Articles | Volume 16, issue 16
https://doi.org/10.5194/amt-16-3931-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3931-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2023
Research article |
| 29 Aug 2023
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Department of Atmospheric Sciences, University of Illinois at
Urbana-Champaign, Urbana, IL 61801, USA
Aviad Levis
Computer and Mathematical Sciences Department, California Institute of Technology, Pasadena, CA 91125, USA
Larry Di Girolamo
Department of Atmospheric Sciences, University of Illinois at
Urbana-Champaign, Urbana, IL 61801, USA
Vadim Holodovsky
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
Linda Forster
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Anthony B. Davis
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Yoav Y. Schechner
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
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Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
EGUsphere, https://doi.org/10.5194/egusphere-2023-353, https://doi.org/10.5194/egusphere-2023-353, 2023
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This study extends MFASIS to simulate 1.6 μm NIR channel reflectances with a neural network, enabling its use in model evaluation and data assimilation. A two-layer model was developed for cloud structure with optimized reflectance errors using IFS forecasts and ICON-D2 hindcasts. Mean absolute reflectance error achieved was 0.01 or less, much smaller than typical differences between observations and models.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-6, https://doi.org/10.5194/amt-2023-6, 2023
Revised manuscript accepted for AMT
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A new database is constructed from satellites comprising millions of deep convective clouds that spans the global tropics and subtropics and greater than 20 years. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirical study of the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
EGUsphere, https://doi.org/10.5194/egusphere-2023-181, https://doi.org/10.5194/egusphere-2023-181, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
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With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-300, https://doi.org/10.5194/amt-2022-300, 2022
Revised manuscript accepted for AMT
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The Level 2 algorithms development of EarthCARE satellite mission requires realistic three-dimensional cloud and aerosols scenes along the satellite orbits. One of the best way to produce these scenes is to use high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This manuscript describes the production and validation of three EarthCARE test scenes.
Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
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This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
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The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
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In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, https://doi.org/10.5194/amt-15-5985-2022, 2022
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Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
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Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
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Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
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This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi
Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, https://doi.org/10.5194/amt-15-5181-2022, 2022
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A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Rachel T. Pinker, Yingtao Ma, Wen Chen, Istvan Laszlo, Hongqing Liu, Hye-Yun Kim, and Jaime Daniels
Atmos. Meas. Tech., 15, 5077–5094, https://doi.org/10.5194/amt-15-5077-2022, https://doi.org/10.5194/amt-15-5077-2022, 2022
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Scene-dependent narrow-to-broadband transformations are developed to facilitate the use of observations from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R, to derive surface shortwave radiative fluxes. This is a first NOAA product at the high resolution of about 5 k over the contiguous United States (CONUS) region. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
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This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429, https://doi.org/10.5194/amt-15-4411-2022, https://doi.org/10.5194/amt-15-4411-2022, 2022
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In this paper we describe the implementation of an interpolation–prediction estimator applied to cloud properties derived from CloudSat observations. The objective is to evaluate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparisons. This paper is didactic and beneficial for anyone interested in kriging estimators.
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
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Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999, https://doi.org/10.5194/amt-15-3969-2022, https://doi.org/10.5194/amt-15-3969-2022, 2022
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We use the C-band POLDIRAD and the Ka-band MIRA-35 to perform snowfall dual-wavelength polarimetric radar measurements. We develop an ice microphysics retrieval for mass, apparent shape, and median size of the particle size distribution by comparing observations to T-matrix ice spheroid simulations while varying the mass–size relationship. We furthermore show how the polarimetric measurements from POLDIRAD help to narrow down ambiguities between ice particle shape and size.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
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This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
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Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Xiaotong Li, Baozhu Wang, Bo Qiu, and Chao Wu
Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, https://doi.org/10.5194/amt-15-3629-2022, 2022
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The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567, https://doi.org/10.5194/amt-15-3555-2022, https://doi.org/10.5194/amt-15-3555-2022, 2022
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It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
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A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Cited articles
Abdoulaev, G. S., Ren, K., and Hielscher, A. H.: Optical tomography as a PDE-constrained optimization problem, Inverse Probl., 21, 1507–1530, https://doi.org/10.1088/0266-5611/21/5/002, 2005.
