Articles | Volume 10, issue 11
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 14 Nov 2017
Research article | 14 Nov 2017
Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI
Johan Strandgren et al.
Related authors
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
Short summary
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Johan Strandgren, David Krutz, Jonas Wilzewski, Carsten Paproth, Ilse Sebastian, Kevin R. Gurney, Jianming Liang, Anke Roiger, and André Butz
Atmos. Meas. Tech., 13, 2887–2904, https://doi.org/10.5194/amt-13-2887-2020, https://doi.org/10.5194/amt-13-2887-2020, 2020
Short summary
Short summary
This paper presents the concept of a spaceborne imaging spectrometer targeting the routine monitoring of CO2 emissions from localized point sources down to an emission strength of about 1 Mt CO2 yr-1. Using high-resolution CO2 emission and albedo data, it is shown that CO2 plumes from point sources with an emission strength down to the order of 0.3 Mt CO2 yr-1 can be resolved in an urban environment (when limited by instrument noise only), hence leaving significant margin for additional errors.
Jonas Simon Wilzewski, Anke Roiger, Johan Strandgren, Jochen Landgraf, Dietrich G. Feist, Voltaire A. Velazco, Nicholas M. Deutscher, Isamu Morino, Hirofumi Ohyama, Yao Té, Rigel Kivi, Thorsten Warneke, Justus Notholt, Manvendra Dubey, Ralf Sussmann, Markus Rettinger, Frank Hase, Kei Shiomi, and André Butz
Atmos. Meas. Tech., 13, 731–745, https://doi.org/10.5194/amt-13-731-2020, https://doi.org/10.5194/amt-13-731-2020, 2020
Short summary
Short summary
Through spectral degradation of GOSAT measurements in the 1.6 and 2.0 μm spectral bands, we mimic a single-band, passive satellite sensor for monitoring of CO2 emissions at fine spatial scales. We compare retrievals of XCO2 from these bands to TCCON and native GOSAT retrievals. At spectral resolutions near 1.3 nm, XCO2 retrievals from both bands show promising performance, but the 2.0 μm band is favorable due to better noise performance and the potential to retrieve some aerosol information.
Johan Strandgren, Luca Bugliaro, Frank Sehnke, and Leon Schröder
Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, https://doi.org/10.5194/amt-10-3547-2017, 2017
Short summary
Short summary
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque pixels and retrieves the corresponding optical thickness, cloud top height and ice water path from the geostationary imager MSG/SEVIRI. CiPS utilises a set of four artificial neural networks trained with space-borne lidar data, thermal MSG/SEVIRI observations, model data and auxiliary data.
To demonstrate the capabilities of CiPS, the life cycle of a thin cirrus cloud is analysed.
Matthieu Plu, Guillaume Bigeard, Bojan Sič, Emanuele Emili, Luca Bugliaro, Laaziz El Amraoui, Jonathan Guth, Beatrice Josse, Lucia Mona, and Dennis Piontek
Nat. Hazards Earth Syst. Sci., 21, 3731–3747, https://doi.org/10.5194/nhess-21-3731-2021, https://doi.org/10.5194/nhess-21-3731-2021, 2021
Short summary
Short summary
Volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, may have huge economic consequences due to flight cancellations. In this article, we demonstrate the benefits of source term improvement and of data assimilation for quantifying volcanic ash concentrations. The work, which was supported by the EUNADICS-AV project, is the first one, to our knowledge, that demonstrates the benefit of the assimilation of ground-based lidar data over Europe during an eruption.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Matthieu Plu, Barbara Scherllin-Pirscher, Delia Arnold Arias, Rocio Baro, Guillaume Bigeard, Luca Bugliaro, Ana Carvalho, Laaziz El Amraoui, Kurt Eschbacher, Marcus Hirtl, Christian Maurer, Marie D. Mulder, Dennis Piontek, Lennart Robertson, Carl-Herbert Rokitansky, Fritz Zobl, and Raimund Zopp
Nat. Hazards Earth Syst. Sci., 21, 2973–2992, https://doi.org/10.5194/nhess-21-2973-2021, https://doi.org/10.5194/nhess-21-2973-2021, 2021
Short summary
Short summary
Past volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, forced the cancellation of thousands of flights and had huge economic consequences.
In this article, an international team in the H2020 EU-funded EUNADICS-AV project has designed a probabilistic model approach to quantify ash concentrations. This approach is evaluated against measurements, and its potential use to mitigate the impact of future large-scale eruptions is discussed.
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. Discuss., https://doi.org/10.5194/nhess-2021-270, https://doi.org/10.5194/nhess-2021-270, 2021
Preprint under review for NHESS
Short summary
Short summary
The dispersion of ash 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 enables to monitor the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This spaceborne retrieval, based on realistic synthetic observations, runs operationally at the German Weather Service.
Ulrich Schumann, Ian Poll, Roger Teoh, Rainer Koelle, Enrico Spinielli, Jarlath Molloy, George S. Koudis, Robert Baumann, Luca Bugliaro, Marc Stettler, and Christiane Voigt
Atmos. Chem. Phys., 21, 7429–7450, https://doi.org/10.5194/acp-21-7429-2021, https://doi.org/10.5194/acp-21-7429-2021, 2021
Short summary
Short summary
The roughly 70 % reduction of air traffic during the COVID-19 pandemic from March–August 2020 compared to 2019 provides a test case for the relationship between air traffic density, contrails, and their radiative forcing of climate change. This paper investigates the induced traffic and contrail changes in a model study. Besides strong weather changes, the model results indicate aviation-induced cirrus and top-of-the-atmosphere irradiance changes, which can be tested with observations.
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
Short summary
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Johan Strandgren, David Krutz, Jonas Wilzewski, Carsten Paproth, Ilse Sebastian, Kevin R. Gurney, Jianming Liang, Anke Roiger, and André Butz
Atmos. Meas. Tech., 13, 2887–2904, https://doi.org/10.5194/amt-13-2887-2020, https://doi.org/10.5194/amt-13-2887-2020, 2020
Short summary
Short summary
This paper presents the concept of a spaceborne imaging spectrometer targeting the routine monitoring of CO2 emissions from localized point sources down to an emission strength of about 1 Mt CO2 yr-1. Using high-resolution CO2 emission and albedo data, it is shown that CO2 plumes from point sources with an emission strength down to the order of 0.3 Mt CO2 yr-1 can be resolved in an urban environment (when limited by instrument noise only), hence leaving significant margin for additional errors.
Jonas Simon Wilzewski, Anke Roiger, Johan Strandgren, Jochen Landgraf, Dietrich G. Feist, Voltaire A. Velazco, Nicholas M. Deutscher, Isamu Morino, Hirofumi Ohyama, Yao Té, Rigel Kivi, Thorsten Warneke, Justus Notholt, Manvendra Dubey, Ralf Sussmann, Markus Rettinger, Frank Hase, Kei Shiomi, and André Butz
Atmos. Meas. Tech., 13, 731–745, https://doi.org/10.5194/amt-13-731-2020, https://doi.org/10.5194/amt-13-731-2020, 2020
Short summary
Short summary
Through spectral degradation of GOSAT measurements in the 1.6 and 2.0 μm spectral bands, we mimic a single-band, passive satellite sensor for monitoring of CO2 emissions at fine spatial scales. We compare retrievals of XCO2 from these bands to TCCON and native GOSAT retrievals. At spectral resolutions near 1.3 nm, XCO2 retrievals from both bands show promising performance, but the 2.0 μm band is favorable due to better noise performance and the potential to retrieve some aerosol information.
