Molecular characterization of alkyl nitrates in atmospheric aerosols by ion mobility mass spectrometry
Xuan Zhang, Haofei Zhang, Wen Xu, Xiaokang Wu, Geoffrey S. Tyndall, John J. Orlando, John T. Jayne, Douglas R. Worsnop, and Manjula R. Canagaratna
Atmos. Meas. Tech., 12, 5535–5545, https://doi.org/10.5194/amt-12-5535-2019, 2019
We develop a new technique to characterize organic nitrates as intact molecules in atmospheric aerosols, and we apply this technique to identify hydroxy nitrates in secondary organic aerosols produced from the photochemical oxidation of isoprene.
Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-345,2019
Manuscript under review for AMT (discussion: open, 0 comments)
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces;The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC’s two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements: a feasibility study
Yun Dong, Elena Spinei, and Anuj Karpatne
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-368,2019
Manuscript under review for AMT (discussion: open, 0 comments)
In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.
Synergistic radar and radiometer retrievals of ice hydrometeors
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-369,2019
Manuscript under review for AMT (discussion: open, 0 comments)
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
ELIFAN, an algorithm for the estimation of cloud cover from sky imagers
Marie Lothon, Paul Barnéoud, Omar Gabella, Fabienne Lohou, Solène Derrien, Sylvain Rondi, Marjolaine Chiriaco, Sophie Bastin, Jean-Charles Dupont, Martial Haeffelin, Jordi Badosa, Nicolas Pascal, and Nadège Montoux
Atmos. Meas. Tech., 12, 5519–5534, https://doi.org/10.5194/amt-12-5519-2019, 2019
In the context of an atmospheric network of instrumented sites equipped with sky cameras for cloud monitoring, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images. ELIFAN is based on red-to-blue ratio thresholding applied on the image pixels and on the use of a blue-sky library. We present its principle and its performance and highlight the interest of combining several complementary instruments.
Evaluation of MOPITT Version 7 joint TIR–NIR XCO retrievals with TCCON
Jacob K. Hedelius, Tai-Long He, Dylan B. A. Jones, Bianca C. Baier, Rebecca R. Buchholz, Martine De Mazière, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Laura T. Iraci, Pascal Jeseck, Matthäus Kiel, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Sébastien Roche, Coleen M. Roehl, Matthias Schneider, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Colm Sweeney, Yao Té, Osamu Uchino, Voltaire A. Velazco, Wei Wang, Thorsten Warneke, Paul O. Wennberg, Helen M. Worden, and Debra Wunch
Atmos. Meas. Tech., 12, 5547–5572, https://doi.org/10.5194/amt-12-5547-2019, 2019
We seek ways to improve the accuracy of column measurements of carbon monoxide (CO) – an important tracer of pollution – made from the MOPITT satellite instrument. We devise a filtering scheme which reduces the scatter and also eliminates bias among the MOPITT detectors. Compared to ground-based observations, MOPITT measurements are about 6 %–8 % higher. When MOPITT data are implemented in a global assimilation model, they tend to reduce the model mismatch with aircraft measurements.
Molecular characterization of alkyl nitrates in atmospheric aerosols by ion mobility mass spectrometry
Xuan Zhang, Haofei Zhang, Wen Xu, Xiaokang Wu, Geoffrey S. Tyndall, John J. Orlando, John T. Jayne, Douglas R. Worsnop, and Manjula R. Canagaratna
Atmos. Meas. Tech., 12, 5535–5545, https://doi.org/10.5194/amt-12-5535-2019, 2019
We develop a new technique to characterize organic nitrates as intact molecules in atmospheric aerosols, and we apply this technique to identify hydroxy nitrates in secondary organic aerosols produced from the photochemical oxidation of isoprene.
Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-345,2019
Manuscript under review for AMT (discussion: open, 0 comments)
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces;The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC’s two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements: a feasibility study
Yun Dong, Elena Spinei, and Anuj Karpatne
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-368,2019
Manuscript under review for AMT (discussion: open, 0 comments)
In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.
Synergistic radar and radiometer retrievals of ice hydrometeors
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-369,2019
Manuscript under review for AMT (discussion: open, 0 comments)
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
ELIFAN, an algorithm for the estimation of cloud cover from sky imagers
Marie Lothon, Paul Barnéoud, Omar Gabella, Fabienne Lohou, Solène Derrien, Sylvain Rondi, Marjolaine Chiriaco, Sophie Bastin, Jean-Charles Dupont, Martial Haeffelin, Jordi Badosa, Nicolas Pascal, and Nadège Montoux
Atmos. Meas. Tech., 12, 5519–5534, https://doi.org/10.5194/amt-12-5519-2019, 2019
In the context of an atmospheric network of instrumented sites equipped with sky cameras for cloud monitoring, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images. ELIFAN is based on red-to-blue ratio thresholding applied on the image pixels and on the use of a blue-sky library. We present its principle and its performance and highlight the interest of combining several complementary instruments.
