Articles | Volume 13, issue 4
Research article 17 Apr 2020
Research article | 17 Apr 2020
SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation
Wanyi Xie et al.
No articles found.
Yanfei Liang, Zengliang Zang, Dong Liu, Peng Yan, Yiwen Hu, Yan Zhou, and Wei You
Geosci. Model Dev., 13, 6285–6301,
Teruyuki Nakajima, Monica Campanelli, Huizheng Che, Victor Estellés, Hitoshi Irie, Sang-Woo Kim, Jhoon Kim, Dong Liu, Tomoaki Nishizawa, Govindan Pandithurai, Vijay Kumar Soni, Boossarasiri Thana, Nas-Urt Tugjsurn, Kazuma Aoki, Sujung Go, Makiko Hashimoto, Akiko Higurashi, Stelios Kazadzis, Pradeep Khatri, Natalia Kouremeti, Rei Kudo, Franco Marenco, Masahiro Momoi, Shantikumar S. Ningombam, Claire L. Ryder, Akihiro Uchiyama, and Akihiro Yamazaki
Atmos. Meas. Tech., 13, 4195–4218,Short summary
This paper overviews the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. The paper also discusses current issues of SKYNET to provide better information for the community.
X. M. Tian, D. Liu, S. L. Fu, D. C. Wu, B. X. Wang, Z. Wang, and Y. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 153–158,
Yang Wang, Steffen Dörner, Sebastian Donner, Sebastian Böhnke, Isabelle De Smedt, Russell R. Dickerson, Zipeng Dong, Hao He, Zhanqing Li, Zhengqiang Li, Donghui Li, Dong Liu, Xinrong Ren, Nicolas Theys, Yuying Wang, Yang Wang, Zhenzhu Wang, Hua Xu, Jiwei Xu, and Thomas Wagner
Atmos. Chem. Phys., 19, 5417–5449,Short summary
A MAX-DOAS instrument was operated to derive tropospheric vertical profiles of NO2, SO2, HONO, HCHO, CHOCHO and aerosols in the central western North China Plain in May and June 2016. The MAX-DOAS results are verified by comparisons with a collocated Raman lidar, overpass aircraft measurements, a sun photometer and in situ measurements. The contributions of regional transports and local emissions to the pollutants are evaluated based on case studies and statistic analysis.
Fang Zhang, Zhanqing Li, Yanan Li, Yele Sun, Zhenzhu Wang, Ping Li, Li Sun, Pucai Wang, Maureen Cribb, Chuanfeng Zhao, Tianyi Fan, Xin Yang, and Qingqing Wang
Atmos. Chem. Phys., 16, 5413–5425,
Zongming Tao, Zhenzhu Wang, Shijun Yang, Huihui Shan, Xiaomin Ma, Hui Zhang, Sugui Zhao, Dong Liu, Chenbo Xie, and Yingjian Wang
Atmos. Meas. Tech., 9, 1369–1376,Short summary
A new measurement technology of PM2.5 mass concentration profile near ground is addressed using a CCD side-scatter lidar and a PM2.5 detector. The PM2.5 mass concentration profile can be built upon the vertical distribution of the extinction coefficient for aerosol. The PM2.5 is always loading in the planet boundary layer with a complex muti-layer structure. The new method for PM2.5 mass concentration profile is useful for improving our understanding of air quality and atmospheric environment.
