Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-5985-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5985-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2022
Research article |
| 20 Oct 2022
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption
Andrew T. Prata
CORRESPONDING AUTHOR
Sub-Department of Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
Roy G. Grainger
Sub-Department of Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
Isabelle A. Taylor
COMET, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford OX1 3PU, UK
Adam C. Povey
National Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford OX1 3PU, UK
Simon R. Proud
National Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford OX1 3PU, UK
RAL Space, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0QX, UK
Caroline A. Poulsen
Science and Innovation Group, Australian Bureau of Meteorology, Melbourne, Australia
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Isabelle A. Taylor, Roy G. Grainger, Andrew T. Prata, Simon R. Proud, Tamsin A. Mather, and David M. Pyle
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-772, https://doi.org/10.5194/acp-2022-772, 2022
Revised manuscript under review for ACP
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This study looks at sulfur dioxide (SO2) and ash emissions from the 2021 eruption of La Soufrière on St Vinent. Using satellite data, 32 eruptive events were identified. Satellite data was used to track SO2 as it was transported around the globe. The majority of SO2 was emitted into the upper troposphere and lower stratosphere. Similarities with the 1979 eruption of La Soufrière highlight the value of studying these eruptions to be better prepared for future eruptions.
Natalie J. Harvey, Helen F. Dacre, Cameron Saint, Andrew T. Prata, Helen N. Webster, and Roy G. Grainger
Atmos. Chem. Phys., 22, 8529–8545, https://doi.org/10.5194/acp-22-8529-2022, https://doi.org/10.5194/acp-22-8529-2022, 2022
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In the event of a volcanic eruption, airlines need to make decisions about which routes are safe to operate and ensure that airborne aircraft land safely. The aim of this paper is to demonstrate the application of a statistical technique that best combines ash information from satellites and a suite of computer forecasts of ash concentration to provide a range of plausible estimates of how much volcanic ash emitted from a volcano is available to undergo long-range transport.
Leonardo Mingari, Arnau Folch, Andrew T. Prata, Federica Pardini, Giovanni Macedonio, and Antonio Costa
Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, https://doi.org/10.5194/acp-22-1773-2022, 2022
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We present a new implementation of an ensemble-based data assimilation method to improve forecasting of volcanic aerosols. This system can be efficiently integrated into operational workflows by exploiting high-performance computing resources. We found a dramatic improvement of forecast quality when satellite retrievals are continuously assimilated. Management of volcanic risk and reduction of aviation impacts can strongly benefit from this research.
Andrew T. Prata, Leonardo Mingari, Arnau Folch, Giovanni Macedonio, and Antonio Costa
Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, https://doi.org/10.5194/gmd-14-409-2021, 2021
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This paper presents FALL3D-8.0, the latest version release of an open-source code with a track record of 15+ years and a growing number of users in the volcanological and atmospheric communities. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. This paper details new model applications and validation of FALL3D-8.0 using satellite, ground-deposit load and radionuclide data.
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, https://doi.org/10.5194/acp-23-4115-2023, 2023
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The impact of aerosols on clouds is one of the largest uncertainties in the human forcing of the climate. Aerosol can increase the concentrations of droplets in clouds, but observational and model studies produce widely varying estimates of this effect. We show that these estimates can be reconciled if only polluted clouds are studied, but this is insufficient to constrain the climate impact of aerosol. The uncertainty in aerosol impact on clouds is currently driven by cases with little aerosol.
Isabelle A. Taylor, Roy G. Grainger, Andrew T. Prata, Simon R. Proud, Tamsin A. Mather, and David M. Pyle
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-772, https://doi.org/10.5194/acp-2022-772, 2022
Revised manuscript under review for ACP
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This study looks at sulfur dioxide (SO2) and ash emissions from the 2021 eruption of La Soufrière on St Vinent. Using satellite data, 32 eruptive events were identified. Satellite data was used to track SO2 as it was transported around the globe. The majority of SO2 was emitted into the upper troposphere and lower stratosphere. Similarities with the 1979 eruption of La Soufrière highlight the value of studying these eruptions to be better prepared for future eruptions.
Natalie J. Harvey, Helen F. Dacre, Cameron Saint, Andrew T. Prata, Helen N. Webster, and Roy G. Grainger
Atmos. Chem. Phys., 22, 8529–8545, https://doi.org/10.5194/acp-22-8529-2022, https://doi.org/10.5194/acp-22-8529-2022, 2022
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In the event of a volcanic eruption, airlines need to make decisions about which routes are safe to operate and ensure that airborne aircraft land safely. The aim of this paper is to demonstrate the application of a statistical technique that best combines ash information from satellites and a suite of computer forecasts of ash concentration to provide a range of plausible estimates of how much volcanic ash emitted from a volcano is available to undergo long-range transport.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
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A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Maria-Elissavet Koukouli, Konstantinos Michailidis, Pascal Hedelt, Isabelle A. Taylor, Antje Inness, Lieven Clarisse, Dimitris Balis, Dmitry Efremenko, Diego Loyola, Roy G. Grainger, and Christian Retscher
Atmos. Chem. Phys., 22, 5665–5683, https://doi.org/10.5194/acp-22-5665-2022, https://doi.org/10.5194/acp-22-5665-2022, 2022
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Volcanic eruptions eject large amounts of ash and trace gases into the atmosphere. The use of space-borne instruments enables the global monitoring of volcanic SO2 emissions in an economical and risk-free manner. The main aim of this paper is to present its extensive verification, accomplished within the ESA S5P+I: SO2LH project, over major recent volcanic eruptions, against collocated space-borne measurements, as well as assess its impact on the forecasts provided by CAMS.
Luca Bugliaro, Dennis Piontek, Stephan Kox, Marius Schmidl, Bernhard Mayer, Richard Müller, Margarita Vázquez-Navarro, Daniel M. Peters, Roy G. Grainger, Josef Gasteiger, and Jayanta Kar
Nat. Hazards Earth Syst. Sci., 22, 1029–1054, https://doi.org/10.5194/nhess-22-1029-2022, https://doi.org/10.5194/nhess-22-1029-2022, 2022
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The monitoring of ash dispersion in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that retrieves the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This algorithm, trained on realistic observations simulated with a radiative transfer model, runs operationally at the German Weather Service.
