From the scientific and social point of view, the forecast of meteorological systems that affect the weather and that sometimes produce dangerous events reaches a high value. Fundamentally, during the rainy season, the transit of tropical waves through the seas adjacent to the Cuban archipelago occurs, stimulating deep convection and later the occurrence of severe local storms; that is why the general objective is to analyze the behavior of the tropical waves that passed through Cuba during the 2012-2020 period. This research will examine the most relevant characteristics of tropical waves, the associated dangerous phenomena and the number of intensification or dissipation that occurred after passing through the country. The sources of information are based on consultation of the archives of the Cuban Institute of Meteorology and the National Hurricane Center of the United States. In order to obtain the frequency of incidents of these systems for years, months and tens, the Microsoft Excel version 2019 program was extracted, as well as for the graphical presentation and calculation of statistics. The main results obtained were: a chronology of tropical waves in the period 2012-2020; of the 579 waves that formed in the Atlantic, 323 passed through Cuba; the African coasts and the eastern Atlantic is the area with the greatest formation of tropical waves with more than 50% of the total that reached the territory; most were classified as “active” moving at speeds between 15-25 km/h; 305 waves arrived in the eastern half and 219 in the western half. It is concluded that the activity of tropical waves during the years 2012-2020 was higher by 28% than that of the period 2003-2011. The results obtained contribute to the expansion of knowledge on Tropical Meteorology and to a better effectiveness in the meteorological forecast related to these systems.

Detailed long-term hydrometeorological hindcast for Russian Arctic was created using regional nonhydrostatic atmospheric model COSMO-CLM ver. 5.05 for 1980–2016 with ~12 km grid size and shared online partially on the figshare service (https://figshare.com/collections/Arctic_COSMO-CLM_reanalysis_all_years/5186714, [1]). The hindcast includes about a hundred hydrometeorological variables at both surface and 50 model levels, and covers the Barents, Kara and Laptev Seas [2].

Surface temperature and wind speed reproduction by the COSMO-CLM Russian Arctic hindcast was evaluated in this study according to 145 stations and satellite data.

The COSMO-CLM Russian Arctic hindcast is generally successful in reproducing the thermal regime of region according to monthly mean values, while a significant part of the errors is explained by a large distance of the nearest model grid from the station, inaccuracies in the coastline or surface altitude description. On the average, the model underestimates the mean monthly temperatures within one degree, the errors are in the range from +2 to -4 ^oC. The patterns of the temperature 5-% quantiles differences are within +-1 ^oC, but there are significant positive errors at more continental stations of the region (from +1 to +2 ^oC), as well at highlands stations (up to +2 ^oC). The ETCDDI indices FD and ID pattern according to the hindcast is climatologically adequate, however the model overestimates the indices in the western and maritime parts of the region, and underestimates them in severe climate continental regions.

The comparison with the station data showed that the monthly average wind speed is well reproduced by the COSMO-CLM hindcast, while the errors relate mainly to cases when the wind speed is overestimated by the model data up to 2 m/s. Unlike the average wind speed, the extreme values (for 95% percentiles) according to the hindcast are underestimated compared to the stations data with up to -5 m/s.

Model data were evaluated according to the SAR high-resolution satellite images including the FSS score. Its evaluation for specific extreme wind speeds cases near the Novaya Zemlya Island shown the hindcast could capture the spatial structure of wind speeds higher than 10 m/s and partially 15 m/s, however could not reproduce 20 m/s. This revealed the model capability to reproduce β-mesoscale processes, unlike the γ-scale processes. An analysis of the difference between the quantiles of model and satellite data showed that the more extreme is the observed wind speed, the greater is the probability of underestimation by the model.

Future perspectives include the hindcast prolongation to 2019, sharing more data online; focus on extreme and severe events statistics assessment (downslope windstorms, polar lows, MCAO climatologies using satellite data); quality estimation based on other datasets (e.g., ERA5, CARRA, satellites climatology).

References:

1. Data from the COSMO-CLM Russian Arctic Hindcast archive, figshare repository https://figshare.com/collections/Arctic_COSMO-CLM_reanalysis_all_years/5186714; https://doi.org/10.6084/m9.figshare.c.5186714

2. Platonov V, Varentsov M. Introducing a New Detailed Long-Term COSMO-CLM Hindcast for the Russian Arctic and the First Results of Its Evaluation. 2021 Atmos. 12(3) 350 http://dx.doi.org/10.3390/atmos12030350

