Projected changes of typhoon intensity in a regional climate model: Development of a machine learning bias correction scheme,International Journal of Climatology

当前位置： X-MOL 学术 › Int. J. Climatol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Projected changes of typhoon intensity in a regional climate model: Development of a machine learning bias correction scheme
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-12-27 , DOI: 10.1002/joc.6987
Jinkai Tan _{1,

2,

3,

4} , Sheng Chen _{1,

2,

3} , Chia‐Ying Lee ₅ , Guangtao Dong ₆ , Wenyan Hu ₇ , Jun Wang _{4,

8}

Affiliation

A machine learning‐based bias correction scheme was developed to adjust the simulated Western North Pacific typhoon intensity in a 25‐km regional climate model (RCM). The bias correction scheme, MLERA, consists of a hybrid neural network, which takes modelled atmospheric and oceanic conditions near the storm centre as input. We use air temperature, specific humidity, and relative vorticity at 300 hPa, geopotential height at 700 hPa, wind speed at 850 hPa, sea‐level pressure, 10‐m wind speed, and total air‐sea fluxes. Those predictors are selected using least absolute shrinkage and selection operator (Lasso) algorithm and principal component analysis (PCA). Because there is no ‘ground truth’ for RCM simulated storms, we train and test MLERA using ERA‐Interim reanalysis and best‐track data. The intensity statistics estimated from MLERA match well with those from observations. MLERA, when applied to RCM history base period, increases the likelihood of simulated typhoons (with wind speed >33 m/s) from 40.8 to 73.9% in the direct simulation. Meanwhile, the RCM itself produces no C3+ storms (wind speed >50 m/s) in both RCP4.5 and RCP8.5 scenarios, while MLERA significantly increases those storms. MLERA suggests an increasing trend of the frequency of violent typhoons from near‐term (2031–2060) to long‐term (2069–2098). Such an increase is consistent with our understanding that a warming climate will increase the storm intensity. The present study shows the potential of the machine learning technique for bias correcting cyclone intensity in RCMs. Furthermore, the proposed algorithm could also be applied to the next generation of high‐resolution global climate models, which may have a spatial grid spacing close to today's RCMs.

中文翻译：

区域气候模型中台风强度的预计变化：机器学习偏差校正方案的开发

开发了一种基于机器学习的偏差校正方案，以在25 km的区域气候模型（RCM）中调整模拟的北太平洋西部台风强度。偏差校正方案MLERA由一个混合神经网络组成，该混合神经网络将风暴中心附近的模拟大气和海洋条件作为输入。我们使用300 hPa的空气温度，比湿度和相对涡度，700 hPa的地势高度，850 hPa的风速，海平面压力，10 m风速以及总的海气通量。使用最小绝对收缩和选择算子（Lasso）算法和主成分分析（PCA）来选择那些预测变量。由于RCM模拟风暴没有“地面真理”，因此我们使用ERA-Interim重新分析和最佳跟踪数据来训练和测试MLERA。MLERA估算的强度统计与观测值相吻合。MLERA在应用于RCM历史基准期时，在直接模拟中将模拟台风（风速> 33 m / s）的可能性从40.8增加到73.9％。同时，RCM本身在RCP4.5和RCP8.5方案中均不会产生C3 +风暴（风速> 50 m / s），而MLERA会显着增加这些风暴。MLERA表明，暴力台风的频率从近期（2031年至2060年）到长期（2069年至2098年）的增加趋势。这样的增加与我们的认识一致，即气候变暖会增加风暴强度。本研究显示了机器学习技术对RCM中的气旋强度进行偏差校正的潜力。此外，

更新日期：2020-12-27

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11