Predicting origins of coherent air mass trajectories using a neural network—the case of dry intrusions,Meteorological Applications

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Predicting origins of coherent air mass trajectories using a neural network—the case of dry intrusions
Meteorological Applications ( IF 2.3 ) Pub Date : 2021-04-30 , DOI: 10.1002/met.1986
Vered Silverman ₁ , Stav Nahum ₁ , Shira Raveh‐Rubin ₁

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Mid‐latitude cyclones are complex weather systems that are tightly related to surface weather impacts. Coherent air streams are known to be associated with such systems, in particular dry intrusions (DIs) in which dry air masses descend slantwise from the vicinity of the tropopause equatorward towards the surface. Often, DIs are associated with severe surface winds, heavy precipitation and frontogenesis. Currently, DIs can only be identified in hindsight by costly Lagrangian calculations using high resolution wind field data. Here, we use a novel method aiming to simplify the detection procedure of DI origins to allow their future identification in climate datasets, previously inaccessible for such diagnostic studies. A novel adaptation of a segmentation‐oriented neural network model is hereby presented as a successful tool to identify DI origins based solely on three ERA‐Interim reanalysis geopotential height fields, representing the state of the atmosphere. The model prediction skill is tested by calculating both the grid‐point and DI object based Matthews correlation coefficient. We find the model highly skilful in both reconstructing accurately the climatological distribution and predicting the vast majority of the individual DI origin objects. The skill decreases for relatively small objects and for objects occurring at locations where such cases are relatively less frequent. This indicates that geopotential height variability is related to the dynamic mechanisms involved in DI initiations. The results serve as a proof of concept for predicting DIs and other coherent air mass trajectories even when high resolution wind field data are not available, such as for model output for future climate projections.

中文翻译：

使用神经网络预测相干空气质量轨迹的起源-干侵入的情况

中纬度气旋是与地面天气影响紧密相关的复杂天气系统。已知连贯的气流与这样的系统相关联，特别是干侵入物（DI），其中干空气团从对流层顶赤道附近朝表面倾斜地下降。DI通常与严重的地面风，大量的降水和锋生有关。当前，只能通过使用高分辨率风场数据进行昂贵的拉格朗日计算来事后识别DI。在这里，我们使用一种新颖的方法，旨在简化DI来源的检测程序，以便将来在气候数据集中识别它们，而这些数据以前是此类诊断研究无法获得的。本文提出了一种新颖的面向分段神经网络模型的改编方法，它是仅基于三个ERA-Interim重新分析地势高度场（代表大气状态）来识别DI起源的成功工具。通过计算基于网格点和DI对象的Matthews相关系数来测试模型预测技能。我们发现该模型非常熟练，既可以准确地重建气候分布，又可以预测绝大多数DI直接起源对象。对于相对较小的物体以及在这种情况相对较少发生的位置处出现的物体，技能会降低。这表明地势高度变异性与DI引发中涉及的动力学机制有关。

更新日期：2021-04-30

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