Tropical cyclone (TC) intensity estimation is an important task in meteorological research. Meanwhile, TC intensity estimation performance can be improved by developing advanced machine learning techniques using the newly emerged high-quality multispectral images (MSIs) acquired by FY-4 meteorological satellite of China. To this end, this article proposes a novel model, tensor-based convolutional neural network (TCNN). Not only being a deep network entirely formulated in tensor algebra, but TCNN also establishes the mathematical connections between tensor decomposition and CNN operations with tensor contraction. Moreover, TCNN adopts a multitask structure, which consists of a classification network for intensity categorization and a regression network for wind speed estimation. It allows the regression network to leverage the outcome of the classification network, thus ensuring intensity estimation accuracy. In addition, two alternative TCNN models, coupled TCNN (C-TCNN) and Tucker TCNN (T-TCNN), are designed to automatically deal with invalid band data in the FY-4 MSIs, which has been a practical issue with the data. Experimental results prove that the proposed network is accurate and functional. They also demonstrate the advantages of TCNN over other TC intensity estimation methods, showing that C-TCNN and T-TCNN outperform several classic models and the state-of-the-art models based on CNN.

A Novel Tensor Network for Tropical Cyclone Intensity Estimation

In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

Interpretable machine learning for weather and climate prediction: A review

In this study, a deep convolutional neural network (CNN) was developed to forecast the movement direction of tropical cyclones (or typhoons) over the Northwestern Pacific basin from Himawari-8 (H-8) satellite images. 2250 infrared images which captured 97 typhoon cases between 2015 and 2018 were used to train the CNN model. By using images from Channels 13 and 15 as input into the CNN model, the mean error of the typhoon movement angle reaches up to 27.8°, which shows the great potential of deep learning in tropical cyclone track prediction.

CNN-Based Tropical Cyclone Track Forecasting from Satellite Infrared Images

A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts.

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A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—a review

In this study, a deep convolutional neural network was designed to estimate the intensity of tropical cyclones over the Northwestern Pacific Ocean from high-frequency Himawari-8 satellite images. Our model achieved good results by using the brightness temperature derived from one single infrared band data. The accuracy of the top (top-1) and the second best (top-2) tropical cyclone intensity classification reaches 81.4% and 93.3%, respectively.

Estimating Typhoon Intensity with Convolutional Neural Network

In this paper, an objective technique was developed for monitoring typhoons over the Northwestern Pacific Ocean with Himawari-8 geostationary satellite infrared imagery. Two convolutional neural networks (CNNs) were designed to locate a typhoon and estimate its intensity, respectively. The mean error of the typhoon center location (CNN-Location) model is 5.4 pixels (54 km), and the top-1 accuracy and root mean square error (RMSE) of the intensity estimation (CNN-Intensity) model are 79.6% and 11.66 kt, respectively. By changing the loss function from categorical_crossentropy to focal_loss in the CNN-Intensity model, higher top-1 accuracy of 82.9% and lower RMSE of 10.84 kt are obtained. The results demonstrate that CNN has great potential in the application of automatic typhoon monitoring.

An Objective Technique for Typhoon Monitoring with Satellite Infrared Imagery


 This paper developed a deep learning (DL) model for forecasting tropical cyclone (TC) intensity in the Northwest Pacifi c. A dataset containing 20,533 synchronized and collocated samples was assembled, which included ERA5 reanalysis data as well as satellite infrared imagery, covering the period from 1979 to 2021. The u-, v- and w-components of wind, sea surface temperature, infrared satellite imagery, and historical TC information were selected as the model inputs. Then, a tropical-cyclone-intensity-forecast-fusion (TCIF-fusion) model was developed, in which two special branches were designed to learn multi-factor information to forecast 24- hour TC intensity. Finally, heat maps capturing the model's insights are generated and applied to the original input data, creating an enhanced input set that results in more accurate forecasting. Employing this refi ned input, the heat maps (model knowledge, MK) were used to guide TCIF-fusion model modeling, and the model- knowledge-guided TCIF-fusion model achieved a 24-hour forecast error of 3.56 m/s for Northwest Paci fic TCs spanning 2020-2021. The results show that the performance of our method is signi ficantly better than the official subjective prediction and advanced deep learning methods in forecasting TC intensity by 4% to 22%. Moreover, in contrast to approaches by operational agencies, the model-knowledge-guided approach can forecast TC intensity landfall better.

Tropical cyclone intensity forecasting using model knowledge guided deep learning model

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