Articles | Volume 16, issue 5
https://doi.org/10.5194/amt-16-1391-2023
© Author(s) 2023. 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-16-1391-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2023
Research article |
| 16 Mar 2023
| 16 Mar 2023
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Stephen W. Nesbitt
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Robert J. Trapp
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Larry Di Girolamo
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Related authors
Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
Short summary
Short summary
Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Yulan Hong and Larry Di Girolamo
Atmos. Chem. Phys., 20, 8267–8291, https://doi.org/10.5194/acp-20-8267-2020, https://doi.org/10.5194/acp-20-8267-2020, 2020
Short summary
Short summary
Cloud phase plays a crucial role in Earth radiation budget but is not well understood. Using A-Train satellite observations, this study provides climatological studies of cloud phase characteristics over Southeast Asia on multiple meteorological scales. Results show that ice, liquid, and ice over liquid clouds display distinct spatial heterogeneity and spectral radiance features. The intraseasonal and interannual behaviors of cloud phases are useful to track the MJO and ENSO.
Jui-Lin Frank Li, Mark Richardson, Wei-Liang Lee, Eric Fetzer, Graeme Stephens, Jonathan Jiang, Yulan Hong, Yi-Hui Wang, Jia-Yuh Yu, and Yinghui Liu
The Cryosphere, 13, 969–980, https://doi.org/10.5194/tc-13-969-2019, https://doi.org/10.5194/tc-13-969-2019, 2019
Short summary
Short summary
Observed summer Arctic sea ice retreat has been faster than simulated by the average CMIP5 models, most of which exclude falling ice particles from their radiative calculations.
We use controlled CESM1-CAM5 simulations to show for the first time that snowflakes' radiative effects can accelerate sea ice retreat. September retreat rates are doubled above current CO2 levels, highlighting falling ice radiative effects as a high priority for inclusion in future modelling of the Arctic.
Matthew Graber, Zhuo Wang, and Robert J. Trapp
EGUsphere, https://doi.org/10.5194/egusphere-2024-3216, https://doi.org/10.5194/egusphere-2024-3216, 2024
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
This study identifies five warm-season weather regimes (WRs) to investigate the link between tornado activity and large-scale atmospheric circulations. Certain WRs strongly affect tornado activity due to their relationship with the environment. When a WR that positively affects tornado activity persists over multiple days, it further increases the tornado activity probability. The links found highlight the potential application of WRs for developing better seasonal prediction of tornadoes.
Puja Roy, Robert M. Rauber, and Larry Di Girolamo
Atmos. Chem. Phys., 24, 11653–11678, https://doi.org/10.5194/acp-24-11653-2024, https://doi.org/10.5194/acp-24-11653-2024, 2024
Short summary
Short summary
Cloud droplet temperature and lifetime impact cloud microphysical processes such as the activation of ice-nucleating particles. We investigate the thermal and radial evolution of supercooled cloud droplets and their surrounding environments with an aim to better understand observed enhanced ice formation at supercooled cloud edges. This analysis shows that the magnitude of droplet cooling during evaporation is greater than estimated from past studies, especially for drier environments.
Michie Vianca De Vera, Larry Di Girolamo, Guangyu Zhao, Robert M. Rauber, Stephen W. Nesbitt, and Greg M. McFarquhar
Atmos. Chem. Phys., 24, 5603–5623, https://doi.org/10.5194/acp-24-5603-2024, https://doi.org/10.5194/acp-24-5603-2024, 2024
Short summary
Short summary
Tropical oceanic low clouds remain a dominant source of uncertainty in cloud feedback in climate models due to their macrophysical properties (fraction, size, height, shape, distribution) being misrepresented. High-resolution satellite imagery over the Philippine oceans is used here to characterize cumulus macrophysical properties and their relationship to meteorological variables. Such information can act as a benchmark for cloud models and can improve low-cloud generation in climate models.
