Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-3147-2020
© Author(s) 2020. 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-13-3147-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2020
Research article |
| 15 Jun 2020
| 15 Jun 2020
An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans
Alexis Hunzinger
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, WA, USA
Joseph C. Hardin
Pacific Northwest National Laboratory, Richland, WA, USA
Nitin Bharadwaj
Pacific Northwest National Laboratory, Richland, WA, USA
Adam Varble
Pacific Northwest National Laboratory, Richland, WA, USA
Alyssa Matthews
Pacific Northwest National Laboratory, Richland, WA, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2025-1868, https://doi.org/10.5194/egusphere-2025-1868, 2025
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Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global ERFaer signal can be robustly reproduced across resolutions when aerosol activation processes are carefully parameterized. Further, we argue that simplified prescribed aerosol schemes will open the door for further process/mechanism studies under controlled conditions.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
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Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
Jerome D. Fast, Adam C. Varble, Fan Mei, Mikhail Pekour, Jason Tomlinson, Alla Zelenyuk, Art J. Sedlacek III, Maria Zawadowicz, and Louisa Emmons
Atmos. Chem. Phys., 24, 13477–13502, https://doi.org/10.5194/acp-24-13477-2024, https://doi.org/10.5194/acp-24-13477-2024, 2024
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Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
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Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
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Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
Atmos. Chem. Phys., 24, 6455–6476, https://doi.org/10.5194/acp-24-6455-2024, https://doi.org/10.5194/acp-24-6455-2024, 2024
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Clouds are essential to keep Earth cooler by reflecting sunlight back to space. We show that an increase in aerosol concentration suppresses precipitation in clouds, causing them to accumulate water and expand in a polluted environment with stronger turbulence and radiative cooling. This process enhances their reflectance by 51 %. It is therefore prudent to account for cloud fraction changes in assessments of aerosol–cloud interactions to improve predictions of climate change.
Charlotte M. Beall, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Adam Varble, Kentaroh Suzuki, and Takuro Michibata
Atmos. Chem. Phys., 24, 5287–5302, https://doi.org/10.5194/acp-24-5287-2024, https://doi.org/10.5194/acp-24-5287-2024, 2024
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Single-layer warm liquid clouds cover nearly one-third of the Earth's surface, and uncertainties regarding the impact of aerosols on their radiative properties pose a significant challenge to climate prediction. Here, we demonstrate how satellite observations can be used to constrain Earth system model estimates of the radiative forcing from the interactions of aerosols with clouds due to warm rain processes.
Yang Wang, Chanakya Bagya Ramesh, Scott E. Giangrande, Jerome Fast, Xianda Gong, Jiaoshi Zhang, Ahmet Tolga Odabasi, Marcus Vinicius Batista Oliveira, Alyssa Matthews, Fan Mei, John E. Shilling, Jason Tomlinson, Die Wang, and Jian Wang
Atmos. Chem. Phys., 23, 15671–15691, https://doi.org/10.5194/acp-23-15671-2023, https://doi.org/10.5194/acp-23-15671-2023, 2023
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We report the vertical profiles of aerosol properties over the Southern Great Plains (SGP), a region influenced by shallow convective clouds, land–atmosphere interactions, boundary layer turbulence, and the aerosol life cycle. We examined the processes that drive the aerosol population and distribution in the lower troposphere over the SGP. This study helps improve our understanding of aerosol–cloud interactions and the model representation of aerosol processes.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
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As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023, https://doi.org/10.5194/acp-23-13523-2023, 2023
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We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model-predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
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This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
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Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Yang Wang, Guangjie Zheng, Michael P. Jensen, Daniel A. Knopf, Alexander Laskin, Alyssa A. Matthews, David Mechem, Fan Mei, Ryan Moffet, Arthur J. Sedlacek, John E. Shilling, Stephen Springston, Amy Sullivan, Jason Tomlinson, Daniel Veghte, Rodney Weber, Robert Wood, Maria A. Zawadowicz, and Jian Wang
Atmos. Chem. Phys., 21, 11079–11098, https://doi.org/10.5194/acp-21-11079-2021, https://doi.org/10.5194/acp-21-11079-2021, 2021
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This paper reports the vertical profiles of trace gas and aerosol properties over the eastern North Atlantic, a region of persistent but diverse subtropical marine boundary layer (MBL) clouds. We examined the key processes that drive the cloud condensation nuclei (CCN) population and how it varies with season and synoptic conditions. This study helps improve the model representation of the aerosol processes in the remote MBL, reducing the simulated aerosol indirect effects.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
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In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Christopher R. Williams, Maximilian Maahn, Joseph C. Hardin, and Gijs de Boer
Atmos. Meas. Tech., 11, 4963–4980, https://doi.org/10.5194/amt-11-4963-2018, https://doi.org/10.5194/amt-11-4963-2018, 2018
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This study presents three signal-processing methods to improve estimates derived from a vertically pointing 35 GHz cloud radar deployed at Oliktok Point, Alaska. The first method removes ground clutter from the Doppler velocity spectra. The second method estimates multiple peaks and high-order moments from the improved spectra. The third method removes high-frequency variability in high-order moments by shifting original 2 s spectra to a common reference before averaging over a 15 s interval.
