Articles | Volume 6, issue 5
https://doi.org/10.5194/amt-6-1227-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-6-1227-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 May 2013
Research article |
| 14 May 2013
Ground-based remote sensing of thin clouds in the Arctic
T. J. Garrett
Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, USA
C. Zhao
Lawrence Livermore National Laboratories, Livermore, California, USA
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Related authors
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, https://doi.org/10.5194/amt-17-4581-2024, 2024
Short summary
Short summary
Accurate measurements of the properties of snowflakes are challenging to make. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), is shown to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density. The method exploits the rate of heat transfer during the melting of a snowflake on a hotplate.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Timothy J. Garrett, Matheus R. Grasselli, and Stephen Keen
Earth Syst. Dynam., 13, 1021–1028, https://doi.org/10.5194/esd-13-1021-2022, https://doi.org/10.5194/esd-13-1021-2022, 2022
Short summary
Short summary
Current world economic production is rising relative to energy consumption. This increase in
production efficiencysuggests that carbon dioxide emissions can be decoupled from economic activity through technological change. We show instead a nearly fixed relationship between energy consumption and a new economic quantity, historically cumulative economic production. The strong link to the past implies inertia may play a more dominant role in societal evolution than is generally assumed.
Karlie N. Rees and Timothy J. Garrett
Atmos. Meas. Tech., 14, 7681–7691, https://doi.org/10.5194/amt-14-7681-2021, https://doi.org/10.5194/amt-14-7681-2021, 2021
Short summary
Short summary
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties according to World Meteorological Organization standards. Measurement accuracy depends on instrument sampling area, time interval, and precipitation rate. Simulations are compared with field measurements taken by an emerging hotplate precipitation sensor. We find that the current collection area is sufficient for light rain, but a larger collection area is required to detect moderate to heavy rain.
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, https://doi.org/10.5194/amt-14-1127-2021, 2021
Short summary
Short summary
Snow measurements are very sensitive to wind. Here, we compare airflow and snowfall simulations to Arctic observations for a Multi-Angle Snowflake Camera to show that measurements of fall speed, orientation, and size are accurate only with a double wind fence and winds below 5 m s−1. In this case, snowflakes tend to fall with a nearly horizontal orientation; the largest flakes are as much as 5 times more likely to be observed. Adjustments are needed for snow falling in naturally turbulent air.
Mathias Gergely, Steven J. Cooper, and Timothy J. Garrett
Atmos. Chem. Phys., 17, 12011–12030, https://doi.org/10.5194/acp-17-12011-2017, https://doi.org/10.5194/acp-17-12011-2017, 2017
Short summary
Short summary
This study investigates the importance of snowflake surface-area-to-volume ratio (SAV) for the interpretation of snowfall triple-frequency radar signatures. The results indicate that snowflake SAV has a strong impact on modeled snowfall radar signatures and therefore may be used to further constrain (the large variety and high natural variability of) snowflake shape for snowfall remote sensing, e.g., to distinguish graupel snow from snowfall characterized by large aggregate snowflakes.
Quentin Coopman, Timothy J. Garrett, Jérôme Riedi, Sabine Eckhardt, and Andreas Stohl
Atmos. Chem. Phys., 16, 4661–4674, https://doi.org/10.5194/acp-16-4661-2016, https://doi.org/10.5194/acp-16-4661-2016, 2016
Short summary
Short summary
We analyze interactions of Arctic clouds with pollution plumes that have been transported long distances from midlatitudes. Constraining for meteorological state, we find that pollution decreases cloud-droplet effective radius and increases cloud optical depth. The impact is highest when the atmosphere is particularly humid and/or stable suggesting that aerosol–cloud interactions depend on the Arctic's climate.
T. J. Garrett
Earth Syst. Dynam., 6, 673–688, https://doi.org/10.5194/esd-6-673-2015, https://doi.org/10.5194/esd-6-673-2015, 2015
Short summary
Short summary
GCMs and economic models are often coupled for climate scenarios. Here, what is examined is how well a simple non-equilibrium thermodynamic model can represent the multi-decadal growth of global civilization. Initialized with growth trends from the 1950s, the model attains high skill at hindcasting how fast the GDP and energy consumption grew during the 2000s. This opens treating the coupled economy and climate as a physically deterministic response to available flows of energy and matter.
