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  • Atmospheric Measurement Techniques (AMT)

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gunter Stober; Alan Liu; Alexander Kozlovsky; Zishun Qiao; +12 Authors

    Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Measurem...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Copernicus Publications
    Other literature type . 2022
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.5194/egusph...
    Preprint . 2022 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    https://doi.org/10.48350/17442...
    Article . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Measurem...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Copernicus Publications
      Other literature type . 2022
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.5194/egusph...
      Preprint . 2022 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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      https://doi.org/10.48350/17442...
      Article . 2022
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B. Alföldy; Jacob Balzani Lööv; F. Lagler; Johan Mellqvist; +13 Authors

    The chemical composition of the plumes of seagoing ships was investigated during a two weeks long measurement campaign in the port of Rotterdam, Hoek van Holland, The Netherlands, in September 2009. Altogether, 497 ships were monitored and a statistical evaluation of emission factors (g kg-1 fuel) was provided. The concerned main atmospheric components were SO2, NO2, NOX and the aerosol particle number. In addition, the elemental and water-soluble ionic composition of the emitted particulate matter was measured. Emission factors were expressed as a function of ship type, power and crankshaft rotational speed. The average SO2 emission factor was found to be roughly half of what is allowed in sulphur emission control areas (16 34 vs. 30 g kg-1 fuel), and exceedances of this limit were rarely registered. A significant linear relationship was observed between the SO2 and particle number emission factor. The slope of the regression line, 2x1018 (kg fuel)-1, provides the average number of sulphate particles from 1 kg sulphur burnt with the fuel, while the intercept, 0.5x1016 (kg fuel)-1, gives the average number of primary particles (mainly soot and ash) formed during the burning of 1 kg fuel. Water-soluble ionic composition analysis of the aerosol samples from the plumes showed that approx. 144 g of sulphate particles were emitted from 1 kg sulphur burnt with the fuel. The mass median diameter of sulphate particles estimated from the measurements was 42 nm. JRC.H.2-Air and Climate

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Measurem...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Atmospheric Measurement Techniques
    Article
    License: CC BY
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    Atmospheric Measurement Techniques
    Article . 2013 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Measurem...arrow_drop_down
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      Atmospheric Measurement Techniques
      Article
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      Atmospheric Measurement Techniques
      Article . 2013 . Peer-reviewed
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: S. Garimella; T. B. Kristensen; K. Ignatius; A. Welti; +18 Authors

    The SPectrometer for Ice Nuclei (SPIN) is a commercially available ice nucleating particle (INP) counter manufactured by Droplet Measurement Technologies in Boulder, CO. The SPIN is a continuous flow diffusion chamber with parallel plate geometry based on the Zurich Ice Nucleation Chamber and the Portable Ice Nucleation Chamber. This study presents a standard description for using the SPIN instrument and also highlights methods to analyze measurements in more advanced ways. It characterizes and describes the behavior of the SPIN chamber, reports data from laboratory measurements, and quantifies uncertainties associated with the measurements. Experiments with ammonium sulfate are used to investigate homogeneous freezing of deliquesced haze droplets and droplet breakthrough. Experiments with kaolinite, NX illite, and silver iodide are used to investigate heterogeneous ice nucleation. SPIN nucleation results are compared to those from the literature. A machine learning approach for analyzing depolarization data from the SPIN optical particle counter is also presented (as an advanced use). Overall, we report that the SPIN is able to reproduce previous INP counter measurements. Atmospheric Measurement Techniques, 9 (7) ISSN:1867-8548 ISSN:1867-1381

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    DSpace@MIT
    Article . 2015
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    Research Collection
    Article . 2016
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    Atmospheric Measurement Techniques
    Article . Preprint
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    Other literature type . 2016
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    https://doi.org/10.5194/amt-20...
    Preprint . 2016 . Peer-reviewed
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    ETH Zürich Research Collection
    Article . 2016
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      DSpace@MIT
      Article . 2015
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      Atmospheric Measurement Techniques
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      NRC Publications Archive
      Other literature type . 2016
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      https://doi.org/10.5194/amt-20...
      Preprint . 2016 . Peer-reviewed
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      ETH Zürich Research Collection
      Article . 2016
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    Authors: Joseph K. Lee; Andreas Christen; Rick Ketler; Zoran Nesic;

