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首页> 外文期刊>Journal of Molecular Spectroscopy >Evaluation of spectroscopic databases through radiative transfer simulations compared to observations. Application to the validation of GEISA 2015 with IASI and TCCON
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Evaluation of spectroscopic databases through radiative transfer simulations compared to observations. Application to the validation of GEISA 2015 with IASI and TCCON

机译:通过辐射转移模拟与观察相比较来评估光谱数据库。申请通过IASI和TCCON验证GEISA 2015

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摘要

The quality of spectroscopic parameters that serve as input to forward radiative transfer models are essential to fully exploit remote sensing of Earth atmosphere. However, the process of updating spectroscopic databases in order to provide the users with a database that insures an optimal characterization of spectral properties of molecular absorption for radiative transfer modeling is challenging. The evaluation of the databases content and the underlying choices made by the managing team is thus a crucial step. Here, we introduce an original and powerful approach for evaluating spectroscopic parameters: the Spectroscopic Parameters And Radiative Transfer Evaluation (SPARTE) chain. The SPARTE chain relies on the comparison between forward radiative transfer simulations made by the 4A radiative transfer model and observations of spectra made from various observations collocated over several thousands of well-characterized atmospheric situations. Averaging the resulting 'calculated-observed spectral' residuals minimizes the random errors coming from both the radiometric noise of the instruments and the imperfect description of the atmospheric state. The SPARTE chain can be used to evaluate any spectroscopic databases, from the visible to the microwave, using any type of remote sensing observations (ground-based, airborne or space-borne). We show that the comparison of the shape of the residuals enables: (i) identifying incorrect line parameters (line position, intensity, width, pressure shift, etc.), even for molecules for which interferences between the lines have to be taken into account; (ii) proposing revised values, in cooperation with contributing teams; and (iii) validating the final updated parameters. In particular, we show that the simultaneous availability of two databases such as GEISA and HITRAN helps identifying remaining issues in each database. The SPARTE chain has been here applied to the validation of the update of GEISA-2015 in 2 spectral regions of particular interest for several currently exploited or planned Earth space missions: the thermal infrared domain and the short-wave infrared domain, for which observations from the space-borne IASI instrument and from the ground-based FTS instruments at the Parkfalls TCCON site are used respectively. Main results include: (i) the validation of the positions and intensities of line parameters, with overall significantly lower residuals for GEISA2015 than for GEISA-2011 and (iii) the validation of the choice made on the parameters (such as pressure shift and air-broadened width) which has not been given by the provider but completed by ourselves. For example, comparisons between residuals obtained with GEISA-2015 and HITRAN-2012 have highlighted a specific issue with some HWHM values in the latter that can be clearly identified on the 'calculated observed' residuals. (C) 2016 Elsevier Inc. All rights reserved.
机译:光谱参数的质量作为正向辐射传递模型的输入,对于充分利用地球大气的遥感至关重要。然而,更新光谱数据库以向用户提供确保辐射吸收建模的分子吸收光谱特性的最佳表征的数据库的过程具有挑战性。因此,评估数据库内容和管理团队做出的基本选择是至关重要的一步。在这里,我们介绍了一种新颖而强大的方法来评估光谱参数:光谱参数和辐射传递评估(SPARTE)链。 SPARTE链依赖于4A辐射传递模型进行的正向辐射传递模拟与由数千个特征明确的大气环境中的各种观测值构成的光谱观测值之间的比较。对所得的“计算出的观测光谱”残差进行平均可以最大程度地减少由仪器的辐射噪声和不完善的大气状态描述引起的随机误差。 SPARTE链可用于使用任何类型的遥感观测值(地面,空中或空间星载)评估从可见光到微波的任何光谱数据库。我们表明,通过比较残差的形状,可以:(i)识别不正确的线参数(线位置,强度,宽度,压力偏移等),即使对于必须考虑线之间干扰的分子也是如此; (ii)与捐助团队合作提出修订后的价值; (iii)验证最终更新的参数。特别是,我们证明了两个数据库(例如GEISA和HITRAN)的同时可用性有助于识别每个数据库中的剩余问题。 SPARTE链已在此处用于两个当前特别开发或计划中的地球空间飞行任务特别感兴趣的2个光谱区域的GEISA-2015更新的验证:热红外域和短波红外域, Parkfalls TCCON站点分别使用了星载IASI仪器和地面FTS仪器。主要结果包括:(i)验证管线参数的位置和强度,GEISA2015的残差总体上显着低于GEISA-2011的残差,以及(iii)验证对参数的选择(如压力变化和空气压力) -宽的宽度),这不是提供者提供的,而是由我们自己完成的。例如,通过GEISA-2015和HITRAN-2012获得的残差之间的比较突出了一个特定的问题,后者中的一些HWHM值可以在“计算得出的观测到”残差上清楚地识别出来。 (C)2016 Elsevier Inc.保留所有权利。

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