Leonardo Mingari, Arnau Folch, Andrew T. Prata, Federica Pardini, Giovanni Macedonio, and Antonio Costa
Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, https://doi.org/10.5194/acp-22-1773-2022, 2022
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We present a new implementation of an ensemble-based data assimilation method to improve forecasting of volcanic aerosols. This system can be efficiently integrated into operational workflows by exploiting high-performance computing resources. We found a dramatic improvement of forecast quality when satellite retrievals are continuously assimilated. Management of volcanic risk and reduction of aviation impacts can strongly benefit from this research.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Johannes de Leeuw, Anja Schmidt, Claire S. Witham, Nicolas Theys, Isabelle A. Taylor, Roy G. Grainger, Richard J. Pope, Jim Haywood, Martin Osborne, and Nina I. Kristiansen
Atmos. Chem. Phys., 21, 10851–10879, https://doi.org/10.5194/acp-21-10851-2021, https://doi.org/10.5194/acp-21-10851-2021, 2021
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Using the NAME dispersion model in combination with high-resolution SO2 satellite data from TROPOMI, we investigate the dispersion of volcanic SO2 from the 2019 Raikoke eruption. NAME accurately simulates the dispersion of SO2 during the first 2–3 weeks after the eruption and illustrates the potential of using high-resolution satellite data to identify potential limitations in dispersion models, which will ultimately help to improve efforts to forecast the dispersion of volcanic clouds.
Andrew T. Prata, Leonardo Mingari, Arnau Folch, Giovanni Macedonio, and Antonio Costa
Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, https://doi.org/10.5194/gmd-14-409-2021, 2021
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This paper presents FALL3D-8.0, the latest version release of an open-source code with a track record of 15+ years and a growing number of users in the volcanological and atmospheric communities. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. This paper details new model applications and validation of FALL3D-8.0 using satellite, ground-deposit load and radionuclide data.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
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We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
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We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Andrew M. Sayer, Yves Govaerts, Pekka Kolmonen, Antti Lipponen, Marta Luffarelli, Tero Mielonen, Falguni Patadia, Thomas Popp, Adam C. Povey, Kerstin Stebel, and Marcin L. Witek
Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020, https://doi.org/10.5194/amt-13-373-2020, 2020
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Satellite measurements of the Earth are routinely processed to estimate useful quantities; one example is the amount of atmospheric aerosols (which are particles such as mineral dust, smoke, volcanic ash, or sea spray). As with all measurements and inferred quantities, there is some degree of uncertainty in this process.
There are various methods to estimate these uncertainties. A related question is the following: how reliable are these estimates? This paper presents a method to assess them.
Isabelle A. Taylor, Elisa Carboni, Lucy J. Ventress, Tamsin A. Mather, and Roy G. Grainger
Atmos. Meas. Tech., 12, 3853–3883, https://doi.org/10.5194/amt-12-3853-2019, https://doi.org/10.5194/amt-12-3853-2019, 2019
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Volcanic ash is a hazard associated with volcanoes. Knowing an ash cloud’s location is essential for minimising the hazard. This includes knowing the height. This study adapted a well-known technique for obtaining the height of meteorological clouds, known as CO2 slicing, for volcanic ash. Modelled data were used to refine the method and then demonstrate that the technique could work for volcanic ash. It was then successfully applied to data from the Eyjafjallajökull and Grímsvötn eruptions.
Gregory R. McGarragh, Caroline A. Poulsen, Gareth E. Thomas, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin Stengel, Rainer Hollmann, and Roy G. Grainger
Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, https://doi.org/10.5194/amt-11-3397-2018, 2018
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Satellites are vital for measuring cloud properties necessary for climate prediction studies. We present a method to retrieve cloud properties from satellite based radiometric measurements. The methodology employed is known as optimal estimation and belongs in the class of statistical inversion methods based on Bayes' theorem. We show, through theoretical retrieval simulations, that the solution is stable and accurate to within 10–20% depending on cloud thickness.
Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, and Rainer Hollmann
Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, https://doi.org/10.5194/amt-11-3373-2018, 2018
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This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
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We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
Matthew W. Christensen, David Neubauer, Caroline A. Poulsen, Gareth E. Thomas, Gregory R. McGarragh, Adam C. Povey, Simon R. Proud, and Roy G. Grainger
Atmos. Chem. Phys., 17, 13151–13164, https://doi.org/10.5194/acp-17-13151-2017, https://doi.org/10.5194/acp-17-13151-2017, 2017
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The cloud-aerosol pairing algorithm (CAPA) is developed to quantify the impact of near-cloud aerosol retrievals on satellite-based aerosol–cloud statistical relationships. We find that previous satellite-based radiative forcing estimates of aerosol–cloud interactions represented in key climate reports are likely exaggerated by up to 50 % due to including retrieval artefacts in the aerosols located near clouds. It is demonstrated that this retrieval artefact can be corrected in current products.
Christopher J. Merchant, Frank Paul, Thomas Popp, Michael Ablain, Sophie Bontemps, Pierre Defourny, Rainer Hollmann, Thomas Lavergne, Alexandra Laeng, Gerrit de Leeuw, Jonathan Mittaz, Caroline Poulsen, Adam C. Povey, Max Reuter, Shubha Sathyendranath, Stein Sandven, Viktoria F. Sofieva, and Wolfgang Wagner
Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, https://doi.org/10.5194/essd-9-511-2017, 2017
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Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.
Lucy J. Ventress, Gregory McGarragh, Elisa Carboni, Andrew J. Smith, and Roy G. Grainger
Atmos. Meas. Tech., 9, 5407–5422, https://doi.org/10.5194/amt-9-5407-2016, https://doi.org/10.5194/amt-9-5407-2016, 2016
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The detection of volcanic ash plumes and knowledge of their properties have been of increasing interest due to the effect ash particles can have on the aviation industry. A new method is shown for use with hyperspectral satellite instruments, such as the Infrared Atmospheric Sounding Interferometer, to derive optical and physical properties of volcanic ash. The results are compared to ancillary data sources, showing good agreement, which indicates better characterisation of volcanic plumes.
