Banca de DEFESA: RICARDO BRUNO DE ARAÚJO TENÓRIO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : RICARDO BRUNO DE ARAÚJO TENÓRIO
DATE: 26/08/2021
TIME: 09:00
LOCAL: Google meet - defesa online
TITLE:

Sea ice modeling with Deep Learning in the Weddell, Bellingshausen and Amundsen Antactic Seas.

KEY WORDS:

Machine Learning;

Long-Short Term Memmory;

Sea-Ice Concentration;

Southern Oceans.

PAGES: 85
BIG AREA: Ciências Exatas e da Terra
AREA: Geociências
SUBÁREA: Meteorologia
SPECIALTY: Climatologia
SUMMARY:

Sea ice, covering approximately 7% of the surface of the Earth’s oceans, is a fundamental climatic component for studying climate in the polar regions, mainly because it acts as a natural barrier at the ocean-atmosphere interface that restricts the exchange of heat, mass, and momentum between the sea and the air, and reflects much of the incident solar radiation. Satellite observations since the 1970s indicate an Arctic with increasingly thinner and younger sea ice, accompanied by a decline in its extent. While some areas of Antarctica (e.g. Ross Sea and Weddell Sea) have shown a slight increase in the extent of this climate component for the same period. In this context, this study had as main objective to evaluate the potential predictability of sea ice cover with the application of ANNs techniques (MLP, LSTM and CNN-LSTM) in 3 Antarctic seas, they are Weddell, Bellingshausen and Amundsen. The data used were the monthly reanalyses from Era-5, for the period 1979 to 2019. SARIMAX models served as reference values for gauging the accuracy of the predictions with ANNs. Analyzing the errors (RMSE and MAE) of Sea Ice Concentration (SIC) for all the studied seas it was observed that the CNN-LSTM was exceeded only in the months with values lower than ± 25%, and the maximum differences between the errors in these events did not reach values above 5% ,as what occurred in the months of July, September and October in the Weddell Sea. With these results it was possible to infer that the CNN-LSTM model was the one that most accurately predicted the periods with the greatest differences (> 0.15) of SIC. With the analyses of the spatial distributions of the SIC differences it can be affirmed that the delimitations of the fields with differences greater than 0.15 were also better predicted with the coupling of the CNN and LSTM architectures, as occurred with the negative anomaly of June in the Weddell Sea (MAE < 0.15) and the positive one, in September, in the Amundsen Sea (MAE < 0.10).

BANKING MEMBERS:
Presidente - 1808127 - JOSE HENRIQUE FERNANDEZ
Interno - 1858120 - DAVID MENDES
Externo ao Programa - 2276514 - MARCIO MACHADO CINTRA
Externo à Instituição - CARLOS ALESSANDRE DOMINGOS LENTINI - UFBA
Externa à Instituição - LOURDES MILAGROS MENDOZA VILLAVICENCIO - UNIFACEX