Die Wang, Scott E. Giangrande, Mary Jane Bartholomew, Joseph Hardin, Zhe Feng, Ryan Thalman, and Luiz A. T. Machado
Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018, https://doi.org/10.5194/acp-18-9121-2018, 2018
McKenna W. Stanford, Adam Varble, Ed Zipser, J. Walter Strapp, Delphine Leroy, Alfons Schwarzenboeck, Rodney Potts, and Alain Protat
Atmos. Chem. Phys., 17, 9599–9621, https://doi.org/10.5194/acp-17-9599-2017, https://doi.org/10.5194/acp-17-9599-2017, 2017
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Radar reflectivity is a valuable observational tool used to guide numerical weather model improvement. Biases in simulated reflectivity help identify potential errors in physical process and property representation in models. This study uniquely compares simulated and observed tropical convective systems to establish that a commonly documented high bias in radar reflectivity values at least partially results from the production of simulated ice particle sizes that are larger than observed.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Optimizing cloud optical parameterizations in Radiative Transfer for TOVS (RTTOV v12.3) for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images
Errors in stereoscopic retrievals of cloud top height for single-layer clouds
Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
A method to retrieve mixed-phase cloud vertical structure from airborne lidar
Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations
Evaluating parallax and shadow correction methods for global horizontal irradiance retrievals from Meteosat SEVIRI
Factors limiting contrail detection in satellite imagery
Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES
Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights
Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation
Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations
Effectiveness of Cirrus Detection with MODIS Cloud Mask data
Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework
An evaluation of microphysics in a numerical model using Doppler velocity measured by ground-based radar for application to the EarthCARE satellite
Investigation of cirrus cloud properties in the tropical tropopause layer using high-altitude limb-scanning near-IR spectroscopy during NASA-ATTREX
Comparing FY-2F/CTA products to ground-based manual total cloud cover observations in Xinjiang under complex underlying surfaces and different weather conditions
Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS
Improved RepVGG ground-based cloud image classification with attention convolution
An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products
First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer
Thundercloud structures detected and analyzed based on coherent Doppler wind lidar
Assessing Arctic low-level clouds and precipitation from above – a radar perspective
What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations
Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic
The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals
Retrieval of microphysical parameters of monsoonal rain using X-band dual-polarization radar: their seasonal dependence and evaluation
Consistency test of precipitating ice cloud retrieval properties obtained from the observations of different instruments operating at Dome C (Antarctica)
Sizing ice hydrometeor populations using the dual-wavelength radar ratio
Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions
The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data
Horizontal geometry of trade wind cumuli – aircraft observations from a shortwave infrared imager versus a radar profiler
Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)
Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data
Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles
Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples
Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems
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
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
Yongbo Zhou, Tianrui Cao, and Lijian Zhu
Atmos. Meas. Tech., 18, 3267–3285, https://doi.org/10.5194/amt-18-3267-2025, https://doi.org/10.5194/amt-18-3267-2025, 2025
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Cloud optical parameterizations (COPs) in Radiative Transfer for TOVS are evaluated by comparing observed and synthetic visible satellite images. The optimal COP for liquid water cloud is parameterized in terms of effective diameter (Deff) and the COP for ice cloud developed by the Space Science and Engineering Center, with Deff for ice cloud parameterized in terms of ice water content and temperature. The findings will benefit the remote sensing and data assimilation community.
Jesse Loveridge and Larry Di Girolamo
Atmos. Meas. Tech., 18, 3009–3033, https://doi.org/10.5194/amt-18-3009-2025, https://doi.org/10.5194/amt-18-3009-2025, 2025
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Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and often have tenuous boundaries. We evaluate the effect of this on stereoscopy using numerical simulations. Stereoscopic techniques retrieve a cloud boundary that is ~100 m interior to the true boundary and is smoother, depending on the cloud shape and resolution of the instrument. This error is similar across views, demonstrating the strength of stereoscopy for detecting changes in cloud geometry.
