We review the major conceptual models of atmospheric moisture transport, which describe
the link between evaporation from the ocean and precipitation over the continents. We begin …

This is largely due to 1) a lack of communication and collaboration between the machine
learning research community and natural scientists, 2) a lack of communication about …

Despite its wide applications as a tool for feature extraction, the Self‐Organizing Map (SOM)
remains a black box to most meteorologists and oceanographers. This paper evaluates the …

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method
itself has only been utilized in the field for around a decade. In this article, we review the …

Coupled ocean-atmosphere science steadily advances with increasing information obtained
from long-records of in situ observations, multiple-year archives of remotely sensed satellite …

Abstract Machine learning, a subfield of artificial intelligence, offers various methods that can
be applied in marine science. It supports data-driven learning, which can result in automated …

More accurate forecasts of climate conditions over time periods of weeks to a few years
could help people plan agricultural activities, mitigate drought, and manage energy …

Abstract North, Central, and South America (collectively referred to as the Americas) extend
across two hemispheres, and together cover approximately 28% of Earth's land area and …

The impacts of global warming/climate change on extreme rainfall events during the Baiu
period in Japan and their dependency on weather patterns (WPs) were examined using the …

Abstract The Self‐Organizing Map (SOM), an unsupervised learning neural network, is
employed to extract patterns evinced by the Loop Current (LC) system and to identify …