Spectral Radiation Measurements and Analysis in the ARM Program
Spectral Radiation Measurements and Analysis in the ARM Program
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ARM 程序中的光谱辐射测量和分析
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发表时间：
2016
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通讯作者：
D. Turner
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作者：
E. Mlawer;D. Turner
各种大气成分产生的辐射的光谱特征是我们理解与气候和天气有关的许多问题的关键。对光谱分辨辐射的地面测量是关于大气气体、云和气溶胶特性的特别丰富的信息来源。为了使包括大气环流模式在内的大气模式的模拟具有可信度，最精确的辐射传输代码的计算必须能够再现各种条件下的光谱测量。这一观点是ARM计划创始目标的核心，在早期提供了该计划的基本重点，并且是该计划历史上许多重要成就的核心。建立大气辐射测量计划的一个关键动机是通过与高质量光谱辐射测量进行广泛比较，发展评估和改进逐线辐射代码的能力，逐线辐射代码是最基于物理的辐射传输算法。特别是，来自气候模式中辐射代码的相互比较（ICRCCM; Ellingson和Foulingson 1991; Ellingson et al. 2016，第1章）的结果，尽管旨在评估快速辐射参数化的性能，但对于建立像ARM这样以光谱辐射为重点的计划的动力至关重要。对长波ICRCCM结果的分析得出的一个关键结论（Ellingson等人，1991年）是，虽然气候模型中使用的许多快速辐射代码在计算宽光谱范围的通量时存在部分抵消的光谱误差，但逐线模型设计者对自己的模型没有足够的信心，不主张将其用作参考。因此，本研究的参与者建议“组织一个计划，同时测量高光谱分辨率下的光谱辐射亮度，沿着计算辐射亮度所需的大气变量，特别是在晴朗天空条件下”（第8952页）。ARM计划就是为了应对这一挑战而制定的，本章（沿着本专题中的其他相关章节）详细介绍了为成功解决这一问题而遵循的研究计划。ICRCCM建议通过分析实地观测来改进辐射传输参数化，对此的最初回应是组织光谱辐射实验（SPECTRE; Ellingson and Wiscombe 1996; Ellingson et al. 2016，chapter 1）。这个为期一个月的现场实验在堪萨斯的科菲维尔部署了几个红外干涉仪，以测量下涌的红外光谱辐射沿着一系列传感器，这两个传感器都在原地（例如，无线电探空仪、烧瓶测量二氧化碳和甲烷等痕量气体）并且是远程的（例如，拉曼激光雷达、无线电声学探测系统、云雷达），以表征作为驱动辐射模型的输入所需的大气状态。幽灵党虽然规模有限，但取得了一些成功。美国威斯康星大学麦迪逊分校研制的大气辐射干涉仪（AERI; Knuteson et al. 2004 a，B）被证明具有很强的稳定性。J. Mlawer，大气和环境研究公司，131 Hartwell Ave.，列克星敦，MA 02421。emlawer@aer.com十四章MLAWER和TURNER 14.1
The spectral signatures of radiation produced by various atmospheric constituents are key to our understanding of many issues related to climate and weather. Groundbased measurements of spectrally resolved radiation are particularly rich sources of information on atmospheric gases, clouds, and aerosol properties. For there to be confidence in simulations by atmospheric models, including general circulation models (GCMs), it is essential that calculations by the most accurate radiative transfer codes be able to reproduce these spectral measurements for a broad range of conditions. This perspective was central to the founding objectives of the ARM Program, provided an essential focus of the program during its early years, and was at the core of many of the program’s important accomplishments during its history. A critical motivation for establishing the Atmospheric RadiationMeasurement (ARM)Programwas to develop the capability to evaluate and improve line-by-line radiation codes, which are themost physically based radiative transfer algorithms, through extensive comparisons with high-quality spectral radiation measurements. In particular, results from the Intercomparison ofRadiationCodes in Climate Models (ICRCCM; Ellingson and Fouquart 1991; Ellingson et al. 2016, chapter 1), although directed at the evaluation of the performance of fast radiation parameterizations, were key to establishing the impetus for a program such as ARM with a spectral radiation focus. A key conclusion from the analysis of longwave ICRCCM results (Ellingson et al. 1991) was that, although many fast radiation codes used within climate models had spectral errors that partially canceled out when fluxes over a wide spectral range were computed, line-by-linemodelers did not have sufficient confidence in their own models to advocate using them as references. The participants in this study therefore recommended that ‘‘a program be organized to simultaneously measure the spectral radiance at high spectral resolution along with the atmospheric variables necessary to calculate the radiance, particularly for clear-sky conditions’’ (p. 8952). The ARM Program was developed as the answer to this challenge, and this chapter (along with other related chapters in this monograph) details the research program that was followed toward its successful resolution. The initial response to the ICRCCM recommendation to improve radiative transfer parameterizations through the analysis of field observations was the organization of the Spectral Radiation Experiment (SPECTRE; Ellingson and Wiscombe 1996; Ellingson et al. 2016, chapter 1). This one-month field experiment deployed several infrared interferometers to Coffeyville, Kansas, to measure the downwelling infrared spectral radiance along with a range of sensors, both in situ (e.g., radiosonde, flask measurements of trace gases like carbon dioxide and methane, etc.) and remote (e.g., Raman lidar, Radio Acoustic Sounding System, cloud radar), to characterize the atmospheric state needed as input to drive the radiation models. SPECTRE, although limited, had a number of successes. The Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004a,b), which was developed by theUniversity of Wisconsin–Madison, was demonstrated to have a robust Corresponding author address: E. J. Mlawer, Atmospheric and Environmental Research Inc., 131 Hartwell Ave., Lexington, MA 02421. E-mail: emlawer@aer.com CHAPTER 14 MLAWER AND TURNER 14.1