Johan Strandgren, Luca Bugliaro, Frank Sehnke, and Leon Schröder
Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, https://doi.org/10.5194/amt-10-3547-2017, 2017
Short summary
Short summary
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque pixels and retrieves the corresponding optical thickness, cloud top height and ice water path from the geostationary imager MSG/SEVIRI. CiPS utilises a set of four artificial neural networks trained with space-borne lidar data, thermal MSG/SEVIRI observations, model data and auxiliary data.
To demonstrate the capabilities of CiPS, the life cycle of a thin cirrus cloud is analysed.
Tobias Sirch, Luca Bugliaro, Tobias Zinner, Matthias Möhrlein, and Margarita Vazquez-Navarro
Atmos. Meas. Tech., 10, 409–429, https://doi.org/10.5194/amt-10-409-2017, https://doi.org/10.5194/amt-10-409-2017, 2017
Short summary
Short summary
A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the geostationary satellite MSG is presented. The basis of the algorithm is an optical flow method to derive cloud motion vectors for low and high level clouds separately. DNI is calculated from the forecasted optical thickness of the clouds. Validation against MSG observations shows good performance: compared to persistence an improvement of forecast horizon by a factor of 2 is reached for 2 h forecasts.
Tobias Zinner, Petra Hausmann, Florian Ewald, Luca Bugliaro, Claudia Emde, and Bernhard Mayer
Atmos. Meas. Tech., 9, 4615–4632, https://doi.org/10.5194/amt-9-4615-2016, https://doi.org/10.5194/amt-9-4615-2016, 2016
Short summary
Short summary
A new retrieval of optical thickness and effective particle size of ice clouds over a wide range of optical thickness from transmittance measurements is presented. A visible range spectral slope is used to resolve the transmittance optical thickness ambiguity. Retrieval sensitivity to ice crystal habit, aerosol, albedo, sensor accuracy and lookup table interpolation is presented as well as an application of the method and comparison to satellite products for 2 days.
Claudia Emde, Robert Buras-Schnell, Arve Kylling, Bernhard Mayer, Josef Gasteiger, Ulrich Hamann, Jonas Kylling, Bettina Richter, Christian Pause, Timothy Dowling, and Luca Bugliaro
Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, https://doi.org/10.5194/gmd-9-1647-2016, 2016
Short summary
Short summary
libradtran is a widely used software package for radiative transfer calculations. It allows one to compute (polarized) radiances, irradiance, and actinic fluxes in the solar and thermal spectral regions. This paper gives an overview of libradtran version 2.0 with focus on new features (e.g. polarization, Raman scattering, absorption parameterization, cloud and aerosol optical properties). libRadtran is freely available at http://www.libradtran.org.
S. Kox, L. Bugliaro, and A. Ostler
Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, https://doi.org/10.5194/amt-7-3233-2014, 2014
U. Hamann, A. Walther, B. Baum, R. Bennartz, L. Bugliaro, M. Derrien, P. N. Francis, A. Heidinger, S. Joro, A. Kniffka, H. Le Gléau, M. Lockhoff, H.-J. Lutz, J. F. Meirink, P. Minnis, R. Palikonda, R. Roebeling, A. Thoss, S. Platnick, P. Watts, and G. Wind
Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, https://doi.org/10.5194/amt-7-2839-2014, 2014
B. Reinhardt, R. Buras, L. Bugliaro, S. Wilbert, and B. Mayer
Atmos. Meas. Tech., 7, 823–838, https://doi.org/10.5194/amt-7-823-2014, https://doi.org/10.5194/amt-7-823-2014, 2014
J.-F. Gayet, V. Shcherbakov, L. Bugliaro, A. Protat, J. Delanoë, J. Pelon, and A. Garnier
Atmos. Chem. Phys., 14, 899–912, https://doi.org/10.5194/acp-14-899-2014, https://doi.org/10.5194/acp-14-899-2014, 2014
F. Ewald, L. Bugliaro, H. Mannstein, and B. Mayer
Atmos. Meas. Tech., 6, 309–322, https://doi.org/10.5194/amt-6-309-2013, https://doi.org/10.5194/amt-6-309-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign
Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks
The effect of low-level thin arctic clouds on shortwave irradiance: evaluation of estimates from spaceborne passive imagery with aircraft observations
Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS
Dissecting effects of orbital drift of polar-orbiting satellites on accuracy and trends of climate data records of cloud fractional cover
Calibration of global MODIS cloud amount using CALIOP cloud profiles
Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products
An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans
Comparing lightning observations of the ground-based European lightning location system EUCLID and the space-based Lightning Imaging Sensor (LIS) on the International Space Station (ISS)
Microwave and submillimeter wave scattering of oriented ice particles
Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images
Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models
Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar
Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in northern Europe
The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations
Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks
Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?
Calibration of a 35 GHz airborne cloud radar: lessons learned and intercomparisons with 94 GHz cloud radars
Airborne validation of radiative transfer modelling of ice clouds at millimetre and sub-millimetre wavelengths
Assessing the impact of different liquid water permittivity models on the fit between model and observations
Cloud liquid water path in the sub-Arctic region of Europe as derived from ground-based and space-borne remote observations
Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements
Cloud heterogeneity on cloud and aerosol above cloud properties retrieved from simulated total and polarized reflectances
Orographic and convective gravity waves above the Alps and Andes Mountains during GPS radio occultation events – a case study
Neural network cloud top pressure and height for MODIS
Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2
Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements
Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record
Analysis of lightning outliers in the EUCLID network
Analysis and evaluation of WRF microphysical schemes for deep moist convection over south-eastern South America (SESA) using microwave satellite observations and radiative transfer simulations
Remote sensing of multiple cloud layer heights using multi-angular measurements
Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS collections 5.1 and 6 over global oceans
In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data
Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS
Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth
Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland
Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data
Next-generation angular distribution models for top-of-atmosphere radiative flux calculation from CERES instruments: validation
Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans
Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study
Meso-scale modelling and radiative transfer simulations of a snowfall event over France at microwaves for passive and active modes and evaluation with satellite observations
Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms
Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements
Characteristics of cloud liquid water path from SEVIRI onboard the Meteosat Second Generation 2 satellite for several cloud types
Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM
Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa
On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset
Depolarization ratio of polar stratospheric clouds in coastal Antarctica: comparison analysis between ground-based Micro Pulse Lidar and space-borne CALIOP observations
An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS
Applying spaceborne reflectivity measurements for calculation of the solar ultraviolet radiation at ground level
David Painemal, Douglas Spangenberg, William L. Smith Jr., Patrick Minnis, Brian Cairns, Richard H. Moore, Ewan Crosbie, Claire Robinson, Kenneth L. Thornhill, Edward L. Winstead, and Luke Ziemba
Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, https://doi.org/10.5194/amt-14-6633-2021, 2021
Short summary
Short summary
Cloud properties derived from satellite sensors are critical for the global monitoring of climate. This study evaluates satellite-based cloud properties over the North Atlantic using airborne data collected during NAAMES. Satellite observations of droplet size and cloud optical depth tend to compare well with NAAMES data. The analysis indicates that the satellite pixel resolution and the specific viewing geometry need to be taken into account in research applications.