    A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference, and log measurements of CO2 mixing ratios on vehicles (car, bicycles). Second, two measurement campaigns, one in summer and one in winter (heating season) were carried out. Five mobile sensors were deployed within a 1 × 12. 7 km transect across the city of Vancouver, BC, Canada. The sensors were operated for 3.5 h on pre-defined routes to map CO2 mixing ratios at street level, which were then averaged to 100 × 100 m grid cells. The averaged CO2 mixing ratios of all grids in the study area were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the grid cells of the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO2 emissions from the gridded CO2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28 m tall eddy-covariance tower located within the study area. These measured emissions showed a range of −12 to 226 CO2 ha−1 h−1 in summer and of −14 to 163 kg CO2 ha−1 h−1 in winter, with an average of 35.1 kg CO2 ha−1 h−1 (summer) and 25.9 kg CO2 ha−1 h−1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO2 ha−1 h−1 (summer) and 28.76 kg CO2 ha−1 h−1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2 mixing ratios and the emissions inventory (R2 = 0. 53 in summer and R2 = 0. 47 in winter). Also, 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order of magnitude, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

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    Atmospheric Measurement Techniques
    Article . 2017 . Peer-reviewed
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    https://doi.org/10.5194/amt-20...
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    Atmospheric Measurement Techniques
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      Atmospheric Measurement Techniques
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      https://doi.org/10.5194/amt-20...
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      Atmospheric Measurement Techniques
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    Authors: Daan Hubert; J. C. Lambert; Tijl Verhoelst; José Granville; +36 Authors

    Abstract. The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of 14 limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20 and 40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5–12 % and the drifts are at most ±5 % decade−1 (or even ±3 % decade−1 for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10 % and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY) and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.

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    Atmospheric Measurement Techniques
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    Atmospheric Measurement Techniques
    Article . 2016 . Peer-reviewed
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    Hal-Diderot
    Article . 2016
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      Atmospheric Measurement Techniques
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      Atmospheric Measurement Techniques
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    Authors: M. Toohey; T. von Clarmann;

    Climatologies of atmospheric observations are often produced by binning measurements according to latitude and calculating zonal means. The uncertainty in these climatological means is characterised by the standard error of the mean (SEM). However, the usual estimator of the SEM, i.e., the sample standard deviation divided by the square root of the sample size, holds only for uncorrelated randomly sampled measurements. Measurements of the atmospheric state along a satellite orbit cannot always be considered as independent because (a) the time-space interval between two nearest observations is often smaller than the typical scale of variations in the atmospheric state, and (b) the regular time-space sampling pattern of a satellite instrument strongly deviates from random sampling. We have developed a numerical experiment where global chemical fields from a chemistry climate model are sampled according to real sampling patterns of satellite-borne instruments. As case studies, the model fields are sampled using sampling patterns of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS) satellite instruments. Through an iterative subsampling technique, and by incorporating information on the random errors of the MIPAS and ACE-FTS measurements, we produce empirical estimates of the standard error of monthly mean zonal mean model O3 in 5° latitude bins. We find that generally the classic SEM estimator is a conservative estimate of the SEM, i.e., the empirical SEM is often less than or approximately equal to the classic estimate. Exceptions occur only when natural variability is larger than the random measurement error, and specifically in instances where the zonal sampling distribution shows non-uniformity with a similar zonal structure as variations in the sampled field, leading to maximum sensitivity to arbitrary phase shifts between the sample distribution and sampled field. The occurrence of such instances is thus very sensitive to slight changes in the sampling distribution, and to the variations in the measured field. This study highlights the need for caution in the interpretation of the oft-used classically computed SEM, and outlines a relatively simple methodology that can be used to assess one component of the uncertainty in monthly mean zonal mean climatologies produced from measurements from satellite-borne instruments.