B. van Diedenhoven, B. Cairns, A. M. Fridlind, A. S. Ackerman, and T. J. Garrett
Atmos. Chem. Phys., 13, 3185–3203, https://doi.org/10.5194/acp-13-3185-2013, https://doi.org/10.5194/acp-13-3185-2013, 2013
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, https://doi.org/10.5194/amt-17-4581-2024, 2024
Short summary
Short summary
Accurate measurements of the properties of snowflakes are challenging to make. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), is shown to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density. The method exploits the rate of heat transfer during the melting of a snowflake on a hotplate.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Timothy J. Garrett, Matheus R. Grasselli, and Stephen Keen
Earth Syst. Dynam., 13, 1021–1028, https://doi.org/10.5194/esd-13-1021-2022, https://doi.org/10.5194/esd-13-1021-2022, 2022
Short summary
Short summary
Current world economic production is rising relative to energy consumption. This increase in
production efficiencysuggests that carbon dioxide emissions can be decoupled from economic activity through technological change. We show instead a nearly fixed relationship between energy consumption and a new economic quantity, historically cumulative economic production. The strong link to the past implies inertia may play a more dominant role in societal evolution than is generally assumed.
Karlie N. Rees and Timothy J. Garrett
Atmos. Meas. Tech., 14, 7681–7691, https://doi.org/10.5194/amt-14-7681-2021, https://doi.org/10.5194/amt-14-7681-2021, 2021
Short summary
Short summary
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties according to World Meteorological Organization standards. Measurement accuracy depends on instrument sampling area, time interval, and precipitation rate. Simulations are compared with field measurements taken by an emerging hotplate precipitation sensor. We find that the current collection area is sufficient for light rain, but a larger collection area is required to detect moderate to heavy rain.
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, https://doi.org/10.5194/amt-14-1127-2021, 2021
Short summary
Short summary
Snow measurements are very sensitive to wind. Here, we compare airflow and snowfall simulations to Arctic observations for a Multi-Angle Snowflake Camera to show that measurements of fall speed, orientation, and size are accurate only with a double wind fence and winds below 5 m s−1. In this case, snowflakes tend to fall with a nearly horizontal orientation; the largest flakes are as much as 5 times more likely to be observed. Adjustments are needed for snow falling in naturally turbulent air.
Mathias Gergely, Steven J. Cooper, and Timothy J. Garrett
Atmos. Chem. Phys., 17, 12011–12030, https://doi.org/10.5194/acp-17-12011-2017, https://doi.org/10.5194/acp-17-12011-2017, 2017
Short summary
Short summary
This study investigates the importance of snowflake surface-area-to-volume ratio (SAV) for the interpretation of snowfall triple-frequency radar signatures. The results indicate that snowflake SAV has a strong impact on modeled snowfall radar signatures and therefore may be used to further constrain (the large variety and high natural variability of) snowflake shape for snowfall remote sensing, e.g., to distinguish graupel snow from snowfall characterized by large aggregate snowflakes.
Quentin Coopman, Timothy J. Garrett, Jérôme Riedi, Sabine Eckhardt, and Andreas Stohl
Atmos. Chem. Phys., 16, 4661–4674, https://doi.org/10.5194/acp-16-4661-2016, https://doi.org/10.5194/acp-16-4661-2016, 2016
Short summary
Short summary
We analyze interactions of Arctic clouds with pollution plumes that have been transported long distances from midlatitudes. Constraining for meteorological state, we find that pollution decreases cloud-droplet effective radius and increases cloud optical depth. The impact is highest when the atmosphere is particularly humid and/or stable suggesting that aerosol–cloud interactions depend on the Arctic's climate.
T. J. Garrett
Earth Syst. Dynam., 6, 673–688, https://doi.org/10.5194/esd-6-673-2015, https://doi.org/10.5194/esd-6-673-2015, 2015
Short summary
Short summary
GCMs and economic models are often coupled for climate scenarios. Here, what is examined is how well a simple non-equilibrium thermodynamic model can represent the multi-decadal growth of global civilization. Initialized with growth trends from the 1950s, the model attains high skill at hindcasting how fast the GDP and energy consumption grew during the 2000s. This opens treating the coupled economy and climate as a physically deterministic response to available flows of energy and matter.