Ahn, E., Huang, Y., Siems, S. T., and Manton, M. J.: A Comparison of Cloud
Microphysical Properties Derived From MODIS and CALIPSO With In Situ
Measurements Over the Wintertime Southern Ocean, J. Geophys.
Res.-Atmos., 123, 11120–11140, https://doi.org/10.1029/2018JD028535, 2018.
Alexandrov, M. D., Emde, C., Van Diedenhoven, B., and Cairns, B.:
Application of Radon Transform to Multi-Angle Measurements Made by the
Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I:
Theory and Tests on Simulated Data, Front. Remote Sens., 2, 791130, https://doi.org/10.3389/frsen.2021.791130, 2021.
Arridge, S. R. and Schotland, J. C.: Optical tomography: forward and inverse
problems, Inverse Probl., 25, 123010, https://doi.org/10.1088/0266-5611/25/12/123010, 2009.
Bal, G.: Introduction to inverse problems, University of Chicago,
https://statistics.uchicago.edu/~guillaumebal/PAPERS/IntroductionInverseProblems.pdf (last access: 21 August 2023), 2012.
Bal, G. and Jollivet, A.: Stability estimates in stationary inverse transport, Inverse Probl. Imag., 2, 427–454, https://doi.org/10.3934/ipi.2008.2.427, 2008.
Bal, G., Chen, J., and Davis, A. B.: Reconstruction of cloud geometry from
high-resolution multi-angle images, Inverse Probl. Imag., 12, 261–280,
https://doi.org/10.3934/ipi.2018011, 2018.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P.,
Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau,
A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman,
A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T.,
Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein,
M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T.,
Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative
Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660,
https://doi.org/10.1029/2019RG000660, 2020.
Bitterli, B., Ravichandran, S., Müller, T., Wrenninge, M., Novák,
J., Marschner, S., and Jarosz, W.: A radiative transfer framework for
non-exponential media, ACM T. Graphic., 37, 225, https://doi.org/10.1145/3272127.3275103, 2018.
Bruegge, C. J., Chrien, N. L., Ando, R. R., Diner, D. J., Abdou, W. A.,
Helmlinger, M. C., Pilorz, S. H., and Thome, K. J.: Early validation of the
Multi-angle Imaging SpectroRadiometer (MISR) radiometric scale, IEEE
T. Geosci. Remote, 40, 1477–1492, https://doi.org/10.1109/TGRS.2002.801583, 2002.
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm
for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208,
https://doi.org/10.1137/0916069, 1995.
Cahalan, R. F., Ridgway, W., Wiscombe, W. J., Bell, T. L., and Snider, J.
B.: The Albedo of Fractal Stratocumulus Clouds, J. Atmos. Sci., 51, 2434–2455, https://doi.org/10.1175/1520-0469(1994)051<2434:TAOFSC>2.0.CO;2, 1994.
Cahalan, R. F., Oreopoulos, L., Marshak, A., Evans, K. F., Davis, A. B.,
Pincus, R., Yetzer, K. H., Mayer, B., Davies, R., Ackerman, T. P., Barker,
H. W., Clothiaux, E. E., Ellingson, R. G., Garay, M. J., Kassianov, E.,
Kinne, S., Macke, A., O'hirok, W., Partain, P. T., Prigarin, S. M., Rublev,
A. N., Stephens, G. L., Szczap, F., Takara, E. E., Várnai, T., Wen, G.,
and Zhuravleva, T. B.: THE I3RC: Bringing Together the Most Advanced
Radiative Transfer Tools for Cloudy Atmospheres, B. Am. Meteorol. Soc., 86, 1275–1294, https://doi.org/10.1175/BAMS-86-9-1275, 2005.
Cairns, B., Lacis, A. A., and Carlson, B. E.: Absorption within
Inhomogeneous Clouds and Its Parameterization in General Circulation Models,
J. Atmos. Sci., 57, 700–714,
https://doi.org/10.1175/1520-0469(2000)057<0700:AWICAI>2.0.CO;2, 2000.
Chazette, P., Totems, J., Baron, A., Flamant, C., and Bony, S.: Trade-wind clouds and aerosols characterized by airborne horizontal lidar measurements during the EUREC4A field campaign, Earth Syst. Sci. Data, 12, 2919–2936, https://doi.org/10.5194/essd-12-2919-2020, 2020.
