The present study analyses the carbonaceous species of PM₁₀ collected at Mohal-Kullu (31.9 °N, 77.11 °E; 1154 m asl), Nainital (29.39 °N, 79.45 °E; 1959 m asl) and Darjeeling (27.01 °N and 88.15 °E; 2200 m asl) during winter (January- February 2019). The concentration of PM₁₀ were recorded as 51 ± 16 μg m^-3, 38 ± 9 μg m^-3 and 52 ± 18 μg m^-3 for Mohal-Kullu, Nainital and Darjeeling, respectively. Organic carbon (OC) dominated over elemental carbon (EC) and was found to be 50.2%, 42.8% and 47% in total carbonaceous aerosols (TCA) at Mohal-Kullu, Nainital and Darjeeling, respectively. The concentrations of carbonaceous species were higher at Mohal-Kullu (OC: 11.1 ± 5.3, EC: 4.2 ± 1.9, WSOC: 5.3 ± 1.3 μg m^-3 and TCA: 22.1 ± 10.4 μg m^-3) followed by Darjeeling (OC: 5.4 ± 2.0, EC: 2.7 ± 1.0, WSOC: 3.9 ± 1.3 μg m^-3 and TCA: 22.1 ± 10.4 μg m^-3) and Nainital (OC: 2.9 ± 1.0, EC: 1.3 ± 0.6, WSOC: 2.1 ± 0.6 μg m^-3 and TCA: 6.7 ± 2.4 μg m^-3). The diagnostic ratios (OC/EC and WSOC/OC) are used to characterize the pollution sources and provides information on the ageing of aerosols and their emission sources The OC/EC and WSOC/OC ratio at Mohal- Kullu (2.6 ± 0.3, 0.6 ± 0.2), Nainital (2.0 ± 0.4, 0.7 ± 0.2) and Darjeeling (2.3 ± 0.5, 0.7 ± 0.2), respectively, indicates the dominance of fossil fuel combustion (coal and vehicular exhaust), with signified additional contribution from secondary organic carbon (SOC).

In Cuba rainfall is a meteorological variable of importance at the national level, economic and social losses could have been avoided knowing the exact position where they would occur. To solve this problem, the NS was created, a system that provides, among many others, precipitation forecasts, although it has errors when providing the quantity, as well as the position of the precipitation to occur. That is why this project aimed to improve the precision of the system's precipitation forecast by implementing the sliding window method. From its implementation, the results obtained were that the spatial error presented by NS was reduced by using a window of size N = 15 and the maximum and average instructions; the quantitative error was more optimally decreased with the mean instruction, using the same window size, and this last instruction was the one that most completely improved the precipitation forecast provided by NS, both in position and in the amount of forecast precipitation.

In this research, the performance of the Short-range Forecast System (SisPI by its acronym in Spanish) to represent the North Atlantic subtropical anticyclone over the parent domain during the 2020 wet season is evaluated. For this, an average of the decade 2010-2019 was calculated using data from the ERA5 reanalysis at different levels of the troposphere for variables geopotential height, relative humidity, temperature and wind, in order to characterize the main systems that disturb the weather in the study area, to obtain the corresponding anomalies and to determine if the errors have more influence of these anomalies or SisPI configuration. For this it was necessary to interpolate SisPI data for make match with the resolution of ERA5 reanalysis and to be able to perform the calculations and generate the maps, for which a Python code was designed. The results suggest that SisPI shows tendencies to locate the high geopotential areas further south of its real position, which produces modifications in the synoptic flow forecasted. On the other hand, the northern and southern borders of the domain have the largest errors, mainly to the north, where, according to the decadal mean and the anomalies obtained in 2020 tends to generate a more baroclinic zone which creates an additional noise over said border. To the south it lies on segments of the ITCZ which may also be the reason for additional sources of errors on the model.