B. van Diedenhoven, B. Cairns, A. M. Fridlind, A. S. Ackerman, and T. J. Garrett
Atmos. Chem. Phys., 13, 3185–3203, https://doi.org/10.5194/acp-13-3185-2013, https://doi.org/10.5194/acp-13-3185-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Assessment of horizontally-oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Riming-dependent Snowfall Rate and Ice Water Content Retrievals for W-band cloud radar
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
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
Radiative Closure Assessment of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACMB-DF Product
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
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
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
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite 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
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
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025, https://doi.org/10.5194/amt-18-2083-2025, 2025
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 based 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 observation experiments.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
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
Short summary
Short summary
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.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Short summary
This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
Short summary
Short summary
Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
Short summary
Short summary
We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
Short summary
Short summary
The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3841, https://doi.org/10.5194/egusphere-2024-3841, 2025
Short summary
Short summary
This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a year-long dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve the atmospheric model.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
EGUsphere, https://doi.org/10.5194/egusphere-2024-3664, https://doi.org/10.5194/egusphere-2024-3664, 2025
Short summary
Short summary
Contrails, the linear clouds formed by aircraft, are have a substantial climate impact. Flight deviations to avoid forming contrails should decrease this impact. We introduce a method for matching contrails seen by satellites to the flights that made them. This can determine if avoidance was successful and improve contrail forecasts. We also introduce a synthetic contrail dataset to evaluate the accuracy of the matches. We show that our attributions are much more accurate than previous methods.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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 found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3916, https://doi.org/10.5194/egusphere-2024-3916, 2025
Short summary
Short summary
Accurate measurements of cloud water content IWC and snowfall rate SR are challenging due to high spatial variability and limitations of our measurement techniques. Here we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path as a proxy for the occurrence of riming, which is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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 a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 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.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles 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 retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of 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.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. 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.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
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.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
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.
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.
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.
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.
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.
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.
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.
Cited articles
Beesley, J. A.: Estimating the effect of clouds on the arctic surface energy budget, J. Geophys. Res., 105, 10103–10117, 2000.
Bourdages, L., Duck, T. J., Lesins, G., Drummond, J. R., and Eloranta, E. W.: Physical properties of High Arctic tropospheric particles during winter, Atmos. Chem. Phys., 9, 6881–6897, https://doi.org/10.5194/acp-9-6881-2009, 2009.
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D., Spinhirne, J. D., and Scott, V. S.: Full-time eye-safe cloud and aerosol lidar observation at atmospheric radiation measurement program sites: Instruments and data processing, J. Atmos. Ocean. Tech., 19, 431–442, 2002.
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, L20804, http://dx.doi.org/10.1029/2012GL053385, 2012.
Chylek, P., Robinson, S., Dubey, M. K., King, M. D., Fu, Q., and Clodius, W. B.: Comparison of near-infrared and thermal infrared cloud phase detections, J. Geophys. Res., 111, D20203, https://doi.org/10.1029/2006JD007140, 2006.
Clough, S. A., Iacono, M. J., and Moncet, J. L.: Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapour, J. Geophys. Res., 97, 15761–15785, 1992.
Comstock, J. M., D'Entremont, R., DeSlover, D., Mace, G. G., Matrosov, S. Y., McFarlane, S. A., Minnis, P., Mitchell, D., Sassen, K., Shupe, M. D., et al.: An intercomparison of microphysical retrieval algorithms for upper-tropospheric ice clouds, Bull. Am. Meteorol. Soc., 88, 191–204, 2007.
Curry, J. A., Rossow, W. B., Randall, D., and Schramm, J. L.: Overview of Arctic cloud and radiation characteristics, J. Climate, 9, 1731–1764, 1996.
Curry, J. A., Hobbs, P. V., King, M. D., Randall, D. A., Minnis, P., Isaac, G. A., Pinto, J. O., Uttal, T., Bucholtz, A., Cripe, D. G., Gerber, H., Fairall, C. W., Garrett, T. J., Hudson, J., Intrieri, J. M., Jakob, C., Jensen, T., Lawson, P., Marcotte, D., Nguyen, L., Pilewskie, P., Rangno, A., Rogers, D. C., Strawbridge, K. B., Valero, F. P. J., Williams, A. G., and Wylie, D.: FIRE Arctic Clouds Experiment, Bull. Amer. Meteor. Soc., 81, 5–29, 2000.
de Boer, G., Collins, W. D., Menon, S., and Long, C. N.: Using surface remote sensors to derive radiative characteristics of Mixed-Phase Clouds: an example from M-PACE, Atmos. Chem. Phys., 11, 11937–11949, https://doi.org/10.5194/acp-11-11937-2011, 2011.
