Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Ceilometer at North Alabama

IF 3.8 2区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Jingjing Tian, Yunyan Zhang, Kevin Knupp, Preston Pangle, Jennifer Comstock
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引用次数: 0

Abstract

Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite-based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground-based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian, Zhang, Klein, & Schumacher, 2021, https://doi.org/10.3390/rs13122309, 2022, https://doi.org/10.1029/2021gl097070). To validate this algorithm, the framework employs ground-based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear-sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground-based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground-based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground-based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land-atmosphere interactions in North Alabama.

Abstract Image

利用GOES-16卫星和地基积云计在北阿拉巴马夏季大陆浅积云探测
在地球系统建模中,边界层云过程的精确模拟仍然是一个挑战。观测对于评价和改进这些过程的模型是必不可少的。本文介绍了一种基于卫星的大陆浅积云(ShCu)白天探测算法的综合验证框架,该算法最初是利用美国能源部大气辐射测量部(ARM)南部大平原站点的立体相机地面观测开发的。舒马赫,2021,https://doi.org/10.3390/rs13122309, 2022, https://doi.org/10.1029/2021gl097070)。为了验证该算法,该框架采用了来自ShCu种群普遍存在的北阿拉巴马州(NA)的地面天花板测量仪。本研究首先生成NA晴空表面反射率图,并利用地球静止运行环境卫星(GOES)反射率数据识别具有检测阈值的ShCu像元。然后,将获得的云分数(CFs)与地面测云仪的云分数进行比较,考虑诸如观测面积差异、卫星视差问题和系统偏差等因素。我们发现,当检测阈值(∆R)为0.05时,ShCu检测算法对NA是有效的,可以使用go再现每小时的ShCu CFs。我们的框架简单明了且易于重复,可用于评估在部署了ceilometer的不同地理区域检测ShCu云的∆R阈值的有效性。这种对ShCu的卫星探测为地面测量提供了一个重要的区域背景,促进了对流开始的跟踪及其与陆地表面条件的耦合。整合局部地面和区域卫星数据将增强我们对北阿拉巴马州云形态和陆地-大气相互作用进行深入研究的能力。
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来源期刊
Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres Earth and Planetary Sciences-Geophysics
CiteScore
7.30
自引率
11.40%
发文量
684
期刊介绍: JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.
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