A. C. Povey and R. G. Grainger
Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, https://doi.org/10.5194/amt-8-4699-2015, 2015
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Clear communication of the uncertainty on data is necessary for users to make appropriate use of it. This paper discusses the representation of uncertainty in satellite observations of the environment, arguing that the dominant sources of error are assumptions made during data analysis. The resulting uncertainty may be more usefully represented using ensemble techniques (a set of analyses using different assumptions to illustrate their impact) than with traditional statistical metrics.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
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Coincident in situ and triple-frequency radar airborne observations in the Arctic
Analysis of improvements in MOPITT observational coverage over Canada
Using artificial neural networks to predict riming from Doppler cloud radar observations
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Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Kamil Mroz, Bernat Puidgomenech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
EGUsphere, https://doi.org/10.5194/egusphere-2023-56, https://doi.org/10.5194/egusphere-2023-56, 2023
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We present the theoretical basis of the algorithm for estimating the size and water content of cloud and precipitation. The algorithm utilizes the data collected by the Cloud Precipitation Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as C-CLD product.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
EGUsphere, https://doi.org/10.5194/egusphere-2022-1240, https://doi.org/10.5194/egusphere-2022-1240, 2022
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The Multi-Spectral Imager (MSI) onboard of the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the across-track direction complementing the measurements from the cloud profiling radar, atmospheric lidar and broadband radiometer. The accurate discrimination between clear and cloudy pixel is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
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With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-301, https://doi.org/10.5194/amt-2022-301, 2022
Revised manuscript accepted for AMT
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the Scene Construction Algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provide the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
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This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
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The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
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In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
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Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
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Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
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This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi
Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, https://doi.org/10.5194/amt-15-5181-2022, 2022
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A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Rachel T. Pinker, Yingtao Ma, Wen Chen, Istvan Laszlo, Hongqing Liu, Hye-Yun Kim, and Jaime Daniels
Atmos. Meas. Tech., 15, 5077–5094, https://doi.org/10.5194/amt-15-5077-2022, https://doi.org/10.5194/amt-15-5077-2022, 2022
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Scene-dependent narrow-to-broadband transformations are developed to facilitate the use of observations from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R, to derive surface shortwave radiative fluxes. This is a first NOAA product at the high resolution of about 5 k over the contiguous United States (CONUS) region. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
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This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429, https://doi.org/10.5194/amt-15-4411-2022, https://doi.org/10.5194/amt-15-4411-2022, 2022
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In this paper we describe the implementation of an interpolation–prediction estimator applied to cloud properties derived from CloudSat observations. The objective is to evaluate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparisons. This paper is didactic and beneficial for anyone interested in kriging estimators.
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
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Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999, https://doi.org/10.5194/amt-15-3969-2022, https://doi.org/10.5194/amt-15-3969-2022, 2022
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We use the C-band POLDIRAD and the Ka-band MIRA-35 to perform snowfall dual-wavelength polarimetric radar measurements. We develop an ice microphysics retrieval for mass, apparent shape, and median size of the particle size distribution by comparing observations to T-matrix ice spheroid simulations while varying the mass–size relationship. We furthermore show how the polarimetric measurements from POLDIRAD help to narrow down ambiguities between ice particle shape and size.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
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This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
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Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Xiaotong Li, Baozhu Wang, Bo Qiu, and Chao Wu
Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, https://doi.org/10.5194/amt-15-3629-2022, 2022
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The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567, https://doi.org/10.5194/amt-15-3555-2022, https://doi.org/10.5194/amt-15-3555-2022, 2022
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It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
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A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022, https://doi.org/10.5194/amt-15-2021-2022, 2022
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Two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) are introduced into a one-dimensional EnVar data assimilation system to demonstrate their benefit for future operational assimilation schemes, with the use of microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D. Results show that the brightness temperatures analysed with the new variables are improved, including the liquid water.
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
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We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
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This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
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We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, https://doi.org/10.5194/amt-15-797-2022, 2022
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This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech., 15, 775–795, https://doi.org/10.5194/amt-15-775-2022, https://doi.org/10.5194/amt-15-775-2022, 2022
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An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
Heba S. Marey, James R. Drummond, Dylan B. A. Jones, Helen Worden, Merritt N. Deeter, John Gille, and Debbie Mao
Atmos. Meas. Tech., 15, 701–719, https://doi.org/10.5194/amt-15-701-2022, https://doi.org/10.5194/amt-15-701-2022, 2022
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In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
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We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
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It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Jean-Marc Nicolas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-414, https://doi.org/10.5194/amt-2021-414, 2022
Revised manuscript accepted for AMT
Short summary
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The uncertainties in cloud remote sensing can propagate to the retrieved cloud properties and they need to be quantified. We present the formalism of error extraction and we apply it on the cloud properties retrieved from the measurements of the airborne radiometer OSIRIS. We show that errors related to measurement uncertainties reach 10 %. Errors related to the simplified model assuming that the clouds are plane-parallel and homogeneous lead to uncertainties exceeding 10 %.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech., 14, 7893–7907, https://doi.org/10.5194/amt-14-7893-2021, https://doi.org/10.5194/amt-14-7893-2021, 2021
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PARAFOG is a near-real-time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows us to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at five different locations presents a good perfomance.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
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Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Frank Werner, Nathaniel J. Livesey, Michael J. Schwartz, William G. Read, Michelle L. Santee, and Galina Wind
Atmos. Meas. Tech., 14, 7749–7773, https://doi.org/10.5194/amt-14-7749-2021, https://doi.org/10.5194/amt-14-7749-2021, 2021
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In this study we present an improved cloud detection scheme for the Microwave Limb Sounder, which is based on a feedforward artificial neural network. This new algorithm is shown not only to reliably detect high and mid-level convection containing even small amounts of cloud water but also to distinguish between high-reaching and mid-level to low convection.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
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A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Cited articles
Barber, C. B., Dobkin, D. P., and Huhdanpaa, H.: The quickhull algorithm for
convex hulls, ACM Trans. Mathe. Softw., 22, 469–483,
https://doi.org/10.1145/235815.235821, 1996. a
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y.,
Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y.,
Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama,
H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to
Himawari-8/9 Japan's New-Generation Geostationary
Meteorological Satellites, J. Meteorol. Soc.