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    https://doi.org/10.5194/amtd-5...
    Preprint . 2012
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    Atmospheric Measurement Techniques
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    Atmospheric Measurement Techniques
    Article . 2013 . Peer-reviewed
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      https://doi.org/10.5194/amtd-5...
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      Atmospheric Measurement Techniques
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      Atmospheric Measurement Techniques
      Article . 2013 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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    Authors: M. Thomas; M. Thomas; J. France; J. France; +13 Authors

    Sea ice is difficult, expensive, and potentially dangerous to observe in nature. The remoteness of the Arctic Ocean and Southern Ocean complicates sampling logistics, while the heterogeneous nature of sea ice and rapidly changing environmental conditions present challenges for conducting process studies. Here, we describe the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC), a laboratory facility designed to reproduce polar processes and overcome some of these challenges. The RvG-ASIC is an open-topped 3.5 m3 glass tank housed in a cold room (temperature range: −55 to +30 ∘C). The RvG-ASIC is equipped with a wide suite of instruments for ocean, sea ice, and atmospheric measurements, as well as visible and UV lighting. The infrastructure, available instruments, and typical experimental protocols are described. To characterise some of the technical capabilities of our facility, we have quantified the timescale over which our chamber exchanges gas with the outside, τl=(0.66±0.07) d, and the mixing rate of our experimental ocean, τm=(4.2±0.1) min. Characterising our light field, we show that the light intensity across the tank varies by less than 10 % near the centre of the tank but drops to as low as 60 % of the maximum intensity in one corner. The temperature sensitivity of our light sources over the 400 to 700 nm range (PAR) is (0.028±0.003) W m−2 ∘C−1, with a maximum irradiance of 26.4 W m−2 at 0 ∘C; over the 320 to 380 nm range, it is (0.16±0.1) W m−2 ∘C−1, with a maximum irradiance of 5.6 W m−2 at 0 ∘C. We also present results characterising our experimental sea ice. The extinction coefficient for PAR varies from 3.7 to 6.1 m−1 when calculated from irradiance measurements exterior to the sea ice and from 4.4 to 6.2 m−1 when calculated from irradiance measurements within the sea ice. The bulk salinity of our experimental sea ice is measured using three techniques, modelled using a halo-dynamic one-dimensional (1D) gravity drainage model, and calculated from a salt and mass budget. The growth rate of our sea ice is between 2 and 4 cm d−1 for air temperatures of (-9.2±0.9) ∘C and (-26.6±0.9) ∘C. The PAR extinction coefficients, vertically integrated bulk salinities, and growth rates all lie within the range of previously reported comparable values for first-year sea ice. The vertically integrated bulk salinity and growth rates can be reproduced well by a 1D model. Taken together, the similarities between our laboratory sea ice and observations in nature, as well as our ability to reproduce our results with a model, give us confidence that sea ice grown in the RvG-ASIC is a good representation of natural sea ice.

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    Atmospheric Measurement Techniques
    Other literature type . Article . 2021 . Peer-reviewed
    License: CC BY
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    MPG.PuRe
    Article . 2021
    License: CC BY
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    Atmospheric Measurement Techniques
    Article . Preprint . 2021
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    https://doi.org/10.5194/amt-20...
    Preprint . 2020 . Peer-reviewed
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      Atmospheric Measurement Techniques
      Other literature type . Article . 2021 . Peer-reviewed
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      Atmospheric Measurement Techniques
      Article . Preprint . 2021
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      https://doi.org/10.5194/amt-20...
      Preprint . 2020 . Peer-reviewed
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    Authors: Roscoe, H. K.; Roozendael, M.; Fayt, C.; Piesanie, A.; +47 Authors

    In June 2009, 22 spectrometers from 14 institutes measured tropospheric and stratospheric NO2 from the ground for more than 11 days during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments (CINDI), at Cabauw, NL (51.97° N, 4.93° E). All visible instruments used a common wavelength range and set of cross sections for the spectral analysis. Most of the instruments were of the multi-axis design with analysis by differential spectroscopy software (MAX-DOAS), whose non-zenith slant columns were compared by examining slopes of their least-squares straight line fits to mean values of a selection of instruments, after taking 30-min averages. Zenith slant columns near twilight were compared by fits to interpolated values of a reference instrument, then normalised by the mean of the slopes of the best instruments. For visible MAX-DOAS instruments, the means of the fitted slopes for NO2 and O4 of all except one instrument were within 10% of unity at almost all non-zenith elevations, and most were within 5%. Values for UV MAX-DOAS instruments were almost as good, being 12% and 7%, respectively. For visible instruments at zenith near twilight, the means of the fitted slopes of all instruments were within 5% of unity. This level of agreement is as good as that of previous intercomparisons, despite the site not being ideal for zenith twilight measurements. It bodes well for the future of measurements of tropospheric NO2, as previous intercomparisons were only for zenith instruments focussing on stratospheric NO2, with their longer heritage.