Z. Wang, D. Liu, Y. Wang, Z. Wang, and G. Shi
Atmos. Meas. Tech., 8, 2901–2907,
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information RetrievalInpainting radar missing data regions with deep learningImproved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS dataTriple-frequency radar retrieval of microphysical properties of snowRetrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observationsComparison of mid-latitude single- and mixed-phase cloud optical depth from co-located infrared spectrometer and backscatter lidar measurementsPhysical characteristics of frozen hydrometeors inferred with parameter estimationCloud height measurement by a network of all-sky imagersIncreasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examplesWhy we need radar, lidar, and solar radiance observations to constrain ice cloud microphysicsCoincident In-situ and Triple-Frequency Radar Airborne Observations in the ArcticEstimating the optical extinction of liquid water clouds in the cloud base regionW-band radar observations for fog forecast improvement: an analysis of model and forward operator errorsIdentification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variablesIdentifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectraEvaluating cloud liquid detection using cloud radar Doppler spectra in a pre-trained artificial neural network against Cloudnet liquid detectionMICRU: an effective cloud fraction algorithm designed for UV–vis satellite instruments with large viewing anglesA simplified method for the detection of convection using high-resolution imagery from GOES-16Using artificial neural networks to predict riming from Doppler cloud radar observationsIntroducing hydrometeor orientation into all-sky microwave and submillimeter assimilationVersion 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceansVersion 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithmsObservation of cirrus clouds with GLORIA during the WISE campaign: detection methods and cirrus characterizationPARAFOG v2.0: a near real-time decision tool to support nowcasting fog formation events at local scalesApplying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) dataApplying self-supervised learning for semantic cloud segmentation of all-sky imagesA new method to detect and classify polar stratospheric nitric acid trihydrate clouds derived from radiative transfer simulations and its first application to airborne infrared limb emission observationsA study of polarimetric error induced by satellite motion: application to the 3MI and similar sensorsA robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radarTwo-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurementsAnalysis of 3D cloud effects in OCO-2 XCO2 retrievalsImproving cloud type classification of ground-based images using region covariance descriptorsGlobal cloud property models for real-time triage on board visible–shortwave infrared spectrometersApplying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphologyMicrowave single-scattering properties of non-spheroidal raindropsDetermining cloud thermodynamic phase from the polarized Micro Pulse LidarImproved cloud detection over sea ice and snow during Arctic summer using MERIS dataA kernel-driven BRDF model to inform satellite-derived visible anvil cloud detectionCloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observationsEstimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radarsA new Orbiting Carbon Observatory 2 cloud flagging method and rapid retrieval of marine boundary layer cloud propertiesCALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing anglesDetection of the cloud liquid water path horizontal inhomogeneity in a coastline area by means of ground-based microwave observations: feasibility studySynergistic radar and radiometer retrievals of ice hydrometeorsImprovement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusionUsing two-stream theory to capture fluctuations of satellite-perceived TOA SW radiances reflected from clouds over oceanExploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric cloudsThree-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler modeLow-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithmA machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observationsSpatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747,Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Frank Werner, Nathaniel J. Livesey, Michael J. Schwartz, William G. Read, Michelle L. Santee, and Galina Wind
Atmos. Meas. Tech., 14, 7749–7773,Short summary
In this study we present an improved cloud detection scheme for the Microwave Limb Sounder, which is based on a feedforward artificial neural network. This new algorithm is shown not only to reliably detect high and mid-level convection containing even small amounts of cloud water but also to distinguish between high-reaching and mid-level to low convection.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254,Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904,Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Gianluca Di Natale, Marco Barucci, Claudio Belotti, Giovanni Bianchini, Francesco D'Amato, Samuele Del Bianco, Marco Gai, Alessio Montori, Ralf Sussmann, Silvia Viciani, Hannes Vogelmann, and Luca Palchetti
Atmos. Meas. Tech., 14, 6749–6758,Short summary
The importance of cirrus and mixed-phase clouds in the Earth radiation budget has been proven by many studies. In this paper the properties that characterize these clouds are retrieved from lidar and far-infrared spectral measurements performed in winter 2018/19 on the Zugspitze (Germany). The synergy of lidar and spectrometer measurements allowed us to assess the exponent k of the power-law relationship between the backscattering and the extinction coefficients.
Alan J. Geer
Atmos. Meas. Tech., 14, 5369–5395,Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Niklas Benedikt Blum, Bijan Nouri, Stefan Wilbert, Thomas Schmidt, Ontje Lünsdorf, Jonas Stührenberg, Detlev Heinemann, Andreas Kazantzidis, and Robert Pitz-Paal
Atmos. Meas. Tech., 14, 5199–5224,Short summary
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it, photovoltaic production. All-sky imagers (ASIs), cameras monitoring the sky above their point of installation, can provide such forecasts and also measure CBH. We present a network of ASIs to measure CBH. The network provides numerous readings of CBH simultaneously. We combine these with a statistical procedure. Validation attests to significantly higher accuracy of the combination compared to two ASIs alone.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126,Short summary
The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Florian Ewald, Silke Groß, Martin Wirth, Julien Delanoë, Stuart Fox, and Bernhard Mayer
Atmos. Meas. Tech., 14, 5029–5047,Short summary
In this study, we show how solar radiance observations can be used to validate and further constrain ice cloud microphysics retrieved from the synergy of radar–lidar measurements. Since most radar–lidar retrievals rely on a global assumption about the ice particle shape, ice water content and particle size biases are to be expected in individual cloud regimes. In this work, we identify and correct these biases by reconciling simulated and measured solar radiation reflected from these clouds.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
A dataset from the RadSnowExp represents the first-ever triple-frequency radar reflectivities at X-Ka-W-band combined with almost perfectly co-located and coincident airborne in situ microphysics probes. Results confirm the main findings of previous modelling works with radar dual frequency ratios (DFRs) occur at different zones of the DFR plane associated with different ice habits. Moreover, the dataset could be used to produce look-up tables for retrieving microphysical properties.
