Applicability of deep learning based detection of surface weather fronts on large scale climate models

About 90% of extreme precipitation in the midlatitudes can be assoicated with front boundaries. Therefore, it is important to identifiy frontal locations for short term weather forecasting or long-term prediction of precipitation in climatology. Deep learning (DL) refers to machine learning alogrithms that use multiple layers of neural networks to derive features from input data. It is generaly useful to process 2-dimensional image data. One of the potential advantages of employing DL to detect surface weather fronts is that the developed DL based functions can be applied to automatic detection of surface weather fronts for climate models. However, justifications of its applicability on climate models are needed.

In this study, we developed deep learning based methodology to detect surface weather fronts. Specifically, a U-shape convolutional network (U-net) based deep learning model is developed to predict surface weather fronts over Japan and surrounding sea in summer (June, July, and August). We justify the applicability of the deep learning model in predicting surface fronts in summer on outputs from large-scale Global Climate Models (i.e. GCMs) from two aspects. First, the coarse resolution of GCMs (e.g., 1.25 degrees) can capture the general morphological features of surface fronts. Second, models trained in a colder climate are applied to predict fronts in a warmer climate with some decrease in predicted peak frequency of fronts, but the general features of the spatial distribution of fronts can be represented by the deep-learning model predictions. We also see that the locations of peak frequency tend to move slightly more southwesterly in a slant zone within the belt region between 25N to 40N as climate warms in the future.