Dergach, A. L., Zabrodsky, G. M., and Morachevsky, V. G.: The results of a complex investigation of the type St-Sc clouds and fogs in the Arctic, Bull. Acad. Sci. USSR, Geophys. Ser., 1, 66–70, 1960.
Devasthale, A., Tjernström, M., Karlsson, K.-G., Thomas, M. A., Jones, C., Sedlar, J., and Omar, A. H.: The vertical distribution of thin features over the Arctic analysed from CALIPSO observations Part I: Optically thin clouds, Tellus B, 63, 77–85, https://doi.org/10.1111/j.1600-0889.2010.00516.x, 2011.
Dong, X. and Mace, G. G.: Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at Barrow, Alaska, J. Climate, 16, 445–461, 2003{a}.
Dong, X. and Mace, G. G.: Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at B}arrow, {A}laska, J. Climate, 16, 445–461, 2003{b.
Dong, X., Minnis, P., and Xi, B.: A climatology of midlatitude continental clouds from the ARM SGP central facility: Part I: Low-level cloud macrophysical, microphysical, and radiative properties, J. Climate, 18, 1391–1410, 2005.
Field, P. R., Wood, R., Brown, R. A., Kaye, P. H., Hirst, E., Greenway, R., and Smith, J. A.: Ice particle interarrival times measured with a Fast FSSP, J. Atmos. Ocean. Technol., 20, 249–261, 2003.
Foot, J. S.: Some observations of the optical properties of clouds, {P}art 2, {C}irrus, Q. J. R. Meteorol. Soc., 114, 145–164, 1988.
Francis, J. A. and Hunter, E.: New insight into the disappearing Arctic sea ice, Eos Trans. AGU, 87, 509–511, https://doi.org/10.1029/2006EO460001, 2006.
Francis, J. A. and Hunter, E.: Changes in the fabric of the Arctic's greenhouse blanket, Env. Res. Lett., 2, 045011–+, https://doi.org/10.1088/1748-9326/2/4/045011, 2007.
Fridlind, A. M., Ackerman, A. S., McFarquhar, G., Zhang, G., Poellot, M. R., DeMott, P. J., Prenni, A. J., and Heymsfield, A. J.: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2. Model results, J. Geophys. Res., 112, D24202, https://doi.org/10.1029/2007JD008646, 2007.
Garrett, T. J. and Zhao, C.: Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes, Nature, 440, 787–789, https://doi.org/10.1038/nature04636, 2006.
Garrett, T. J., Radke, L. F., and Hobbs, P. V.: Aerosol effects on the cloud emissivity and surface longwave heating in the Arctic, J. Atmos. Sci., 59, 769–778, 2002.
Garrett, T. J., Zhao, C., Dong, X., Mace, G. G., and Hobbs, P. V.: Effects of varying aerosol regimes on low-level Arctic stratus, Geophys. Res. Lett., 31, 17105–17109, 2004.
Han, W., Stamnes, K., and Lubin, D.: Retrieval of surface and cloud properties in the Arctic from NOAA AVHRR measurements, J. Appl. Meteor., 38, 989–1012, 1999.
Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527–610, 1974.
Harrington, J. Y., Feingold, G., and Cotton, W. R.: Radiative impacts on the growth of a population of drops within simulated summertime Arctic stratus, J. Atmos. Sci., 57, 766–785, 2000.
Hildenbrand, B., Bittner, M., Baier, F., and Erbertseder, T.: European Rocket and Balloon Programmes and Related Research, Vol. 530, chap. Derivation of vertically resolved ozone profiles by assimilating total column ozone data into the 3D-NCAR-ROSE chemical-transport model, 487–491, 2003.
Hobbs, P. V. and Rangno, A. L.: Microstructure of low and middle-level clouds over the beaufort sea, Q. J. R. Meteorol. Soc., 124, 2035–2071, 1998{a}.
Hobbs, P. V. and Rangno, A. L.: Microstructures of low and middle-level clouds over the B}eaufort {Sea, Quart. J. Roy. Meteor. Soc., 124, 2035–2071, 1998{b}.