Chen, K., Li, Q., and Wang, L.: Stability of stationary inverse transport
equation in diffusion scaling, Inverse Probl., 34, 025004,
https://doi.org/10.1088/1361-6420/aa990c, 2018.
Dandini, P., Cornet, C., Binet, R., Fenouil, L., Holodovsky, V., Y. Schechner, Y., Ricard, D., and Rosenfeld, D.: 3D cloud envelope and cloud development velocity from simulated CLOUD (C3IEL) stereo images, Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, 2022.
Davis, A., Marshak, A., Wiscombe, W., and Cahalan, R.: Scale Invariance of
Liquid Water Distributions in Marine Stratocumulus. Part I: Spectral
Properties and Stationarity Issues, J. Atmos. Sci., 53,
1538–1558, https://doi.org/10.1175/1520-0469(1996)053<1538:SIOLWD>2.0.CO;2, 1996.
Davis, A., Marshak, A., Cahalan, R., and Wiscombe, W.: The Landsat Scale
Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect:
Implications for Cloud Remote Sensing, J. Atmos. Sci.,
54, 241–260, https://doi.org/10.1175/1520-0469(1997)054<0241:TLSBIS>2.0.CO;2, 1997.
Davis, A. B. and Marshak, A.: Photon propagation in heterogeneous optical
media with spatial correlations: enhanced mean-free-paths and
wider-than-exponential free-path distributions, J. Quant.
Spectrosc. Ra., 84, 3–34, https://doi.org/10.1016/S0022-4073(03)00114-6, 2004.
Davis, A. B., Marshak, A., Gerber, H., and Wiscombe, W. J.: Horizontal
structure of marine boundary layer clouds from centimeter to kilometer
scales, J. Geophys. Res.-Atmos., 104, 6123–6144,
https://doi.org/10.1029/1998JD200078, 1999.
Davis, A. B., Forster, L., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space using MISR and MODIS: The Physics of Image Formation
for Opaque Convective Clouds, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14537, 27 July 2021.
Delanoë, J. and Hogan, R. J.: A variational scheme for retrieving ice
cloud properties from combined radar, lidar, and infrared radiometer,
J. Geophys. Res.-Atmos., 113, D07204, https://doi.org/10.1029/2007JD009000, 2008.
De Sterck, H. and Howse, A. J. M.: Nonlinearly preconditioned L-BFGS as an
acceleration mechanism for alternating least squares with application to
tensor decomposition, Numer. Linear Algebr., 25, e2202,
https://doi.org/10.1002/nla.2202, 2018.
Di Girolamo, L., Liang, L., and Platnick, S.: A global view of
one-dimensional solar radiative transfer through oceanic water clouds,
Geophys. Res. Lett., 37, L18809, https://doi.org/10.1029/2010GL044094, 2010.
Diner, D. J., Xu, F., Garay, M. J., Martonchik, J. V., Rheingans, B. E., Geier, S., Davis, A., Hancock, B. R., Jovanovic, V. M., Bull, M. A., Capraro, K., Chipman, R. A., and McClain, S. C.: The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing, Atmos. Meas. Tech., 6, 2007–2025, https://doi.org/10.5194/amt-6-2007-2013, 2013.
Doicu, A. and Efremenko, D. S.: Linearizations of the Spherical Harmonic
Discrete Ordinate Method (SHDOM), Atmosphere, 10, 292,
https://doi.org/10.3390/atmos10060292, 2019.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. I: Surrogate minimization method, J. Quant. Spectrosc. Ra., 277, 107954, https://doi.org/10.1016/j.jqsrt.2021.107954, 2022a.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. II: Adjoint method, J. Quant. Spectrosc. Ra., 285, 108177, https://doi.org/10.1016/j.jqsrt.2022.108177, 2022b.
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011.
Evans, K. F.: The Spherical Harmonics Discrete Ordinate Method for
Three-Dimensional Atmospheric Radiative Transfer, J. Atmos. Sci., 55, 429–446, https://doi.org/10.1175/1520-0469(1998)055<0429:TSHDOM>2.0.CO;2, 1998.