Genevieve Rose Lorenzo, Avelino F. Arellano, Maria Obiminda Cambaliza, Christopher Castro, Melliza Templonuevo Cruz, Larry Di Girolamo, Glenn Franco Gacal, Miguel Ricardo A. Hilario, Nofel Lagrosas, Hans Jarett Ong, James Bernard Simpas, Sherdon Niño Uy, and Armin Sorooshian
Atmos. Chem. Phys., 23, 10579–10608, https://doi.org/10.5194/acp-23-10579-2023, https://doi.org/10.5194/acp-23-10579-2023, 2023
Short summary
Short summary
Aerosol and weather interactions in Southeast Asia are complex and understudied. An emerging aerosol climatology was established in Metro Manila, the Philippines, from aerosol particle physicochemical properties and meteorology, revealing five sources. Even with local traffic, transported smoke from biomass burning, aged dust, and cloud processing, background marine particles dominate and correspond to lower aerosol optical depth in Metro Manila compared to other Southeast Asian megacities.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
Short summary
Short summary
We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on 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.
Rose Marie Miller, Robert M. Rauber, Larry Di Girolamo, Matthew Rilloraza, Dongwei Fu, Greg M. McFarquhar, Stephen W. Nesbitt, Luke D. Ziemba, Sarah Woods, and Kenneth Lee Thornhill
Atmos. Chem. Phys., 23, 8959–8977, https://doi.org/10.5194/acp-23-8959-2023, https://doi.org/10.5194/acp-23-8959-2023, 2023
Short summary
Short summary
The influence of human-produced aerosols on clouds remains one of the uncertainties in radiative forcing of Earth’s climate. Measurements of aerosol chemistry from sources around the Philippines illustrate the linkage between aerosol chemical composition and cloud droplet characteristics. Differences in aerosol chemical composition in the marine layer from biomass burning, industrial, ship-produced, and marine aerosols are shown to impact cloud microphysical structure just above cloud base.
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
Short summary
Short summary
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.
Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
Short summary
Short summary
Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Yulan Hong and Larry Di Girolamo
Atmos. Chem. Phys., 20, 8267–8291, https://doi.org/10.5194/acp-20-8267-2020, https://doi.org/10.5194/acp-20-8267-2020, 2020
Short summary
Short summary
Cloud phase plays a crucial role in Earth radiation budget but is not well understood. Using A-Train satellite observations, this study provides climatological studies of cloud phase characteristics over Southeast Asia on multiple meteorological scales. Results show that ice, liquid, and ice over liquid clouds display distinct spatial heterogeneity and spectral radiance features. The intraseasonal and interannual behaviors of cloud phases are useful to track the MJO and ENSO.
Michel M. Verstraete, Linda A. Hunt, Hugo De Lemos, and Larry Di Girolamo
Earth Syst. Sci. Data, 12, 611–628, https://doi.org/10.5194/essd-12-611-2020, https://doi.org/10.5194/essd-12-611-2020, 2020
Short summary
Short summary
The radiometric camera-by-camera cloud mask product, available for each of the nine cameras of the MISR instrument, contains a variable number of missing values, especially wherever and whenever the instrument is switched from the Global to Local Mode of operation. This paper proposes a simple method for effectively replacing those missing values and demonstrates the performance of the process. MISR data and software tools are obtainable from public domain websites to explore this issue further.
Jui-Lin Frank Li, Mark Richardson, Wei-Liang Lee, Eric Fetzer, Graeme Stephens, Jonathan Jiang, Yulan Hong, Yi-Hui Wang, Jia-Yuh Yu, and Yinghui Liu
The Cryosphere, 13, 969–980, https://doi.org/10.5194/tc-13-969-2019, https://doi.org/10.5194/tc-13-969-2019, 2019
Short summary
Short summary
Observed summer Arctic sea ice retreat has been faster than simulated by the average CMIP5 models, most of which exclude falling ice particles from their radiative calculations.
We use controlled CESM1-CAM5 simulations to show for the first time that snowflakes' radiative effects can accelerate sea ice retreat. September retreat rates are doubled above current CO2 levels, highlighting falling ice radiative effects as a high priority for inclusion in future modelling of the Arctic.