Hobbs, P. V., Rangno, A. L., Shupe, M., and Uttal, T.: Airborne studies of cloud structures over the Arctic ocean comparisons with retrievals from ship-based remote sensing measurements, J. Geophys. Res., 106, 15029–15044, 2001.
Jayaweera, K. O. L. F. and Ohtake, T.: Concentration of ice crystals in Arctic Stratus Clouds, Geophys. Res. Lett., 9, 94–97, 1982.
Jouan, C., Girard, E., Pelon, J., Gultepe, I., Delano{ë}, J., and Blanchet, J.-P.: Characterization of Arctic ice cloud properties observed during ISDAC, J Geophys Res, 117, D23207, https://doi.org/10.1029/2012JD017889, 2012.
Jourdan, O., Mioche, G., Garrett, T. J., Schwarzenb{ö}ck, A., Vidot, J., Xie, Y., Shcherbakov, V., Yang, P., and Gayet, J.-F.: Coupling of the microphysical and optical properties of an Arctic nimbostratus cloud during the ASTAR 2004 experiment: Implications for light-scattering modeling, J. Geophys. Res., 115, D23206, https://doi.org/10.1029/2010JD014016, 2010.
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal Arctic sea ice loss, J. Geophys. Res., 114, D18204, https://doi.org/10.1029/2009JD011773, 2009.
Kay, J. E., L'Ecuyer, T., Gettelman, A., Stephens, G., and O'Dell, C.: The contribution of cloud and radiation anomalies to the 2007 Arctic sea ice extent minimum, Geophys. Res. Lett., 35, L08503, https://doi.org/10.1029/2008GL033451, 2008.
Kay, J. E., Holland, M. M., Bitz, C. M., Blanchard-Wrigglesworth, E., Gettelman, A., Conley, A., and Bailey, D.: The Influence of Local Feedbacks and Northward Heat Transport on the Equilibrium Arctic Climate Response to Increased Greenhouse Gas Forcing, J. Climate, 25, 5433–5450, https://doi.org/10.1175/JCLI-D-11-00622.1, http://dx.doi.org/10.1175/JCLI-D-11-00622.1, 2012.
King, M. D., Platnick, S., Yang, P., Arnold, G. T., Gray, M. A., Riedi, J. C., Ackerman, S. A., and Liou, K. N.: Remote sensing of liquid water and ice cloud optical thickness and effective radius in the Arctic: Application of airborne multispectral MAS data, J. Atmos. Ocean. Tech., 21, 857–875, 2004.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer, G. d., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M., Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M., Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C., Turner, D. D., Veron, D. E., Salzen, K. v., Walker, G. K., Wang, Z., Wolf, A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteorol. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, http://dx.doi.org/10.1002/qj.416, 2009.
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker, R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell, H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric emitted radiance interferometer. Part I: Instrument design, J. Atmos. Oceanic Technol., 21, 1763–1776, 2004.
Lampert, A., Ehrlich, A., Dörnbrack, A., Jourdan, O., Gayet, J.-F., Mioche, G., Shcherbakov, V., Ritter, C., and Wendisch, M.: Microphysical and radiative characterization of a subvisible midlevel Arctic ice cloud by airborne observations– a case study, Atmos. Chem. Phys., 9, 2647–2661, https://doi.org/10.5194/acp-9-2647-2009, 2009.
Lapaolo, M., Godin-Beekmann, S., DelFrate, F., Casadio, S., Petitdidier, M., McDermid, I. S., Leblanc, T., D. Swart, Y. M., Hansen, G., and Stebel, K.: Gome ozone profiles retrieved by neural network techniques: A global validation with lidar measurements, J. Quant. Spectr. Rad. Transfer, 107, 105–119, 2007.
Liljegren, J. C., Clothiaux, E. E., Mace, G. G., Kato, S., and Dong, X.: A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature, J. Geophys. Res., 106, 14485–14500, 2001.
Mahesh, A., Walden, V. P., and Warren, S. G.: Ground-based infrared remote sensing of cloud properties over the Antarctic plateau. Part II: Cloud optical depths and particle sizes, J. Appl. Meteor., 40, 1279–1294, 2001.