Evans, K. F.: Spherical Harmonics Discrete Ordinate Method, University of Colorado [code], Boulder,
https://nit.coloradolinux.com/~evans/shdom/shdom.tar.gz, last access: 23 March 2023.
Ewald, F., Zinner, T., Kölling, T., and Mayer, B.: Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization, Atmos. Meas. Tech., 12, 1183–1206, https://doi.org/10.5194/amt-12-1183-2019, 2019.
Ewald, F., Groß, S., Wirth, M., Delanoë, J., Fox, S., and Mayer, B.: Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics, Atmos. Meas. Tech., 14, 5029–5047, https://doi.org/10.5194/amt-14-5029-2021, 2021.
Eytan, E., Khain, A., Pinsky, M., Altaratz, O., Shpund, J., and Koren, I.:
Shallow Cumulus Properties as Captured by Adiabatic Fraction in
High-Resolution LES Simulations, J. Atmos. Sci., 79,
409–428, https://doi.org/10.1175/JAS-D-21-0201.1, 2022.
Fielding, M. D., Chiu, J. C., Hogan, R. J., and Feingold, G.: A novel
ensemble method for retrieving properties of warm cloud in 3-D using
ground-based scanning radar and zenith radiances, J. Geophys. Res.-Atmos., 119, 10912–10930, https://doi.org/10.1002/2014JD021742, 2014.
Forster, L., Davis, A. B., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in
Opaque Convective Clouds, J. Atmos. Sci., 78, 155–166,
https://doi.org/10.1175/JAS-D-19-0262.1, 2021.
Fu, D., Di Girolamo, L., Liang, L., and Zhao, G.: Regional Biases in MODIS
Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With
MISR, J. Geophys. Res.-Atmos., 124, 13182–13196,
https://doi.org/10.1029/2019JD031063, 2019.
Fu, D., Di Girolamo, L., Rauber, R. M., McFarquhar, G. M., Nesbitt, S. W., Loveridge, J., Hong, Y., van Diedenhoven, B., Cairns, B., Alexandrov, M. D., Lawson, P., Woods, S., Tanelli, S., Schmidt, S., Hostetler, C., and Scarino, A. J.: An evaluation of the liquid cloud droplet effective radius derived from MODIS, airborne remote sensing, and in situ measurements from CAMP2Ex, Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, 2022.
Gao, M., Franz, B. A., Knobelspiesse, K., Zhai, P.-W., Martins, V., Burton, S., Cairns, B., Ferrare, R., Gales, J., Hasekamp, O., Hu, Y., Ibrahim, A., McBride, B., Puthukkudy, A., Werdell, P. J., and Xu, X.: Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model, Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021, 2021.
Gao, M., Knobelspiesse, K., Franz, B. A., Zhai, P.-W., Sayer, A. M., Ibrahim, A., Cairns, B., Hasekamp, O., Hu, Y., Martins, V., Werdell, P. J., and Xu, X.: Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, 2022.
Guillaume, A., Kahn, B. H., Yue, Q., Fetzer, E. J., Wong, S., Manipon, G.
J., Hua, H., and Wilson, B. D.: Horizontal and Vertical Scaling of Cloud
Geometry Inferred from CloudSat Data, J. Atmos. Sci.,
75, 2187–2197, https://doi.org/10.1175/JAS-D-17-0111.1, 2018.
Hu, C.-C. and van Leeuwen, P. J.: A particle flow filter for
high-dimensional system applications, Q. J. Roy. Meteor. Soc., 147, 2352–2374, https://doi.org/10.1002/qj.4028, 2021.
Iwabuchi, H. and Hayasaka, T.: Effects of Cloud Horizontal Inhomogeneity on
the Optical Thickness Retrieved from Moderate-Resolution Satellite Data,
J. Atmos. Sci., 59, 2227–2242,
https://doi.org/10.1175/1520-0469(2002)059<2227:EOCHIO>2.0.CO;2, 2002.