Joseph A. Finlon, Greg M. McFarquhar, Stephen W. Nesbitt, Robert M. Rauber, Hugh Morrison, Wei Wu, and Pengfei Zhang
Atmos. Chem. Phys., 19, 3621–3643, https://doi.org/10.5194/acp-19-3621-2019, https://doi.org/10.5194/acp-19-3621-2019, 2019
Short summary
Short summary
A new approach describing the relationship between ice crystal mass (m) and dimension (D) is derived, characterizing it as a set of
equally realizableparameters based on the natural variability in cloud conditions observed by aircraft over the Great Plains. Results from this approach address shortcomings of microphysical parameterization schemes and remote sensing retrievals that employ a single m–D relation for a given ice species or environment.
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.
I. Astin and L. Di Girolamo
Atmos. Chem. Phys., 14, 9917–9922, https://doi.org/10.5194/acp-14-9917-2014, https://doi.org/10.5194/acp-14-9917-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Using machine learning algorithm to retrieve cloud fraction based on FY-4A AGRI observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
Retrieval of cloud fraction and optical thickness from multi-angle polarization observations
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
PEAKO and peakTree: Tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2090, https://doi.org/10.5194/egusphere-2024-2090, 2024
Short summary
Short summary
This article presents a novel technique to estimate the liquid water content (LWC) in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Jinyi Xia and Li Guan
EGUsphere, https://doi.org/10.5194/egusphere-2024-977, https://doi.org/10.5194/egusphere-2024-977, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY4A AGRI observations using LSTM neural networks. The results demonstrate excellent performance in distinguishing clear sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
Short summary
Short summary
This study demonstrates the potential for enhancing severe hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
Short summary
Short summary
We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-837, https://doi.org/10.5194/egusphere-2024-837, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information about the cloud and precipitation particles present in clouds from data measured by ground-based radar instruments. The data consist of Doppler spectra, in which several peaks are formed by hydrometeor populations with different fall velocities. The detection of the specific peaks makes it possible to assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
Short summary
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Short summary
The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary
Short summary
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
Short summary
Short summary
The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
Short summary
Short summary
A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
Short summary
Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
Short summary
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
Short summary
Short summary
Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
Short summary
Short summary
We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on 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.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
Short summary
Short summary
We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
Short summary
Short summary
The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
Short summary
Short summary
We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
Short summary
Short summary
JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
Short summary
Short summary
This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
Short summary
Short summary
The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
Short summary
Short summary
The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
Short summary
Short summary
We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling 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 the C-CLD product.
Cited articles
Ackerman, S. A.: Global satellite observations of negative brightness
temperature differences between 11 and 6.7 µm, J. Atmos. Sci., 53,
2803–2812, https://doi.org/10.1175/1520-0469(1996)053<2803:GSOONB>2.0.CO;2, 1996.
Adams, D. K. and Comrie, A. C.: The North American Monsoon, B. Am.
Meteorol. Soc., 78, 2197–2213, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2, 1997.
Alcala, C. M. and Dessler, A. E.: Observations of deep convection in the
tropics using the Tropical Rainfall Measuring Mission (TRMM) precipitation
radar, J. Geophys. Res.-Atmos., 107, 4792, https://doi.org/10.1029/2002JD002457, 2002.
Astin, I., Di Girolamo, L., and Van De Poll, H. M.: Bayesian confidence
intervals for true fractional coverage from finite transect measurements:
Implications for cloud studies from space, J. Geophys. Res.-Atmos.,
106, 17303–17310, https://doi.org/10.1029/2001JD900168, 2001.
Barnes, W. L., Pagano, T. S., and Salomonson, V. V.: Prelaunch
characteristics of the moderate resolution, IEEE Trans. Geosci. Remote
Sens., 36, 1088–1100, 1998.
Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.:
Objective satellite-based detection of overshooting tops using infrared
window channel brightness temperature gradients, J. Appl. Meteorol.
Climatol., 49, 181–202, https://doi.org/10.1175/2009JAMC2286.1, 2010.
Bedka, K. M.: Overshooting cloud top detections using MSG SEVIRI Infrared
brightness temperatures and their relationship to severe weather over
Europe, Atmos. Res., 99, 175–189, https://doi.org/10.1016/j.atmosres.2010.10.001,
2011.