This study focuses on the comparison of carbonaceous aerosol measurements in the air at Cotonou in 2015 compared to 2005. In the framework of two international programs, AMMA (African Monsoon Multidisciplinary Analysis) and DACCIWA (Dynamics Aerosol-Cloud-Chemistry Interactions in West Africa) monitoring data for PM2.5 microns were collected at one of the most polluted urban site of Cotonou (Dantokpa) in Benin (West Africa) respectively in 2005 and 2015. The results obtained indicate that the carbonaceous aerosol measures, Black carbon (BC), and organic carbon (OC) in 2005 are higher than those obtained in 2015. PM2.5 concentrations related mainly to traffic sources for 2 wheeled vehicles, were 34g/m³ in May 2005 and 28µg/m³ in May 2015. In May 2005, OC and BC concentrations were from 15µg/m³ and 2.3µg/m³ while in May 2015, they were from 8µg/m³ for OC and 1.3 µg/m³ for BC. In May 2005 and 2015, the total carbon (TC) accounted for 50 % and 32 % of the PM2.5, respectively. In this study, the OC/EC ratio exceeds 2.0, which confirms the presence of secondary organic aerosols.

In the article certain statistical characteristics are analyzed as they are the gusts, the persistence and the conditioned probabilities of days with Condition of Intense Cold. The indicative bioclimatic used Condition of Intense (IFC) Cold is generated starting from the thermal sensations of people in contrasting schedules of the day (7 am and 1 pm), in two modalities, according to the presence or not of the wind. The climatic data came from the meteorological stations of Casablanca and Santiago de las Vegas, representative of the climatic conditions of the territory, in the period 1981-2018. Through the statistical selected characteristics it was possible to obtain additional information enlarged on the behavior and manifestations of the Condition of Intense Cold in the county of Havana, sitting down the bases stops in later investigations to extend their use to other counties of the country. The persistence values ​​of the phenomenon become less appreciable as the gusts increase, which shows that these events are generally limited to periods of a few days. There are high values ​​of persistence of the phenomenon in Havana during the months of December to February, being more likely in the windy CFIs, representative of the worst bioclimatic conditions, although this phenomenon is characteristic of occurring in isolation.

Modelling the dispersion of atmospheric pollutants plays an important role in regulatory and epidemiological settings. Although the majority of modelling concepts were developed in the 1980s, a significant amount of optimisation and refinement of dispersion models has occurred since this time. In addition, some completely novel models such as computational fluid dynamics have emerged. Furthermore, next generation models are continually improving the accuracies of the results obtained. This review provides a non-technical outline of the mechanisms of atmospheric pollutant dispersion modelling and discusses common model types and their applications.

The types of emergency situations associated with weather conditions include fires in forests, fields and peat deposits. Fires destroy and damage forests over large areas, which have a negative impact on the social and economic development of countries. A significant role in the occurrence of extensive fires is play by dry weather, stormy wind.

The purpose of this study is to assess the fire weather conditions over the territory of Belarus during the warm season (March-October) for the period 1990-2020 using the monthly area-averaged data of Fire Weather Index (FWI).

It is shown that the lowest seasonal average values of the FWI were observed in the Vitebsk and Grodno regions, the highest – in the Gomel region. The seasonal course of the FWI was characterized by increasing the average monthly values of the index from March to May, but in June a decrease of FWI values was observed. In July the index values reached a maximum. Analysis of the frequency of the fire weather in separate time intervals showed that the high level of FWI was observed only in period 2015-2020 over the south of Belarus (2%). During the study period the months with negative FWI anomalies generally were dominated in all regions of Belarus, but in some years the positive FWI anomalies were prevailed: 2002, 2007, 2011, 2014-2015, and in the period from 2018 to 2020.

Additionally three periods for different seasons with real large-scale wildfires were considered: June 2015, August-October 2016 and April 2020. It was revealed that FWI maxima values were observed on the first days of fires and start to decrease in the next days.

The obtained dynamics of the FWI indicates significant changes in the temperature and precipitation regime of the territory of Belarus in recent decades.

In this study, we investigated the variations in the intensity of the tropical cyclones (TCs) formed in the North Atlantic basin from 1982 to 2021, based on the outputs from the Hurricane Maximum Potential Intensity (HuMPI) model. To feed HuMPI, we computed the annual Sea Surface Temperature (SST) as the SST average from 1 June to 30 November using the Daily Optimum Interpolation SST database. The information for all major hurricanes (MHs, category 3+ on the Saffir-Simpson wind scale) was from the HURDAT2 dataset. While the trend (p<0.05) in the mean maximum potential intensity (MPI) was approximately 1.1 m/s per decade for the maximum sustained wind speed and -1.5 hPa/decade for the minimum central pressure, the MHs intensity did not exhibit any statistically significant trend. The behaviour of the MPI could be explained by the increase (p<0.05) of the SST at a rate of 0.20ºC/decade. In addition, the increase of the TCs intensity in the last 20 seasons (2002 – 2021) concerning the period 1982 – 2001 was quite similar for MHs and MPI, being an increase of 3.89% and 3.20% for the mean maximum wind speed, respectively. Meanwhile, the minimum central pressure decreased by about 0.36% in both cases. This latter result is promising for investigating the changes in the TC intensity in global warming based on the HuMPI model.