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    Authors: Roscoe, H. K.; Brough, N.; Jones, A. E.; Wittrock, F.; +3 Authors

    Abstract. Tropospheric BrO was measured by a ground-based remote-sensing spectrometer at Halley in Antarctica, and BrO was measured by remote-sensing spectrometers in space using similar spectral regions and Differential Optical Absorption Spectroscopy (DOAS) analyses. Near-surface BrO was simultaneously measured at Halley by Chemical Ionisation Mass Spectrometry (CIMS), and in an earlier year near-surface BrO was measured at Halley over a long path by a DOAS spectrometer. During enhancement episodes, total amounts of tropospheric BrO from the ground-based remote-sensor were similar to those from space, but if we assume that the BrO was confined to the boundary layer they were very much larger than values measured by either near-surface technique. This large apparent discrepancy can be resolved if substantial amounts of BrO were in the free troposphere during most enhancement episodes. Amounts observed by the ground-based remote sensor at different elevation angles, and their formal inversions to vertical profiles, also show that much of the BrO was often in the free troposphere. This is consistent with the ~5 day lifetime of Bry, from the enhanced BrO observed during some Antarctic blizzards, and from aircraft measurements of BrO well above the surface in the Arctic.

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    https://doi.org/10.5194/amtd-5...
    Preprint . 2012
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      https://doi.org/10.5194/amtd-5...
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gunter Stober; Alan Liu; Alexander Kozlovsky; Zishun Qiao; +12 Authors

    Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.

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    Copernicus Publications
    Other literature type . 2022
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    https://doi.org/10.5194/egusph...
    Preprint . 2022 . Peer-reviewed
    License: CC BY
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    https://doi.org/10.48350/17442...
    Article . 2022
    License: CC BY
    Data sources: Datacite
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      Copernicus Publications
      Other literature type . 2022
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      https://doi.org/10.5194/egusph...
      Preprint . 2022 . Peer-reviewed
      License: CC BY
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      https://doi.org/10.48350/17442...
      Article . 2022
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B. Alföldy; Jacob Balzani Lööv; F. Lagler; Johan Mellqvist; +13 Authors

    The chemical composition of the plumes of seagoing ships was investigated during a two weeks long measurement campaign in the port of Rotterdam, Hoek van Holland, The Netherlands, in September 2009. Altogether, 497 ships were monitored and a statistical evaluation of emission factors (g kg-1 fuel) was provided. The concerned main atmospheric components were SO2, NO2, NOX and the aerosol particle number. In addition, the elemental and water-soluble ionic composition of the emitted particulate matter was measured. Emission factors were expressed as a function of ship type, power and crankshaft rotational speed. The average SO2 emission factor was found to be roughly half of what is allowed in sulphur emission control areas (16 34 vs. 30 g kg-1 fuel), and exceedances of this limit were rarely registered. A significant linear relationship was observed between the SO2 and particle number emission factor. The slope of the regression line, 2x1018 (kg fuel)-1, provides the average number of sulphate particles from 1 kg sulphur burnt with the fuel, while the intercept, 0.5x1016 (kg fuel)-1, gives the average number of primary particles (mainly soot and ash) formed during the burning of 1 kg fuel. Water-soluble ionic composition analysis of the aerosol samples from the plumes showed that approx. 144 g of sulphate particles were emitted from 1 kg sulphur burnt with the fuel. The mass median diameter of sulphate particles estimated from the measurements was 42 nm. JRC.H.2-Air and Climate

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Atmospheric Measurement Techniques
    Article
    License: CC BY
    Data sources: UnpayWall
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    Atmospheric Measurement Techniques
    Article . 2013 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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