陈锦鹏,冯业荣,黄奕丹,蔡乐天,洪晓湘,文秋实. 2024. 利用机器学习模拟湿物理参数化方案. 气象学报,82(1):113-126. DOI: 10.11676/qxxb2024.20230030
引用本文: 陈锦鹏,冯业荣,黄奕丹,蔡乐天,洪晓湘,文秋实. 2024. 利用机器学习模拟湿物理参数化方案. 气象学报,82(1):113-126. DOI: 10.11676/qxxb2024.20230030
Chen Jinpeng, Feng Yerong, Huang Yidan, Cai Letian, Hong Xiaoxiang, Wen Qiushi. 2024. Development of four machine learning schemes used for moist physics parameterization in CMA-TRAMS. Acta Meteorologica Sinica, 82(1):113-126. DOI: 10.11676/qxxb2024.20230030
Citation: Chen Jinpeng, Feng Yerong, Huang Yidan, Cai Letian, Hong Xiaoxiang, Wen Qiushi. 2024. Development of four machine learning schemes used for moist physics parameterization in CMA-TRAMS. Acta Meteorologica Sinica, 82(1):113-126. DOI: 10.11676/qxxb2024.20230030

利用机器学习模拟湿物理参数化方案

Development of four machine learning schemes used for moist physics parameterization in CMA-TRAMS

  • 摘要: 数值天气预报模式的湿物理参数化方案对降水预报有很大影响。常规湿物理参数化方案计算复杂、计算量大,且存在较大不确定性。文中采用4种机器学习算法即基于决策树的梯度提升算法(LightGBM)、全连接神经网络(FC)、卷积神经网络(CNN)和卷积块注意力模块(CBAM)提取数值预报模式变量网格点周围的局部信息建模。针对一次中国南海台风过程开展湿物理参数化方案模拟试验,试验表明,4种机器学习模型均能较好地模拟湿物理参数化方案的温、湿效应,能够刻画台风对流活动产生的热源和水汽汇的螺旋结构。位温倾向在对流层中层误差较大,比湿倾向在对流层低层误差较大,随着预报时效延长模型的模拟能力有所降低。

     

    Abstract: Moist physics parameterization in numerical weather prediction models has great influences on precipitation forecast. Conventional moist physics parameterization is complicated, computation-intensive and has great uncertainties. In this paper, four machine learning (ML) architectures, i.e., decision tree based light gradient-boosting machine (LightGBM), fully connected neural network (FC), convolutional neural network (CNN) and convolutional block attention module (CBAM), are developed for moist physics parameterization via extracting local information of model variables at each grid point. Simulation experiments are carried out for a typhoon process in the South China Sea. Results show that the four ML-based schemes can well simulate the thermal and moisture effects of the moist physics parameterization and can present the spiral structure of heat source and moisture sink related to typhoon convection. Large errors of potential temperature tendency and specific humidity tendency are found in the middle and lower troposphere respectively. All the four machine learning schemes deteriorate with forecast time. This paper provides a useful reference for the development of ML-based physical parameterization scheme.

     

/

返回文章
返回