Karolina Sarna, David P. Donovan, and Herman W. J. Russchenberg
Atmos. Meas. Tech., 14, 4959–4970,Short summary
We show a method for obtaining cloud optical extinction with a lidar system. We use a scheme in which a lidar signal is inverted based on the estimated value of cloud extinction at the far end of the cloud and apply a correction for multiple scattering within the cloud and a range resolution correction. By applying our technique, we show that it is possible to obtain the cloud optical extinction with an error better than 5 % up to 90 m within the cloud.
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946,Short summary
This paper presents work towards making retrievals on the liquid water content in fog and low clouds. Future retrievals will rely on a radar simulator and high-resolution forecast. In this work, real observations are used to assess the errors associated with the simulator and forecast. A selection method to reduce errors associated with the forecast is proposed. It is concluded that the distribution of errors matches the requirements for future retrievals.
Noémie Planat, Josué Gehring, Étienne Vignon, and Alexis Berne
Atmos. Meas. Tech., 14, 4543–4564,Short summary
We implement a new method to identify microphysical processes during cold precipitation events based on the sign of the vertical gradient of polarimetric radar variables. We analytically asses the meteorological conditions for this vertical analysis to hold, apply it on two study cases and successfully compare it with other methods informing about the microphysics. Finally, we are able to obtain the main vertical structure and characteristics of the different processes during these study cases.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444,Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine-learning technique is better at detecting liquid, but in convective clouds where lidar data is available, Cloudnet performs better.
Holger Sihler, Steffen Beirle, Steffen Dörner, Marloes Gutenstein-Penning de Vries, Christoph Hörmann, Christian Borger, Simon Warnach, and Thomas Wagner
Atmos. Meas. Tech., 14, 3989–4031,Short summary
MICRU is an algorithm for the retrieval of effective cloud fractions (CFs) from satellite measurements. CFs describe the amount of clouds, which have a significant impact on the vertical sensitivity profile of trace gases like NO2 and HCHO. MICRU retrieves small CFs with an accuracy of 0.04 over the entire satellite swath. It features an empirical surface reflectivity model accounting for physical anisotropy (BRDF, sun glitter) and instrumental effects. MICRU is also applicable to imager data.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 14, 3755–3771,Short summary
This study suggests two methods to detect convection using 1 min data from GOES-16: one method detects early convective clouds using their vertical growth rate and the other method detects mature convective clouds using their lumpy cloud top surfaces. Applying the two methods to 1-month data showed that the accuracy of the combined methods was 85.8 % and showed their potential to be used in regions where radar data are not available.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. It is important to better detect this process, because its dynamics are not sufficiently well understood. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal.
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447,Short summary
Oriented nonspherical ice particles induce polarization that is ignored when cloud-sensitive satellite observations are used in numerical weather prediction systems. We present a simple approach for approximating particle orientation, requiring minor adaption of software and no additional calculation burden. With this approach, the system realistically simulates the observed polarization patterns, increasing the physical consistency between instruments with different polarizations.
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299,Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 as a result of the significant changes implemented in the version 4 algorithms, which are presented in a companion paper (Part I).
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3253–3276,Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results are presented in a companion paper (Part II).
Irene Bartolome Garcia, Reinhold Spang, Jörn Ungermann, Sabine Griessbach, Martina Krämer, Michael Höpfner, and Martin Riese
Atmos. Meas. Tech., 14, 3153–3168,Short summary
Cirrus clouds contribute to the general radiation budget of the Earth. Measuring optically thin clouds is challenging but the IR limb sounder GLORIA possesses the necessary technical characteristics to make it possible. This study analyses data from the WISE campaign obtained with GLORIA. We developed a cloud detection method and derived characteristics of the observed cirrus-like cloud top, cloud bottom or position with respect to the tropopause.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
PARAFOG is a near-real time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at 5 different locations present a good perfomance.
