Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity
Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2023-10-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022MS003596 |
| _version_ | 1827769669094735872 |
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| author | Dan Fu Ping Chang Xue Liu |
| author_facet | Dan Fu Ping Chang Xue Liu |
| author_sort | Dan Fu |
| collection | DOAJ |
| description | Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model. |
| first_indexed | 2024-03-11T12:28:08Z |
| format | Article |
| id | doaj.art-7f1a1ec776e946eaa920d09161a1c255 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-03-11T12:28:08Z |
| publishDate | 2023-10-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-7f1a1ec776e946eaa920d09161a1c2552023-11-06T06:42:17ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662023-10-011510n/an/a10.1029/2022MS003596Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone ActivityDan Fu0Ping Chang1Xue Liu2Department of Oceanography Texas A&M University College Station TX USADepartment of Oceanography Texas A&M University College Station TX USADepartment of Oceanography Texas A&M University College Station TX USAAbstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.https://doi.org/10.1029/2022MS003596tropical cyclonemachine learningconvolutional neural workseasonal predictionsclimate projections |
| spellingShingle | Dan Fu Ping Chang Xue Liu Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity Journal of Advances in Modeling Earth Systems tropical cyclone machine learning convolutional neural work seasonal predictions climate projections |
| title | Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity |
| title_full | Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity |
| title_fullStr | Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity |
| title_full_unstemmed | Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity |
| title_short | Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity |
| title_sort | using convolutional neural network to emulate seasonal tropical cyclone activity |
| topic | tropical cyclone machine learning convolutional neural work seasonal predictions climate projections |
| url | https://doi.org/10.1029/2022MS003596 |
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