Evaluation of MOPITT Version 7 joint TIR–NIR XCO retrievals with TCCON
Jacob K. Hedelius, Tai-Long He, Dylan B. A. Jones, Bianca C. Baier, Rebecca R. Buchholz, Martine De Mazière, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Laura T. Iraci, Pascal Jeseck, Matthäus Kiel, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Sébastien Roche, Coleen M. Roehl, Matthias Schneider, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Colm Sweeney, Yao Té, Osamu Uchino, Voltaire A. Velazco, Wei Wang, Thorsten Warneke, Paul O. Wennberg, Helen M. Worden, and Debra Wunch
Atmos. Meas. Tech., 12, 5547–5572, https://doi.org/10.5194/amt-12-5547-2019, 2019
We seek ways to improve the accuracy of column measurements of carbon monoxide (CO) – an important tracer of pollution – made from the MOPITT satellite instrument. We devise a filtering scheme which reduces the scatter and also eliminates bias among the MOPITT detectors. Compared to ground-based observations, MOPITT measurements are about 6 %–8 % higher. When MOPITT data are implemented in a global assimilation model, they tend to reduce the model mismatch with aircraft measurements.
Molecular characterization of alkyl nitrates in atmospheric aerosols by ion mobility mass spectrometry
Xuan Zhang, Haofei Zhang, Wen Xu, Xiaokang Wu, Geoffrey S. Tyndall, John J. Orlando, John T. Jayne, Douglas R. Worsnop, and Manjula R. Canagaratna
Atmos. Meas. Tech., 12, 5535–5545, https://doi.org/10.5194/amt-12-5535-2019, 2019
We develop a new technique to characterize organic nitrates as intact molecules in atmospheric aerosols, and we apply this technique to identify hydroxy nitrates in secondary organic aerosols produced from the photochemical oxidation of isoprene.
Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2019-345,2019
Manuscript under review for AMT (discussion: open, 0 comments)
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces;The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC’s two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements: a feasibility study
Yun Dong, Elena Spinei, and Anuj Karpatne
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2019-368,2019
Manuscript under review for AMT (discussion: open, 0 comments)
In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.
Synergistic radar and radiometer retrievals of ice hydrometeors
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2019-369,2019
Manuscript under review for AMT (discussion: open, 0 comments)
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
Application of Levy Processes in Modelling (Geodetic) Time Series With Mixed Spectra
Jean-Philippe Montillet, Xiaoxing He, and Kegen Yu
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-48,2019
Manuscript under review for NPG (discussion: open, 0 comments)
Geodetic time series, series of observations measured from various satellites, must be modelled carefully to extract accurate information about geophysical processes. These models take into account the properties of the noise in these time series, which are generally a mixed of several kinds of noise. This work proposes a model based on the family of Levy processes (Gaussian, fractional and stable) as an alternative with real and simulated data.
Arctic regions are responding rapidly to modern climate change, as high latitudes have warmed more than twice as fast as the global average. Among the changes in recent decades are thawing and degradation of permafrost, and hydrologic shifts that include earlier snowmelt and higher river discharge.
Zheng et al. [2019] developed a heat-exchange model to investigate how changes in river flow affect permafrost within floodplains, and applied their model to the Kuparuk River, Alaska, where mean annual flow has increased by 35% since the 1970s and snowmelt floods now arrive earlier. Their results indicate that the changes to inundation extent and timing of river discharge cause floodplain permafrost to thaw more rapidly, as heat is transferred from the warmer floodwater down into the cooler subsurface. The model shows that the earlier arrival of spring flooding impacts permafrost warming more than a prolonged warm season would.
Accelerated degradation of permafrost due to more sustained floodwater inundation could enhance the release of old carbon, large quantities of which are currently stored in Arctic floodplains.
Citation: Zheng, L., Overeem, I., Wang, K., & Clow, G. D. [2019]. Changing Arctic river dynamics cause localized permafrost thaw. Journal of Geophysical Research: Earth Surface, 124. https://doi.org/10.1029/2019JF005060
—Amy East, Editor in Chief, JGR: Earth Surface
When the photographer Walter Mittelholzer snapped pictures of Mont Blanc from his plane in 1919, he pointed his lens at the landscape’s rugged beauty. One century later, his images reveal the rapid loss of ice on the Alps’ highest peak.
This summer, researchers re-created Mittelholzer’s images of three Mont Blanc glaciers by photographing the glaciers 100 years later. The scientists triangulated Mittelholzer’s original location on the basis of nearby peaks and flew a helicopter to an elevation of 4,700 meters at the same spot near the Mont Blanc summit, which straddles the border of Italy and France. Viewed side by side, the images show the drastic effect of climate change on the region.