Marchand, R., Ackerman, T., Westwater, E. R., Clough, S. A., Pereira, K. C., and Liljegren, J. C.: An assessment of microwave absorption models and retrievals of cloud liquid water using clear-sky data, J. Geophys. Res., 108, 4773, https://doi.org/10.1029/2003JD003,843, 2003.
McFarquhar, G. M., Ghan, S., Verlinde, J., Korolev, A., Strapp, J. W., Schmid, B., Tomlinson, J. M., Wolde, M., Brooks, S. D., Cziczo, D., Dubey, M. K., Fan, J., Flynn, C., Gultepe, I., Hubbe, J., Gilles, M. K., Laskin, A., Lawson, P., Leaitch, W. R., Liu, P., Liu, X., Lubin, D., Mazzoleni, C., MacDonald, A.-M., Moffet, R. C., Morrison, H., Ovchinnikov, M., Shupe, M. D., Turner, D. D., Xie, S., Zelenyuk, A., Bae, K., Freer, M., and Glen, A.: Indirect and Semi-direct Aerosol Campaign, Bull. Am. Meteorol. Soc., 92, 183–201, https://doi.org/10.1175/2010BAMS2935.1, 2011.
Morrison, H., Zuidema, P., Ackerman, A. S., Avramov, A., de Boer, G., Fan, J., Fridlind, A. M., Hashino, T., Harrington, J. Y., Luo, Y., Ovchinnikov, M., and Shipway, B.: Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during SHEBA/FIRE-ACE, Journal of Advances in Modeling Earth Systems, 30, M06003, https://doi.org/10.1029/2011MS000066, 2011.
Nasiri, S. L. and Kahn, B. H.: Limitations of bispectral infrared cloud phase determination and potential for improvement, J. Appl. Meteorol. Clim., 47, 2895–2910, 2008.
Peppler, R. A., Long, C. N., Sisterson, D. L., Turner, D. D., Bahrmann, C. P., Christensen, S. W., Doty, K. J., C., E. R., Halter, T. D., Ivey, M. D., Keck, N. N., Kehoe, K. E., Liljegren, J. C., Macduf, M. C., Mather, J. H., McCord, R. A., Monroe, J. W., Moore, S. T., Nitschke, K. L., Orr, B. W., Perez, R. C., Perkins, B. D., Richardson, S. J., Sonntag, K. L., Voyles, J. W., , and Wagener, R.: An overview of ARM Program Climate Research Facility data quality assurance, The Open Atmos. Sci. J., 2, 192–216, https://doi.org/10.2174/1874282300802010192, 2008.
Pinto, J. O., Curry, J. A., and Intrieri, J. M.: Cloud-aerosol interactions during autumn over Beaufort Sea, J. Geophys. Res., 106, 15077–15098, https://doi.org/10.1029/2000JD900267, 2001.
Rangno, A. L. and Hobbs, P. V.: Ice particles in stratiform clouds in the Arctic and possible mechanisms for the production of high ice concentrations, J. Geophys. Res., 106, 15065–15076, https://doi.org/10.1029/2000JD900286, 2001.
Riedi, J., Marchant, B., Platnick, S., Baum, B., Thieuleux, F., Oudard, C., Parol, F., Nicolas, J., and Dubuisson, P.: Cloud thermodynamic phase inferred from merged POLDER and MODIS data, Atmos. Chem. Phys. Disc., 7, 14103–14137, 2007.
Shupe, M. D.: Clouds at Arctic Atmospheric Observatories. Part II: Thermodynamic Phase Characteristics, Journal of Applied Meteorology and Climatology, 50, 645–661, https://doi.org/10.1175/2010JAMC2468.1, 2011.
Shupe, M. D., Uttal, T., and Matrosov, S. Y.: Arctic cloud microphysics retrievals from surface-based remote sensors at SHEBA, J. Appl. Meteor., 44, 1544–1562, 2005.
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA, J. Atmos. Sci., 63, 697–711, 2006.
Smith, W. S. and Kao, C. Y.: Numerical simulations of observed Arctic stratus clouds using a second-order turbulence closure model, J. Appl. Meteor., 35, 47–59, 1996.
Stamnes, K., Tsay, S. C., Wiscombe, W., and Jayaweera, K.: A numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Opt., 27, 2502–2509, 1988.
Strabala, K. I., Ackerman, S. A., and Menzel, W. P.: Cloud properties inferred from 8–12 μm data, J. Appl. Meteor., 33, 212–229, 1994.
