Editorial: Atmospheric processes and applications in urban, coastal and mountainous terrain

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Meteorological Applications Pub Date : 2023-05-28 DOI:10.1002/met.2129
Meinolf Kossmann, Lorenzo Giovannini
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Abstract

This editorial introduces the contributions to the Special Issue ‘Atmospheric processes and applications in urban, coastal, and mountainous terrain’ of the journal Meteorological Applications. The scientific background and motivation for this Special Issue are described and followed by a brief summary of the 15 articles of this Special Issue. The conclusions of this Editorial summarise the approaches taken and address future developments expected in process-oriented studies and for the development of applications in complex terrain.

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社论:大气过程及其在城市、沿海和山区的应用
这篇社论介绍了对《气象应用》杂志特刊“城市、沿海和山区的大气过程和应用”的贡献。介绍了本期特刊的科学背景和动机,并简要总结了本期的15篇文章。本社论的结论总结了所采取的方法,并阐述了面向过程的研究和在复杂地形中开发应用程序的未来发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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Issue Information Evaluation of forecasted wind speed at turbine hub height and wind ramps by five NWP models with observations from 262 wind farms over China Tall tower observations of a northward surging gust front in central Amazon and its role in the mesoscale transport of carbon dioxide Fidelity of global tropical cyclone activity in a new reanalysis dataset (CRA40) Predicting dryland winter wheat yield in cold regions of Iran
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