Bedka, K. M. and Khlopenkov, K.: A probabilistic multispectral pattern
recognition method for detection of overshooting cloud tops using passive
satellite imager observations, J. Appl. Meteorol. Climatol., 55,
1983–2005, https://doi.org/10.1175/JAMC-D-15-0249.1, 2016.
Bedka, K. M., Allen, J. T., Punge, H. J., Kunz, M., and Simanovic, D.: A
long-term overshooting convective cloud-top detection database over
Australia derived from MTSAT Japanese Advanced Meteorological Imager
Observations, J. Appl. Meteorol. Climatol., 57, 937–951,
https://doi.org/10.1175/JAMC-D-17-0056.1, 2018.
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File Specification,
Earth, 9, 73, http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: 12 May 2022), 2016.
Chung, E. S., Sohn, B. J., Schmetz, J., and Koenig, M.: Diurnal variation of upper tropospheric humidity and its relations to convective activities over tropical Africa, Atmos. Chem. Phys., 7, 2489–2502, https://doi.org/10.5194/acp-7-2489-2007, 2007.
Chung, E. S., Sohn, B. J., and Schmetz, J.: CloudSat shedding new light on
high-reaching tropical deep convection observed with Meteosat, Geophys. Res.
Lett., 35, 1–5, https://doi.org/10.1029/2007GL032516, 2008.
Cloudsat: CloudSat DPC, CloudSat [data set], https://www.cloudsat.cira.colostate.edu/ (last access: 26 November 2021), 2023.
Dworak, R., Bedka, K., Brunner, J., and Feltz, W.: Comparison between GOES-12
overshooting-top detections, WSR-88D radar reflectivity, and severe storm
reports, Weather Forecast., 27, 684–699, https://doi.org/10.1175/WAF-D-11-00070.1,
2012.
EarthData: Your Source for Level-1 and Atmospheric Data, EarthData [data set], https://ladsweb.modaps.eosdis.nasa.gov/ (last access: 8 February 2022), 2023.
Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., and Husak, G.: A global satellite-assisted precipitation climatology, Earth Syst. Sci. Data, 7, 275–287, https://doi.org/10.5194/essd-7-275-2015, 2015.
Geerts, B., Giangrande, S. E., McFarquhar, G. M., Xue, L., Abel, S. J.,
Comstock, J. M., Crewell, S., DeMott, P. J., Ebell, K., Field, P., Hill, T.
C. J., Hunzinger, A., Jensen, M. P., Johnson, K. L., Juliano, T. W.,
Kollias, P., Kosovic, B., Lackner, C., Luke, E., Lüpkes, C., Matthews,
A. A., Neggers, R., Ovchinnikov, M., Powers, H., Shupe, M. D., Spengler, T.,
Swanson, B. E., Tjernström, M., Theisen, A. K., Wales, N. A., Wang, Y.,
Wendisch, M., and Wu, P.: The COMBLE Campaign: A Study of Marine Boundary
Layer Clouds in Arctic Cold-Air Outbreaks, B. Am. Meteorol. Soc., 103,
E1371–E1389, https://doi.org/10.1175/bams-d-21-0044.1, 2022.
Gettelman, A., Salby, M. L., and Sassi, F.: Distribution and influence of
convection in the tropical tropopause region, J. Geophys. Res.-Atmos.,
107, 9–10, https://doi.org/10.1029/2001jd001048, 2002.
Gettelman, A., Forster, P. M. de F., Fujiwara, M., Fu, Q., Voömel, H., Gohar, L. K., Johanson, C., and Ammerman, M.:
Radiation balance of the tropical tropopause layer, J. Geophys. Res.,
109, D07103, https://doi.org/10.1029/2003JD004190, 2004.
Griffin, S. M.: Climatology of tropical overshooting tops in North Atlantic
tropical cyclones, J. Appl. Meteorol. Climatol., 56, 1783–1796,
https://doi.org/10.1175/JAMC-D-16-0413.1, 2017.
