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• W3096113005 abstract "ABSTRACTThis 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." @default.
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• W3096113005 date "2020-04-28" @default.
• W3096113005 modified "2023-09-23" @default.
• W3096113005 title "Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil" @default.
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• W3096113005 doi "https://doi.org/10.22564/rbgf.v38i2.2042" @default.
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