Chen, R.; Toumi, R.; Shi, X.; Wang, X.; Duan, Y.; Zhang, W. An Adaptive Learning Approach for Tropical Cyclone Intensity Correction. Remote Sens.2023, 15, 5341.
Chen, R.; Toumi, R.; Shi, X.; Wang, X.; Duan, Y.; Zhang, W. An Adaptive Learning Approach for Tropical Cyclone Intensity Correction. Remote Sens. 2023, 15, 5341.
Chen, R.; Toumi, R.; Shi, X.; Wang, X.; Duan, Y.; Zhang, W. An Adaptive Learning Approach for Tropical Cyclone Intensity Correction. Remote Sens.2023, 15, 5341.
Chen, R.; Toumi, R.; Shi, X.; Wang, X.; Duan, Y.; Zhang, W. An Adaptive Learning Approach for Tropical Cyclone Intensity Correction. Remote Sens. 2023, 15, 5341.
Abstract
Tropical cyclones (TC) are dangerous weather events and accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and still 19 kts after simple linear correction even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems because of the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use TC knowledge to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in IBTrACS in the North Atlantic. The method may have wide applicability, such as extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.
Keywords
tropical cyclones; ERA5 reanalysis; deep learning; generalisability; domain adaptation
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
