Applying Data Assimilation on the Urban Environment

Khassenova, Z. T.; Kussainova, Ainur

doi:10.1007/978-3-030-12203-4_12

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…erefore, the approach is used to restore the "real" state of the system as accurately as possible, using a mathematical model, a priori information, and measurement data. e problem statement with the nonstationary transfer equation and diffusion was considered for this study [46,47]. After multiplying the original equation by a sufficiently smooth conjugate function, the integral identity was obtained to construct discrete approximations.…”

Section: Methodsmentioning

confidence: 99%

A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City

Oralbekova

Zhukabayeva

Iskakov

et al. 2021

Scientific Programming

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In order to ensure optimal operation of the existing environmental monitoring information system, it has become essential to use mathematical modeling based on the data assimilation algorithm. In this paper, a data assimilation algorithm has been designed and implemented. An algorithmic approach was tested for the assimilation of city atmosphere monitoring data from an industrial area. An industrial district of Karaganda city was selected for the investigation of the algorithm. The industrial district of Karaganda was taken as a research object due to the high level of atmospheric air pollution in industrial cities in the Republic of Kazakhstan. The result of our research and testing of the algorithm showed the effectiveness of the data assimilation algorithm for monitoring the atmosphere of the selected city. The practical value of the work lies on the fact that the presented results can be used to assess the state of atmospheric air in real time, to model the state of atmospheric air at each point of the city, and to determine the zone of increased environmental risk in an industrial city.

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Section: Methodsmentioning

confidence: 99%

A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City

Oralbekova

Zhukabayeva

Iskakov

et al. 2021

Scientific Programming

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“…Considerable studies have discussed how to accomplish this data assimilation. The different algorithms rely mainly on either variational principles [88], nonlinear Kalman filters [89], and/or Bayesian techniques [90].…”

Section: Data Assimilation and Validationmentioning

confidence: 99%

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

García-Gutiérrez

Gonzalo

López

et al. 2022

Fluids

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The feasibility, safety, and efficiency of a drone mission in an urban environment are heavily influenced by atmospheric conditions. However, numerical meteorological models cannot cope with fine-grained grids capturing urban geometries; they are typically tuned for best resolutions ranging from 1 to 10 km. To enable urban air mobility, new now-casting techniques are being developed based on different techniques, such as data assimilation, variational analysis, machine-learning algorithms, and time series analysis. Most of these methods require generating an urban wind field database using CFD codes coupled with the mesoscale models. The quality and accuracy of that database determines the accuracy of the now-casting techniques. This review describes the latest advances in CFD simulations applied to urban wind and the alternatives that exist for the coupling with the mesoscale model. First, the distinct turbulence models are introduced, analyzing their advantages and limitations. Secondly, a study of the meshing is introduced, exploring how it has to be adapted to the characteristics of the urban environment. Then, the several alternatives for the definition of the boundary conditions and the interpolation methods for the initial conditions are described. As a key step, the available order reduction methods applicable to the models are presented, so the size and operability of the wind database can be reduced as much as possible. Finally, the data assimilation techniques and the model validation are presented.

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Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil

et al. 2020

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This study investigates the use of neural networks for data assimilation of local data in the WRF model in Rio de Janeiro, Brazil. Surface and upper-air data (air temperature, relative humidity and wind speed and direction) from airport stations and 6-hour forecast from WRF are used as input for the model and the 3D-Var analysis for each grid point is used as target variable. Periods of 168h from 2014 and 2015 are used with 6h and 12h assimilation cycles for surface and upper-air data, respectively. The neural network model was built using the Multi-Particle Collision Algorithm (MPCA) where different topologies are tested until the optimum solution is found. Results show that the neural network is able to emulate the 3D-Var with root mean squared error (standard deviation), respectively, of 0.31 K (0.37 K), 3.10% (4.04%), 0.63 ms-1 (1.05 ms-1), 1.10 ms-1 (1.56 ms-1) for air temperature, relative humidity, u-component of the wind and v-component of the wind. Also, the results show the neural network method is able to run 71 times faster than the conventional method under similar hardware configurations. Redes Neurais Artificiais para Assimilação de Dados no Modelo WRF no Rio de Janeiro, BrazilRESUMOEste estudo investiga o uso de redes neurais para assimilação de dados locais no modelo WRF no Rio de Janeiro. Dados de superfície e do ar superior (temperatura do ar, umidade relativa e velocidade e direção do vento) das estações do aeroporto e previsão de 6 horas do WRF são usados como entrada para o modelo, e a análise 3D-Var para cada ponto da grade é usada como variável destino. Períodos de 168h de 2014 e 2015 são utilizados com ciclos de assimilação de 6h e 12h para dados de superfície e do ar superior, respectivamente. O modelo de rede neural foi construído usando o algoritmo de colisão de partículas múltiplas (MPCA), onde diferentes topologias são testadas até que a solução ideal seja encontrada. Os resultados mostram que a rede neural é capaz de emular o 3D-Var com raiz do erro quadrático médio (desvio padrão) de 0,31 K (0,37 K), 3,10% (4,04%), 0,63 ms -1 (1,05 ms -1), 1,10 ms -1 (1,56 ms -1) para temperatura do ar, umidade relativa, componente u do vento e componente v do vento. Além disso, os resultados mostram que o método de rede neural é capaz de rodar 71 vezes mais rápido que o método convencional em configurações de hardware semelhantes.

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Applying Data Assimilation on the Urban Environment

Cited by 3 publications

References 24 publications

A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City

A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil

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