JPN Ser. II, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. a
Bruckert, J., Hoshyaripour, G. A., Horváth, Á., Muser, L. O.,
Prata, F. J., Hoose, C., and Vogel, B.: Online treatment of eruption
dynamics improves the volcanic ash and SO2 dispersion forecast: case
of the 2019 Raikoke eruption, Atmos. Chem. Phys., 22, 3535–3552,
https://doi.org/10.5194/acp-22-3535-2022, 2022. a
Bursik, M. I., Sparks, R. S. J., Gilbert, J. S., and Carey, S. N.:
Sedimentation of tephra by volcanic plumes: I. Theory and its comparison
with a study of the Fogo A plinian deposit, Sao Miguel (Azores),
Bull. Volcanol., 54, 329–344, https://doi.org/10.1007/BF00301486, 1992. a, b
Clarisse, L., Hurtmans, D., Prata, A. J., Karagulian, F., Clerbaux, C.,
De Maziére, M., and Coheur, P.-F.: Retrieving radius, concentration, optical
depth, and mass of different types of aerosols from high-resolution infrared
nadir spectra, Appl. Opt., 49, 3713, https://doi.org/10.1364/AO.49.003713, 2010. a
Cooper, S. J., L'Ecuyer, T. S., Gabriel, P., Baran, A. J., and Stephens,
G. L.: Objective Assessment of the Information Content of Visible and
Infrared Radiance Measurements for Cloud Microphysical Property
Retrievals over the Global Oceans. Part II: Ice Clouds, J. Appl. Meteorol. Climatol., 45, 42–62, https://doi.org/10.1175/JAM2327.1,
2006. a
Corradini, S., Spinetti, C., Carboni, E., Tirelli, C., Buongiorno, M.,
Pugnaghi, S., and Gangale, G.: Mt. Etna tropospheric ash retrieval and
sensitivity analysis using moderate resolution imaging spectroradiometer
measurements, J. Appl. Remote Sens., 2, 023550,
https://doi.org/10.1117/1.3046674, 2008. a, b, c, d, e
Corradini, S., Montopoli, M., Guerrieri, L., Ricci, M., Scollo, S., Merucci,
L., Marzano, F., Pugnaghi, S., Prestifilippo, M., Ventress, L., Grainger, R.,
Carboni, E., Vulpiani, G., and Coltelli, M.: A Multi-Sensor Approach
for Volcanic Ash Cloud Retrieval and Eruption Characterization:
The 23 November 2013 Etna Lava Fountain, Remote Sens., 8, 58,
https://doi.org/10.3390/rs8010058, 2016. a
Dacre, H. F., Grant, A. L. M., and Johnson, B. T.: Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud, Atmos. Chem. Phys., 13, 1277–1291, https://doi.org/10.5194/acp-13-1277-2013, 2013. a
de Leeuw, J., Schmidt, A., Witham, C. S., Theys, N., Taylor, I. A., Grainger, R. G., Pope, R. J., Haywood, J., Osborne, M., and Kristiansen, N. I.: The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide, Atmos. Chem. Phys., 21, 10851–10879, https://doi.org/10.5194/acp-21-10851-2021, 2021. a
Deguine, A., Petitprez, D., Clarisse, L., Gudmundsson, S., Outes, V.,
Villarosa, G., and Herbin, H.: Complex refractive index of volcanic ash
aerosol in the infrared, visible, and ultraviolet, Appl. Opt., 59, 884,
https://doi.org/10.1364/AO.59.000884, 2020. a
Dioguardi, F., Beckett, F., Dürig, T., and Stevenson, J. A.: The Impact of
Eruption Source Parameter Uncertainties on Ash Dispersion
Forecasts During Explosive Volcanic Eruptions, J.
Geophys. Res.-Atmos., 125, e2020JD032717, https://doi.org/10.1029/2020JD032717, 2020. a
Folch, A., Mingari, L., Gutierrez, N., Hanzich, M., Macedonio, G., and Costa, A.: FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 1: Model physics and numerics, Geosci. Model Dev., 13, 1431–1458, https://doi.org/10.5194/gmd-13-1431-2020, 2020. a
Folch, A., Mingari, L., and Prata, A. T.: Ensemble-Based Forecast of
Volcanic Clouds Using FALL3D-8.1, Front. Earth Sci., 9,
741841, https://doi.org/10.3389/feart.2021.741841, 2022. a
Gouhier, M., Eychenne, J., Azzaoui, N., Guillin, A., Deslandes, M., Poret, M.,
Costa, A., and Husson, P.: Low efficiency of large volcanic eruptions in
transporting very fine ash into the atmosphere, Sci. Rep., 9, 1449,
https://doi.org/10.1038/s41598-019-38595-7, 2019. a, b
Grainger, R. G., Peters, D. M., Thomas, G. E., Smith, A. J. A., Siddans, R.,
Carboni, E., and Dudhia, A.: Measuring volcanic plume and ash properties from
space, Geological Society, London, Special Pub., 380, 293–320,
https://doi.org/10.1144/SP380.7, 2013. a
Gray, T. M. and Bennartz, R.: Automatic volcanic ash detection from MODIS observations using a back-propagation neural network, Atmos. Meas. Tech., 8, 5089–5097, https://doi.org/10.5194/amt-8-5089-2015, 2015. a
Gu, Y., Rose, W. I., and Bluth, G. J. S.: Retrieval of mass and sizes of
particles in sandstorms using two MODIS IR bands: A case study of
April 7, 2001 sandstorm in China, Geophys. Res. Lett., 30, 1805,
https://doi.org/10.1029/2003GL017405, 2003. a
Gu, Y., Rose, W. I., Schneider, D. J., Bluth, G. J. S., and Watson, I. M.:
Advantageous GOES IR results for ash mapping at high latitudes:
Cleveland eruptions 2001, Geophys. Res. Lett., 32, L02305,
https://doi.org/10.1029/2004GL021651, 2005. a
Harvey, N. J., Dacre, H. F., Webster, H. N., Taylor, I. A., Khanal, S.,
Grainger, R. G., and Cooke, M. C.: The Impact of Ensemble Meteorology
on Inverse Modeling Estimates of Volcano Emissions and Ash
Dispersion Forecasts: Grímsvötn 2011, Atmosphere, 11, 1022,
https://doi.org/10.3390/atmos11101022, 2020. a
Harvey, N. J., Dacre, H. F., Saint, C., Prata, A. T., Webster, H. N., and Grainger, R. G.: Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption, Atmos. Chem. Phys., 22, 8529–8545, https://doi.org/10.5194/acp-22-8529-2022, 2022. a, b
Heidinger, A. K., Pavolonis, M. J., Holz, R. E., Baum, B. A., and Berthier, S.:
Using CALIPSO to explore the sensitivity to cirrus height in the infrared
observations from NPOESS/VIIRS and GOES-R/ABI, J.