Rohit Mangla, Mary Borderies, Philippe Chambon, Alan Geer, and James Hocking
Atmos. Meas. Tech., 18, 2751–2779, https://doi.org/10.5194/amt-18-2751-2025, https://doi.org/10.5194/amt-18-2751-2025, 2025
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This study provides a detailed description of the radar simulator available within version 13 of the RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) software. It is applied to the Météo-France global numerical weather prediction model, with the objective of simulating Dual-frequency Precipitation Radar reflectivity observations. Additionally, the simulation of the bright band is addressed and then successfully applied to model forecasts for the purpose of classifying NWP (numerical weather prediction) model columns between stratiform and convective categories.
Ewan Crosbie, Johnathan W. Hair, Amin R. Nehrir, Richard A. Ferrare, Chris Hostetler, Taylor Shingler, David Harper, Marta Fenn, James Collins, Rory Barton-Grimley, Brian Collister, K. Lee Thornhill, Christiane Voigt, Simon Kirschler, and Armin Sorooshian
Atmos. Meas. Tech., 18, 2639–2658, https://doi.org/10.5194/amt-18-2639-2025, https://doi.org/10.5194/amt-18-2639-2025, 2025
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A method was developed to extract information from airborne lidar observations about the distribution of ice and liquid water within clouds. The method specifically targets signatures of horizontal and vertical gradients in ice and water that appear in the polarization of the lidar signals. The method was tested against direct measurements of the cloud properties collected by a second aircraft.
Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert
Atmos. Meas. Tech., 18, 1499–1517, https://doi.org/10.5194/amt-18-1499-2025, https://doi.org/10.5194/amt-18-1499-2025, 2025
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This study demonstrates the ability of a new method delivering the vertical distribution of particle shape to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the new method to identify rimed particles and aggregates without differentiating them can simplify statistical work.
Job I. Wiltink, Hartwig Deneke, Chiel C. van Heerwaarden, and Jan Fokke Meirink
EGUsphere, https://doi.org/10.5194/egusphere-2024-4139, https://doi.org/10.5194/egusphere-2024-4139, 2025
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Global horizontal irradiance retrievals from satellite observations are affected by spatial displacements due to parallax and cloud shadows. We assess different approaches to correct for these displacements and quantify their added value by comparison with a network of ground-based pyranometer observations. The corrections are found to become increasingly important at higher spatial resolutions and are most relevant for variable cloud types.
Oliver G. A. Driver, Marc E. J. Stettler, and Edward Gryspeerdt
Atmos. Meas. Tech., 18, 1115–1134, https://doi.org/10.5194/amt-18-1115-2025, https://doi.org/10.5194/amt-18-1115-2025, 2025
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Contrails (clouds caused by planes) play a large role in the climate warming caused by aviation. Satellites are a good tool to validate modelled impact estimates. Many contrails are either too narrow or too disperse to detect. This work shows that only around half of contrails are observable but that the most climatically important are easier to detect. It supports the use of satellites for contrail observation but highlights the need for observability considerations for specific applications.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Nandana Amarasinghe, Daniel Miller, Jennifer Small-Griswold, Mikael Witte, Brian Cairns, Siddhant Gupta, Greg McFarquhar, and Joseph O'Brien
Atmos. Meas. Tech., 18, 981–1011, https://doi.org/10.5194/amt-18-981-2025, https://doi.org/10.5194/amt-18-981-2025, 2025
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Satellite remote sensing retrievals of cloud droplet size are used to understand clouds and their interactions with aerosols and radiation but require many simplifying assumptions. Evaluation of these retrievals is typically done by comparing against direct measurements of droplets from airborne cloud probes. This paper details an evaluation of proxy airborne remote sensing droplet size retrievals against several cloud probes and explores the impact of key assumptions on retrieval agreement.
Lea Volkmer, Tobias Kölling, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6807–6817, https://doi.org/10.5194/amt-17-6807-2024, https://doi.org/10.5194/amt-17-6807-2024, 2024
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The importance of the consideration of cloud motion for the stereographic determination of cloud top height from aircraft observations is demonstrated using measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). A method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts is presented and validated using both real measurements and realistic radiative transfer simulations.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
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The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.
Job I. Wiltink, Hartwig Deneke, Yves-Marie Saint-Drenan, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 17, 6003–6024, https://doi.org/10.5194/amt-17-6003-2024, https://doi.org/10.5194/amt-17-6003-2024, 2024
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Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) global horizontal irradiance (GHI) retrievals are validated at standard and increased spatial resolution against a network of 99 pyranometers. GHI accuracy is strongly dependent on the cloud regime. Days with variable cloud conditions show significant accuracy improvements when retrieved at higher resolution. We highlight the benefits of dense network observations and a cloud-regime-resolved approach in validating GHI retrievals.
