Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, Graydon Aulich
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引用次数: 0

Abstract

Abstract. Cloud feedbacks associated with deep convective anvils remain highly uncertain. In part, this uncertainty arises from a lack of understanding of how microphysical processes influence the cloud radiative effect. In particular, climate models have a poor representation of microphysics processes, thereby encouraging the collection and study of observation data to enable better representation of these processes in models. As such, the Deep Convective Microphysics Experiment (DCMEX) undertook an in situ aircraft and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar, thermodynamics, dynamics, electric fields, and weather. This paper introduces the potential data user to DCMEX observational campaign characteristics, relevant instrument details, and references to more detailed instrument descriptions. Also included is information on the structure and important files in the dataset in order to aid the accessibility of the dataset to new users. Our overview of the campaign cases illustrates the complementary operational observations available and demonstrates the breadth of the campaign cases observed. During the campaign, a wide selection of environmental conditions occurred, ranging from dry, northerly air masses with low wind shear to moist, southerly air masses with high wind shear. This provided a wide range of different convective growth situations. Of 19 flight days, only 2 d lacked the formation of convective cloud. The dataset presented (https://doi.org/10.5285/B1211AD185E24B488D41DD98F957506C; Facility for Airborne Atmospheric Measurements et al., 2024) will help establish a new understanding of processes on the smallest cloud- and aerosol-particle scales and, once combined with operational satellite observations and modelling, can support efforts to reduce the uncertainty of anvil cloud radiative impacts on climate scales.
协调飞机和地面观测的深对流微物理实验(DCMEX):积雨云形成过程中的微物理、气溶胶和动力学
摘要与深对流砧相关的云反馈仍具有很大的不确定性。造成这种不确定性的部分原因是对微物理过程如何影响云辐射效应缺乏了解。特别是,气候模式对微物理过程的代表性较差,因此需要收集和研究观测数据,以便在模式中更好地体现这些过程。因此,深对流微物理实验(DCMEX)在 2022 年 7 月至 8 月期间对新墨西哥州的深对流云进行了实地飞机和地面测量活动。该活动协调了一系列测量气溶胶、云物理、雷达、热力学、动力学、电场和天气的仪器。本文向潜在数据用户介绍了 DCMEX 观测活动的特点、相关仪器的详细信息,以及更详细仪器说明的参考资料。此外,本文还介绍了数据集的结构和重要文件,以帮助新用户使用数据集。我们对活动案例的概述说明了可用的互补性业务观测,并展示了活动案例观测的广度。活动期间出现了多种环境条件,既有干燥、低风切变的偏北气团,也有潮湿、高风切变的偏南气团。这提供了各种不同的对流生长情况。在 19 个飞行日中,只有 2 天没有形成对流云。所提供的数据集(https://doi.org/10.5285/B1211AD185E24B488D41DD98F957506C;机载大气测量设施等,2024 年)将有助于建立对最小云和气溶胶粒子尺度过程的新认识,一旦与业务卫星观测和建模相结合,将有助于减少砧云对气候尺度辐射影响的不确定性。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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