Geophys. Res., 115, D00H20, https://doi.org/10.1029/2009JD012152, 2010. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Horváth, Á., Carr, J. L., Girina, O. A., Wu, D. L., Bril, A. A., Mazurov, A. A., Melnikov, D. V., Hoshyaripour, G. A., and Buehler, S. A.: Geometric estimation of volcanic eruption column height from GOES-R near-limb imagery – Part 1: Methodology, Atmos. Chem. Phys., 21, 12189–12206, https://doi.org/10.5194/acp-21-12189-2021, 2021a. a, b
Horváth, Á., Girina, O. A., Carr, J. L., Wu, D. L., Bril, A. A., Mazurov, A. A., Melnikov, D. V., Hoshyaripour, G. A., and Buehler, S. A.: Geometric estimation of volcanic eruption column height from GOES-R near-limb imagery – Part 2: Case studies, Atmos. Chem. Phys., 21, 12207–12226, https://doi.org/10.5194/acp-21-12207-2021, 2021b. a, b, c, d, e, f
Hyman, D. M. and Pavolonis, M. J.: Probabilistic retrieval of volcanic SO2
layer height and cumulative mass loading using the Cross-track Infrared
Sounder (CrIS), https://doi.org/10.5194/amt-2020-41, 2020. a
ICAO: Roadmap for International Airways Volcano Watch (IAVW) in
Support of International Air Navigation, https://www.icao.int/airnavigation/METP/ MOGVA ReferenceDocuments/IAVW Roadmap.pdf (last access: 29 November
2021), 2019. a
Inoue, T.: On the Temperature and Effective Emissivity Determination of
Semi-Transparent Cirrus Clouds by Bi-Spectral Measurements in
the 10 – Window Region, J. Meteorol. Soc. JPN,
Ser. II, 63, 88–99, https://doi.org/10.2151/jmsj1965.63.1_88, 1985. a
Iwabuchi, H. and Hayasaka, T.: Effects of Cloud Horizontal Inhomogeneity
on the Optical Thickness Retrieved from Moderate-Resolution
Satellite Data, J. Atmos. Sci., 59, 2227–2242,
https://doi.org/10.1175/1520-0469(2002)059<2227:EOCHIO>2.0.CO;2, 2002. a
Key, J. R.: Retrieval of cloud optical depth and particle effective radius at
high latitudes using visible and thermal satellite data, p. 318, Paris,
France, https://doi.org/10.1117/12.228928, 1995. a
Kylling, A., Kahnert, M., Lindqvist, H., and Nousiainen, T.: Volcanic ash infrared signature: porous non-spherical ash particle shapes compared to homogeneous spherical ash particles, Atmos. Meas. Tech., 7, 919–929, https://doi.org/10.5194/amt-7-919-2014, 2014. a, b, c, d
Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde, C., and Gasteiger, J.: A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash, Atmos. Meas. Tech., 8, 1935–1949, https://doi.org/10.5194/amt-8-1935-2015, 2015. a, b, c
Levenberg, K.: A method for the solution of certain non-linear problems in
least squares, Q. Appl. Mathe., 2, 164–168,
https://doi.org/10.1090/qam/10666, 1944. a
Lindsey, D., Schmit, T. J., MacKenzie, W. M., Jewitt, C. P., Gunshor, M. M.,
and Grasso, L.: 10.35: atmospheric window on the GOES-R Advanced
Baseline Imager with less moisture attenuation, J. Appl. Remote
Sens., 6, 1, https://doi.org/10.1117/1.JRS.6.063598, 2012. a
Lu, S., Lin, H. X., Heemink, A., Segers, A., and Fu, G.: Estimation of volcanic
ash emissions through assimilating satellite data and ground-based
observations, J. Geophys. Res.-Atmos., 121,
10971–10994, https://doi.org/10.1002/2016JD025131, 2016. a
Mackie, S., Millington, S., and Watson, I. M.: How assumed composition affects
the interpretation of satellite observations of volcanic ash: How assumed
composition affects interpretation of ash observations, Meteorol.
Appl., 21, 20–29, https://doi.org/10.1002/met.1445, 2014. a
Marquardt, D. W.: An Algorithm for Least-Squares Estimation of
Nonlinear Parameters, J. Soc. Indust. Appl.
Mathe., 11, 431–441, https://doi.org/10.1137/0111030, 1963. a
Mastin, L., Guffanti, M., Servranckx, R., Webley, P., Barsotti, S., Dean, K.,
Durant, A., Ewert, J., Neri, A., Rose, W., Schneider, D., Siebert, L.,
Stunder, B., Swanson, G., Tupper, A., Volentik, A., and Waythomas, C.: A
multidisciplinary effort to assign realistic source parameters to models of
volcanic ash-cloud transport and dispersion during eruptions, J.