Żaneta Nguyen Huu, Andrzej Z. Kotarba, and Agnieszka Wypych
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-163, https://doi.org/10.5194/amt-2024-163, 2024
Revised manuscript accepted for AMT
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Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares MODIS and CALIOP data, testing six MODIS methods and two ISCCP tests. The ATC test was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it suitable for creating a high-level cloud mask.
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
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In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Woosub Roh, Masaki Satoh, Yuichiro Hagihara, Hiroaki Horie, Yuichi Ohno, and Takuji Kubota
Atmos. Meas. Tech., 17, 3455–3466, https://doi.org/10.5194/amt-17-3455-2024, https://doi.org/10.5194/amt-17-3455-2024, 2024
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The advantage of the use of Doppler velocity in the categorization of the hydrometeors is that Doppler velocities suffer less impact from the attenuation of rain and wet attenuation on an antenna. The ground Cloud Profiling Radar observation of the radar reflectivity for the precipitation case is limited because of wet attenuation on an antenna. We found the main contribution to Doppler velocities is the terminal velocity of hydrometeors by analysis of simulation results.
Santo Fedele Colosimo, Nathaniel Brockway, Vijay Natraj, Robert Spurr, Klaus Pfeilsticker, Lisa Scalone, Max Spolaor, Sarah Woods, and Jochen Stutz
Atmos. Meas. Tech., 17, 2367–2385, https://doi.org/10.5194/amt-17-2367-2024, https://doi.org/10.5194/amt-17-2367-2024, 2024
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Cirrus clouds are poorly understood components of the climate system, in part due to the challenge of observing thin, sub-visible ice clouds. We address this issue with a new observational approach that uses the remote sensing of near-infrared ice water absorption features from a high-altitude aircraft. We describe the underlying principle of this approach and present a new procedure to retrieve ice concentration in cirrus clouds. Our retrievals compare well with in situ observations.
Shuai Li, Hua Zhang, Yonghang Chen, Zhili Wang, Xiangyu Li, Yuan Li, and Yuanyuan Xue
Atmos. Meas. Tech., 17, 2011–2024, https://doi.org/10.5194/amt-17-2011-2024, https://doi.org/10.5194/amt-17-2011-2024, 2024
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In this paper, Xinjiang was the test area, and nine evaluation indexes of FY-2F/CTA, including precision rate, false rate, missing rate, consistency rate, strong rate, weak rate, bias, AE, and RMSE, were calculated and analyzed under complex underlying surface (subsurface types, temperature and altitude conditions) and different weather conditions (dust effects and different cloud cover levels). The precision, consistency, and error indexes of FY-2F/CTA were tested and evaluated.
Lea Volkmer, Veronika Pörtge, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, https://doi.org/10.5194/amt-17-1703-2024, 2024
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Three-dimensional radiative transfer simulations are used to evaluate the performance of retrieval algorithms in the derivation of cloud geometry (cloud top heights) and cloud droplet size distributions from two-dimensional polarized radiance measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner. The cloud droplet size distributions are derived for the effective radius and variance. The simulations are based on cloud data from highly resolved large-eddy simulations.
Chaojun Shi, Leile Han, Ke Zhang, Hongyin Xiang, Xingkuan Li, Zibo Su, and Xian Zheng
Atmos. Meas. Tech., 17, 979–997, https://doi.org/10.5194/amt-17-979-2024, https://doi.org/10.5194/amt-17-979-2024, 2024
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This article mainly studies the problem of ground cloud classification and significantly improves the accuracy of ground cloud classification by applying an improved deep-learning method. The research results show that the method proposed in this article has a significant impact on the classification results of ground cloud images. These conclusions have important implications for providing new insights and future research directions in the field of ground cloud classification.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
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When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024, https://doi.org/10.5194/amt-17-453-2024, 2024
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This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.