Charles H. White, Andrew K. Heidinger, and Steven A. Ackerman
Atmos. Meas. Tech., 14, 3371–3394, https://doi.org/10.5194/amt-14-3371-2021, https://doi.org/10.5194/amt-14-3371-2021, 2021
Short summary
Short summary
Automated detection of clouds in satellite imagery is an important practice that is useful for predicting and understanding both weather and climate. Cloud detection is often difficult at night and over cold surfaces. In this paper, we discuss how a complex statistical model (a neural network) can more accurately detect clouds compared to currently used approaches. Overall, our results suggest that our approach could result in more reliable assessments of global cloud cover.
Hong Chen, Sebastian Schmidt, Michael D. King, Galina Wind, Anthony Bucholtz, Elizabeth A. Reid, Michal Segal-Rozenhaimer, William L. Smith, Patrick C. Taylor, Seiji Kato, and Peter Pilewskie
Atmos. Meas. Tech., 14, 2673–2697, https://doi.org/10.5194/amt-14-2673-2021, https://doi.org/10.5194/amt-14-2673-2021, 2021
Short summary
Short summary
In this paper, we accessed the shortwave irradiance derived from MODIS cloud optical properties by using aircraft measurements. We developed a data aggregation technique to parameterize spectral surface albedo by snow fraction in the Arctic. We found that undetected clouds have the most significant impact on the imagery-derived irradiance. This study suggests that passive imagery cloud detection could be improved through a multi-pixel approach that would make it more dependable in the Arctic.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
Short summary
Short summary
The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Jędrzej S. Bojanowski and Jan P. Musiał
Atmos. Meas. Tech., 13, 6771–6788, https://doi.org/10.5194/amt-13-6771-2020, https://doi.org/10.5194/amt-13-6771-2020, 2020
Short summary
Short summary
Satellites such as NOAA's Advanced Very High Resolution Radiometer can uniquely observe changes in cloud cover but are affected by orbital drift that results in shifted image acquisition times, which in turn lead to spurious trends in cloud cover detected during climatological analyses. Providing a detailed quantification of these trends, we show that climate data records must be analysed with caution, as for some periods and regions they do not comply with the requirements for climate data.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 13, 4995–5012, https://doi.org/10.5194/amt-13-4995-2020, https://doi.org/10.5194/amt-13-4995-2020, 2020
Short summary
Short summary
This paper evaluates the operational approach for producing global (Level 3) cloud amount based on MODIS cloud masks (Level 2). Using CALIPSO we calculate the actual cloud fractions for each cloud mask category, which are 21.5 %, 27.7 %, 66.6 %, and 94.7 % instead of assumed 0 %, 0 %, 100 %, and 100 %. Consequently we find the operational procedure unreliable, especially on a regional/local scale. A method of how to correct and calibrate MODIS global data using CALIPSO detections is suggested.
Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, https://doi.org/10.5194/amt-13-3263-2020, 2020
Short summary
Short summary
Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds, and this paper presents an evaluation of this algorithm by comparing it with
other instruments.
Alexis Hunzinger, Joseph C. Hardin, Nitin Bharadwaj, Adam Varble, and Alyssa Matthews
Atmos. Meas. Tech., 13, 3147–3166, https://doi.org/10.5194/amt-13-3147-2020, https://doi.org/10.5194/amt-13-3147-2020, 2020
Short summary
Short summary
The calibration of weather radars is one of the most dominant sources of errors hindering their use. This work takes a technique for tracking the changes in radar calibration using the radar clutter from the ground and extends it to higher-frequency research radars. It demonstrates that after modifications the technique is successful but that special care needs to be taken in its application at high frequencies. The technique is verified using data from multiple DOE ARM field campaigns.
Dieter R. Poelman and Wolfgang Schulz
Atmos. Meas. Tech., 13, 2965–2977, https://doi.org/10.5194/amt-13-2965-2020, https://doi.org/10.5194/amt-13-2965-2020, 2020
Short summary
Short summary
The objective of this work is to quantify the similarities and contrasts between the lightning observations from the Lightning Imaging Sensor (LIS) on the International Space Station (ISS) and the ground-based European Cooperation for Lightning Detection (EUCLID) network. This work is timely, given that the Meteosat Third Generation (MTG), which has a lightning imager (LI) on board, is going to be launched in 2 years.
Manfred Brath, Robin Ekelund, Patrick Eriksson, Oliver Lemke, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, https://doi.org/10.5194/amt-13-2309-2020, 2020
Short summary
Short summary
Microwave dual-polarization observations consistently show that larger atmospheric ice particles tend to have a preferred orientation. We provide a publicly available database of microwave and submillimeter wave scattering properties of oriented ice particles based on discrete dipole approximation scattering calculations. Detailed radiative transfer simulations, recreating observed polarization patterns, are additionally presented in this study.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Robin Ekelund, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 13, 501–520, https://doi.org/10.5194/amt-13-501-2020, https://doi.org/10.5194/amt-13-501-2020, 2020
Short summary
Short summary
Atmospheric ice particles (e.g. snow and ice crystals) are an important part of weather, climate, and the hydrological cycle. This study investigates whether combined satellite measurements by radar and radiometers at microwave wavelengths can be used to find the most likely shape of such ice particles. The method was limited when using only currently operating sensors (CloudSat radar and the GPM Microwave Imager) but shows promise if the upcoming Ice Cloud Imager is also considered.
Juan Huo, Daren Lu, Shu Duan, Yongheng Bi, and Bo Liu
Atmos. Meas. Tech., 13, 1–11, https://doi.org/10.5194/amt-13-1-2020, https://doi.org/10.5194/amt-13-1-2020, 2020
Short summary
Short summary
Cloud top height (CTH) is one of the important cloud parameters providing information about the vertical structure of cloud water content. To better understand the accuracy of CTH derived from passive satellite data, 2 years of ground-based Ka-band radar measurements are compared with CTH inferred from Terra/Aqua MODIS and Himawari AHI. It is found that MODIS and AHI underestimate CTH relative to radar by −1.10 km. Both MODIS and AHI CTH retrieval accuracy depend strongly on cloud depth.
Vladimir S. Kostsov, Anke Kniffka, Martin Stengel, and Dmitry V. Ionov
Atmos. Meas. Tech., 12, 5927–5946, https://doi.org/10.5194/amt-12-5927-2019, https://doi.org/10.5194/amt-12-5927-2019, 2019
Short summary
Short summary
Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms. The LWP data delivered by the satellite instruments SEVIRI and AVHRR together with the data provided by the ground-based radiometer RPG-HATPRO near St. Petersburg, Russia, have been compared. Our study revealed considerable differences between LWP data from SEVIRI and AVHRR in winter over ice-covered relatively small water bodies in this region.