We study aridity in Buryatia (Russia) during the summer periods from 1979 to 2019. Selyaninov's hydrothermal coefficient (HTC) was used as a aridity criterion. The HTC was calculated on the basis of precipitation and 2 m temperature data from two datasets: meteorological stations that are freely available [meteo.ru] and the ECMWF ERA5 project [Hersbach, H. et al. The ERA5 Global Reanalysis QJRMS 2020. doi:10.1002/qj.3803]. Comparison of HTC calculations for these two data sets was performed. The ERA5 data show underestimated HTC values ​​compared to the station data. The worst data fit is typical for June, as well as for stations in western Buryatia (Eastern Sayan and Tunka Valley) in all summer months. The good agreement of HTC is typical for central and southern stations. The inconsistencies found are mainly related to the underestimation of the precipitation amount in the ERA5 project compared to the observational data.

The study was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Subsidy no.075-GZ/C3569/278) and the grant No. 075-15-2020-787 for implementation of Major scientific projects on priority areas of scientific and technological development (the project «Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory»).

This research carries out an evaluation of the 3DVAR, 3DEnVAR and 4DEnVAR methods applied to Short-range Forecast System (SisPI) with the objetive of determining which scheme presents the best results for short-term forecasting purposes. For this, three study cases are selected with initializations at 00:00 and 12:00 UTC, assimilating in a combined way PrepBufr and radiances data. The assimilation was carried out on the domain with the highest spatial resolution (3km), using a domain-dependent covariance matrix (BEC) generated from 15 days prior to each case study. Multiple outers loops are used in the case of the 3DVAR method, where the multiplicative weight of its control variables is also modified. This does not applied to hybrid schemes, a selection based on results obtained in international studies. On the other hand, the ensemble required for the hybrid methods was obtained from the previous outputs of SisPI, establishing a weight of 75/25 in relation to the perturbations of the ensemble and the BEC. The results suggest that 3DVAR modifies the background field poorly, which causes its results to rapidly converge to the solution without assimilation. On the contrary, the contribution of the flow-dependent errors in combination with the static errors contained in the BEC, in the hybrid schemes, show a significantly modify the first guess, leading to the assimilation effect being prolonged by an approximate threshold of 6 to 12 hours. The performance of the 3DEnVAR method is unstable, as it can lead to very realistic forecasts or others comparable with 3DVAR in the same situation. The 4DEnVAR scheme turns out to be the most appropriate because, although it does not always turn out to be the one that exhibits the most realistic solutions, it is the only one whose forecast always presented fewer errors with respect to the run without assimilation.

In recent years, NOAA Earth System Research Laboratories (ESRL) have been launching very high quality and high resolution ozonesondes from eight sites across the globe: Antarctica; Greenland; American Samoa; Fiji; and several sites in USA (Alabama, California, Colorado and Hawai'i). These locations collectively cover the tropics, mid-latitudes and polar regions. The balloons provide in-situ measurements approximately every second throughout their vertical ascent and descent in the troposphere, tropopause and stratosphere (up to ~30-35 km altitude) with readings of: pressure; temperature; water vapor; ozone; horizontal wind speed and direction; and vertical ascent and descent velocity.

This unique high quality and publicly archived dataset allows direct inter-comparisons between various new and old techniques for analyzing the troposphere/stratosphere transitions that were not previously possible. With this in mind, we have analyzed one complete year (2016) of ozonesonde data from these eight locations in terms of several widely-used definitions of the tropopause, as well as some new definitions. We find a surprising cohesiveness between many of the independent definitions of the tropopause that does not appear to have been properly recognized until now. These definitions appear to hold over all eight locations – from the tropics to the poles – for all seasons.

We discuss the implications of these new results for our understanding of the interrelationships between the upper troposphere, tropopause and stratosphere.

We have developed remote ground-based and satellite methods, hardware and software for studying atmospheric aerosol, clouds and underlying surface in Eastern and Western Antarctica. Ground-based equipment includes: 1) CIMEL solar spectrum photometer, which measures the spectrum of solar radiation transmitted and scattered by the atmosphere, 2) multi-wavelength Raman lidar, which measures the vertical backscatter profile, 3) albedometer, which measures the spectral albedo of the surface, primarily snow, 4) a reflectometer that measures the directional spectral reflectance of snow. Ground-based measurement data were integrated with data from satellite radiometers MODIS or OLCI and the satellite lidar CALIOP. A synergy of the manifold data allows retrieval of various atmosphere and surface characteristics such as the aerosol optical thickness of the atmosphere, profiles of concentration of the fine and coarse aerosol fractions, spatial distribution of the effective snow grain size, fraction of naked rocks etc. An experimental implementation of the technique has been made to investigate the regions of the Antarctic Peninsula (Turkish Research Station) and Enderby Land (Mount Vechernyaya, Belarusian Research Station) and to compare the characteristics of the atmosphere and Earth's surface in these regions.

