Cirrus Cloud Identification from Airborne Far-Infrared and Mid-Infrared Spectra

Magurno, D.; Cossich, William; Maestri, Tiziano; Bantges, R.; Brindley, Helen E.; Fox, Stuart; Harlow, Chawn; Murray, J. E.; Pickering, Juliet C.; Warwick, Laura; Oetjen, H.

doi:10.3390/rs12132097

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…For example, several studies showed that a large fraction of the OLR energy lies in the FIR region (Bellisario et al., 2019; Chen et al., 2014; Harries et al., 2008; X. Huang et al., 2008; Martinazzo et al., 2021; Turner & Mlawer, 2010). Moreover, as the OLR in FIR is especially sensitive to atmospheric water vapor and clouds (e.g., cirrus), investigations and observations in FIR could potentially improve the retrieval of their information (Blanchet et al., 2011; Clough et al., 1992; Cox et al., 2015; Delamere et al., 2010; Harries et al., 2008; Libois & Blanchet, 2017; Maestri & Rizzi, 2003; Magurno et al., 2020; Palchetti et al., 2015, 2016; Sinha & Harries, 1995). To give a few examples, Clough et al.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

Huang

2022

JGR Atmospheres

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The outgoing longwave radiation (OLR), which consists of the thermal radiation from both the atmosphere and surface, is of critical importance to the Earth radiation energy budget. To understand the global OLR distribution, it is important to quantify the varying atmospheric and surface contributions. In this work, we present such a quantification using radiative transfer computations based on global reanalysis atmospheric data. By dissecting the OLR simulated following the radiative transfer equation, we quantitatively measure the layer‐wise atmospheric contributions to OLR and compare it to the surface contribution in different spectral bands. One focus of this study is on the OLR in the far‐infrared (FIR), for which new satellites are expected to provide unprecedented measurements. We find that around 45% of the global mean OLR is radiated in the FIR and in polar regions the FIR contribution can increase to 60%. Our vertical decomposition of OLR discloses that although the atmospheric contribution generally surpasses the surface contribution, the enhanced FIR contribution in the polar regions mainly results from a relatively stronger surface (as opposed to atmospheric) contribution. This is allowed by the opening of the atmospheric window in the FIR in these extremely dry regions. Our analysis also reveals that the tropopause layer makes a minimum contribution to the OLR, which may be a unique spectroscopic feature of the Earth atmosphere. Clouds are found to reduce the atmospheric contribution in the FIR while enhancing it in the mid‐infrared.

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Section: Introductionmentioning

confidence: 99%

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

Huang

2022

JGR Atmospheres

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“…The results fulfill the high requirements of climatologists with a mean bias of −0.15% and a bias-corrected root-mean-square deviation of 7% against SYNOP observations (Bojanowski et al 2018). Cloud classification is significantly improved by machine learning algorithms with classification accuracies of 95% and more (e.g., Chen et al 2018;Ozkan et al 2018;Reguiegue and Chouireb 2018;Gao et al 2019;Jeppesen et al 2019;Li et al 2019;Magurno et al 2020;Mahajan and Fataniya 2020;Meraner et al 2020;Sun et al 2020;Dubovik et al 2021).…”

Section: Appendix C: Cloud Observations From Spacementioning

confidence: 99%

“…louds are omnipresent in the Earth atmosphere, where they are significant actors in weather, hydrology, climate, air chemistry, and several practical applications such as atmospheric aviation hazards and solar energy use (e.g., Bonkaney et al 2017;Romano 2020;Mahajan and Fataniya 2020;Yuchechen et al 2020;Prata 2020). With a global annual mean cloud cover of ≈66% (Zhang et al 2004;Wang et al 2020), clouds strongly affect the radiation budget of the Earth both in the solar and thermal spectral ranges, thus governing the state and forcing of Earth's climate (L. Dai et al 2019;Magurno et al 2020;Romano 2020;Wang et al 2020;Dubovik et al 2021). It is generally accepted that clouds are the most uncertain determinants in model estimates of global warming (e.g., Dai et al 2019;Romano 2020).…”

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confidence: 99%

What Is a Cloud? Toward a More Precise Definition

Spänkuch

Hellmuth

Görsdorf

2022

Bulletin of the American Meteorological Society

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There are a couple of reasons to stimulate a discussion about the definition of clouds. One reason is that the American Meteorological Society (AMS) and the World Meteorological Organisation (WMO) define clouds differently, and that in two aspects. AMS defines clouds as visible objects, and WMO as perceivable objects. Furthermore, AMS includes all minute particles independent of their nature and composition whereas WMO considers only such minute particles that consist of water, ice or a mixture of both. Additionally, the so called invisible or subvisible clouds are perceivable objects in the definition of WMO but not of AMS. Clouds can be observed by humans and be detected by active and passive sensors from ground and space. The detection limits of instruments span more than two orders of magnitude in visible optical thickness ranging from about 0.001 from satellite lidar (in case of horizontal and vertical averaging or 0.07 for single lidar shots) to about 0.3 to 0.45 for multispectral satellite imaging spectrometers. Human observers see clouds when their optical thickness is larger than about 0.03 at day and 0.05 at night. This paper gives a brief overview of cloud detection from ground and space, of the occurrence, characteristics, and impacts of subvisible clouds. Pros and cons of these definitions are discussed followed by a proposal for a more precise definition. This definition is like those of AMS and WMO a one-parametric definition and does not cover all aspects of clouds.

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“…The far-infrared (FIR) spectral band is essential to the radiation spectrum, ranging from about 100 to 667 cm −1 (15-100 µm) spectra [19]. The equivalent blackbody temperature of the Earth is 255 K, whose peak energy occurs at about 500 cm −1 in the FIR [20].…”

Section: Introductionmentioning

confidence: 99%

Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic

Ren,

Liu,

et al. 2023

Remote Sensing

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The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.

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Cirrus Cloud Identification from Airborne Far-Infrared and Mid-Infrared Spectra

Cited by 9 publications

References 40 publications

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

What Is a Cloud? Toward a More Precise Definition

Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic

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