The study evaluates the impact of air temperature on the pathogenesis and epidemiology of COVID-19, using the example of the pandemic wave caused by the Omicron strain in Tomsk. The results show that any short-term decrease in daily temperature by more than 3°C has a significant impact on the course of the disease in infected individuals, leading to an increase in severity and symptoms of the disease, and consequently, in the number of hospitalizations with a lag of 1-2 days after exposure. The findings can help healthcare systems and the population develop more effective preventive measures and protect those at greatest risk from serious complications. The study also highlights the potential use of temperature changes to predict hospitalizations, aiding clinics and medical facilities in quickly preparing for an influx of critically ill patients. Further detailed research is required.
The study aimed to assess the relationship between adverse meteorological conditions and the dynamics of morbidity and hospitalizations associated with SARS-CoV-2. The assessment was conducted using data collected from daily weather reports and reports from the COVID-19 Surveillance Center. The results showed that adverse weather conditions, such as high levels of air pollution and low air temperature, were significantly associated with the incidence of COVID-19. These results provide insight into the potential impact of weather conditions, particularly "Black Sky" conditions, on the spread and transmission of COVID-19 in regions with extreme continental climates.
The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required.
In this work, we present estimates of the predictions of global horizontal irradiance, as well as of the profiles of air temperature and relative humidity in the interval of 10-40 m under the clear-sky summer conditions near Tomsk. The numerical experiments are carried out for three different configurations of the WRF-Solar model. Data of measurements are obtained using the measurement complex, located on the territory of “Fonovaya” observatory (IAO SB RAS). The statistical analysis of the calculation errors showed that the mean absolute errors and the root-mean-square errors are: 30 W/m2 and 40 W/m2 for GHI, 1.2°C and 1.5°C for air temperature, as well as 10% and 15% for the relative air humidity, averaged over the altitude interval of 10-40 m. Preliminary results showed that the chosen sets of parameterizations make it possible to simulate these characteristics with the accuracy no worse than that of similar calculations by other authors under the analogous conditions.
An approach to the formation of a description of the city's transport system is considered in order to identify the most polluted road spans with vehicle exhaust gases. The quantitative characteristics of pollution are determined in accordance with the standard Russian GOST R 56162-2019. When estimating emissions from road transport, information presented on the web in graphical form about traffic jam is used. In addition, the information on the number of cars, average speed and type of transport obtained by analyzing the video stream from web cameras by the YOLO neural network (version 4) is also used. The binding of pollution results to the transport system is formalized using the transport system ontology. The results can be used in dynamic models of pollution in urbanized areas.
We consider meteorological situations in Tomsk accompanied by weak (less than 1 m/s) and strong wind (leading to wind gusts over 11 m/s). Meteorological equipment of the Joint Use Center "Atmosphere" of V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the RAS (hereinafter referred to as JUC “Atmosphere”) and numerical mesoscale meteorological and photochemical models developed in TSU were used to study these phenomena. Dates (cases) when the considered meteorological phenomena were most pronounced were selected for the analysis of observation results in 2019. For low wind conditions, the TSUNM3 meteorological model confirmed the periods of the day when observed wind speed did not exceed 1m/s. The photochemical mesoscale model applied for the conditions of Tomsk city confirmed the interrelation of weak surface wind with surface air quality deterioration. For strong wind conditions, the calculated values of wind speed change synchronously with the actual values measured both by the ultrasonic meteorological station of the JUC "Atmosphere" and the aeronautical meteorological station of Bogashevo airport.
The paper presents the results of calculations of meteorological parameters and parameters that characterize air quality in the city, obtained with the use of mesoscale meteorology and impurity transport models. Changes in the numerically predicted wind velocity fields, temperature, and concentration of major air pollutants were considered in detail for the selected dates, when calm, cloudless, dry, and anticyclonic weather was observed in Tomsk. The numerical calculation results were compared with the experimental data obtained with the use of instruments of the Joint Use Center "Atmosphere" of V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the RAS (hereinafter referred to as JUC "Atmosphere"). The studies, confirmed experimentally, have shown that the most unfavorable meteorological conditions at low (-30°- 20°C) ambient air temperatures, which lead to the accumulation of pollutants near the earth's surface, are weak wind of variable direction and stable or neutral stratification of the surface air.
The results of numerical simulation of the concentrations of atmospheric air pollutants in the city of Tomsk, obtained using the CAMx model, are presented. The results of modeling using the CAMx model are compared with the experimental data measured at the TOR-station and the BEC of the IAO SB RAS for historical dates.
The results of use of TSUNM3 mesoscale meteorological model developed in TSU for studying the development of local atmospheric processes above the city during the day in different seasons of the year are presents. The model takes into account various factors accompanying the circulation of air over the city which differs significantly from the surrounding area the dynamic and thermal interaction with the atmospheric boundary layer. The equipment of JUC “Atmosphere” of V.E. Zuev Institute of Atmospheric Optics of Siberian Branch of the Russian Academy of Science (IAO SB RAS) was applied for quality assessment of the calculation results using the TSU supercomputer.
An updated nonhydrostatic mesoscale meteorological model TSUNM3 is represented herein. The model was improved by inclusion of the moisture microphysics parameterization scheme WSM6 which allows to simulate the formation of ice crystals and precipitation in the form of rain, snow or graupel (hail). The results of the application of this model as well as the Weather Research and Forecasting (WRF) model adapted to the Siberian region conditions showed the prospects of TSUNM3 model application for different Siberian seasons.