Volcanol. Geothermal Res., 186, 10–21,
https://doi.org/10.1016/j.jvolgeores.2009.01.008, 2009. a, b, c, d, e, f, g, h, i, j, k, l
McGarragh, G. R., Thomas, G. E., Povey, A. C., Poulsen, C. A., and Grainger,
R. G.: Volcanic ash retrievals using ORAC and satellite measurements in the
visible and IR, Proc. ˜ATMOS 2015, Advances in Atmospheric Science and
Applications, Heraklion, Greece, 8–12 June 2015 (ESA SP-735, November
2015), p. 8, 2015. a
McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, 2018. a, b, c, d, e, f, g
McKee, K., Smith, C. M., Reath, K., Snee, E., Maher, S., Matoza, R. S., Carn,
S., Mastin, L., Anderson, K., Damby, D., Roman, D. C., Degterev, A., Rybin,
A., Chibisova, M., Assink, J. D., de Negri Leiva, R., and Perttu, A.:
Evaluating the state-of-the-art in remote volcanic eruption characterization
Part I: Raikoke volcano, Kuril Islands, J. Volcanol.
Geotherm. Res., 419, 107354, https://doi.org/10.1016/j.jvolgeores.2021.107354,
2021. a, b, c, d, e, f, g, h
Mingari, L., Folch, A., Prata, A. T., Pardini, F., Macedonio, G., and Costa, A.: Data assimilation of volcanic aerosol observations using FALL3D+PDAF, Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, 2022. a, b
Morton, B. R., Taylor, G. T., and Turner, J. S.: Turbulent gravitational
convection from maintained and instantaneous sources, P.
Roy. Soc. London Series A, 234,
1–23, https://doi.org/10.1098/rspa.1956.0011, 1956. a, b
Muser, L. O., Hoshyaripour, G. A., Bruckert, J., Horváth, Á., Malinina, E., Wallis, S., Prata, F. J., Rozanov, A., von Savigny, C., Vogel, H., and Vogel, B.: Particle aging and aerosol–radiation interaction affect volcanic plume dispersion: evidence from the Raikoke 2019 eruption, Atmos. Chem. Phys., 20, 15015–15036, https://doi.org/10.5194/acp-20-15015-2020, 2020. a, b, c
Newman, S. M., Clarisse, L., Hurtmans, D., Marenco, F., Johnson, B., Turnbull,
K., Havemann, S., Baran, A. J., and Haywood, J.: A case study of observations
of volcanic ash from the Eyjafjallajókull eruption: 2. Airborne and
satellite radiative measurements, p. 19, https://doi.org/10.1029/2011JD016780, 2012. a
Osborne, M. J., de Leeuw, J., Witham, C., Schmidt, A., Beckett, F., Kristiansen, N., Buxmann, J., Saint, C., Welton, E. J., Fochesatto, J., Gomes, A. R., Bundke, U., Petzold, A., Marenco, F., and Haywood, J.: The 2019 Raikoke volcanic eruption – Part 2: Particle-phase dispersion and concurrent wildfire smoke emissions, Atmos. Chem. Phys., 22, 2975–2997, https://doi.org/10.5194/acp-22-2975-2022, 2022. a, b, c, d, e, f
Pardini, F., Corradini, S., Costa, A., Esposti Ongaro, T., Merucci, L., Neri,
A., Stelitano, D., and de Michieli Vitturi, M.: Ensemble-Based Data
Assimilation of Volcanic Ash Clouds from Satellite Observations:
Application to the 24 December 2018 Mt. Etna Explosive Eruption,
Atmosphere, 11, 359, https://doi.org/10.3390/atmos11040359, 2020. a
Parol, F., Buriez, J. C., Brogniez, G., and Fouquart, Y.: Information Content
of AVHRR Channels 4 and 5 with Respect to the Effective Radius of
Cirrus Cloud Particles, J. Appl. Meteorol., 30, 973–984,
https://doi.org/10.1175/1520-0450-30.7.973, 1991. a
Pavolonis, M. and Sieglaff, J.: GOES-R Advanced Baseline Imager
(ABI) Algorithm Theoretical Basis Document For Volcanic Ash
(Detection and Height), p. 71, https://www.star.nesdis.noaa.gov/goesr/documents/ATBDs/Baseline/ATBD_GOES-R_VolAsh_v3.0_July2012.pdf (last access: 17 October 2022), 2012. a
Pavolonis, M. J., Heidinger, A. K., and Sieglaff, J.: Automated retrievals of
volcanic ash and dust cloud properties from upwelling infrared measurements:
Retrieval of ash/dust cloud properties, J. Geophys. Res.-Atmos., 118, 1436–1458, https://doi.org/10.1002/jgrd.50173, 2013. a, b, c, d, e, f, g, h, i, j, k, l, m
Pavolonis, M. J., Sieglaff, J., and Cintineo, J.: Spectrally Enhanced Cloud
Objects - A generalized framework for automated detection of volcanic ash
and dust clouds using passive satellite measurements: 2. Cloud object
analysis and global application, J. Geophys. Res.-Atmos., 120, 7842–7870, https://doi.org/10.1002/2014JD022969, 2015a. a
Pavolonis, M. J., Sieglaff, J., and Cintineo, J.: Spectrally Enhanced Cloud
Objects-A generalized framework for automated detection of volcanic ash
and dust clouds using passive satellite measurements: 1. Multispectral
analysis, J. Geophys. Res.-Atmos., 120, 7813–7841,
https://doi.org/10.1002/2014JD022968, 2015b. a
Pavolonis, M. J., Sieglaff, J. M., and Cintineo, J. L.: Remote Sensing of
Volcanic Ash with the GOES-R Series, in: The GOES-R Series,
103–124, Elsevier, https://doi.org/10.1016/B978-0-12-814327-8.00010-X, 2020. a
Picchiani, M., Chini, M., Corradini, S., Merucci, L., Sellitto, P., Del Frate, F., and Stramondo, S.: Volcanic ash detection and retrievals using MODIS data by means of neural networks, Atmos. Meas. Tech., 4, 2619–2631, https://doi.org/10.5194/amt-4-2619-2011, 2011. a
