Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites

Zakšek, Klemen; Hort, Matthias; Zaletelj, Janez; Langmann, Bärbel

doi:10.5194/acp-13-2589-2013

Cited by 54 publications

(50 citation statements)

References 53 publications

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“…Ash layers were observed from a large set of measurement instruments, allowing for tracking the volcanic ash cloud movement over Europe (Gasteiger et al, 2011a;Zakšek et al, 2013;Mona et al, 2012;Dacre et al, 2013;Waquet et al, 2014). Using images from the geostationary instrument SEVIRI (Spinning Enhanced Visible and Infrared Imager) the spatial extend of the ash cloud and its movement could be tracked and compared to the measurement of ground-based instruments (see Fig.…”

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confidence: 99%

A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

Geisinger

Behrendt

Wulfmeyer

et al. 2016

Preprint

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Abstract.State-of-the-art atmospheric chemistry models are capable of simulating the transport and evolution of particles and trace gases but there is a lack of reliable methods for model validation and data assimilation. Networks of automated ceilometer lidar systems (ACLs) could be used to fill this gap. These are already used for the detection of clouds and aerosols, providing a 3D dataset of atmospheric backscatter profiles. However, as the aerosol number concentration cannot be obtained from the ACL 5 data alone, one needs a backscatter-lidar forward model to simulate lidar profiles from the model variables. Such an operator allows then for a qualitative and quantitative model validation based on ACL data. In this work, we present a new backscatterlidar operator which contains most of the microphysical properties of aerosol particles, discuss sensitivity studies and compare simulated with measured ACL profiles. A major challenge is the high sensitivity of the optical cross sections to the particle size and shape: A slightly different particle radius may lead to quite a large change of the scattering properties. As most particle 10 size distributions are continuous in reality, the optical cross sections are averaged over certain size-intervals which also reduces the problematic and unrealistic sensitivity significantly. To calculate the attenuated backscatter coefficient, the size-dependent particle number concentration and the scattering properties of each particle type and size have to be simulated. While the particle number concentration is a model output variable, the scattering properties have to be determined by extensive scattering calculations. As these scattering calculations require assumptions about the particle refractive indices and shapes, sensitivity 15 studies were performed to estimate the uncertainties related to the particle properties as represented by the model system.The strong sensitivity of the scattering characteristics to the particle radius was largely reduced by size-averaging algorithms.We focus on a case study of the eruption of the Islandic volcano Eyjafjallajökull from 20 March 2010 to 24 May 2010. The Consortium for Small-scale Modeling -Aerosols and Reactive Trace gases (COSMO-ART) model of DWD (Deutscher Wetterdienst) and KIT (Karlsruhe Institute of Technology) was used during this event for ash-dispersion simulation over Europe. 20For the forward model, the attenuated backscatter coefficient is used as lidar-independent variable, as it only relies on the laser wavelength. Finally, the forward modeled lidar profiles were compared to ACL measurements. Significant differences between ACL profiles and the output of the forward operator applied to the COSMO-ART data were found but also several identical features have been observed. Comparing the data quantitatively revealed that the model-predicted ash number concentration is slightly too high. We identified the following key issues which are mandatory for performing quantitative comparisons be-

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confidence: 99%

A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

Geisinger

Behrendt

Wulfmeyer

et al. 2016

Preprint

View full text Add to dashboard Cite

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“…BT methods are based on comparing the measured plume BT to atmospheric temperature profiles, and the absorption technique uses the wavelength dependence of CO 2 , but these methods generally do not provide the same accuracy as the purely geometric stereo matching methods. These alternative methods have recently been discussed, e.g., by Zakšek et al (2013) and Ekstrand et al (2013).…”

Section: T H Virtanen Et Al: Ash Plume Top Height Estimation Usingmentioning

confidence: 99%

“…A large window can detect large features but miss smaller ones, while a smaller window can create a lot of noise (Zakšek et al, 2013). Figure 4 shows retrievals made with three different CW sizes.…”

Section: Correlation Window Sizementioning

confidence: 99%

“…Fortunately, there are some statistical tricks to remove part of the background noise and improve the results. Instead of following the method of Prata and Turner (1997) as such, the approach adopted here is to consider the deviation of the measured values from the local average, instead of the measured values themselves (Muller et al, 2007;Zakšek et al, 2013;Fisher et al, 2013). The cross-correlation coefficient C at point (x, y) between the nadir and forwardview data is defined as…”

Section: Spatial-correlation Plume Height Estimatementioning

confidence: 99%

“…The MISR height estimate methods have been applied to volcanic ash plumes, e.g., by Scollo et al (2012). Recently, Zakšek et al (2013) proposed a method combining Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Ash plume heights have also been studied by Grainger et al (2013) using AATSR, SEVIRI, and MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) data.…”

Section: Introductionmentioning

confidence: 99%

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Ash plume top height estimation using AATSR

et al. 2014

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Abstract. An algorithm is presented for the estimation of volcanic ash plume top height using the stereo view of the Advanced Along Track Scanning Radiometer (AATSR) aboard Envisat. The algorithm is based on matching top of the atmosphere (TOA) reflectances and brightness temperatures of the nadir and 55 • forward views, and using the resulting parallax to obtain the height estimate. Various retrieval parameters are discussed in detail, several quality parameters are introduced, and post-processing methods for screening out unreliable data have been developed. The method is compared to other satellite observations and in situ data. The proposed algorithm is designed to be fully automatic and can be implemented in operational retrieval algorithms. Combined with automated ash detection using the brightness temperature difference between the 11 and 12 µm channels, the algorithm allows efficient simultaneous retrieval of the horizontal and vertical dispersion of volcanic ash. A case study on the eruption of the Icelandic volcano Eyjafjallajökull in 2010 is presented.

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Quantification of volcanic cloud top heights and thicknesses using A‐train observations for the 2008 Chaitén eruption

2015

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New evidence of vertically thin (< 400 m), low‐level (< 10 km) volcanic ash clouds, as well as confirmation of previously reported high‐level (>10 km) ash clouds, from the May 2008 Chaitén eruption in southern Chile is presented. A‐train remote sensors were used to measure high‐resolution volcanic cloud top heights (VCTHs) during the explosive phase (2–10 May 2008) of the eruption. Ash clouds were identified using a reverse absorption technique applied to hyperspectral measurements taken by the Atmospheric Infrared Sounder. Once identified, heights and thicknesses were derived from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. As these two instruments are part of the same constellation of satellites, coincident retrievals are routinely possible. Collocation of the data allowed for detection of volcanic ash within CALIOP profiles. Using a simple thresholding algorithm, VCTH and thickness were derived from the CALIOP profiles. A total of 12 VCTH measurements, ranging from 3.7 to 16.6 km, have been derived. Back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory dispersion model were used as a check on volcanic origin of the detected ash clouds. Ensemble forward trajectories were generated to demonstrate how the new data could be used to improve Volcanic Ash Advisory Center operations. The findings reported here demonstrate several cases where low‐level ash was not recorded previously and include observations of thin ash clouds at large distances (∼4000 km) from the volcano.

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Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites

Cited by 54 publications

References 53 publications

A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

A Backscatter Lidar Forward Operator for Particle-Representing Atmospheric Chemistry Models

Ash plume top height estimation using AATSR

Quantification of volcanic cloud top heights and thicknesses using A‐train observations for the 2008 Chaitén eruption

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