Jonathan K. P. Shonk, Jui-Yuan Christine Chiu, Alexander Marshak, David M. Giles, Chiung-Huei Huang, Gerald G. Mace, Sally Benson, Ilya Slutsker, and Brent N. Holben
Atmos. Meas. Tech., 12, 5087–5099, https://doi.org/10.5194/amt-12-5087-2019, https://doi.org/10.5194/amt-12-5087-2019, 2019
Short summary
Short summary
Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.
Chaojun Shi, Yatong Zhou, Bo Qiu, Jingfei He, Mu Ding, and Shiya Wei
Atmos. Meas. Tech., 12, 4713–4724, https://doi.org/10.5194/amt-12-4713-2019, https://doi.org/10.5194/amt-12-4713-2019, 2019
Short summary
Short summary
Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. We propose a new automatic cloud segmentation algorithm to segment cloud pixels from diurnal and nocturnal ASI images called an enhancement fully convolutional network (EFCN). Experiments showed that the proposed EFCN was much more accurate in cloud segmentation for diurnal and nocturnal ASI images.
Maximilian Maahn, Fabian Hoffmann, Matthew D. Shupe, Gijs de Boer, Sergey Y. Matrosov, and Edward P. Luke
Atmos. Meas. Tech., 12, 3151–3171, https://doi.org/10.5194/amt-12-3151-2019, https://doi.org/10.5194/amt-12-3151-2019, 2019
Short summary
Short summary
Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Here, we present three novel methods for calibrating vertically pointing cloud radars. These calibration methods are based on microphysical processes of liquid clouds, such as the transition of cloud droplets to drizzle drops. We successfully apply the methods to cloud radar data from the North Slope of Alaska (NSA) and Oliktok Point (OLI) ARM sites.
Florian Ewald, Silke Groß, Martin Hagen, Lutz Hirsch, Julien Delanoë, and Matthias Bauer-Pfundstein
Atmos. Meas. Tech., 12, 1815–1839, https://doi.org/10.5194/amt-12-1815-2019, https://doi.org/10.5194/amt-12-1815-2019, 2019
Short summary
Short summary
This study gives a summary of lessons learned during the absolute calibration of the airborne, high-power Ka-band cloud radar HAMP MIRA on board the German research aircraft HALO. The first part covers the internal calibration of the instrument where individual instrument components are characterized in the laboratory. In the second part, the internal calibration is validated with external reference sources like the ocean surface backscatter and different air- and spaceborne cloud radars.
Stuart Fox, Jana Mendrok, Patrick Eriksson, Robin Ekelund, Sebastian J. O'Shea, Keith N. Bower, Anthony J. Baran, R. Chawn Harlow, and Juliet C. Pickering
Atmos. Meas. Tech., 12, 1599–1617, https://doi.org/10.5194/amt-12-1599-2019, https://doi.org/10.5194/amt-12-1599-2019, 2019
Short summary
Short summary
Airborne observations of ice clouds are used to validate radiative transfer simulations using a state-of-the-art database of cloud ice optical properties. Simulations at these wavelengths are required to make use of future satellite instruments such as the Ice Cloud Imager. We show that they can generally reproduce observed cloud signals, but for a given total ice mass there is considerable sensitivity to the cloud microphysics, including the particle shape and distribution of ice mass.
Katrin Lonitz and Alan J. Geer
Atmos. Meas. Tech., 12, 405–429, https://doi.org/10.5194/amt-12-405-2019, https://doi.org/10.5194/amt-12-405-2019, 2019
Short summary
Short summary
Permittivity models for microwave frequencies of liquid water below 0°C are poorly constrained due to limited laboratory experiments and observations, especially for high microwave frequencies. This uncertainty translates directly into errors in retrieved liquid water paths of up to 80 %. This study investigates the effect of different liquid water permittivity models including models based on the most recent observations.
Vladimir S. Kostsov, Anke Kniffka, and Dmitry V. Ionov
Atmos. Meas. Tech., 11, 5439–5460, https://doi.org/10.5194/amt-11-5439-2018, https://doi.org/10.5194/amt-11-5439-2018, 2018
Short summary
Short summary
Clouds are a very important component of the climate system and of the hydrological cycle in the Arctic and sub-Arctic. A joint analysis of the cloud parameters obtained remotely from satellite and ground-based observations near St Petersburg, Russia, has been made. Our study has revealed considerable differences between the cloud properties over land and over water areas in the region under investigation.
Fanny Jeanneret, Giovanni Martucci, Simon Pinnock, and Alexis Berne
Atmos. Meas. Tech., 11, 4153–4170, https://doi.org/10.5194/amt-11-4153-2018, https://doi.org/10.5194/amt-11-4153-2018, 2018
Short summary
Short summary
Above mountainous regions, satellites may have difficulty in discriminating snow from clouds: this study proposes a new method that combines different ground-based measurements to assess the sky cloudiness with high temporal resolution. The method's output is used as input to a model capable of identifying false satellite cloud detections. Results show that 62 ± 13 % of these false detections can be identified by the model when applied to the AVHRR-PM and MODIS Aqua data sets of the Cloud_cci.
Céline Cornet, Laurent C.-Labonnote, Fabien Waquet, Frédéric Szczap, Lucia Deaconu, Frédéric Parol, Claudine Vanbauce, François Thieuleux, and Jérôme Riédi
Atmos. Meas. Tech., 11, 3627–3643, https://doi.org/10.5194/amt-11-3627-2018, https://doi.org/10.5194/amt-11-3627-2018, 2018
Short summary
Short summary
Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo of the cloudy scenes, effective radius and variance of the cloud droplet size distribution, cloud top pressure and aerosol above cloud are analyzed.
Rodrigo Hierro, Andrea K. Steiner, Alejandro de la Torre, Peter Alexander, Pablo Llamedo, and Pablo Cremades
Atmos. Meas. Tech., 11, 3523–3539, https://doi.org/10.5194/amt-11-3523-2018, https://doi.org/10.5194/amt-11-3523-2018, 2018
Short summary
Short summary
This paper analyzed the collocated GPS radio occultation profiles near the convective systems identified from ISCCP over two orographic regions of the Alps and Andes. Gravity wave (GW) analysis over both selected regions was also carried out. The gravity wave signature from the two case studies were investigated using mesoscale WRF simulations, ERA-Interim reanalysis data, and measured RO temperature profiles. The absence of fronts or jets during both case studies reveals similar relevant GWs.
Nina Håkansson, Claudia Adok, Anke Thoss, Ronald Scheirer, and Sara Hörnquist
Atmos. Meas. Tech., 11, 3177–3196, https://doi.org/10.5194/amt-11-3177-2018, https://doi.org/10.5194/amt-11-3177-2018, 2018
Short summary
Short summary
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS is presented. It uses artificial neural networks and reduces the mean absolute error by 32 % compared to two other operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies is possible.
