TROPOMI Aerosol Products: Evaluation and Observations of Synoptic Scale Carbonaceous Aerosol Plumes during 2018–2020
Omar Torres, Hiren Jethva, Changwoo Ahn, Glen Jaross, and Diego G. Loyola
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-124,2020
Preprint under review for AMT (discussion: open, 0 comments)
TROPOMI measures the amount of small suspended particles (aerosols). We describe initial results of aerosol measurements using a NASA algorithm that retrieves the UV Aerosol Index, as well as Aerosol Optical Depth and Single Scattering Albedo. An evaluation of derived products using sun-photometer observations shows close agreement. We also use these results to discuss important biomass burning and wildfire events around the world that got the attention of scientists and news media alike.
Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study
Pauline Martinet, Domenico Cimini, Frédéric Burnet, Benjamin Ménétrier, Yann Michel, and Vinciane Unger
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-166,2020
Preprint under review for AMT (discussion: open, 0 comments)
Each year large human and economical losses are due to fog episodes. However, fog forecasts remain quite inaccurate partly due to a lack of observations in the atmospheric boundary layer. The benefit of ground-based microwave radiometers has been investigated and has demonstrated their capability of significantly improving the initial state of temperature and liquid water content profiles in current numerical weather prediction model paving the way for improved fog forecasts in the future.
Stratospheric Extinction Profiles from SCIAMACHY Solar Occultation
Stefan Noël, Klaus Bramstedt, Alexei Rozanov, Elizaveta Malinina, Heinrich Bovensmann, and John P. Burrows
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-113,2020
Preprint under review for AMT (discussion: open, 0 comments)
A new approach to derive stratospheric aerosol extinction profiles between 15 and 30 km from SCIAMACHY solar occultation measurements is presented.
Except for some oscillating features the results for 452, 525 and 750 nm agree well with collocated SAGE II and SCIAMACHY limb data products.
Volcanic eruptions and polar stratospheric clouds can be identified in the time series.
Linear changes of extinction between 2003 and 2011 reach 20–30 % per year, mainly due to volcanic eruptions after 2006.
Quantifying the impact of aerosol scattering on the retrieval of methane from airborne remote sensing measurements
Yunxia Huang, Vijay Natraj, Zhaocheng Zeng, and Yuk L. Yung
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-51,2020
Preprint under review for AMT (discussion: open, 0 comments)
As a greenhouse gas with strong global warming potential, atmospheric methane emissions have attracted a great deal of attention. However, accurate assessment of these emissions is challenging in the presence of atmospheric particulates called aerosols. We quantify the aerosol impact on methane quantification from airborne measurements using two techniques, one that has traditionally been used by the imaging spectroscopy community and the other commonly employed in trace gas remote sensing.
Generalized Canonical Transform method for radio occultation sounding with improved retrieval in the presence of horizontal gradients
Michael Gorbunov, Gottfried Kirchengast, and Kent B. Lauritsen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-147,2020
Preprint under review for AMT (discussion: open, 0 comments)
By now, the Canonical Transform (CT) approach to the processing of Radio Occultation (RO) observations is widely used. For the spherically symmetric atmosphere, the applicability of this method can be strictly proven. However, in the presence of horizontal gradients, this approach may not work. Here we introduce a generalization of the CT method in order to reduce the errors due to horizontal gradients.
Quantifying the impact of aerosol scattering on the retrieval of methane from airborne remote sensing measurements
Yunxia Huang, Vijay Natraj, Zhaocheng Zeng, and Yuk L. Yung
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-51,2020
Preprint under review for AMT (discussion: open, 0 comments)
As a greenhouse gas with strong global warming potential, atmospheric methane emissions have attracted a great deal of attention. However, accurate assessment of these emissions is challenging in the presence of atmospheric particulates called aerosols. We quantify the aerosol impact on methane quantification from airborne measurements using two techniques, one that has traditionally been used by the imaging spectroscopy community and the other commonly employed in trace gas remote sensing.