Yoonjin Lee, Christian D. Kummerow, and Imme Ebert-Uphoff
Atmos. Meas. Tech., 14, 2699–2716,Short summary
Convective clouds are usually associated with intense rain that can cause severe damage, and thus it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling areas. The results look promising when compared to radar-derived products, which are commonly used for detecting convection.
Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets leading to improved accuracy in cloud detection and less need for manual image labeling.
Christoph Kalicinsky, Sabine Griessbach, and Reinhold Spang
Atmos. Meas. Tech., 14, 1893–1915,Short summary
For an airborne viewing geometry, radiative transfer simulations of infrared limb emission spectra in the presence of polar stratospheric clouds – nitric acid trihydrate (NAT), supercooled ternary solution, ice, and mixtures – were used to develop a size-sensitive NAT detection algorithm. Characteristic size-dependent spectral features in the 810–820 cm−1 region were exploited to subgroup the NAT into three size regimes: small NAT (≤ 1.0 μm), medium NAT (1.5–4.0 μm), and large NAT (≥ 3.5 μm).
Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali
Atmos. Meas. Tech., 14, 1801–1816,Short summary
This research estimates the magnitude of a motion-induced error in the measurement of polarimetric state of light by a planned instrument on a future satellite. We discovered that the motion-induced error can not be cancelled out by spatiotemporal averaging, but it can be predicted from the along-track change of the intensity of light. With the estimated statistics and the simulation model, this research paves a way to provide pixel-level quality information in the future satellite products.
Xiaoyu Hu, Jinming Ge, Jiajing Du, Qinghao Li, Jianping Huang, and Qiang Fu
Atmos. Meas. Tech., 14, 1743–1759,Short summary
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutter. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutter by considering their different features in several variables. We applied this method to the radar observations at the SACOL site and found the results have good agreement with lidar detection.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech., 14, 1593–1613,Short summary
We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Christopher O'Dell, K. Sebastian Schmidt, Hong Chen, and David Baker
Atmos. Meas. Tech., 14, 1475–1499,Short summary
The OCO-2 science team is working to retrieve CO2 measurements that can be used by the carbon cycle community to calculate regional sources and sinks of CO2. The retrieved data, however, are in need of improvements in accuracy. This paper discusses several ways in which 3D cloud metrics (such as the distance of a measurement to the nearest cloud) can be used to account for cloud effects in the OCO-2 CO2 data files.
Yuzhu Tang, Pinglv Yang, Zeming Zhou, Delu Pan, Jianyu Chen, and Xiaofeng Zhao
Atmos. Meas. Tech., 14, 737–747,Short summary
An automatic cloud classification method on whole-sky images is presented. We first extract multiple pixel-level features to form region covariance descriptors (RCovDs) and then encode RCovDs by the Riemannian bag-of-feature (BoF) method to output the histogram representation. Reults show that a very high prediction accuracy can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech., 13, 7047–7057,Short summary
We demonstrate an onboard cloud-screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997,Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech., 13, 6933–6944,Short summary
Raindrops become flattened due to aerodynamic drag as they increase in mass and fall speed. This study calculated the electromagnetic interaction between microwave radiation and non-spheroidal raindrops. The calculations are made publicly available to the scientific community, in order to promote accurate representations of raindrops in measurements. Tests show that the drop shape can have a noticeable effect on microwave observations of heavy rainfall.
Jasper R. Lewis, James R. Campbell, Sebastian A. Stewart, Ivy Tan, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 13, 6901–6913,Short summary
In this work, the authors describe a process to determine the thermodynamic cloud phase using the Micro Pulse Lidar Network volume depolarization ratio measurements and temperature profiles from the Global Modeling and Assimilation Office GEOS-5 model. A multi-year analysis and comparisons to supercooled liquid water fractions derived from CALIPSO satellite measurements are used to demonstrate the efficacy of the method.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech., 13, 6459–6472,
Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.
Atmos. Meas. Tech., 13, 5491–5511,Short summary
This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
Bangsheng Yin, Qilong Min, Emily Morgan, Yuekui Yang, Alexander Marshak, and Anthony B. