Dynamical downscaling of a multimodel ensemble prediction system: Application to tropical cyclones,Atmospheric Science Letters

当前位置： X-MOL 学术 › Atmos. Sci. Lett. › 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.)

Dynamical downscaling of a multimodel ensemble prediction system: Application to tropical cyclones
Atmospheric Science Letters ( IF 2.0 ) Pub Date : 2020-04-08 , DOI: 10.1002/asl.971
Manpreet Kaur _{1,

2} , R. Phani Murali Krishna ₁ , Susmitha Joseph ₁ , Avijit Dey ₁ , Raju Mandal ₁ , Rajib Chattopadhyay ₁ , Atul Kumar Sahai ₁ , Parthasarathi Mukhopadhyay ₁ , Sukumarapillai Abhilash ₃

Affiliation

This study attempts dynamical downscaling to improve north Indian ocean (NIO) tropical cyclone prediction from a global multimodel ensemble prediction system using weather research and forecasting (WRF) model. A total of 16 ensembles are used in the WRF simulations, these ensembles are bias‐corrected prior to downscaling for model climatological errors. The ensemble mean constructed from the output of all downscaled ensembles is analyzed for added value to global predictions. This mean is also compared against observation as well as high‐resolution (12 km) deterministic forecast from global forecast system (GFS). Two devastating NIO tropical cyclone cases of year 2017 which were not reliably predicted by global systems have been selected for this study. The results show that downscaled predictions well simulate the intensity and spatial distribution of the rainfall and relative vorticity associated with these cyclonic storms. The wind and temperature vertical profiles during cyclone mature stage are also captured more accurately than raw prediction and high‐resolution global deterministic forecast. The study affirms the adequacy of dynamical downscaling in predicting the cyclonic storms over global real‐time weather forecasting system.

中文翻译：

多模型集合预报系统的动态降尺度：在热带气旋中的应用

这项研究尝试通过动态降尺度，从使用天气研究和预报（WRF）模型的全球多模型总体预报系统中改善北印度洋（NIO）热带气旋的预报。WRF仿真中总共使用了16个合奏，这些合奏在按比例缩小之前因模型气候误差而经过了偏差校正。分析所有缩小比例的合奏的输出构成的集合均值，以为全局预测增加价值。还将该平均值与观测值以及来自全球预报系统（GFS）的高分辨率（12 km）确定性预报进行了比较。这项研究选择了两个全球系统无法可靠预测的2017年毁灭性NIO热带气旋病例。结果表明，降尺度的预测很好地模拟了与这些气旋风暴有关的降雨强度和空间分布以及相对涡度。与原始预报和高分辨率全球确定性预报相比，旋风成熟阶段的风和温度垂直剖面也能更准确地捕获。该研究肯定了动态降尺度在预测全球实时天气预报系统上的气旋风暴方面的适当性。

更新日期：2020-04-08

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11