Generalized Canonical Transform method for radio occultation sounding with improved retrieval in the presence of horizontal gradients
Michael Gorbunov, Gottfried Kirchengast, and Kent B. Lauritsen
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-147,2020
Preprint under review for AMT (discussion: open, 0 comments)
By now, the Canonical Transform (CT) approach to the processing of Radio Occultation (RO) observations is widely used. For the spherically symmetric atmosphere, the applicability of this method can be strictly proven. However, in the presence of horizontal gradients, this approach may not work. Here we introduce a generalization of the CT method in order to reduce the errors due to horizontal gradients.
Stratospheric Extinction Profiles from SCIAMACHY Solar Occultation
Stefan Noël, Klaus Bramstedt, Alexei Rozanov, Elizaveta Malinina, Heinrich Bovensmann, and John P. Burrows
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-113,2020
Preprint under review for AMT (discussion: open, 0 comments)
A new approach to derive stratospheric aerosol extinction profiles between 15 and 30 km from SCIAMACHY solar occultation measurements is presented.
Except for some oscillating features the results for 452, 525 and 750 nm agree well with collocated SAGE II and SCIAMACHY limb data products.
Volcanic eruptions and polar stratospheric clouds can be identified in the time series.
Linear changes of extinction between 2003 and 2011 reach 20–30 % per year, mainly due to volcanic eruptions after 2006.
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, 2020
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model
Guillaume Thomas, Jean-François Mahfouf, and Thibaut Montmerle
Atmos. Meas. Tech., 13, 2279–2298, https://doi.org/10.5194/amt-13-2279-2020, 2020
This paper presents the potential of a polarimetric weather radar observation operator for hydrometeor content initialization. The non-linear operator allows to simulate ZHH, ZDR, KDP and ρHV, using the T-Matrix method, prognostic variables forecasted by the AROME-France NWP model and a one-moment microphysical scheme. After sensitivity studies, it has been found that ZHH and ZDR are good candidates for hydrometeor initialization and that KDP seems useful for rain content only.
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, 2020
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model
Guillaume Thomas, Jean-François Mahfouf, and Thibaut Montmerle
Atmos. Meas. Tech., 13, 2279–2298, https://doi.org/10.5194/amt-13-2279-2020, 2020
This paper presents the potential of a polarimetric weather radar observation operator for hydrometeor content initialization. The non-linear operator allows to simulate ZHH, ZDR, KDP and ρHV, using the T-Matrix method, prognostic variables forecasted by the AROME-France NWP model and a one-moment microphysical scheme. After sensitivity studies, it has been found that ZHH and ZDR are good candidates for hydrometeor initialization and that KDP seems useful for rain content only.
Validation of the vertical profiles of HCl over the wide range of the stratosphere to the lower thermosphere measured by SMILES
Seidai Nara, Tomohiro O. Sato, Takayoshi Yamada, Tamaki Fujinawa, Kota Kuribayashi, Takeshi Manabe, Lucien Froidevaux, Nathaniel J. Livesey, Kaley A. Walker, Jian Xu, Franz Schreier, Yvan J. Orsolini, Varavut Limpasuvan, Nario Kuno, and Yasuko Kasai
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-105,2020
Preprint under review for AMT (discussion: open, 0 comments)
In the atmosphere, more than 80 % of chlorine compounds are anthropogenic. Hydrogen chloride (HCl), the main stratospheric chlorine reservoir, is useful to estimate the total budget of the atmospheric chlorine compounds. We for the first time report the HCl vertical distribution from the middle troposphere to the lower thermosphere using a high sensitive SMILES measurement; the data quality is quantified by comparisons with other measurements and via theoretical error analysis.
Validation of the vertical profiles of HCl over the wide range of the stratosphere to the lower thermosphere measured by SMILES
Seidai Nara, Tomohiro O. Sato, Takayoshi Yamada, Tamaki Fujinawa, Kota Kuribayashi, Takeshi Manabe, Lucien Froidevaux, Nathaniel J. Livesey, Kaley A. Walker, Jian Xu, Franz Schreier, Yvan J. Orsolini, Varavut Limpasuvan, Nario Kuno, and Yasuko Kasai
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-105,2020
Preprint under review for AMT (discussion: open, 0 comments)
In the atmosphere, more than 80 % of chlorine compounds are anthropogenic. Hydrogen chloride (HCl), the main stratospheric chlorine reservoir, is useful to estimate the total budget of the atmospheric chlorine compounds. We for the first time report the HCl vertical distribution from the middle troposphere to the lower thermosphere using a high sensitive SMILES measurement; the data quality is quantified by comparisons with other measurements and via theoretical error analysis.
