KARINA: An Efficient Deep Learning Model for Global Weather Forecast
Minjong Cheon Yo-Hwan Choi Seon-Yu Kang Yumi Choi Jeong-Gil Lee Daehyun Kang
Deep learning-based, data-driven models are gaining prevalence in climate
research, particularly for global weather prediction. However, training the
global weather data at high resolution requires massive computational
resources. Therefore, we present a new model named KARINA to overcome the
substantial computational demands typical of this field. This model achieves
forecasting accuracy comparable to higher-resolution counterparts with
significantly less computational resources, requiring only 4 NVIDIA A100 GPUs
and less than 12 hours of training. KARINA combines ConvNext, SENet, and
Geocyclic Padding to enhance weather forecasting at a 2.5{\deg} resolution,
which could filter out high-frequency noise. Geocyclic Padding preserves pixels
at the lateral boundary of the input image, thereby maintaining atmospheric
flow continuity in the spherical Earth. SENet dynamically improves feature
response, advancing atmospheric process modeling, particularly in the vertical
column process as numerous channels. In this vein, KARINA sets new benchmarks
in weather forecasting accuracy, surpassing existing models like the ECMWF S2S
reforecasts at a lead time of up to 7 days. Remarkably, KARINA achieved
competitive performance even when compared to the recently developed models
(Pangu-Weather, GraphCast, ClimaX, and FourCastNet) trained with
high-resolution data having 100 times larger pixels. Conclusively, KARINA
significantly advances global weather forecasting by efficiently modeling
Earth's atmosphere with improved accuracy and resource efficiency.