Kato, S. and Marshak, A.: Solar zenith and viewing geometry-dependent errors
in satellite retrieved cloud optical thickness: Marine stratocumulus case,
J. Geophys. Res.-Atmos., 114, D01202, https://doi.org/10.1029/2008JD010579, 2009.
King, N. J. and Vaughan, G.: Using passive remote sensing to retrieve the
vertical variation of cloud droplet size in marine stratocumulus: An
assessment of information content and the potential for improved retrievals
from hyperspectral measurements, J. Geophys. Res.-Atmos., 117, D15206, https://doi.org/10.1029/2012JD017896, 2012.
Kobayashi, T.: Parameterization of Reflectivity for Broken Cloud Fields,
J. Atmos. Sci., 45, 3034–3045,
https://doi.org/10.1175/1520-0469(1988)045<3034:PORFBC>2.0.CO;2, 1988.
Kutulakos, K. N. and Seitz, S. M.: A theory of shape by space carving, in:
Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20–27 September 1999, vol. 1, 307–314, https://doi.org/10.1109/ICCV.1999.791235, 1999.
Lebsock, M. and Su, H.: Application of active spaceborne remote sensing for
understanding biases between passive cloud water path retrievals, J. Geophys. Res.-Atmos., 119, 8962–8979, https://doi.org/10.1002/2014JD021568, 2014.
Lee, B., Di Girolamo, L., Zhao, G., and Zhan, Y.: Three-Dimensional Cloud
Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer, Remote
Sens., 10, 1858, https://doi.org/10.3390/rs10111858, 2018.
Levis, A., Schechner, Y. Y., Aides, A., and Davis, A. B.: Airborne
Three-Dimensional Cloud Tomography, in: 2015 IEEE International Conference
on Computer Vision (ICCV), Santiago, Chile, 2–13 December 2015, 3379–3387, https://doi.org/10.1109/ICCV.2015.386, 2015.
Levis, A., Schechner, Y. Y., and Davis, A. B.: Multiple-Scattering
Microphysics Tomography, in: 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, 5797–5806,
https://doi.org/10.1109/CVPR.2017.614, 2017.
Levis, A., Schechner, Y. Y., Davis, A. B., and Loveridge, J.: Multi-View
Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size,
Remote Sens., 12, 2831, https://doi.org/10.3390/rs12172831, 2020.
Lewis, G. M., Austin, P. H., and Szczodrak, M.: Spatial statistics of marine
boundary layer clouds, J. Geophys. Res.-Atmos., 109, D04104,
https://doi.org/10.1029/2003JD003742, 2004.
Liang, L. and Girolamo, L. D.: A global analysis on the view-angle
dependence of plane-parallel oceanic liquid water cloud optical thickness
using data synergy from MISR and MODIS, J. Geophys. Res.-Atmos., 118, 2389–2403, https://doi.org/10.1029/2012JD018201, 2013.
Lovejoy, S., Schertzer, D., Silas, P., Tessier, Y., and Lavallee, D.: The
unified scaling model of the atmospheric dynamics and systematic analysis of
scale invariance in cloud radiances, Ann. Geophys., 11, 119–127, 1993.
Loveridge, J.: AT3D-PART2: Stochastically Generated Clouds, Zenodo [data set], https://doi.org/10.5281/zenodo.8270210, 2023.
Loveridge, J., Levis, A., Aides, A., Forster, L., and Holodovsky, V.:
Atmospheric Tomography with 3D Radiative Transfer, v4.1.2,
Zenodo [code], https://doi.org/10.5281/zenodo.7062466, 2022.
Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation, Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, 2023a.
Loveridge, J., Levis, A., Holodovsky, V., and Forster, L.: Atmospheric Tomography with 3D Radiative Transfer, GitHub [code], https://github.com/CloudTomography/AT3D (last access: 28 March 2023), 2023b.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative smoothing
in fractal clouds, J. Geophys. Res.-Atmos., 100, 26247–26261, https://doi.org/10.1029/95JD02895, 1995.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative effects of
sub-mean free path liquid water variability observed in stratiform clouds,
J. Geophys. Res.-Atmos., 103, 19557–19567,
https://doi.org/10.1029/98JD01728, 1998.
Marshak, A., Platnick, S., Várnai, T., Wen, G., and Cahalan, R. F.:
Impact of three-dimensional radiative effects on satellite retrievals of
cloud droplet sizes, J. Geophys. Res.-Atmos., 111, D09207,
https://doi.org/10.1029/2005JD006686, 2006.
