Purely satellite data–driven deep learning forecast of complicated tropical instability waves
Citations Over TimeTop 1% of 2020 papers
Abstract
Forecasting fields of oceanic phenomena has long been dependent on physical equation-based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observation itself. Thus, inspired by largely available satellite remote sensing data and the advance of deep learning technology, we developed a purely satellite data-driven deep learning model for forecasting the sea surface temperature evolution associated with a typical phenomenon: a tropical instability wave. During the testing period of 9 years (2010-2019), our model accurately and efficiently forecasts the sea surface temperature field. This study demonstrates the strong potential of the satellite data-driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.
Related Papers
- → A High-Resolution Global Sea Surface Temperature Climatology(1995)403 cited
- → Dominant Influence of ENSO-Like and Global Sea Surface Temperature Patterns on Changes in Prevailing Boreal Summer Tropical Cyclone Tracks over the Western North Pacific(2020)16 cited
- → Linear Relation of Central and Eastern North American Precipitation to Tropical Pacific Sea Surface Temperature Anomalies(1997)81 cited
- ANALYSIS OF INTERDECADAL AND INTERANNUAL CHARACTERISTICS OF THE TROPICAL WESTERN PACIFIC WARM POOL HEAT STATUS(2004)
- → Bias of ENSO-like SST breaks the connection between the North Atlantic SST and Northeast China spring precipitation in the NCEP CFSv2(2024)