利用机器学习改善模拟温度和湿度数据的垂直细节

IF 2 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Joana D. da Silva Rodrigues, Cyril J. Morcrette
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引用次数: 0

摘要

用于天气预报和气候预测的大气模型将大气离散到一个垂直网格上。然而,也有一些大气现象发生在比这些模式层厚度更小的尺度上。由于逆温而形成的低空云就是一个例子。这导致大气模型低估,甚至忽略了这些云及其辐射效应。使用无线电探空仪观测作为训练数据,使用机器学习模型来改善模拟温度和比湿度剖面的垂直细节。此外,开发了一个物理信息的机器学习模型,并与传统方法进行了比较;显示了根据其预测计算出的云分剖面的改进。垂直增强剖面也改善了对流抑制层和异常折射梯度的表现。这项工作有助于有针对性地改进某些大气过程的表示,而不会增加整个模型中垂直分辨率增加的内存负担和计算成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving vertical detail in simulated temperature and humidity data using machine learning

Improving vertical detail in simulated temperature and humidity data using machine learning

Atmospheric models used for weather forecasting and climate predictions discretise the atmosphere onto a vertical grid. There are however atmospheric phenomena that occur on scales smaller than the thickness of those model layers. The formation of low-level clouds due to temperature inversions is an example. This leads to atmospheric models underestimating, or even missing, these clouds and their radiative effects. Using radiosonde observations as training data, a machine learning model is used to improve the vertical detail of modelled profiles of temperature and specific humidity. In addition, a physics-informed machine learning model is developed and compared to the traditional approach; showing improvements in the cloud fraction profiles calculated from its predictions. The vertically enhanced profiles also improve the representation of layers of convective inhibition and anomalous refractivity gradients. This work facilitates targeted improvements to the representation of certain atmospheric processes without the burden of increased memory and computational cost from increasing vertical resolution throughout the whole model.

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来源期刊
Atmospheric Science Letters
Atmospheric Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.90
自引率
3.30%
发文量
73
审稿时长
>12 weeks
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.
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