A prototype of an information computer system for decision making support is described. The system is based on a simple 1D model of non-stationary thermal conductivity for the calculation of detailed temperature profiles from the permafrost depth. Numerical forecast data on the temperature near the Earth's surface from the databank of IMCES SB RAS are used as boundary conditions. An approach to the correct choice of climatic and meteorological quantities from the databank during the solution of applied climatic problems is considered separately. Incorrectness is due to semantic uncertainty, both of data in the databank and in the intensional of input data required for solution of applied problems. For this choice, we suggested to use the formalized taxonomy of WMO meteorological parameters in the form of OWLontology. A version of this ontology is presented, which is used for the work with the databank of IMCES SB RAS and solution of a number of some applied problems. Output data (numerical simulation results) are represented in the information computer system in the form of an ontological knowledge base, used for the decision making support.
The first version of a primitive OWL-ontology of collections climate and meteorological data of Institute of Monitoring of Climatic and Ecological Systems SB RAS is presented. The ontology is a component of expert and decision support systems intended for quick search for climate and meteorological data required for solution of a certain class of applied problems.
The article presents a forecast of dangerous convective phenomena using one of the indices of instability (Total Totals index). The index values were calculated by Semi-Lagrangian Absolute Vorticity Numerical weather prediction (SL-AV NWP) model for the south of Western Siberia on 13 July 2014. The simulation result was compared with the Total Totals index values retrieved from product MOD07_L2 of spectroradiometer MODIS (the space platform "Terra"). Synoptic conditions and dangerous weather phenomena weather report for the selected date are also given in this study.
This paper presents the results of the analysis and forecast of meteorological conditions that promote icing of aircrafts in the atmospheric boundary layer. The forecasting results were obtained using the mesoscale meteorological model TSU-NM3. The Godske method, the NCEP method, and the statistical method of the Hydro-meteorological Centre of Russia were used as the criteria of aircraft icing probability during take-off or landing. The numeric forecast results were compared with pilot reports. The forecast accuracy rates, the probability of false detection, and the Peirce's skill score confirm the prospects of the proposed approach for the sphere of forecasting aircraft icing zones. On the basis of this technology, a regional method for forecasting aircraft icing can be developed.
The report describes and argues in favor of the data base for numerical experiments with hydrodynamic and photochemical atmospheric models with the purpose of the meteorological visibility forecast. The obtained results demonstrate methodological potential of this approach.
The accuracy of the results of numerical modeling of local atmospheric processes, in particular those in the ground layer, to a great extend depends on the input data characterizing the initial state of meteorological parameters within the area of research or their variation at the boundary. The paper considers a mesoscale model of atmospheric ground layer where medium-range weather prediction data calculated with the help of the global SL-AV model of the Hydrometcenter of the RF are applied as the initial and boundary conditions. Besides, the results of measurements carried out in the Institute of Monitoring of Climatic and Ecological Systems, SB RAS (IMCES) with the help of meteorological stations and temperature profiler are used for quality improvement.
The results of calculation of meteorological parameters using a meteorological model, TSU-NM3, as well as prediction of some indices of atmospheric air pollution in the city of Tomsk obtained from a mesoscale photochemical model are presented. The calculation results are compared with observational data on the atmosphere and pollutants.
KEYWORDS: Atmospheric modeling, Temperature metrology, Error analysis, Climatology, Systems modeling, Data modeling, Atmospheric monitoring, Air contamination, Atmospheric physics, Statistical analysis
In the paper, the possible use of a WRF mesoscale model for the detailed restoring of a temperature profile in the atmosphere boundary layer (ABL) during winter anticyclone is studied. The correctness of air temperature modeling as well as the possible use of a WRF model for predicting a vertical temperature distribution was shown.
The paper presents an approach to specify initial and boundary conditions from the output data of global model SLAV for mesoscale modelling of atmospheric processes in areas not covered by meteorological observations. From the data and the model equations for a homogeneous atmospheric boundary layer the meteorological and turbulent characteristics of the atmospheric boundary layer are calculated.
A mathematical model of atmospheric transport of impurities above urban areas which takes into account chemical transformations resulting in the formation of secondary pollutants has been presented. The model considers an isoprene supply component used to simulate the formation of formaldehyde and ozone as result the chemical interaction of isoprene with anthropogenic pollutants. The results of comparing the calculated and the measured values of the near surface wind speed and direction, temperature and concentration of ozone, nitrogen dioxide, carbon monoxide have been provided. An informational computational system capable of making numerical prediction calculations and representing the calculation results has been described.
KEYWORDS: Atmospheric modeling, Mathematical modeling, Meteorology, Solar radiation models, Clouds, Temperature metrology, Data modeling, Humidity, Atmospheric physics, Process modeling
A high-resolution mesoscale meteorological model TSU-NM3 has been presented for use in forecasting and investigating the weather phenomena and ground air quality over a limited urbanized area and over a major industrial centre or road junction. To solve mesoscale model equations an efficient explicit-implicit differencing method of the second order of approximation has been developed and supercomputer-oriented. The calculation results are well correlated with the measurements made for the City of Tomsk.
In this paper the numerical simulation results of mean wind velocity vector and its measurement error for VAD
technique using Weather Research and Forecasting Model (WRF) and Yamada-Mellor models are presented. The
numerical model takes into account the non-Gaussian and nonstationary characteristics of the Doppler lidar signal. The
numerical simulation results were compared with CASES-99 experimental data from balloon sonde (GLASS) and the
Doppler Lidar. It shows that results of numerical simulation by WRF and Yamada-Mellor models agree well with
experimental data for potential temperature. Yamada-Mellor model describes the nocturnal low-level jet only up to
100 m and above the fit is fairly bad. But WRF model allows us to have a good comparison for all levels. In case of the
strong turbulence the value of measurement error can greatly surpass the value 0.5 m/s; therefore it does not satisfy
World Meteorological Organization (WMO) requirements for wind. For the high spatial resolution we cannot get the
required accuracy.
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