Griffin, S. M., Bedka, K. M., and Velden, C. S.: A method for calculating the
height of overshooting convective cloud tops using satellite-based IR imager
and CloudSat cloud profiling radar observations, J. Appl. Meteorol.
Climatol., 55, 479–491, https://doi.org/10.1175/JAMC-D-15-0170.1, 2016.
Grise, K. M., Thompson, D. W. J., and Birner, T.: A global survey of static
stability in the stratosphere and upper troposphere, J. Climate, 23,
2275–2292, https://doi.org/10.1175/2009JCLI3369.1, 2010.
Groenemeijer, P., Púcik, T., Holzer, A. M., Antonescu, B.,
Riemann-Campe, K., Schultz, D. M., Kühne, T., Feuerstein, B., Brooks, H.
E., Doswell, C. A., Koppert, H. J., and Sausen, R.: Severe convective storms
in Europe: Ten years of research and education at the European Severe Storms
Laboratory, B. Am. Meteorol. Soc., 98, 2641–2651,
https://doi.org/10.1175/BAMS-D-16-0067.1, 2017.
Heymsfield, G. M., Tian, L., Heymsfield, A. J., Li, L., and Guimond, S.:
Characteristics of deep tropical and subtropical convection from
nadir-viewing high-altitude airborne doppler radar, J. Atmos. Sci., 67,
285–308, https://doi.org/10.1175/2009JAS3132.1, 2010.
Hong, G., Heygster, G., Miao, J., and Kunzi, K.: Detection of tropical deep
convective clouds from AMSU-B water vapor channels measurements, J. Geophys.
Res.-Atmos., 110, 1–15, https://doi.org/10.1029/2004JD004949, 2005.
Hong, G., Heygster, G., Notholt, J., and Buehler, S. A.: Interannual to
diurnal variations in tropical and subtropical deep convective clouds and
convective overshooting from seven years of AMSU-B measurements, J. Climate,
21, 4168–4189, https://doi.org/10.1175/2008JCLI1911.1, 2008.
Hong, Y. and Liu, G.: The characteristics of ice cloud prop- erties derived from CloudSat and CALIPSO measurements, J. Climate, 28, 3880–3901, https://doi.org/10.1175/JCLI-D-14-00666.1, 2015.
Hong, Y. and Di Girolamo, L.: Cloud phase characteristics over Southeast Asia from A-Train satellite observations, Atmos. Chem. Phys., 20, 8267–8291, https://doi.org/10.5194/acp-20-8267-2020, 2020.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation
measurement mission, B. Am. Meteorol. Soc., 95, 701–722,
https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Hourngir, D., Panegrossi, G., Casella, D., Sanò, P., D'adderio, L. P.,
and Liu, C.: A 4-year climatological analysis based on gpm observations of
deep convective events in the mediterranean region, Remote Sens., 13,
1–21, https://doi.org/10.3390/rs13091685, 2021.
Iguchi, T. and Meneghini, R.: GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA [data set], https://doi.org/10.5067/GPM/DPR/GPM/2A/07, 2021.
Janiga, M. A. and Thorncroft, C. D.: Convection over tropical Africa and the
East Atlantic during the West African monsoon: Regional and diurnal
variability, J. Climate, 27, 4189–4208, https://doi.org/10.1175/JCLI-D-13-00449.1,
2014.
Jeyaratnam, J., Luo, Z. J., Giangrande, S. E., Wang, D., and Masunaga, H.: A
Satellite-Based Estimate of Convective Vertical Velocity and Convective Mass
Flux: Global Survey and Comparison With Radar Wind Profiler Observations,
Geophys. Res. Lett., 48, 1–11, https://doi.org/10.1029/2020GL090675, 2021.
Johnson, R. H.: Diurnal cycle of monsoon convection, The Global Monsoon System: Research and Forecast, edited by: Chang, C.-P., World Scientific Series on Asia-Pacific Weather and Climate, Vol. 5, World Scientific Publishing Company, 257–276, https://doi.org/10.1142/9789814343411_0015, 2011.