Tietze, K. V., Riedi, J., Stohl, A., and Garrett, T. J.: Space-based evaluation of interactions between aerosols and low-level Arctic clouds during the Spring and Summer of 2008, Atmos. Chem. Phys., 11, 3359–3373, https://doi.org/10.5194/acp-11-3359-2011, 2011.
Turner, D. and Eloranta, E.: Validating Mixed-Phase Cloud Optical Depth Retrieved From Infrared Observations With High Spectral Resolution Lidar, Geoscience and Remote Sensing Letters, IEEE, 5, 285–288, https://doi.org/10.1109/LGRS.2008.915940, 2008.
Turner, D. D.: Arctic Mixed-Phase Cloud Properties from AERI Lidar Observations: Algorithm and Results from SHEBA, J. Appl. Meteorol., 44, 427–444, https://doi.org/10.1175/JAM2208.1, 2005.
Turner, D. D., Ackerman, S. A., Baum, B. A., Revercomb, H. E., and Yang, P.: Cloud phase determination using ground-based AERI observations at SHEBA, J. Appl. Meteor., 42, 701–715, 2003.
Uppala, S., Kallberg, P., Simmons, A., Andrae, U., da Costa Bechtold, V., Fiorino, M., Gibson, J., Haseler, J., Hernandez, A., Kelly, G., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R., Andersson, E., Arpe, K., Balmaseda, M., Beljaars, A., van de Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Holm, E., Hoskins, B., Isaksen, L., Janssen, P., Jenne, R., McNally, A., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N., Saunders, R., Simon, P., Sterl, A., Trenberth, K., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Quart. J. Roy. Meteor. Soc., 131, 2961–3012, 2005.
van Diedenhoven, B., Fridlind, A. M., Ackerman, A. S., Eloranta, E. W., and McFarquhar, G. M.: An evaluation of ice formation in large-eddy simulations of supercooled Arctic stratocumulus using ground-based lidar and cloud radar, J. Geophys. Res.-Atmos., 114, D10203, https://doi.org/10.1029/2008JD011198, 2009.
Verlinde, J., Harrington, J. Y., McFarquhar, G. M., Yannuzzi, V. T., Avramov, A., Greenberg, S., Johnson, N., Zhang, G., Poellot, M. R., Mather, J. H., Turner, D. D., Eloranta, E. W., Zak, B. D., Prenni, A. J., Daniel, J. S., Kok, G. L., Tobin, D. C., Holz, R., Sassen, K., Spangenberg, D., Minnis, P., Tooman, T. P., Ivey, M. D., Richardson, S. J., Bahrmann, C. P., Shupe, M., DeMott, P. J., Heymsfield, A. J., and Shofield, R.: The Mixed-Phase Arctic Cloud Experiment, Bull. Amer. Meteorol. Soc., 88, 205–221, 2007.
Wang, X. and Key, J. R.: Recent trends in Arctic surface, cloud, and radiation properties from space, Science, 299, 1725–1728, 2003.
Wang, X. and Key, J. R.: Arctic surface, cloud, and radiation properties based on the AVHRR polar pathfinder dataset. Part I: Spatial and temporal characteristics, J. Climate, 18, 2558–2574, 2005.
Warren, S. G.: Optical constants of ice from the ultraviolet to the microwave, Appl. Opt., 23, 1206–1225, 1984.
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008.
Wieliczka, D. M., Weng, S., and Querry, M. R.: Wedge shaped cell for highly absorbent liquids: infrared optical constants of water, Appl. Opt., 28, 1714–1719, 1989.
Wiscombe, W. J.: Improved Mie scattering algorithms, Appl. Opt., 19, 1505–1509, 1980.
Witte, H. J.: Airborne observations of cloud particles and infrared flux density in the Arctic, Master's thesis, University of Washington, 1968.
Xiong, X., Lubin, D., Li, W., and Stamnes, K.: A critical examination of satellite cloud retrieval from AVHRR in the Arctic using SHEBA data, J. Appl. Meteor., 41, 1195–1209, 2002.
Zhao, C. and Garrett, T. J.: Ground-based remote-sensing of precipitation in the Arctic, J. Geophys. Res., 113, D14204, https://doi.org/10.1029/2007JD009222, 2008.
