@InProceedings{CamposVelho:2021:NeNeNe,
author = "Campos Velho, Haroldo Fraga de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Neural Network as a New Method for Data Assimilation",
booktitle = "Proceedings...",
year = "2021",
organization = "Mathematical Congress of the Americas (MCA)",
abstract = "Data assimilation is an essential issue for all operational
centers with focus on numerical weather prediction, hydrology,
ocean circulation, environmental forecasting, space weather, and
ionospheric dynamics. Several methods has been proposed for data
assimilation based on Kalman filter, variational schemes, and
particle filter. However, such strategies has very high
computational effort. Our investigation is to apply a
self-configuring supervised artificial neural network to address
the data assimilation process, with significant reduction of the
CPU-time. Results will be shown for different models: shallow
water 2D for ocean circulation simulation, global spectral 3D
meteorological models (SPEED, and COAPS-FSU), and a regional
meteorological model (WRF-NCAR).",
conference-location = "Online",
conference-year = "19-23 July",
language = "en",
targetfile = "S20-02123-FragadeCamposVelho.pdf",
urlaccessdate = "29 jun. 2024"
}