Khlopenkov, K. V., Bedka, K. M., Cooney, J. W., and Itterly, K.: Recent
Advances in Detection of Overshooting Cloud Tops From Longwave Infrared
Satellite Imagery, J. Geophys. Res.-Atmos., 126, 1–25,
https://doi.org/10.1029/2020jd034359, 2021.
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 Trans. Geosci. Remote Sens., 30, 2–27,
https://doi.org/10.1109/36.124212, 1992.
Li, H., Wei, X., Min, M., Li, B., Nong, Z., and Chen, L.: A Dataset of
Overshooting Cloud Top from 12-Year CloudSat/CALIOP Joint Observations,
Remote Sens., 14, 2417, https://doi.org/10.3390/rs14102417, 2022.
Liu, C. and Zipser, E. J.: Global distribution of convection penetrating the
tropical tropopause, J. Geophys. Res.-Atmos., 110, 1–12,
https://doi.org/10.1029/2005JD006063, 2005.
Liu, N. and Liu, C.: Global distribution of deep convection reaching
tropopause in 1 year GPM observations, J. Geophys. Res.-Atmos.,
121, 3924–3842, https://doi.org/10.1002/ 2015JD024430, 2016.
Liu, N., Liu, C., and Hayden, L.: Climatology and Detection of Overshooting
Convection From 4 Years of GPM Precipitation Radar and Passive Microwave
Observations, J. Geophys. Res.-Atmos., 125, 1–14,
https://doi.org/10.1029/2019JD032003, 2020.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
detection using Cloudsat - An earth-orbiting 94-GHz cloud radar, J. Atmos.
Ocean. Technol., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008.
Marion, G. R., Trapp, R. J., and Nesbitt, S. W.: Using overshooting top area
to discriminate potential for large, intense tornadoes, Geophys. Res. Lett.,
46, 12520–12526, https://doi.org/10.1029/2019GL084099, 2019.
Monette, S. A., Velden, C. S., Griffin, K. S., and Rozoff, C. M.: Examining
trends in satellite-detected tropical overshooting tops as a potential
predictor of tropical cyclone rapid intensification, J. Appl. Meteorol.
Climatol., 51, 1917–1930, https://doi.org/10.1175/JAMC-D-11-0230.1, 2012.
Murphy, A. M., Rauber, R. M., McFarquhar, G. M., Finlon, J. A., Plummer, D.
M., Rosenow, A. A., and Jewett, B. F.: A microphysical analysis of elevated
convection in the comma head region of continental winter cyclones, J.
Atmos. Sci., 74, 69–91, https://doi.org/10.1175/JAS-D-16-0204.1, 2017.
NASA: goldsmr4.gesdisc.eosdis.nasa.gov, NASA [data set], https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2/M2I1NXASM.5.12.4/ (last access: 12 May 2022), 2023.
Nesbitt, S. W. and Zipser, E. J.: The diurnal cycle of rainfall and
convective intensity according to three years of TRMM measurements, J.
Climate, 16, 1456–1475, https://doi.org/10.1175/1520-0442(2003)016<1456:TDCORA>2.0.CO;2, 2003.
Papritz, L., Rouges, E., Aemisegger, F., and Wernli, H.: On the Thermodynamic
Preconditioning of Arctic Air Masses and the Role of Tropopause Polar
Vortices for Cold Air Outbreaks From Fram Strait, J. Geophys. Res.-Atmos.,
124, 11033–11050, https://doi.org/10.1029/2019JD030570, 2019.
Partain, P.: Cloudsat ECMWF-AUX auxiliary data process description and
interface control document, Coop. Inst. Res. Atmos. Color. State Univ.,
http://129.82.109.192/ICD/ECMWF-AUX/ECMWF-AUX_PDICD_3.0.pdf (last access: 26 November 2021), 2007.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A.,
Riédi, J. C., and Frey, R. A.: The MODIS cloud products: Algorithms and
examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459–473,
https://doi.org/10.1109/TGRS.2002.808301, 2003.
Proud, S. R.: Analysis of overshooting top detections by Meteosat Second
Generation: A 5-year dataset, Q. J. Roy. Meteorol. Soc., 141, 909–915,
https://doi.org/10.1002/qj.2410, 2015.
