Three-dimensional models allow realistic modeling of atmospheric transport and transformation processes, but at the same time require a large amount of a priori data to set model parameters and significant computational resources to solve inverse modeling tasks. Emission sources identification problem is a key inverse modeling task for the air quality studies. In the paper we numerically evaluate in a realistic regional scenario an emission source identification algorithm based on sensitivity operators and adjoint equations solution ensembles for a three-dimensional case and image-type concentration measurements.
In these notes, we discuss issues of complexity, sensitivity, and uncertainty in integrated environmental models and in modeling system frameworks. First, we will focus on the current state of environmental forecasting and design tools, on those products that are currently used to predict the quality of the natural environment and make estimates of its variability. We will briefly present the developed concept of environmental forecasting and design; mention the main provisions of mathematical modeling methods in the field of environmental protection that we are developing. In the end, we will outline a number of primary tasks that need to be addressed in current conditions.
KEYWORDS: Data modeling, Inverse problems, Atmospheric modeling, Atmospheric chemistry, Algorithm development, Reconstruction algorithms, Chemical elements, Chemical species
Data assimilation algorithms are an important part of modern air quality modeling techniques. To study the real-time operation mode features of the data assimilation algorithms we numerically compare its performance to the solution in the “inverse problem mode”, when the same set of data is available “at once”. The objective of the paper is to compare the gradient-based (variational) and derivative-free solvers in the data assimilation mode to the solution of the reference inverse problem of reconstructing unobservable chemical species concentrations for the atmospheric chemistry model with a derivative-free solver.
KEYWORDS: Data modeling, Inverse problems, Atmospheric chemistry, Atmospheric modeling, Algorithm development, Chemical elements, Data acquisition, Process modeling, Mathematical modeling
The development of efficient data assimilation algorithms for atmospheric chemistry models is an important part of modern air quality studies. In the data assimilation framework considered, the identification of the chosen model parameters is used to continue the model state function to the unobservable part of the domain. This continuation problem is solved sequentially on the set of time intervals called the data assimilation windows. The framework is illustrated on a low-dimensional atmospheric chemistry model.
The paper presents some results of a numerical scenario on pollutants dispersion in the south of the Lake Baikal region. We used the scenario approach and simulated the case corresponding to the late fall or early winter. At this time, in Eastern Siberia, low temperatures are established in the atmosphere and on the ground. However, Lake Baikal is not yet covered with ice: according to climatic data, ice cover appears only in January. Therefore, a situation arises when large temperature gradients between land and lake form in vast areas of the region. Under such conditions, we examined the formation of atmospheric circulation in the Baikal region. To describe the meteorological processes, we use a mesoscale model for the dynamics of the atmosphere, developed in ICMMG SB RAS. Performing the scenario, we use the results of the COSMO-SIB6 predictive mesoscale model to specify the initial distributions of meteorological fields. Based on hydrodynamic processes, we also simulated the processes of impurity propagation from the sources of the Irkutsk-Cheremkhovo industrial hub and other major industrial centers in the region. Under the conditions of the considered winter scenario with north-west background flow, the calculation results showed that the lightweight impurities from high sources at the enterprises of the region not only from Irkutsk, but also from more distant enterprises of the Angarsk complex can reach the water area of Lake Baikal.
We discuss the issues of solving a wide range of environmental forecasting and design problems, which are formulated as mathematical continuation problems. Our approach's main idea is that we improve the quality of state function recovery by applying sensitivity theory methods, solving inverse problems, and data assimilation problems. This approach is demonstrated for the urban air quality problem in the example of the city of Novosibirsk.
The results of simulation scenarios of passive impurities transport in the city of Krasnoyarsk during the warm and cold periods are presented. The calculations are based on a mesoscale non-hydrostatic model. The purpose of the ongoing research is to study the characteristics of the formation of mesoclimates in Krasnoyarsk, a description of the characteristic meteorological conditions and an assessment of their impact on the accumulation of impurities in the urban atmosphere. We concentrate on meteorological scenarios with weak winds, since such conditions are most unfavorable from the point of view of the accumulation of impurities in the lower atmosphere. Under these conditions, we compare scenarios of the distribution of impurities from high sources. Numerical experiments with the parameters we chose showed that there was no significant effect of high sources of impurities on the accumulation of pollution in the surface layer, both in the conditions of winter surface inversions and in summer conditions. The differences are in the level of concentrations and their localization: in winter, the concentrations are higher and the areas of distribution are smaller, while in summer, the areas are larger and the concentrations are smaller. In summer, in contrast to the winter, diurnal variability is more pronounced. The daily rotation of the plumes is due to the formation of local circulations like mountain-valley type.