Martin, W., Cairns, B., and Bal, G.: Adjoint methods for adjusting
three-dimensional atmosphere and surface properties to fit
multi-angle/multi-pixel polarimetric measurements, J. Quant. Spectrosc. Ra., 144, 68–85, https://doi.org/10.1016/j.jqsrt.2014.03.030, 2014.
Martin, W. G. K. and Hasekamp, O. P.: A demonstration of adjoint methods for
multi-dimensional remote sensing of the atmosphere and surface, J. Quant. Spectrosc. Ra., 204, 215–231, https://doi.org/10.1016/j.jqsrt.2017.09.031, 2018.
Miller, D. J., Zhang, Z., Platnick, S., Ackerman, A. S., Werner, F., Cornet, C., and Knobelspiesse, K.: Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator, Atmos. Meas. Tech., 11, 3689–3715, https://doi.org/10.5194/amt-11-3689-2018, 2018.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van
Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud
and Precipitation Microphysics, J. Adv. Model. Earth Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Nakajima, T. and Tanaka, M.: Algorithms for radiative intensity calculations
in moderately thick atmospheres using a truncation approximation, J.
Quant. Spectrosc. Ra., 40, 51–69, https://doi.org/10.1016/0022-4073(88)90031-3, 1988.
NASA: AtmOS Tandem Stereographic Cameras Instrumentation, National Aeronautics and Space Administration, Goddard Space Flight Center,
https://aos.gsfc.nasa.gov/docs/ATMOS_Tandem_Stereographic_Cameras_210602+final+revised.pdf, last access: 8 June 2021.
Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network, Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, 2022.
Niu, H., Lin, Z.-J., Tian, F., Dhamne, S., and Liu, H.: Comprehensive
investigation of three-dimensional diffuse optical tomography with depth
compensation algorithm, J. Biomed. Opt., 15, 046005,
https://doi.org/10.1117/1.3462986, 2010.
Painemal, D., Spangenberg, D., Smith Jr., W. L., Minnis, P., Cairns, B., Moore, R. H., Crosbie, E., Robinson, C., Thornhill, K. L., Winstead, E. L., and Ziemba, L.: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign, Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, 2021.
Peherstorfer, B., Willcox, K., and Gunzburger, M.: Survey of Multifidelity
Methods in Uncertainty Propagation, Inference, and Optimization, SIAM Rev.,
60, 550–591, https://doi.org/10.1137/16M1082469, 2018.
Pincus, R. and Evans, K. F.: Computational Cost and Accuracy in Calculating
Three-Dimensional Radiative Transfer: Results for New Implementations of
Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131–3146,
https://doi.org/10.1175/2009JAS3137.1, 2009.
Prigarin, S. M. and Marshak, A.: A Simple Stochastic Model for Generating
Broken Cloud Optical Depth and Cloud-Top Height Fields, J. Atmos. Sci., 66, 92–104, https://doi.org/10.1175/2008JAS2699.1, 2009.
Raveendran, T., Gupta, S., Vasu, R. M., and Roy, D.: Pseudo-time particle
filtering for diffuse optical tomography, J. Opt. Soc. Am. A, 28,
2070–2081, https://doi.org/10.1364/JOSAA.28.002070, 2011.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, World Scientific,
https://doi.org/10.1142/3171, 2000.
Ronen, R., Holodovsky, V., and Schechner, Y. Y.: Variable Imaging Projection
Cloud Scattering Tomography, IEEE T. Pattern Anal., 1–12, https://doi.org/10.1109/TPAMI.2022.3195920, 2022.
Schilling, K., Schechner, Y. Y., and Koren, I.: CloudCT – Computed
Tomography Of Clouds By A Small Satellite Formation, 12th IAA symposium on
Small Satellites for Earth Observation, Berlin, Germany, 6–10 May 2019,
https://www.cloudct.space/_files/ugd/aef793_e2334d0332014022aeb4a5891ff12e92.pdf
(last access: 20 March 2023), 2019.
Schmetz, J.: On the parameterization of the radiative properties of broken
clouds, Tellus A, 36, 417–432, https://doi.org/10.3402/tellusa.v36i5.11644, 1984.
