Sahara Ali, PhD Student, Wins Best Paper at BDCAT 2022
CONGRATULATIONS!
Congratulations to Sahara Ali, PhD Student, for winning best paper award at ACM/IEEE BDCAT 2022 conference.
Long Paper Acceptance rate: 27%
Paper Title
MT-IceNet – A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
Abstract
Arctic amplification has altered the climate patterns
both regionally and globally, resulting in more frequent and
more intense extreme weather events in the past few decades.
The essential part of Arctic amplification is the unprecedented
sea ice loss as demonstrated by satellite observations. Accurately
forecasting Arctic sea ice from sub-seasonal to seasonal scales
has been a major research question with fundamental challenges
at play. In addition to physics-based Earth system models,
researchers have been applying multiple statistical and machine
learning models for sea ice forecasting. Looking at the potential
of data-driven approaches to study sea ice variations, we propose
MT-IceNet – a UNet-based spatial and multi-temporal (MT)
deep learning model for forecasting Arctic sea ice concentration
(SIC). The model uses an encoder-decoder architecture with
skip connections and processes multi-temporal input streams to
regenerate spatial maps at future timesteps. Using bi-monthly and
monthly satellite retrieved sea ice data from NSIDC as well as
atmospheric and oceanic variables from ERA5 reanalysis product
during 1979-2021, we show that our proposed model provides
promising predictive performance for per-pixel SIC forecasting
with up to 60% decrease in prediction error for a lead time of
6 months as compared to its state-of-the-art counterparts.
Index Terms—spatiotemporal data mining, neural networks,
UNet, sea ice forecasting, climate change
both regionally and globally, resulting in more frequent and
more intense extreme weather events in the past few decades.
The essential part of Arctic amplification is the unprecedented
sea ice loss as demonstrated by satellite observations. Accurately
forecasting Arctic sea ice from sub-seasonal to seasonal scales
has been a major research question with fundamental challenges
at play. In addition to physics-based Earth system models,
researchers have been applying multiple statistical and machine
learning models for sea ice forecasting. Looking at the potential
of data-driven approaches to study sea ice variations, we propose
MT-IceNet – a UNet-based spatial and multi-temporal (MT)
deep learning model for forecasting Arctic sea ice concentration
(SIC). The model uses an encoder-decoder architecture with
skip connections and processes multi-temporal input streams to
regenerate spatial maps at future timesteps. Using bi-monthly and
monthly satellite retrieved sea ice data from NSIDC as well as
atmospheric and oceanic variables from ERA5 reanalysis product
during 1979-2021, we show that our proposed model provides
promising predictive performance for per-pixel SIC forecasting
with up to 60% decrease in prediction error for a lead time of
6 months as compared to its state-of-the-art counterparts.
Index Terms—spatiotemporal data mining, neural networks,
UNet, sea ice forecasting, climate change
Click here to see research lab page with details on the award/paper: BDAL Publications
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Posted: December 20, 2022, 12:17 PM