Yu Oishi, Haruma Ishida, Takashi Y. Nakajima, Ryosuke Nakamura, and Tsuneo Matsunaga
Atmos. Meas. Tech., 11, 2863–2878, https://doi.org/10.5194/amt-11-2863-2018, https://doi.org/10.5194/amt-11-2863-2018, 2018
Short summary
Short summary
Preparations are continuing for the launch of the Greenhouse Gases Observing Satellite 2 (GOSAT-2) in the fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing cloud discrimination algorithm. In this paper we showed a new cloud discrimination algorithm of pre-launch version for GOSAT-2, and compared the existing algorithm with the new algorithm.
Alessandro Damiani, Hitoshi Irie, Takashi Horio, Tamio Takamura, Pradeep Khatri, Hideaki Takenaka, Takashi Nagao, Takashi Y. Nakajima, and Raul R. Cordero
Atmos. Meas. Tech., 11, 2501–2521, https://doi.org/10.5194/amt-11-2501-2018, https://doi.org/10.5194/amt-11-2501-2018, 2018
Short summary
Short summary
The Tohoku Earthquake of March 2011 stressed the need for energy source diversity, and the governmental policy in Japan has been stimulating a broader use of
renewable energy. Solar power is potentially able to mitigate climate change triggered by greenhouse gas emissions, but its instability caused by cloudiness
is a critical issue for suppliers. To develop an appropriate control system, surface solar radiation data must be made available as accurately as possible.
Karl-Göran Karlsson and Nina Håkansson
Atmos. Meas. Tech., 11, 633–649, https://doi.org/10.5194/amt-11-633-2018, https://doi.org/10.5194/amt-11-633-2018, 2018
Short summary
Short summary
Data from the high-sensitivity CALIOP cloud lidar onboard the CALIPSO satellite have been used to evaluate cloud amounts estimated from satellite imagery and, specifically, from the climate data record CLARA-A2. The main purpose has been to study the limit of how thin clouds that can be detected efficiently (i.e., detected at the 50 % level) in CLARA-A2 data and how this limit varies globally. The study revealed very large geographical differences in the cloud detection efficiency.
Dieter R. Poelman, Wolfgang Schulz, Rudolf Kaltenboeck, and Laurent Delobbe
Atmos. Meas. Tech., 10, 4561–4572, https://doi.org/10.5194/amt-10-4561-2017, https://doi.org/10.5194/amt-10-4561-2017, 2017
Short summary
Short summary
Lightning data as observed by the European Cooperation for Lightning Detection network EUCLID are used in combination with radar data to retrieve the temporal and spatial behavior of lightning outliers, i.e. discharges located in a wrong place, over a 5-year period from 2011 to 2016 in Belgium and Austria.
Victoria Sol Galligani, Die Wang, Milagros Alvarez Imaz, Paola Salio, and Catherine Prigent
Atmos. Meas. Tech., 10, 3627–3649, https://doi.org/10.5194/amt-10-3627-2017, https://doi.org/10.5194/amt-10-3627-2017, 2017
Short summary
Short summary
Three meteorological events with deep convection and severe weather, characteristic of the SESA region, are considered. High-resolution models, a powerful tool to study convection, can be operated with different microphysics schemes (predict the development of hydrometeors, their interactions, growth, precipitation). We present a systematic evaluation of the microphysical schemes available in the WRF model by a direct comparison between satellite-based simulated and observed microwave radiances.
Kenneth Sinclair, Bastiaan van Diedenhoven, Brian Cairns, John Yorks, Andrzej Wasilewski, and Matthew McGill
Atmos. Meas. Tech., 10, 2361–2375, https://doi.org/10.5194/amt-10-2361-2017, https://doi.org/10.5194/amt-10-2361-2017, 2017
Short summary
Short summary
We present a multi-angular contrast approach to retrieve cloud top height (CTH) using photogrammetry. We demonstrate the method’s ability to retrieve heights of multiple cloud layers within single footprints, using the multiple views available for each footprint. This paper provides an in-depth description and performance analysis of the CTH retrieval technique and the retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar.
John Rausch, Kerry Meyer, Ralf Bennartz, and Steven Platnick
Atmos. Meas. Tech., 10, 2105–2116, https://doi.org/10.5194/amt-10-2105-2017, https://doi.org/10.5194/amt-10-2105-2017, 2017
Short summary
Short summary
This paper documents the observed differences in the aggregated (Level-3) cloud droplet effective radius and droplet number concentration estimates inferred from the Aqua–MODIS cloud product collections 5.1 and 6 for warm oceanic cloud scenes over the year 2008. We note significant differences in effective radius and droplet concentration between the two products and discuss the algorithmic and calibration changes which may contribute to observed results.
Holger Sihler, Peter Lübcke, Rüdiger Lang, Steffen Beirle, Martin de Graaf, Christoph Hörmann, Johannes Lampel, Marloes Penning de Vries, Julia Remmers, Ed Trollope, Yang Wang, and Thomas Wagner
Atmos. Meas. Tech., 10, 881–903, https://doi.org/10.5194/amt-10-881-2017, https://doi.org/10.5194/amt-10-881-2017, 2017
Short summary
Short summary
This paper presents the independent and simple IFR method to retrieve the FOV of an instrument, i.e. the two-dimensional sensitivity distribution. IFR relies on correlated measurements featuring a higher spatial resolution and was applied to two satellite instruments, GOME-2 and OMI, and a DOAS instrument integrated in an SO2 camera. Our results confirm the commonly applied FOV distributions. IFR is applicable for verification exercises as well as degradation monitoring in the field.
Frank Werner, Galina Wind, Zhibo Zhang, Steven Platnick, Larry Di Girolamo, Guangyu Zhao, Nandana Amarasinghe, and Kerry Meyer
Atmos. Meas. Tech., 9, 5869–5894, https://doi.org/10.5194/amt-9-5869-2016, https://doi.org/10.5194/amt-9-5869-2016, 2016
Short summary
Short summary
A research–level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. This yields reliable estimates of important cloud variables at a horizontal resolution of 30 m. Comparisons of the ASTER retrieval results with the operational cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) show a high agreement for 48 example cloud fields.
Felipe A. Mejia, Ben Kurtz, Keenan Murray, Laura M. Hinkelman, Manajit Sengupta, Yu Xie, and Jan Kleissl
Atmos. Meas. Tech., 9, 4151–4165, https://doi.org/10.5194/amt-9-4151-2016, https://doi.org/10.5194/amt-9-4151-2016, 2016
Short summary
Short summary
A method for retrieving cloud optical depth using a sky imager is presented. The method is applied to images taken at the Atmospheric Radiation Measurement site and validated against measurements from a microwave radiometer (MWR), output from the Min method for overcast skies, and τc retrieved by Beer's law from direct normal irradiance (DNI) measurements.
Moa K. Sporre, Ewan J. O'Connor, Nina Håkansson, Anke Thoss, Erik Swietlicki, and Tuukka Petäjä
Atmos. Meas. Tech., 9, 3193–3203, https://doi.org/10.5194/amt-9-3193-2016, https://doi.org/10.5194/amt-9-3193-2016, 2016
Short summary
Short summary
Satellite measurements of cloud top height and liquid water path are compared to ground-based remote sensing to evaluate the satellite retrievals. The overall performance of the satellite retrievals of cloud top height are good, but they become more problematic when several layers of clouds are present. The liquid water path retrievals also agree well, and the average differences are within the estimated measurement uncertainties.
