@Article{AnochiAlmeCamp:2021:MaLeCl,
author = "Anochi, Juliana Aparecida and Almeida, Vin{\'{\i}}cius
Albuquerque de and Campos Velho, Haroldo Fraga de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Machine Learning for Climate Precipitation Prediction Modeling
over South America",
journal = "Remote Sensing",
year = "2021",
volume = "13",
number = "13",
pages = "e2468",
month = "July",
keywords = "machine learningclimate precipitation predictionneural
networksoptimal neural architecturedeep learning.",
abstract = "Many natural disasters in South America are linked to
meteorological phenomena. Therefore, forecasting and monitoring
climatic events are fundamental issues for society and various
sectors of the economy. In the last decades, machine learning
models have been developed to tackle different issues in society,
but there is still a gap in applications to applied physics. Here,
different machine learning models are evaluated for precipitation
prediction over South America. Currently, numerical weather
prediction models are unable to precisely reproduce the
precipitation patterns in South America due to many factors such
as the lack of region-specific parametrizations and data
availability. The results are compared to the general circulation
atmospheric model currently used operationally in the National
Institute for Space Research (INPE: Instituto Nacional de
Pesquisas Espaciais), Brazil. Machine learning models are able to
produce predictions with errors under 2 mm in most of the
continent in comparison to satellite-observed precipitation
patterns for different climate seasons, and also outperform INPE's
model for some regions (e.g., reduction of errors from 8 to 2 mm
in central South America in winter). Another advantage is the
computational performance from machine learning models, running
faster with much lower computer resources than models based on
differential equations currently used in operational centers.
Therefore, it is important to consider machine learning models for
precipitation forecasts in operational centers as a way to improve
forecast quality and to reduce computation costs.",
doi = "10.3390/rs13132468",
url = "http://dx.doi.org/10.3390/rs13132468",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-13-02468.pdf",
urlaccessdate = "15 jun. 2024"
}