This study is a result of the bilateral cooperation project between the Scientific and Technological Research Council of Turkey (TUBITAK) and the National Academy of Sciences of Belarus (NAS).

Wildfires emit large quantities of air pollutants into the atmosphere. As wildfires increase in frequency, intensity, duration, and coverage area, such emissions have become a significant health hazard for residential populations, particularly the vulnerable groups. This health hazard is exacerbated by two factors: first, wildfires are expected to increase in frequency as a result of climate change; and second, fine particulate matter, PM2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. We will present how to forecast the human health burden of wildfire emissions via deep learning models. We are developing a novel statistical framework for forecasting future emissions from active wildfires by integrating physicochemical models of emissions and satellite observations with statistical forecasting models. This is allowing us to model human health impacts of poor air quality, and use this to forecast the burden of diseases associated with exposures to wildfire events, both short- and long-term, and help develop mitigation strategies.

Fire activities have significant impacts on air quality by emitting various trace gases (e.g. carbon monoxide (CO) and nitrogen oxides (NO_x)) and fine particulate matter (PM_2.5) into the atmosphere. Fire emissions are critical inputs of chemical transport models (CTMs), which are used to understand, and even predict, the influence of fires on the atmosphere and air quality. Most of the current fire emission inventories use ground and space-borne measurements of fire radiance to estimate trace gas and particle emissions from fire activities depending on prescribed emission factors. In this study, the total-column CO and nitrogen dioxide (NO₂) measurements from the TROPOspheric Monitoring Instrument (TROPOMI) satellite are used to quantify the CO and NO₂ fire emissions over North America in 2020. We use high resolution total-column CO and NO₂ measurements from TROPOMI and atmospheric transport and chemical loss processes to derive estimates of CO and NO₂ fire emissions (E_CO and E_NO2). We further use the emission ratio (E_NO2/E_CO) as a proxy for biomass burning combustion efficiency. Preliminary results show that, although the TROPOMI-based emissions have a weak linear relationship compared to the U.S. EPA’s 2020 draft National Emission Inventory (EPA NEI), which is based on a “bottom-up” approach to derive biomass burning emissions, the TROPOMI-based biomass burning combustion efficiency has a relatively high correlation coefficient. Compared to other satellite-based, “top-down” biomass burning inventory approaches (e.g. the Blended Global Biomass Burning Emissions Product (GBBEPx) and the Global Fire Assimilation System (GFAS)), there are weaker linear relationships and relatively low correlation coefficients in terms of biomass burning emissions and combustion efficiency. Also, the TROPOMI-based burning combustion efficiency is able to reflect the fire type of different regions, which could be a useful input for CTMs.

Understanding the water cycle change under the warming climate is essential, particularly the ocean to land moisture transport, which affects the precipitation over land areas. Using ERA5 data and satellite observations from 1979-2020, the moisture transport and its trend around the boundary of each continent, including the Eurasia, Africa, North America, South America, Antarctic, Australia and Greenland, have been investigated. The inflow and outflow sections of the moisture have been identified for each continent. The trends of moisture convergence over Eurasia, Africa, North America, Antarctic and Australia have positive trends, with the value of 1.78±3.11, 2.43±3.16, 12.92±2.27 and 0.34±0.45 (in 10⁶ kg/s/decade), respectively, but only the trend of 12.92±2.27 (in 10⁶ kg/s/decade) over North America is significant at 0.1 significance level. The moisture convergence trend of −0.87±3.64 (in 10⁶ kg/s/decade) over South America is negative but insignificant. The positive trend of 0.04±0.35 (in 10⁶ kg/s/decade) over Greenland is very weak.

Both evaporation and moisture convergence (or transport) contribute to the continental precipitation, but the convergence dominates the precipitation variability over all continents, and the correlation coefficients between the time series of continental mean convergence and precipitation anomalies are higher than 0.8 in all continents.