Possible formulations of mathematical modeling problems intended for estimating transboundary transport are considered. Two approaches are possible. The first approach is related to direct modeling methods. It actually consists in obtaining forecasts of impurity distribution with all the necessary data to solve such problems. The second approach, using the methods of inverse modeling, makes it possible to obtain estimates of some functionals characterizing the desired solutions, for example, related to the search for possible sources of disturbances that led to transboundary transport. We develop both of these approaches based on the variational principle. Some remarks on both approaches are discussed.
The work is focused on studying the features of local circulation formation and impurity transport processes in the Baikal region. To carry out the research, we use the basic numerical mesoscale model of the atmospheric dynamics and impurity transport in regions with complex relief, developed at the ICMMG SB RAS. On its basis, we created a special version of the model adapted to the climatic and orographic conditions of the region. The results of a numerical experiment on modeling the transport of a smoke tracer under the conditions of a summer meteorological scenario are presented.
Prospective issues of the organization of modeling technology for studying the climatic and ecological processes and solving the practical environmental problems are discussed. The combination of process models and observational data as well as the construction of numerical schemes is carried out within the framework of the variational principle with weak constraints. Some of the basic models, developed in the ICM and MG of the SB RAS for climate-ecological research, are presented.
Prospective issues of the organization of modeling technology for studying climatic and environmental processes and solving practical problems are discussed. We study the problems of predictability analysis and uncertainty estimates. To this goal, the combination of process models and observational data is carried out within the framework of the variational principle with weak constraints. This makes it possible to obtain the direct non-iterative algorithms for estimating the state and uncertainty functions.
This work is dedicated to the studies of peculiarities of local circulations and pollutants transport in urban area. The research is fulfilled by means of a numerical mesoscale model of atmosphere dynamics and a model of pollutants transport in the orographically complex areas. The results of numerical experiments for winter meteorological scenarios made in the framework of Krasnoyarsk mesoclimate description are represented.
This paper presents a meso-scale non-hydrostatic model of atmospheric dynamics and some scenario calculations on formation of the local atmospheric circulations near the city of Krasnoyarsk. The results of numerical experiments are presented for summer and winter meteorological scenarios performed as a study of mesoclimates of the city and its surroundings. Using the numerical model it was possible to reproduce the processes of formation and development of the surface and elevated temperature inversions in the atmosphere of the territory in question. Experiments with basic state winds have shown that the orographic and thermal inhomogeneities of the underlying surface considerably transform the large-scale basic state flux.
We discuss an adaptive strategy of targeted monitoring for “real time” estimating the atmospheric situation as well as the air quality changes. To this goal, we develop some theoretical aspects related to mathematical modeling of investigated processes based on observations of their actual behavior fulfilled with monitoring tools. The concept of combined analysis of the solutions of direct and adjoint problems, sensitivity and uncertainty functions is described. We use the obtained information to detect the domains of the high-energy activity and increased uncertainty in the results of prognoses to place the additional mobile tools of observation in them and (or) to configure the remote sensing instruments.
A new method for the joint use of mathematical models and heterogeneous data from various monitoring systems for ground and space based assets, including the sequence of images, is presented. The method is based on variational principles with weak constraints. The algorithm for its implementation allows us to use images in the problems of prediction and reconstruction of multidimensional fields of the state functions.
Atmospheric chemistry dynamics is studied with convection-diffusion-reaction model. The numerical Data Assimilation algorithm presented is based on the additive-averaged splitting schemes. It carries out ''fine-grained'' variational data assimilation on the separate splitting stages with respect to spatial dimensions and processes i.e. the same measurement data is assimilated to different parts of the split model. This design has efficient implementation due to the direct data assimilation algorithms of the transport process along coordinate lines. Results of numerical experiments with chemical data assimilation algorithm of in situ concentration measurements on real data scenario have been presented. In order to construct the scenario, meteorological data has been taken from EnviroHIRLAM model output, initial conditions from MOZART model output and measurements from Airbase database.
KEYWORDS: Atmospheric modeling, Data modeling, Process modeling, Climatology, Inverse problems, Mathematical modeling, Systems modeling, Chemical elements, Atmospheric particles, Aerosols
We continue to develop our concept of environmental forecasting for various impacts of natural and anthropogenic factors on the climate and ecological systems. To this goal, we apply hybrid methods for constructing forecast scenarios using the ideas of scales decomposition and methods of direct and inverse modeling. To illustrate the approach, we discuss some results of scenario calculations for assessment of ecological perspectives.
KEYWORDS: Atmospheric modeling, Meteorology, Solar radiation models, Computer simulations, Motion models, Solar radiation, Atmospheric physics, Data modeling, Pollution, Basic research
The results of scenario estimation of summer conditions for the formation of atmospheric circulations and transport of pollutants of natural and anthropogenic origin in the Baikal region atmosphere and over the Baikal water area are presented. Possible changes in air quality are studied with a mesoscale nonhydrostatic model of atmospheric dynamics and pollutant transport. The investigation has revealed some meteorological situations that are unfavorable for air quality in the Baikal region and over its water area.
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