Piontek, D., Bugliaro, L., Kar, J., Schumann, U., Marenco, F., Plu, M., and
Voigt, C.: The New Volcanic Ash Satellite Retrieval VACOS Using
MSG/SEVIRI and Artificial Neural Networks: 2. Validation, Remote
Sens., 13, 3128, https://doi.org/10.3390/rs13163128, 2021a. a
Piontek, D., Hornby, A., Voigt, C., Bugliaro, L., and Gasteiger, J.:
Determination of complex refractive indices and optical properties of
volcanic ashes in the thermal infrared based on generic petrological
compositions, J. Volcanol. Geotherm. Res., 411, 107174,
https://doi.org/10.1016/j.jvolgeores.2021.107174, 2021b. a
Pouget, S., Bursik, M., Webley, P., Dehn, J., and Pavolonis, M.: Estimation of
eruption source parameters from umbrella cloud or downwind plume growth rate,
J. Volcanol. Geotherm. Res., 258, 100–112,
https://doi.org/10.1016/j.jvolgeores.2013.04.002, 2013. a
Poulsen, C. A., Siddans, R., Thomas, G. E., Sayer, A. M., Grainger, R. G., Campmany, E., Dean, S. M., Arnold, C., and Watts, P. D.: Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR, Atmos. Meas. Tech., 5, 1889–1910, https://doi.org/10.5194/amt-5-1889-2012, 2012. a, b, c, d, e, f
Prabhakara, C., Fraser, R. S., Dalu, G., Wu, M.-L. C., Curran, R. J., and
Styles, T.: Thin Cirrus Clouds: Seasonal Distribution over Oceans
Deduced from Nimbus-4 IRIS, J. Appl. Meteorol., 27,
379–399, https://doi.org/10.1175/1520-0450(1988)027<0379:TCCSDO>2.0.CO;2, 1988. a
Prata, A. J.: Infrared radiative transfer calculations for volcanic ash clouds,
Geophys. Res. Lett., 16, 1293–1296, https://doi.org/10.1029/GL016i011p01293,
1989a. a, b, c
Prata, A. J.: Observations of volcanic ash clouds in the 10–12 µm window using
AVHRR/2 data, Int. J. Remote Sens., 10, 751–761,
https://doi.org/10.1080/01431168908903916, 1989b. a
Prata, A. T., Siems, S. T., and Manton, M. J.: Quantification of volcanic cloud
top heights and thicknesses using A-train observations for the 2008
Chaitén eruption, J. Geophys. Res.-Atmos., 120,
2928–2950, https://doi.org/10.1002/2014JD022399, 2015. a
Prata, A. T., Young, S. A., Siems, S. T., and Manton, M. J.: Lidar ratios of stratospheric volcanic ash and sulfate aerosols retrieved from CALIOP measurements, Atmos. Chem. Phys., 17, 8599–8618, https://doi.org/10.5194/acp-17-8599-2017, 2017a. a, b
Prata, A. T., Folch, A., Prata, A. J., Biondi, R., Brenot, H., Cimarelli, C.,
Corradini, S., Lapierre, J., and Costa, A.: Anak Krakatau triggers volcanic
freezer in the upper troposphere, Sci. Rep., 10,
https://doi.org/10.1038/s41598-020-60465-w, 2020. a
Prata, A. T., Mingari, L., Folch, A., Macedonio, G., and Costa, A.: FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation, Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, 2021. a, b, c, d
Prata, F., Woodhouse, M., Huppert, H. E., Prata, A., Thordarson, T., and Carn, S.: Atmospheric processes affecting the separation of volcanic ash and SO2 in volcanic eruptions: inferences from the May 2011 Grímsvötn eruption, Atmos. Chem. Phys., 17, 10709–10732, https://doi.org/10.5194/acp-17-10709-2017, 2017b. a, b
Prata, G. S., Ventress, L. J., Carboni, E., Mather, T. A., Grainger, R. G., and
Pyle, D. M.: A New Parameterization of Volcanic Ash Complex
Refractive Index Based on NBO/T and SiO 2
Content, J. Geophys. Res.-Atmos., 124, 1779–1797,
https://doi.org/10.1029/2018JD028679, 2019. a, b, c
Reed, B. E., Peters, D. M., McPheat, R., and Grainger, R. G.: The Complex
Refractive Index of Volcanic Ash Aerosol Retrieved From
Spectral Mass Extinction, J. Geophys. Res.-Atmos.,
123, 1339–1350, https://doi.org/10.1002/2017JD027362, 2018. a, b
Rodgers, C. D.: Retrieval of atmospheric temperature and composition from
remote measurements of thermal radiation, Rev. Geophys., 14, 609,
https://doi.org/10.1029/RG014i004p00609, 1976. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Schneider, D. J., Rose, W. I., Coke, L. R., Bluth, G. J. S., Sprod, I. E., and
Krueger, A. J.: Early evolution of a stratospheric volcanic eruption cloud as
observed with TOMS and AVHRR, J. Geophys. Res.-Atmos., 104, 4037–4050, https://doi.org/10.1029/1998JD200073, 1999. a
Sears, T. M., Thomas, G. E., Carboni, E., A. Smith, A. J., and Grainger, R. G.:
SO 2 as a possible proxy for volcanic ash in aviation hazard
avoidance, J. Geophys. Res.-Atmos., 118, 5698–5709,
https://doi.org/10.1002/jgrd.50505, 2013. a
Settle, M.: Volcanic eruption clouds and the thermal power output of explosive
eruptions, J. Volcanol. Geotherm. Res., 3, 309–324,
https://doi.org/10.1016/0377-0273(78)90041-0, 1978. a
Smirnov, S., Nizametdinov, I., Timina, T., Kotov, A., Sekisova, V., Kuzmin, D.,
Kalacheva, E., Rashidov, V., Rybin, A., Lavrenchuk, A., Degterev, A.,
Maksimovich, I., and Abersteiner, A.: High explosivity of the June 21, 2019
eruption of Raikoke volcano (Central Kuril Islands); mineralogical
and petrological constraints on the pyroclastic materials, J.