Proud, S. R. and Bachmeier, S.: Record-low cloud temperatures associated with a tropical deep convective event, Geophys. Res. Lett., 48, e2020GL092261, https://doi.org/10.1029/2020GL092261, 2021.
Rauber, R. M., Wegman, J., Plummer, D. M., Rosenow, A. A., Peterson, M.,
McFarquhar, G. M., Jewett, B. F., Leon, D., Market, P. S., Knupp, K. R.,
Keeler, J. M., and Battaglia, S. M.: Stability and charging characteristics
of the comma head region of continental winter cyclones, J. Atmos. Sci.,
71, 1559–1582, https://doi.org/10.1175/JAS-D-13-0253.1, 2014.
Rauber, R. M., Plummer, D. M., Macomber, M. K., Rosenow, A. A., McFarquhar,
G. M., Jewett, B. F., Leon, D., Owens, N., and Keeler, J. M.: The role of
cloud-top generating cells and boundary layer circulations in the finescale
radar structure of a winter cyclone over the great lakes, Mon. Weather Rev.,
143, 2291–2318, https://doi.org/10.1175/MWR-D-14-00350.1, 2015.
Rysman, J. F., Claud, C., and Delanoe, J.: Monitoring Deep Convection and
Convective Overshooting from 60∘ S to 60∘ N Using MHS: A
Cloudsat/CALIPSO-Based Assessment, IEEE Geosci. Remote Sens. Lett., 14,
159–163, https://doi.org/10.1109/LGRS.2016.2631725, 2017.
Schmetz, J., Tjemkes, S. A., Gube, M., and Van De Berg, L.: Monitoring deep
convection and convective overshooting with METEOSAT, Adv. Sp. Res., 19,
433–441, https://doi.org/10.1016/S0273-1177(97)00051-3, 1997.
Setvák, M., Rabin, R. M., and Wang, P. K.: Contribution of the MODIS
instrument to observations of deep convective storms and stratospheric
moisture detection in GOES and MSG imagery, Atmos. Res., 83,
505–518, https://doi.org/10.1016/j.atmosres.2005.09.015, 2007.
Setvák, M., Bedka, K., Lindsey, D. T., Sokol, A., Charvát, Z.,
Šťástka, J., and Wang, P. K.: A-Train observations of deep
convective storm tops, Atmos. Res., 123, 229–248,
https://doi.org/10.1016/j.atmosres.2012.06.020, 2013.
Shikhov, A., Chernokulsky, A., Kalinin, N., Bykov, A., and Pischalnikova, E.:
Climatology and Formation Environments of Severe Convective Windstorms and
Tornadoes in the Perm Region (Russia) in 1984–2020, Atmosphere (Basel),
(12), 1407, https://doi.org/10.3390/atmos12111407, 2021.
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., and Mitrescu, C.: The cloudsat
mission and the A-Train: A new dimension of space-based observations of
clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790,
https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J.,
Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang,
Z., and Marchand, R.: CloudSat mission: Performance and early science after
the first year of operation, J. Geophys. Res., 113, D00A18,
https://doi.org/10.1029/2008JD009982, 2008.
Stewart, R. E., Szeto, K. K., Reinking, R. F., Clough, S. A., and Ballard, S.
P.: Midlatitude cyclonic cloud systems and their features affecting large
scales and climate, Rev. Geophys., 36, 245–273, https://doi.org/10.1029/97RG03573,
1998.
Sun, L. X., Zhuge, X. Y., and Wang, Y.: A Contour-Based Algorithm for
Automated Detection of Overshooting Tops Using Satellite Infrared Imagery,
IEEE Trans. Geosci. Remote Sens., 57, 497–508,
https://doi.org/10.1109/TGRS.2018.2857486, 2019.
Tao, C. and Jiang, H.: Global distribution of hot towers in tropical
cyclones based on 11-Yr TRMM data, J. Climate, 26, 1371–1386,
https://doi.org/10.1175/JCLI-D-12-00291.1, 2013.