Seiz, G. and Davies, R.: Reconstruction of cloud geometry from multi-view
satellite images, Remote Sens. Environ., 100, 143–149,
https://doi.org/10.1016/j.rse.2005.09.016, 2006.
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
https://doi.org/10.1038/nature12829, 2014.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J.
R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678,
https://doi.org/10.1029/2019RG000678, 2020.
Shi, H.-J. M., Xie, Y., Byrd, R., and Nocedal, J.: A Noise-Tolerant
Quasi-Newton Algorithm for Unconstrained Optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.04352, 9 September 2021.
Tarvainen, T., Kolehmainen, V., Pulkkinen, A., Vauhkonen, M., Schweiger, M.,
Arridge, S. R., and Kaipio, J. P.: An approximation error approach for
compensating for modelling errors between the radiative transfer equation
and the diffusion approximation in diffuse optical tomography, Inverse Probl., 26, 015005, https://doi.org/10.1088/0266-5611/26/1/015005, 2009.
Tian, F., Niu, H., Khadka, S., Lin, Z.-J., and Liu, H.: Algorithmic depth
compensation improves quantification and noise suppression in functional
diffuse optical tomography, Biomed. Opt. Express, 1, 441–452,
https://doi.org/10.1364/BOE.1.000441, 2010.
Tzabari, M., Holodovsky, V., Shubi, O., Eytan, E., Koren, I., and Schechner,
Y. Y.: Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase
Clouds by the CloudCT Mission, IEEE T. Geosci. Remote, 60, 1–16, https://doi.org/10.1109/TGRS.2022.3198525, 2022.
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich,
S.: Particle filters for high-dimensional geoscience applications: A review,
Q. J. Roy. Meteor. Soc., 145, 2335–2365, https://doi.org/10.1002/qj.3551, 2019.
Veikherman, D., Aides, A., Schechner, Y. Y., and Levis, A.: Clouds in the
Cloud, in: Computer Vision – ACCV 2014, Springer, Cham, 659–674,
https://doi.org/10.1007/978-3-319-16817-3_43, 2015.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: fundamental
algorithms for scientific computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020.
Weinman, J. A. and Harshvardhan: Solar reflection from a regular array of
horizontally finite clouds, Appl. Optics, 21, 2940–2944,
https://doi.org/10.1364/AO.21.002940, 1982.
Wiscombe, W. J.: The Delta–M Method: Rapid Yet Accurate Radiative Flux
Calculations for Strongly Asymmetric Phase Functions, J. Atmos. Sci., 34, 1408–1422, https://doi.org/10.1175/1520-0469(1977)034<1408:TDMRYA>2.0.CO;2, 1977.
Xu, Z., Mace, G. G., and Posselt, D. J.: Impact of Rain on Retrieved Warm
Cloud Properties Using Visible and Near-Infrared Reflectances Using Markov
Chain Monte Carlo Techniques, IEEE T. Geosci. Remote, 60, 1–10, https://doi.org/10.1109/TGRS.2022.3208007, 2022.
Yang, Y. and Di Girolamo, L.: Impacts of 3-D radiative effects on satellite
cloud detection and their consequences on cloud fraction and aerosol optical
depth retrievals, J. Geophys. Res.-Atmos., 113, D04213,
https://doi.org/10.1029/2007JD009095, 2008.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue,
H.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing
of cloud droplet effective radius: Case studies based on large-eddy
simulations, J. Geophys. Res.-Atmos., 117, D19208,
https://doi.org/10.1029/2012JD017655, 2012.
Zhao, G. and Di Girolamo, L.: Statistics on the macrophysical properties of
trade wind cumuli over the tropical western Atlantic, J. Geophys. Res.-Atmos., 112, D10204, https://doi.org/10.1029/2006JD007371, 2007.
Zhao, H. and Zhong, Y.: Instability of an Inverse Problem for the Stationary
Radiative Transport Near the Diffusion Limit, SIAM J. Math. Anal., 51,
3750–3768, https://doi.org/10.1137/18M1222582, 2019.
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
Trans. Math. Softw., 23, 550–560, https://doi.org/10.1145/279232.279236, 1997.
Short summary
We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
We test a new method for measuring the 3D spatial variations of water within clouds, using...