The scientists chose three of the mountain’s largest glaciers: Argentière, Bossons, and Mer de Glace. In the photographs taken at Mer de Glace, the black-and-white image from 1919 shows a channel of ice nearly 2 kilometers wide in places flowing down a deep valley. In 2019, the glacier is sunken, covered in brown sediment, and peters out into a melt pond at what used to be the glacier’s far end.
Aerial images of Mer de Glace glacier taken in 1919 (left) and 2019 (right). Mer de Glace means “sea of ice” in French and is the largest glacier on Mont Blanc. Credit: Walter Mittelholzer, ETH-Bibliothek Zürich; Kieran Baxter, University of Dundee
University of Dundee scientist Kieran Baxter, who took the new images, said in a press release that “it was both a breathtaking and heartbreaking experience, particularly knowing that the melt has accelerated massively in the last few decades.”
The ice loss on Mont Blanc is hardly unique, said Baxter. Glaciers in the European Alps lost half their volume between 1850 and 1975, according to a study published in the Annals of Glaciology. Over the next 30 years, 40% of their remaining volume melted away.
“The ice loss visible in these pictures is representative of the type of melt that is happening to the vast majority of glaciers across the Alps and in other glaciated regions around the world,” Baxter told Eos.
Disappearing Landscape
Glaciers used to be viewed as permanent features of the landscape, said Baxter, and even Mittelholzer was more interested in mountain summits than glaciers. Now “we recognize that our actions have made [glaciers] something much more ephemeral,” Baxter said, and pointed to the example of mourners who gathered for a funeral for the Swiss Pizol glacier recently stripped of its title.
Two thirds of the ice in the Alps will vanish by 2100 under the best-case emissions scenario.Rapidly shrinking glaciers could be hazardous as well. Mont Blanc’s Planpincieux glacier grew so unstable in September that an Italian mayor called for evacuations and road closures. A recent report from the United Nations warns that climate change will bring disasters to high mountain regions.
The three photographs are just a “tiny fraction” of Mittelholzer’s collection, said Baxter, and the researchers hope to further explore his archives. They also plan to search for photographs in personal collections that may have been overlooked.
Future projections of glaciers in the Alps are grim: Two thirds of the ice will vanish by 2100 under the best-case emissions scenario, according to a study published in April 2019. Yet if emissions continue at their current rate, more than 90% could be gone by the end of the century. No matter what humans emit, the study found that half the Alpine ice will be gone by 2050.
Side-by-side images of Mont Blanc’s Bossons glacier in 1919 (left) and 2019 (right). Credit: Walter Mittelholzer, ETH-Bibliothek Zürich; Kieran Baxter, University of Dundee
When it comes to photography, Baxter said it’s a “race against time” to capture images before it’s too late.
“Unless we drastically reduce our dependence on fossil fuels, there will be little ice left to photograph in another hundred years,” Baxter said in a press release.
—Jenessa Duncombe (@jrdscience), News Writing and Production Fellow
21 October 2019: This article has been updated to accurately state the elevation of the photographs.
Many natural and human-made phenomena obey power law distributions. In one of the most well known examples, a power law distribution describes how small earthquakes occur much more frequently than large, potentially destructive ones.
Generally, the power law distribution in earthquake moment holds when considering seismic events over time on multiple faults. However, scientists still puzzle over the distribution of rupture sizes along individual faults. Exceptions to the power law statistics have been observed in rare sequences known as repeating earthquakes. Instead of many small events and a few large ones, these sequences are characterized by periodic earthquakes of fixed size, raising various questions for researchers. Why do these sequences depart from the otherwise ubiquitous power law statistics of earthquake sizes? And what distributions can we expect to occur on faults large enough to produce destructive earthquakes?
In a new theoretical study, Cattania explored the factors controlling earthquake statistics on a single isolated fault. The author used a two-dimensional earthquake cycle model of a simple fault experiencing both earthquakes and slow aseismic slip, or creep, and compared the results to records of earthquakes observed in nature.
The research revealed that although small seismic sources can produce identical and periodic earthquakes, tremors on large faults exhibit different traits, including the power law distributions observed in nature. For bigger faults, the rupture lengths of earthquakes may span several orders of magnitude and cluster in time—instead of spacing out more evenly as they do on small seismic sources. On the basis of straightforward physical concepts related to fault strain and the energy released during fracture formation, the study showed that the transition between these types of behavior is controlled by the ratio of a fault’s size to a length related to the earthquake nucleation dimension.
In essence, the study demonstrated that simple, isolated faults do not necessarily produce regular and periodic earthquakes, especially when the faults are relatively large. The conclusions offer insights into seismic hazard analysis. Although the simplified model used in the study may not adequately represent individual faults found in nature, the theory can be extended to more realistic cases. (Geophysical Research Letters, https://doi.org/10.1029/2019GL083628, 2019)
—Aaron Sidder, Freelance Writer
