20 min. In addition to the measurements, simulations were performed using the regional climate model CCLM with a 5 km resolution. CCLM was run with nesting in ERA5 data in a forecast mode, and the ABL measurements were used for comparison with a LLJ occurring from 31 December 2014 to 1 January 2015. The CCLM simulations agreed well with near-surface and SODAR observations and represented the LLJ development very well. The simulations showed that the LLJ at Tiksi was part of a downslope wind event and that LLJ structures were present over a large region. The flow was preconditioned by a barrier wind and channeling in the Lena Valley in the initial phase, but synoptic forcing from a low over the Laptev Sea dominated the mature and dissipation phases of the LLJ. High turbulence intensity occurred in the mature phase of the LLJ, which seemed to be associated with wave breaking. Downslope wind events are likely the reason for most LLJs at Tiksi. Full article

A machine learning (ML)-based methodology for predicting frosts was applied to the southern and southeastern regions of Brazil, as well as to other countries including Uruguay, Paraguay, northern Argentina, and southeastern Bolivia. The machine learning model (using TensorFlow (TF)) was compared to the frost index (IG from the Portuguese: Índice de Geada) developed by the National Institute for Space Research (INPE, Brazil). The IG is estimated using meteorological variables from a regional weather numerical model (RWNM). After calculating the two indices using the ML model and the RWNM, a voting committee (VC) was trained to select between the computed outputs. The AdaBoostClassifier algorithm was employed to implement the voting committee. The study area was subdivided into three distinct subregions: R1 (outside Brazil), R2 (the south of Brazil), and R3 (southeastern Brazil). Two forecasting time scales were evaluated: 24 h and 72 h. The 24 h forecasts from both approaches (TF and RWNM) exhibited a similar performance in terms of the number of accurate predictions. However, in the region covering Uruguay and northern Argentina, the TensorFlow model demonstrated superior frost prediction accuracy. Additionally, the TensorFlow model outperformed the RWNM for the 72 h forecast horizon. Full article

This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region. Full article

This study investigates the formation of cold air pools during calm, anticyclonic winter nights in a topographic basin bounded by a medium-sized mountain to the east and near-flat terrain elsewhere. The main objective is to understand how local topography drives unique topoclimatic conditions—specifically cold air lakes and an inversion layer at approximately 100/120 m altitude—in a peri-urban depression where a major cement factory and several residential areas are located. To achieve this, the research design combined surface measurements (collected at 10:00 p.m., 3:00 a.m., 7:00 a.m., and 3:00 p.m.) using a motorized vehicle, with vertical measurements (at 7:00 a.m.) collected via two unmanned aerial vehicles (UAVs), with the three vehicles equipped with Tinytag data loggers. The Empirical Bayesian Kriging tool in ArcGIS Pro was employed to generate the surface temperature cartograms. The results show that shortly after sunset, a cold air layer of approximately 100–120 m thickness forms, with nocturnal air temperature variations of up to 8 °C on the night measurements. An inversion layer was detected at around 120–130 m, while near-zero wind speeds in the basin’s core facilitate the retention of cold air. Surface spatialization confirms earlier findings of a cold air lake and thermal belts on the basin’s perimeter, forming in the early evening and dissipating by late morning. A 3D visualization underscores the influence of the mountain in directing cold air downslope, leading to stabilization and stratification within the lower atmospheric layers. These findings carry significant health implications: air pollutants released by the cement plant tend to accumulate within the cold air pool and beneath the inversion layer, posing potential risks to nearby populations. Full article

On 15 September 2022, a flood affected the Marche region, an Italian region that faces the Adriatic Sea. Unlike previous floods that affected the same area, no typical weather system, such as cyclones or synoptic fronts, caused the recorded extreme precipitation. In fact, the synoptic situation was characterized by a zonal flow, which normally does not cause intense precipitation over that area. The aim of this study was to understand which ingredients led to extraordinary precipitation in the region. ERA5 and the Weather Research Forecast (WRF) model were used to describe the synoptic situation and to reproduce rainfall. While limited area models with a horizontal resolution of a few km failed to forecast the precipitation, as confirmed by a WRF simulation with a horizontal resolution of 3 km, reducing the horizontal grid spacing to about 500 m improved the rain’s reproducibility. Together with a zonal flow that interested most of Italy, an atmospheric river starting in the eastern Mediterranean Sea transported moisture over the region. The interaction between the zonal flow and orography resulted in frontogenesis in the Apennine Lee. This process deformed the thermal structures in the area and created conditions of convective instability, transforming the moisture into copious rainfall. Moreover, ERA5 and the time series of observed rainfall from 1959 to 2022 were used to explore whether similar events, in terms of geopotential height configuration and rainfall, occurred in the past. Three metrics were employed to compare the event’s 700 hPa geopotential height pattern with all the other patterns, and the result was that the event was unique in the sense that a zonal flow, like that observed during the event of 15 September 2022, had never produced such an amount of precipitation in the time range considered, while all the events with the highest rainfall were usually associated with cyclonic structures. Full article

Coastal lagoons play an essential role in the energy balance and heat exchange to the atmosphere. Furthermore, at mesoscale Monsoon systems and at local scales, sea breeze influences surface processes; however, there is a lack of information on such processes in arid and semiarid regions. We aimed to characterize the atmospheric conditions during sea and land breeze in different seasons and analyze at different temporal scales the variation of atmospheric stability, turbulent fluxes, lifting condensation level, and atmospheric boundary layer height. The study site is a subtropical semiarid coastal lagoon, Estero El Soldado, located in Northwestern Mexico (27°57.248′ N, 110°58.350′ W). Measurements were performed from January 2019 to September 2020 with an Eddy Covariance system (EC) and micrometeorological instruments over the water surface. Results show that there is a strong seasonality that enhances sea–land breeze dominance; sea breeze was 83% more frequent during the Monsoon, and the land breeze was 55% more frequent in the Post-Monsoon. Specific humidity (23.32 ± 3.84 g kg⁻¹, q), potential temperature (307 ± 2.98 K, θ_p), latent heat (135 W m⁻², LE), and turbulent kinetic energy (0.81 m² s⁻², TKE) were significantly higher during the Monsoon season at sea breeze events. Atmospheric boundary layer (ABL) and lifting condensation level (LCL) were higher in the Pre-Monsoon season (3250 ± 71 m and 1142 ± 565 m, respectively). During the Monsoon, surface conditions lead to lower LCL (~800 m) due to the amount of water vapor (q = 23.3 g kg⁻¹). Full article

Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management. Full article

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

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