Typhoon cloud image prediction based on enhanced multi-scale deep neural network

Xin Wang, Mengjiao Qin, Zhe Zhang, Yuanyuan Wang, Zhenhong Du, Nan Wang

Frontiers in Marine Science · 11 January 2023

In one sentence. Enhanced multi-scale ConvLSTM network predicts typhoon cloud images three hours ahead from satellite sequences with sharper detail than prior methods.

Abstract

Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature.

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