Kenan Wu, Tianwen Wei, Jinlong Yuan, Haiyun Xia, Xin Huang, Gaopeng Lu, Yunpeng Zhang, Feifan Liu, Baoyou Zhu, and Weidong Ding
Atmos. Meas. Tech., 16, 5811–5825, https://doi.org/10.5194/amt-16-5811-2023, https://doi.org/10.5194/amt-16-5811-2023, 2023
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A compact all-fiber coherent Doppler wind lidar (CDWL) working at the 1.5 µm wavelength is applied to probe the dynamics and microphysics structure of thunderstorms. It was found that thunderclouds below the 0 ℃ isotherm have significant spectrum broadening and an increase in skewness, and that lightning affects the microphysics structure of the thundercloud. It is proven that the precise spectrum of CDWL is a promising indicator for studying the charge structure of thunderstorms.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
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CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
Atmos. Meas. Tech., 16, 3531–3546, https://doi.org/10.5194/amt-16-3531-2023, https://doi.org/10.5194/amt-16-3531-2023, 2023
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In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Hong Chen, K. Sebastian Schmidt, Steven T. Massie, Vikas Nataraja, Matthew S. Norgren, Jake J. Gristey, Graham Feingold, Robert E. Holz, and Hironobu Iwabuchi
Atmos. Meas. Tech., 16, 1971–2000, https://doi.org/10.5194/amt-16-1971-2023, https://doi.org/10.5194/amt-16-1971-2023, 2023
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We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) and propose a radiance self-consistency approach for quantifying and mitigating 3D bias in legacy airborne and spaceborne imagery retrievals due to spatially inhomogeneous clouds and surfaces.
Kumar Abhijeet, Thota Narayana Rao, Nidamanuri Rama Rao, and Kasimahanthi Amar Jyothi
Atmos. Meas. Tech., 16, 871–888, https://doi.org/10.5194/amt-16-871-2023, https://doi.org/10.5194/amt-16-871-2023, 2023
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The present study focuses on retrieving and validating raindrop size distribution (DSD) relations for monsoonal rainfall, which are required for retrieving DSDs with polarimetric radar measurements. The seasonal variation in DSD is quite large and significant, and as a result the coefficients also vary considerably between the seasons and from those existing elsewhere. Among the existing DSD methods, the N-gamma method performs better than the other methods.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
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In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Sergey Y. Matrosov, Alexei Korolev, Mengistu Wolde, and Cuong Nguyen
Atmos. Meas. Tech., 15, 6373–6386, https://doi.org/10.5194/amt-15-6373-2022, https://doi.org/10.5194/amt-15-6373-2022, 2022
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A remote sensing method to retrieve sizes of particles in ice clouds and precipitation from radar measurements at two wavelengths is described. This method is based on relating the particle size information to the ratio of radar signals at these two wavelengths. It is demonstrated that this ratio is informative about different characteristic particle sizes. Knowing atmospheric ice particle sizes is important for many applications such as precipitation estimation and climate modeling.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, https://doi.org/10.5194/amt-15-4307-2022, 2022
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Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies are infrequently sampled: every 7 to 91 d, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
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Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Henning Dorff, Heike Konow, and Felix Ament
Atmos. Meas. Tech., 15, 3641–3661, https://doi.org/10.5194/amt-15-3641-2022, https://doi.org/10.5194/amt-15-3641-2022, 2022
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This study elaborates how aircraft-based horizontal geometries of trade wind cumuli differ whether a one-dimensional profiling radar or a two-dimensional imager is used. Cloud size distributions are examined in terms of sensitivity to sample size, resolution, and instrument field of view. While the radar cannot reproduce the double power law distribution due to coarse resolution and restriction to vertical transects, the imager also reveals the elliptic cloud structure enhancing with wind speed.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
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The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Pradeep Khatri, Tadahiro Hayasaka, Hitoshi Irie, Husi Letu, Takashi Y. Nakajima, Hiroshi Ishimoto, and Tamio Takamura
Atmos. Meas. Tech., 15, 1967–1982, https://doi.org/10.5194/amt-15-1967-2022, https://doi.org/10.5194/amt-15-1967-2022, 2022
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Cloud properties observed by the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite are evaluated using surface observation data. The study finds that SGLI-observed cloud properties are qualitative enough, although water cloud properties are suggested to be more qualitative, and both water and ice cloud properties can reproduce surface irradiance quite satisfactorily. Thus, SGLI cloud products are very useful for different studies.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Gregor Köcher, Tobias Zinner, Christoph Knote, Eleni Tetoni, Florian Ewald, and Martin Hagen
Atmos. Meas. Tech., 15, 1033–1054, https://doi.org/10.5194/amt-15-1033-2022, https://doi.org/10.5194/amt-15-1033-2022, 2022
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We present a setup for systematic characterization of differences between numerical weather models and radar observations for convective weather situations. Radar observations providing dual-wavelength and polarimetric variables to infer information about hydrometeor shapes and sizes are compared against simulations using microphysics schemes of varying complexity. Differences are found in ice and liquid phase, pointing towards issues of some schemes in reproducing particle size distributions.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
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We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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.
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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.
The calibration of weather radars is one of the most dominant sources of errors hindering their...