The Barents Sea is one of the “hot spots” of the ocean-atmosphere energy system. Warm water spreads here from the North Atlantic, which causes an increase in turbulent heat exchange between the sea surface and the atmosphere. This process is especially pronounced during strong winds, the frequency of which over ​​the Barents Seas is very high. Much attention is paid to this problem; however, the effect of mesoscale circulations, in particular, orographic winds, on the ocean-atmosphere heat exchange has been studied rather poorly so far. To investigate the influence of Novaya Zemlya bora (which is a frequent and very strong downslope windstorm) on turbulent heat exchange in the eastern Barents Sea, simulation of one late-autumn bora episode was carried out, when the wind gusts reached 45 m/s. A coupled model COAWST including atmosphere model WRF-ARW, ocean model ROMS (with the sea ice) and sea waves model SWAN with a grid spacing of 3 km was used. To distinguish the roles of different processes, both coupled and uncoupled experiments were carried out, as well as an idealized experiment with a flat topography (simulating thus the conditions without bora).

Based on the modeling results, three zones can be distinguished in which turbulent heat fluxes in numerical experiments with coupled models will differ from those in the uncoupled experiment. In a narrow strip near the coast, turbulent fluxes decrease (in some places by 20-30%) due to mixing and cooling (by 1°C in this episode) of the ocean. At a distance of 5-100 km from the coast turbulent fluxes increase (up to 50%) due to the formation of rough steep waves. In the open sea, the effect of coupling on turbulent fluxes is generally small. When averaged over the entire region, the difference in the turbulent heat fluxes between coupled and uncoupled simulations is 3%. In addition to ocean cooling and mixing near the coast, the bora also contributes to the strengthening of the southern coastal current (with a speed of up to 0.9 m/s on the surface). The heat content of the entire water column in the Novaya Zemlya region decreases by an average of 3% during the bora. However, in the experiment with a flat topography, i.e. excluding bora, these heat losses are even greater, 4%. Thus, bora in the considered episode reduces the heat exchange between the ocean and the atmosphere for the whole region (compared to similar conditions in the absence of bora), although heat transfer locally increases in the coastal region. The effect of bora on the ocean does not extend far from the coast. On the contrary, in the experiment without bora, the zone of more intense mixing extends further from the coast.

Growing evidence has shown that rapid development and urbanization have been associated with the alteration of the thermal environment of the urban area. Massive burning of fossil fuels in the transportation, urban, and industrial sectors results in increased temperatures and deterioration of air quality as a result of carbon emissions. However, the COVID-19-induced lockdown situation resulted in the shutdown of industry, the transportation system, and day-to-day regular operations, as well as changes in air quality and weather. The reduction in the number of running cars and moving people on the road during the lockdown time reduces pollutants and has a direct beneficial effect on the urban environment. The present study examined the changes in land surface temperature (LST) and normalized difference vegetation index (NDVI) during the lockdown period in Dhaka city, Bangladesh with the earlier periods (2017 to 2019) to compare the environmental status. The findings show that the LST of Dhaka City decreased and the NDVI increased throughout the lockdown period, with the LST-NDVI connection becoming more negative. Additionally, the analysis demonstrates that the city's climate was improved during the lockdown. Numerous actions have been made at the global and regional levels to control increasing temperature and climate change, but no positive consequence has been achieved yet. While such a lockdown (temporarily) is detrimental to economic progress, it demonstrated the curative impact of urban climate. Thus, the findings of this study could provide a quantitative foundation for decision-makers for surface heat island mitigation and public health care.

Climate forcings are natural processes that drive climate variability in the short, medium and long term. Characterizing the forcings behind climate variability is important to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate forcings in specific regions, the magnitude of that influence in meteorological records remains usually unclear.

The central peruvian Andes are affected by most of the common climate forcings of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature, Solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as South American Low Level Jet (SALLJ), South American Monsoon System, Bolivian High and Humboldt Current.

The aim of this research is to study the climate variability of precipitation, maximum temperature and minimum temperature records over Cordillera Blanca (Peru) in order to link this climate variability with the intensity and periodicity of the common climate forcings that affects this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram has been performed over meteorological records (1986 - 2019 period) and over different climate forcing Indexes.

Results show a coincidence in periodicity between MJO and SALLJ with monthly cycles for precipitation and temperature (27 days, 56 days, 90 days cycles). The most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of Humboldt current and SALLJ. Interannual periodicities (3 years, 4.5 years, 5.6 - 7 years and 11 years cycles) would have coincidence with the ENSO - Solar combination, while the longest cycles (16 years) could match PDO variability.