@Article{AlmeidaCampFranEbec:2022:NeNeDa,
author = "Almeida, Vin{\'{\i}}cius Albuquerque de and Campos Velho,
Haroldo Fraga de and Fran{\c{c}}a, Gutemberg Borges and Ebecken,
Nelson Francisco Favilla",
affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal
do Rio de Janeiro (UFRJ)} and {Universidade Federal do Rio de
Janeiro (UFRJ)}",
title = "Neural networks for data assimilation of surface and upper-air
data in Rio de Janeiro",
journal = "Geoscientific Model Development Discussions",
year = "2022",
volume = "2022",
month = "Sept.",
abstract = "The practical feasibility of neural networks models for data
assimilation using local observations data in the WRF model for
the Rio de Janeiro metropolitan region in Brazil is evaluated.
Surface and multi-level variables retrieved from airport
meteorological stations are used: air temperature, relative
humidity, and wind (speed and direction). Also, 6-hour forecast
from WRF high-resolution simulations are used domain centered in
the Rio de Janeiro city with nested grids of 8 and 2.6 km. Periods
of 168h from 2015-2019 are used with 6h and 12h assimilation
cycles for surface and upper-air data, respectively, applied to
6-hour forecast fields. The observed data (interpolated to grid
points close to airport locations and influence computed in its
surroundings) and short-range forecasts are used as input for
training model and the 3D-Var analysis on 6-hour forecast fields
for each grid point is used as target variable. The neural network
models are built using two different approaches: WEKA multilayer
perceptron model and TensorFlows deep learning implementation. The
year of 2019 is used as an independent dataset for forecast
validation from the trained models. Results employing 6-hour
forecast fields with neural network models are able to emulate the
3D-Var results for surface and multi-level variables, with better
results for the NN-TensoFlow implementation. The main result
refers to CPU time reduction enabled by the neural networks
models, reducing the data assimilation CPU-time by 121 times and
25 times for NN-TensorFlow and NN-WEKA, respectively, in
comparison to the 3D-Var method under the same hardware
configurations.",
doi = "10.5194/gmd-2022-50",
url = "http://dx.doi.org/10.5194/gmd-2022-50",
issn = "1991-962X and 1991-9611",
language = "en",
targetfile = "gmd-2022-50.pdf",
urlaccessdate = "21 maio 2024"
}