Terpstra, A., Renfrew, I. A., and Sergeev, D. E.: Characteristics of cold-air
outbreak events and associated polar mesoscale cyclogenesis over the north
Atlantic region, J. Climate, 34, 4567–4584, https://doi.org/10.1175/JCLI-D-20-0595.1,
2021.
Tian, B., Soden, B. J., and Wu, X.: Diurnal cycle of convection, clouds, and
water vapor in the tropical upper troposphere: Satellites versus a general
circulation model, J. Geophys. Res.-Atmos., 109, 1–16,
https://doi.org/10.1029/2003JD004117, 2004.
Tian, B., Held, I. M., Lau, N. C., and Soden, B. J.: Diurnal cycle of
summertime deep convection over North America: A satellite perspective, J.
Geophys. Res.-Atmos., 110, 1–10, https://doi.org/10.1029/2004JD005275, 2005.
Trapp, R. J., Marion, G. R., and Nesbitt, S. W.: The regulation of tornado
intensity by updraft width, J. Atmos. Sci., 74, 4199–4211,
https://doi.org/10.1175/JAS-D-16-0331.1, 2017.
Vergados, P., Luo, Z. J., Emanuel, K., and Mannucci, A. J.: Observational
tests of hurricane intensity estimations using GPS radio occultations, J.
Geophys. Res.-Atmos., 119, 1936–1948, https://doi.org/10.1002/2013JD020934, 2014.
Wang, C., Luo, Z. J., and Huang, X.: Parallax correction in collocating
CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS)
observations: Method and application to convection study, J. Geophys. Res.-Atmos., 116, 1–9, https://doi.org/10.1029/2011JD016097, 2011.
Wang, Z., Vane, D., and Staphens, G.: Level 2 Combined Radar and Lidar Cloud
Scenario Classification Product Process Description and Interface Control
document, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Level+2+Combined+Radar+and+Lidar+Cloud+Scenario+Classification+Product+Process+Description+and+Interface+Control+Document#1 (last access: 26 November 2021),
2012.
Wilcox, L. J., Hoskins, B. J., and Shine, K. P.: A global blended tropopause
based on ERA data. Part I: Climatology, Q. J. Roy. Meteorol. Soc., 138,
561–575, https://doi.org/10.1002/qj.951, 2012.
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission:
Spaceborne lidar for observation of aerosols and clouds, Lidar Remote Sens.
Ind. Environ. Monit. III, 4893, 1–11, https://doi.org/10.1117/12.466539, 2003.
Xiong, X., Sun, J., Wu, A., Chiang, K.-F., Esposito, J., and Barnes, W.:
Terra and Aqua MODIS calibration algorithms and uncertainty analysis,
Sensors, Syst. Next-Generation Satell. IX, 5978, 59780V,
https://doi.org/10.1117/12.627631, 2005.
Xiong, X., Angal, A., Barnes, W. L., Chen, H., Chiang, V., Geng, X., Li, Y.,
Twedt, K., Wang, Z., Wilson, T., and Wu, A.: Updates of Moderate Resolution
Imaging Spectroradiometer on-orbit calibration uncertainty assessments, J.
Appl. Remote Sens., 12, 1, https://doi.org/10.1117/1.jrs.12.034001, 2018.
Yuter, S. E. and Houze, R. A.: Three-dimensional kinematic and microphysical
evolution of Florida cumulonimbus. Part II: Frequency distributions of
vertical velocity, reflectivity, and differential reflectivity, Mon. Weather
Rev., 123, https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2, 1995.
Zhuge, X. Y., Ming, J., and Wang, Y.: Reassessing the use of inner-core hot
towers to predict tropical cyclone rapid intensification, Weather Forecast.,
30, 1265–1279, https://doi.org/10.1175/WAF-D-15-0024.1, 2015.
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where
are the most intense thunderstorms on Earth?, B. Am. Meteorol. Soc.,
87, 1057–1071, https://doi.org/10.1175/BAMS-87-8-1057, 2006.
Short summary
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.
Deep convective updrafts form overshooting tops (OTs) when they extend into the upper...