Thomas E. Taylor, Christopher W. O'Dell, Christian Frankenberg, Philip T. Partain, Heather Q. Cronk, Andrey Savtchenko, Robert R. Nelson, Emily J. Rosenthal, Albert Y. Chang, Brenden Fisher, Gregory B. Osterman, Randy H. Pollock, David Crisp, Annmarie Eldering, and Michael R. Gunson
Atmos. Meas. Tech., 9, 973–989, https://doi.org/10.5194/amt-9-973-2016, https://doi.org/10.5194/amt-9-973-2016, 2016
Short summary
Short summary
NASA's Orbiting Carbon Observatory-2 (OCO-2) is providing approximately 1 million soundings per day of the total column of carbon dioxide (XCO2). The retrieval of XCO2 can only be performed for soundings sufficiently free of cloud and aerosol. This work highlights comparisons of OCO-2 cloud screening algorithms to the MODIS cloud mask product. We find agreement approximately 85 % of the time with some significant spatial and small seasonal dependencies.
W. Su, J. Corbett, Z. Eitzen, and L. Liang
Atmos. Meas. Tech., 8, 3297–3313, https://doi.org/10.5194/amt-8-3297-2015, https://doi.org/10.5194/amt-8-3297-2015, 2015
A. Garnier, J. Pelon, M. A. Vaughan, D. M. Winker, C. R. Trepte, and P. Dubuisson
Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, https://doi.org/10.5194/amt-8-2759-2015, 2015
Short summary
Short summary
Cloud absorption optical depths retrieved at 12.05 microns are compared to extinction optical depths retrieved at 0.532 microns from perfectly co-located observations of single-layered semi-transparent cirrus over oceans made by the space-borne CALIPSO IIR infrared radiometer and CALIOP lidar. A new relationship describing the temperature-dependent effect of multiple scattering in the CALIOP retrievals is derived and discussed.
A. Werkmeister, M. Lockhoff, M. Schrempf, K. Tohsing, B. Liley, and G. Seckmeyer
Atmos. Meas. Tech., 8, 2001–2015, https://doi.org/10.5194/amt-8-2001-2015, https://doi.org/10.5194/amt-8-2001-2015, 2015
V. S. Galligani, C. Prigent, E. Defer, C. Jimenez, P. Eriksson, J.-P. Pinty, and J.-P. Chaboureau
Atmos. Meas. Tech., 8, 1605–1616, https://doi.org/10.5194/amt-8-1605-2015, https://doi.org/10.5194/amt-8-1605-2015, 2015
U. Hamann, A. Walther, B. Baum, R. Bennartz, L. Bugliaro, M. Derrien, P. N. Francis, A. Heidinger, S. Joro, A. Kniffka, H. Le Gléau, M. Lockhoff, H.-J. Lutz, J. F. Meirink, P. Minnis, R. Palikonda, R. Roebeling, A. Thoss, S. Platnick, P. Watts, and G. Wind
Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, https://doi.org/10.5194/amt-7-2839-2014, 2014
P. Wang and P. Stammes
Atmos. Meas. Tech., 7, 1331–1350, https://doi.org/10.5194/amt-7-1331-2014, https://doi.org/10.5194/amt-7-1331-2014, 2014
A. Kniffka, M. Stengel, M. Lockhoff, R. Bennartz, and R. Hollmann
Atmos. Meas. Tech., 7, 887–905, https://doi.org/10.5194/amt-7-887-2014, https://doi.org/10.5194/amt-7-887-2014, 2014
D. Merk and T. Zinner
Atmos. Meas. Tech., 6, 1903–1918, https://doi.org/10.5194/amt-6-1903-2013, https://doi.org/10.5194/amt-6-1903-2013, 2013
T. Zinner, C. Forster, E. de Coning, and H.-D. Betz
Atmos. Meas. Tech., 6, 1567–1583, https://doi.org/10.5194/amt-6-1567-2013, https://doi.org/10.5194/amt-6-1567-2013, 2013
K.-G. Karlsson and E. Johansson
Atmos. Meas. Tech., 6, 1271–1286, https://doi.org/10.5194/amt-6-1271-2013, https://doi.org/10.5194/amt-6-1271-2013, 2013
C. Córdoba-Jabonero, J. L. Guerrero-Rascado, D. Toledo, M. Parrondo, M. Yela, M. Gil, and H. A. Ochoa
Atmos. Meas. Tech., 6, 703–717, https://doi.org/10.5194/amt-6-703-2013, https://doi.org/10.5194/amt-6-703-2013, 2013
F. Ewald, L. Bugliaro, H. Mannstein, and B. Mayer
Atmos. Meas. Tech., 6, 309–322, https://doi.org/10.5194/amt-6-309-2013, https://doi.org/10.5194/amt-6-309-2013, 2013
P. N. den Outer, A. van Dijk, H. Slaper, A. V. Lindfors, H. De Backer, A. F. Bais, U. Feister, T. Koskela, and W. Josefsson
Atmos. Meas. Tech., 5, 3041–3054, https://doi.org/10.5194/amt-5-3041-2012, https://doi.org/10.5194/amt-5-3041-2012, 2012
Cited articles
Ackerman, S. A., Smith, W. L., Revercomb, H. E., and Spinhirne, J. D.: The 27–28 October 1986 FIRE IFO cirrus case study: spectral properties of cirrus clouds in the 8–12 µm window, Mon. Weather Rev., 118, 2377–2388, https://doi.org/10.1175/1520-0493(1990)118<2377:TOFICC>2.0.CO;2, 1990.
Bugliaro, L., Zinner, T., Keil, C., Mayer, B., Hollmann, R., Reuter, M., and Thomas, W.: Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI, Atmos. Chem. Phys., 11, 5603–5624, https://doi.org/10.5194/acp-11-5603-2011, 2011.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.01, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-01_L2-003.01, 2015a.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.02, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-02_L2-003.02, 2015b.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Aerosol Layer Data, version 3.01, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Prov-V3-01_L2-003.01, 2015c.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Aerosol Layer Data, version 3.02, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Prov-V3-02_L2-003.02, 2015d.
Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., and Pelon, J.: From CloudSat-CALIPSO to EarthCare: evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res.-Atmos., 118, 7962–7981, https://doi.org/10.1002/jgrd.50579, 2013.
Cesana, G., Chepfer, H., Winker, D., Getzewich, B., Cai, X., Jourdan, O., Mioche, G., Okamoto, H., Hagihara, Y., Noel, V., and Reverdy, M.: Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO, J. Geophys. Res., 121, 5788–5808, https://doi.org/10.1002/2015JD024334, 2015JD024334, 2016.
Delanoë, J. and Hogan, R. J.: A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer, J. Geophys. Res.-Atmos., 113, D07204, https://doi.org/10.1029/2007JD009000, 2008.
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res.-Atmos., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010.
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical composition, cloud and climate coupling experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res.-Atmos., 115, D00J15, https://doi.org/10.1029/2009JD013104, 2010.
De Paepe, B. and Dewitte, S.: Dust aerosol optical depth retrieval over a desert surface using the SEVIRI window channels, J. Atmos. Ocean. Tech., 26, 704–718, 2009.