Evaluation of single-footprint AIRS CH4 Profile Retrieval Uncertainties Using Aircraft Profile Measurements
Susan S. Kulawik, John R. Worden, Vivienne H. Payne, Dejian Fu, Steve C. Wofsy, Kathryn McKain, Colm Sweeney, Bruce C. Daube Jr., Alan Lipton, Igor Polonsky, Yuguang He, Karen E. Cady-Pereira, Edward J. Dlugokencky, Daniel J. Jacob, and Yi Yin
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-145,2020
Preprint under review for AMT (discussion: open, 0 comments)
This paper shows comparisons of a new methane product from the AIRS satellite to aircraft-based observations. We show that this AIRS methane product provides useful information to study seasonal and global methane trends of this important greenhouse gas.
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, 2020
Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Intercomparison study of atmospheric 222Rn and 222Rn progeny monitors
Claudia Grossi, Scott D. Chambers, Olivier Llido, Felix R. Vogel, Victor Kazan, Alessandro Capuana, Sylvester Werczynski, Roger Curcoll, Marc Delmotte, Arturo Vargas, Josep-Anton Morguí, Ingeborg Levin, and Michel Ramonet
Atmos. Meas. Tech., 13, 2241–2255, https://doi.org/10.5194/amt-13-2241-2020, 2020
The sustainable support of radon metrology at the environmental level offers new scientific possibilities for the quantification of greenhouse gas (GHG) emissions and the determination of their source terms as well as for the identification of radioactive sources for the assessment of radiation exposure. This study helps to harmonize the techniques commonly used for atmospheric radon and radon progeny activity concentration measurements.
Validation of XCO2 and XCH4 retrieved from a portable Fourier transform spectrometer with those from in-situ profiles from aircraft borne instruments
Hirofumi Ohyama, Isamu Morino, Voltaire A. Velazco, Theresa Klausner, Gerry Bagtasa, Matthäus Kiel, Matthias Frey, Akihiro Hori, Osamu Uchino, Tsuneo Matsunaga, Nicholas Deutscher, Joshua P. DiGangi, Yonghoon Choi, Glenn S. Diskin, Sally E. Pusede, Alina Fiehn, Anke Roiger, Michael Lichtenstern, Hans Schlager, Pao K. Wang, Charles C.-K. Cho, Maria Dolores Andrés-Hernández, and John P. Burrows
Atmos. Meas. Tech. Discuss., https//doi.org/10.5194/amt-2020-170,2020
Preprint under review for AMT (discussion: open, 0 comments)
Column-averaged dry-air mole fractions of CO2 and CH4 measured by a solar viewing portable Fourier transform spectrometer (EM27/SUN) were validated with in-situ profile data obtained during the transfer flights of two aircraft campaigns. Atmospheric dynamical properties based on ERA5 and WRF-Chem were used as criteria for selecting the best aircraft profiles for the validation. The resulting air mass independent correction factors for the EM27/SUN data were 0.9878 for CO2 and 0.9833 for CH4.
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, 2020
Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Intercomparison study of atmospheric 222Rn and 222Rn progeny monitors
Claudia Grossi, Scott D. Chambers, Olivier Llido, Felix R. Vogel, Victor Kazan, Alessandro Capuana, Sylvester Werczynski, Roger Curcoll, Marc Delmotte, Arturo Vargas, Josep-Anton Morguí, Ingeborg Levin, and Michel Ramonet
Atmos. Meas. Tech., 13, 2241–2255, https://doi.org/10.5194/amt-13-2241-2020, 2020
The sustainable support of radon metrology at the environmental level offers new scientific possibilities for the quantification of greenhouse gas (GHG) emissions and the determination of their source terms as well as for the identification of radioactive sources for the assessment of radiation exposure. This study helps to harmonize the techniques commonly used for atmospheric radon and radon progeny activity concentration measurements.
