Climate Change Impacts on Heavy Snowfall in Sapporo Using 5-km Mesh Large Ensemble Simulations

Abstract

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Localized Strong Warming and Humidification Over Winter Japan Tied to Sea Ice Retreat

“…The SOMs is a type of neural network that performs unsupervised learning, which offers visualization of high‐dimensional data sets by projecting the data set onto a low‐dimension (two‐dimensional) map. This technique has been widely used in the classification of atmospheric conditions (Cavazos, 2000), and applied to studies of the East Asian winter (Kawazoe et al., 2020; Ohba et al., 2015). In this study, trend analysis was performed for each classified SLP pattern.…”

“…Recently, climate change assessments have been conducted based on pressure pattern classification (J. J. Cassano et al., 2006). This statistical approach offers effective climate change assessment for extreme events because it allows analysis of each of the various atmospheric conditions on a daily basis (Inatsu et al., 2021; Kawazoe et al., 2020). Furthermore, the frequency changes of each pressure pattern can be interpreted as a “dynamic” response to climate change, while the change in atmospheric fields for each respective pressure pattern can be regarded as a “thermodynamic” response (Horton et al., 2015; Ohba et al., 2022).…”

Decrease of winter cyclone passage over northern Japan due to the reduction in the regional cyclogenesis associated with cold air outbreak

“…The SOM approach offers visualization of high‐dimensional datasets via reduction to a two‐dimensional map through neural network techniques. In meteorological studies, this technique has been used widely in classification of temporally varying atmospheric conditions such as SLP patterns (e.g., Lennard and Hegerl, 2015; Ohba et al ., 2015; Kawazoe et al ., 2020). In this study, the SOM method was used to classify the SLP patterns leading to cyclogenesis.…”

Predictability of Heavy Snowfall Days in Western Hokkaido from JMA Operational 1-Month Ensemble Predictions Using Self-Organizing Maps

“…Inatsu et al (2021) showed that even subtle shifts in EAWM associated wind directions could result in marked differences between observed heavy snowfall in Iwamizawa and Sapporo. As an extension of Kawazoe et al (2020a), their work utilized self-organizing maps (SOMs; Kohonen 1995) to establish a linkage between large-scale weather patterns and localized heavy snowfall. SOMs are a type of artificial neural network to reduce high-dimensional datasets to a two-dimensional visual representation through pattern recognition.…”