DeSlover, D., Smith, W., Piironen, P., and Eloranta, E.: A methodology of measuring cirrus cloud visible-to-infrared spectral optical depth ratios, J. Atmos. Ocean. Tech., 16, 251–262, 1999.
Devasthale, A., Tjernström, M., Karlsson, K.-G., Thomas, M. A., Jones, C., Sedlar, J., and Omar, A. H.: The vertical distribution of thin features over the Arctic analysed from CALIPSO observations, Tellus B, 63, 77–85, https://doi.org/10.1111/j.1600-0889.2010.00516.x, 2011.
Donovan, D. and van Lammeren, A.: Cloud effective particle size and water content profile retrievals using combined lidar and radar observations. 1. Theory and examples, J. Geophys. Res.-Atmos., 106, 27425–27448, 2001.
Eliasson, S., Buehler, S. A., Milz, M., Eriksson, P., and John, V. O.: Assessing observed and modelled spatial distributions of ice water path using satellite data, Atmos. Chem. Phys., 11, 375–391, https://doi.org/10.5194/acp-11-375-2011, 2011.
EUMETSAT: Typical Radiometric Accuracy and Noise for MSG-1/2, available at: http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_TYP_RADIOMET_ACC_MSG-1-2&RevisionSelectionMethod=LatestReleased&Rendition=Web (last access: 19 October 2017), EUM/OPS/TEN/07/0314, 2007.
Ewald, F., Bugliaro, L., Mannstein, H., and Mayer, B.: An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS, Atmos. Meas. Tech., 6, 309–322, https://doi.org/10.5194/amt-6-309-2013, 2013.
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R., and Wang, X.: Cloud detection with MODIS. Part I: improvements in the MODIS cloud mask for collection 5, J. Atmos. Ocean. Tech., 25, 1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gao, B.-C., Han, W., Tsay, S. C., and Larsen, N. F.: Cloud detection over the Arctic region using airborne imaging spectrometer data during the daytime, J. Appl. Meteorol., 37, 1421–1429, https://doi.org/10.1175/1520-0450(1998)037<1421:CDOTAR>2.0.CO;2, 1998.
Garnier, A., Pelon, J., Dubuisson, P., Faivre, M., Chomette, O., Pascal, N., and Kratz, D. P.: Retrieval of cloud properties using CALIPSO Imaging Infrared Radiometer. Part I: Effective emissivity and optical depth, J. Appl. Meteorol. Clim., 51, 1407–1425, 2012.
Garnier, A., Pelon, J., Dubuisson, P., Yang, P., Faivre, M., Chomette, O., Pascal, N., Lucker, P., and Murray, T.: Retrieval of cloud properties using CALIPSO imaging infrared radiometer. Part II: effective diameter and ice water path, J. Appl. Meteorol. Clim., 52, 2582–2599, 2013.
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015.
Groß, S., Freudenthaler, V., Schepanski, K., Toledano, C., Schäfler, A., Ansmann, A., and Weinzierl, B.: Optical properties of long-range transported Saharan dust over Barbados as measured by dual-wavelength depolarization Raman lidar measurements, Atmos. Chem. Phys., 15, 11067–11080, https://doi.org/10.5194/acp-15-11067-2015, 2015.
Hastings, D. A. and Emery, W. J.: The advanced very high resolution radiometer (AVHRR)-A brief reference guide, Photogramm. Eng. Rem. S., 58, 1183–1188, 1992.
Heidinger, A. K., Li, Y., Baum, B. A., Holz, R. E., Platnick, S., and Yang, P.: Retrieval of cirrus cloud optical depth under day and night conditions from MODIS Collection 6 cloud property data, Remote Sens., 7, 7257–7271, 2015.
Heymsfield, A. J., Winker, D., and van Zadelhoff, G.-J.: Extinction-ice water content-effective radius algorithms for CALIPSO, Geophys. Res. Lett., 32, L10807, https://doi.org/10.1029/2005GL022742, 2005.
Holl, G., Eliasson, S., Mendrok, J., and Buehler, S. A.: SPARE-ICE: synergistic ice water path from passive operational sensors, J. Geophys. Res.-Atmos., 119, 1504–1523, https://doi.org/10.1002/2013JD020759, 2014.
Holz, R., Ackerman, S., Nagle, F., Frey, R., Dutcher, S., Kuehn, R., Vaughan, M., and Baum, B.: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res.-Atmos., 113, D00A19, https://doi.org/10.1029/2008JD009837, 2008.
Hulley, G. C., Hook, S. J., Abbott, E., Malakar, N., Islam, T., and Abrams, M.: The ASTER Global Emissivity Dataset (ASTER GED): mapping Earth's emissivity at 100 meter spatial scale, Geophys. Res. Lett., 42, 7966–7976, https://doi.org/10.1002/2015GL065564, 2015GL065564, 2015.
Inoue, T.: On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region, J. Meteorol. Soc. Jpn., 63, 88–99, 1985.
Iwabuchi, H., Saito, M., Tokoro, Y., Putri, N. S., and Sekiguchi, M.: Retrieval of radiative and microphysical properties of clouds from multispectral infrared measurements, Prog. in Earth and Planet. Sci., 3, 32, https://doi.org/10.1186/s40645-016-0108-3, 2016.
King, M. D., Kaufman, Y. J., Menzel, W. P., and Tanré, D.: Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS), IEEE T. Geosci. Remote, 30, 2–27, 1992.
Kox, S., Bugliaro, L., and Ostler, A.: Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing, Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, 2014.
Krebs, W., Mannstein, H., Bugliaro, L., and Mayer, B.: Technical note: A new day- and night-time Meteosat Second Generation Cirrus Detection Algorithm MeCiDA, Atmos. Chem. Phys., 7, 6145–6159, https://doi.org/10.5194/acp-7-6145-2007, 2007.
LeCun, Y., Denker, J. S., and Solla, S. A.: Optimal brain damage, in: Advances in Neural Information Processing Systems 2, edited by: Touretzky, D. S., 598–605, Morgan-Kaufmann, San Francisco, CA, USA, 1990.
Loveland, T. R. and Belward, A. S.: The IGBP-DIS global 1km land cover data set, DISCover: first results, Int. J. Remote Sens., 18, 3289–3295, https://doi.org/10.1080/014311697217099, 1997.
Meerkötter, R., Schumann, U., Doelling, D., Minnis, P., Nakajima, T., and Tsushima, Y.: Radiative forcing by contrails, Ann. Geophys., 17, 1080–1094, 1999.
Menzel, W., Smith, W., and Stewart, T.: Improved cloud motion wind vector and altitude assignment using VAS, J. Appl. Meteorol., 22, 377–384, 1983.
Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.: MODIS global cloud-top pressure and amount estimation: algorithm description and results, J. Appl. Meteorol. Clim., 47, 1175–1198, https://doi.org/10.1175/2007JAMC1705.1, 2008.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K. N., Xie, Y., and Yang, P.: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data – Part I: algorithms, IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011.
Minnis, P., Hong, G., Sun-Mack, S., Smith, W. L., Chen, Y., and Miller, S. D.: Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method, J. Geophys. Res.-Atmos., 121, 4907–4932, https://doi.org/10.1002/2015JD024456, 2016.