Volcanol. Geotherm. Res., 418, 107346,
https://doi.org/10.1016/j.jvolgeores.2021.107346, 2021. a, b
Soda, R.: Infrared Absorption Spectra of Quartz and Some other Silica
Modification, B. Chem. Soc. JPN, 34, 1491–1495,
https://doi.org/10.1246/bcsj.34.1491, 1961. a
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Laszlo, I.: DISORT, a
general-purpose Fortran program for discrete-ordinate-method radiative
transfer in scattering and emitting layered media: documentation of
methodology, Tech. rep., Dept. of Physics and Engineering Physics, Stevens
Institute of Technology Hoboken, NJ 07030, 112 pp., http://www.libradtran.org/lib/exe/fetch.php?media=disortreport1.1.pdf (last access: 17 October 2022), 2000. a
Stohl, A., Prata, A. J., Eckhardt, S., Clarisse, L., Durant, A., Henne, S., Kristiansen, N. I., Minikin, A., Schumann, U., Seibert, P., Stebel, K., Thomas, H. E., Thorsteinsson, T., Tørseth, K., and Weinzierl, B.: Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: the 2010 Eyjafjallajökull eruption, Atmos. Chem. Phys., 11, 4333–4351, https://doi.org/10.5194/acp-11-4333-2011, 2011. a
Thomas, G. E., Poulsen, C. A., Sayer, A. M., Marsh, S. H., Dean, S. M., Carboni, E., Siddans, R., Grainger, R. G., and Lawrence, B. N.: The GRAPE aerosol retrieval algorithm, Atmos. Meas. Tech., 2, 679–701, https://doi.org/10.5194/amt-2-679-2009, 2009. a, b
Van Eaton, A. R., Amigo, Ã., Bertin, D., Mastin, L. G., Giacosa, R. E.,
González, J., Valderrama, O., Fontijn, K., and Behnke, S. A.: Volcanic
lightning and plume behavior reveal evolving hazards during the April 2015
eruption of Calbuco volcano, Chile, Geophys. Res. Lett., 43,
3563–3571, https://doi.org/10.1002/2016GL068076, 2016. a
Vicente, G. A., Davenport, J. C., and Scofield, R. A.: The role of orographic
and parallax corrections on real time high resolution satellite rainfall rate
distribution, Int. J. Remote Sens., 23, 221–230,
https://doi.org/10.1080/01431160010006935, 2002. a
Wang, C., Yang, P., Baum, B. A., Platnick, S., Heidinger, A. K., Hu, Y., and
Holz, R. E.: Retrieval of Ice Cloud Optical Thickness and Effective
Particle Size Using a Fast Infrared Radiative Transfer Model,
J. Appl. Meteorol. Climatol., 50, 2283–2297,
https://doi.org/10.1175/JAMC-D-11-067.1, 2011. a
Watts, P. D., Bennartz, R., and Fell, F.: Retrieval of two-layer cloud
properties from multispectral observations using optimal estimation, J. Geophys. Res., 116, D16203, https://doi.org/10.1029/2011JD015883, 2011. a
Western, L. M., Watson, M. I., and Francis, P. N.: Uncertainty in two-channel
infrared remote sensing retrievals of a well-characterised volcanic ash
cloud, B. Volcanol., 77, 67, https://doi.org/10.1007/s00445-015-0950-y, 2015. a
Wilkins, K. L., Mackie, S., Watson, M., Webster, H. N., Thomson, D. J., and
Dacre, H. F.: Data insertion in volcanic ash cloud forecasting, Ann.
Geophys., 57, 1–6, https://doi.org/10.4401/ag-6624, 2015. a
Wilkins, K. L., Watson, I. M., Kristiansen, N. I., Webster, H. N., Thomson,
D. J., Dacre, H. F., and Prata, A. J.: Using data insertion with the NAME
model to simulate the 8 May 2010 Eyjafjallajókull volcanic ash cloud,
J. Geophys. Res.-Atmos., 121, 306–323,
https://doi.org/10.1002/2015JD023895, 2016. a, b
Wilson, L., Sparks, R. S. J., Huang, T. C., and Watkins, N. D.: The control of
volcanic column heights by eruption energetics and dynamics, J.
Geophys. Res.-Solid Earth, 83, 1829–1836,
https://doi.org/10.1029/JB083iB04p01829, 1978. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt,
W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP
Data Processing Algorithms, J. Atmos. Ocean.
Technol., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Winker, D. M., Liu, Z., Omar, A., Tackett, J., and Fairlie, D.: CALIOP
observations of the transport of ash from the Eyjafjallajókull volcano in
April 2010, J. Geophys. Res.-Atmos., 117, D00U15,
https://doi.org/10.1029/2011JD016499, 2012. a
Witham, C., Hort, M., Thomson, D., Devenish, B., Webster, H., and Beckett, F.:
The current volcanic ash modelling set- up at the London VAAC, p. 11,
https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/services/transport/aviation/vaac/london_vaac_current_modelling_setup.pdf (last access: 17 October 2022), 2019.
a
Yamanouchi, T., Suzuki, K., and Kawaguchi, S.: Detection of Clouds in
Antarctica from Infrared Multispectral Data of AVHRR, J.
Meteorol. Soc. JPN Ser. II, 65, 949–962,
https://doi.org/10.2151/jmsj1965.65.6_949, 1987. a
Yang, P., Feng, Q., Hong, G., Kattawar, G. W., Wiscombe, W. J., Mishchenko, M. I., Dubovik, O.,
Laszlo, I., and Sokolik, I. N.: Modeling of the scattering and radiative properties of nonspherical
dust-like aerosols, J. Aerosol Sci., 38, 995–1014,
https://doi.org/10.1016/j.jaerosci.2007.07.001, 2007. a
Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for
satellite-based volcanic ash mapping and retrievals using “split window”
IR data from GOES and AVHRR, J. Geophys. Res., 107, D16,
https://doi.org/10.1029/2001JD000706, 2002. a
Zidikheri, M. J. and Lucas, C.: A Computationally Efficient Ensemble
Filtering Scheme for Quantitative Volcanic Ash Forecasts, J. Geophys. Res.-Atmos., 126, e2020JD033094, https://doi.org/10.1029/2020JD033094, 2021. a
Short summary
Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Satellite observations are often used to track ash clouds and estimate their height, particle...
