{"title":"使用云解析模型模拟分析聚集对流云特征的深度学习框架","authors":"Yi-Chang Chen, Chien-Ming Wu, Wei-Ting Chen","doi":"10.1002/asl.1150","DOIUrl":null,"url":null,"abstract":"<p>This study introduces a framework to extract the high-dimensional nonlinear relationships among state variables for aggregated convection. The prototype of such a framework is developed that applies the convolutional neural network models (CNN models) to retrieve the cloud characteristics from cloud-resolving model (CRM) simulations. CNN model prediction factors are hidden in the high dimensional weighted parameters in each neural network layer. Therefore, we can dig out relevant physics processes by iterating the CNN models' training process and eliminating the features with the physics explanation we can provide at a given stage. Within a few iterations, explainable nonlinear relationships among variables can be provided. We identified that the averaged cloud water path (CWP), the maximum value of CWP in each cloud, and the cloud coverage rate are essential for identifying aggregation. Furthermore, by analyzing the encoded channels of the CNN model, we found a strong relationship between aggregation, cloud peripherals, and fractal dimensions. The results suggest that the important nonlinear cloud characteristics for identifying the aggregation can be captured with the proper adjustment and limitation of the input data to the CNN models. Our framework provides a possibility that we can explore the high dimensional relationship between the physics process with the assistance of the CNN model.</p>","PeriodicalId":50734,"journal":{"name":"Atmospheric Science Letters","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1150","citationCount":"0","resultStr":"{\"title\":\"A deep learning framework for analyzing cloud characteristics of aggregated convection using cloud-resolving model simulations\",\"authors\":\"Yi-Chang Chen, Chien-Ming Wu, Wei-Ting Chen\",\"doi\":\"10.1002/asl.1150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study introduces a framework to extract the high-dimensional nonlinear relationships among state variables for aggregated convection. The prototype of such a framework is developed that applies the convolutional neural network models (CNN models) to retrieve the cloud characteristics from cloud-resolving model (CRM) simulations. CNN model prediction factors are hidden in the high dimensional weighted parameters in each neural network layer. Therefore, we can dig out relevant physics processes by iterating the CNN models' training process and eliminating the features with the physics explanation we can provide at a given stage. Within a few iterations, explainable nonlinear relationships among variables can be provided. We identified that the averaged cloud water path (CWP), the maximum value of CWP in each cloud, and the cloud coverage rate are essential for identifying aggregation. Furthermore, by analyzing the encoded channels of the CNN model, we found a strong relationship between aggregation, cloud peripherals, and fractal dimensions. The results suggest that the important nonlinear cloud characteristics for identifying the aggregation can be captured with the proper adjustment and limitation of the input data to the CNN models. Our framework provides a possibility that we can explore the high dimensional relationship between the physics process with the assistance of the CNN model.</p>\",\"PeriodicalId\":50734,\"journal\":{\"name\":\"Atmospheric Science Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1150\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Science Letters\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/asl.1150\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Science Letters","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asl.1150","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
A deep learning framework for analyzing cloud characteristics of aggregated convection using cloud-resolving model simulations
This study introduces a framework to extract the high-dimensional nonlinear relationships among state variables for aggregated convection. The prototype of such a framework is developed that applies the convolutional neural network models (CNN models) to retrieve the cloud characteristics from cloud-resolving model (CRM) simulations. CNN model prediction factors are hidden in the high dimensional weighted parameters in each neural network layer. Therefore, we can dig out relevant physics processes by iterating the CNN models' training process and eliminating the features with the physics explanation we can provide at a given stage. Within a few iterations, explainable nonlinear relationships among variables can be provided. We identified that the averaged cloud water path (CWP), the maximum value of CWP in each cloud, and the cloud coverage rate are essential for identifying aggregation. Furthermore, by analyzing the encoded channels of the CNN model, we found a strong relationship between aggregation, cloud peripherals, and fractal dimensions. The results suggest that the important nonlinear cloud characteristics for identifying the aggregation can be captured with the proper adjustment and limitation of the input data to the CNN models. Our framework provides a possibility that we can explore the high dimensional relationship between the physics process with the assistance of the CNN model.
期刊介绍:
Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques.
We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.