Mioche, G., Jourdan, O., Ceccaldi, M., and Delanoë, J.: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. Phys., 15, 2445–2461, https://doi.org/10.5194/acp-15-2445-2015, 2015.
Nakajima, T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Rosário, N. E., Yamasoe, M. A., Brindley, H., Eck, T. F., and Schafer, J.: Downwelling solar irradiance in the biomass burning region of the southern Amazon: dependence on aerosol intensive optical properties and role of water vapor, J. Geophys. Res.-Atmos., 116, D18304, https://doi.org/10.1029/2011JD015956, 2011.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning internal representations by error propagation, in: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, 318–362, MIT Press, Cambridge, MA, USA, 1986.
Schmetz, J., Holmlund, K., Hoffman, J., Strauss, B., Mason, B., Gaertner, V., Koch, A., and van de Berg, L.: Operational cloud-motion winds from Meteosat infrared images, J. Appl. Meteorol., 32, 1206–1225, https://doi.org/10.1175/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2, 1993.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Meteosat Second Generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002.
Schmit, T. J., Goodman, S. J., Gunshor, M. M., Sieglaff, J., Heidinger, A. K., Bachmeier, A. S., Lindstrom, S. S., Terborg, A., Feltz, J., Bah, K., Rudlosky, S., Lindsey, D. T., Rabin, R. M., and Schmidt, C. C.: Rapid refresh information of significant events: preparing users for the next generation of geostationary operational satellites, B. Am. Meteorol. Soc., 96, 561–576, 2015.
Shettle, E.: Models of aerosols, clouds and precipitation for atmospheric propagation studies, in: Atmospheric propagation in the UV, visible, IR and mm-region and related system aspects, AGARD Conf. Proc. no. 454, 15-1–15-13, Copenhagen, Denmark, 9–13 October 1989.
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA, J. Atmos. Sci., 63, 697–711, https://doi.org/10.1175/JAS3659.1, 2006.
Stengel, M., Kniffka, A., Meirink, J. F., Lockhoff, M., Tan, J., and Hollmann, R.: CLAAS: the CM SAF cloud property data set using SEVIRI, Atmos. Chem. Phys., 14, 4297–4311, https://doi.org/10.5194/acp-14-4297-2014, 2014.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and Team, T. C. S.: The CloudSat mission and the A-train, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stevens, B. and Bony, S.: What are climate models missing?, Science, 340, 1053–1054, https://doi.org/10.1126/science.1237554, 2013.
Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, 2017.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Girolamo, L. D., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.: Assessment of global cloud datasets from satellites: project and database initiated by the GEWEX radiation panel, B. Am. Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013.
Ten Hoeve, J. E., Jacobson, M. Z., and Remer, L. A.: Comparing results from a physical model with satellite and in situ observations to determine whether biomass burning aerosols over the Amazon brighten or burn off clouds, J. Geophys. Res.-Atmos., 117, D08203, https://doi.org/10.1029/2011JD016856, 2012.
Toth, T. D., Zhang, J., Campbell, J. R., Reid, J. S., Shi, Y., Johnson, R. S., Smirnov, A., Vaughan, M. A., and Winker, D. M.: Investigating enhanced Aqua MODIS aerosol optical depth retrievals over the mid-to-high latitude Southern Oceans through intercomparison with co-located CALIOP, MAN, and AERONET data sets, J. Geophys. Res.-Atmos., 118, 4700–4714, https://doi.org/10.1002/jgrd.50311, 2013.
Trigo, I. F., Peres, L. F., DaCamara, C. C., and Freitas, S. C.: Thermal land surface emissivity retrieved from SEVIRI/Meteosat, IEEE T. Geosci. Remote, 46, 307–315, https://doi.org/10.1109/TGRS.2007.905197, 2008.
Verlinde, J., Harrington, J. Y., Yannuzzi, V. T., Avramov, A., Greenberg, S., Richardson, S. J., Bahrmann, C. P., McFarquhar, G. M., Zhang, G., Johnson, N., Poellot, M. R., Mather, J. H., Turner, D. D., Eloranta, E. W., Tobin, D. C., Holz, R., Zak, B. D., Ivey, M. D., Prenni, A. J., DeMott, P. J., Daniel, J. S., Kok, G. L., Sassen, K., Spangenberg, D., Minnis, P., Tooman, T. P., Shupe, M., Heymsfield, A. J., and Schofield, R.: The mixed-phase Arctic cloud experiment, B. Am. Meteorol. Soc., 88, 205–221, https://doi.org/10.1175/BAMS-88-2-205, 2007.
Waliser, D. E., Li, J.-L. F., Woods, C. P., Austin, R. T., Bacmeister, J., Chern, J., Del Genio, A., Jiang, J. H., Kuang, Z., Meng, H., Minnis, P., Platnick, S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K., Tompkins, A. M., Vane, D. G., Walker, C., and Wu, D.: Cloud ice: a climate model challenge with signs and expectations of progress, J. Geophys. Res.-Atmos., 114, D00A21, https://doi.org/10.1029/2008JD010015, 2009.
Wang, C., Platnick, S., Zhang, Z., Meyer, K., and Yang, P.: Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content, J. Geophys. Res.-Atmos., 121, 5809–5826, https://doi.org/10.1002/2015JD024526, 2015JD024526, 2016.
Weinzierl, B., Sauer, D., Esselborn, M., Petzold, A., Veira, A., Rose, M., Mund, S., Wirth, M., Ansmann, A., Tesche, M., Gross, S., and Freudenthaler, V.: Microphysical and optical properties of dust and tropical biomass burning aerosol layers in the Cape Verde region – an overview of the airborne in situ and lidar measurements during SAMUM-2, Tellus B, 63, 589–618, https://doi.org/10.1111/j.1600-0889.2011.00566.x, 2011.
Wetzel, C. and Brümmer, B.: An Arctic inversion climatology based on the European Centre Reanalysis ERA-40, Meteorol. Z., 20, 589–600, https://doi.org/10.1127/0941-2948/2011/0295, 2011.
Wilson, L. D., Curry, J. A., and Ackerman, T. P.: Satellite retrieval of lower-tropospheric ice crystal clouds in the polar regions, J. Climate, 6, 1467–1472, https://doi.org/10.1175/1520-0442(1993)006<1467:SROLTI>2.0.CO;2, 1993.
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission: spaceborne lidar for observation of aerosols and clouds, in: Proc. SPIE, vol. 4893, Proc. SPIE 4893, Lidar Remote Sensing for Industry and Environment Monitoring III (21 March 2003), https://doi.org/10.1117/12.466539, 2003.
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. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Short summary
We characterise the the performance of a set of artificial neural networks used for the remote sensing of cirrus clouds from the geostationary Meteosat Second Generation satellites. The retrievals show little interference with the underlying land surface type as well as with possible liquid water clouds or aerosol layers below the cirrus cloud. We also characterise the retrievals as a funtion of optical thickness and top height and gain better understanding of the retrival uncertainties of CiPS
We characterise the the performance of a set of artificial neural networks used for the remote...
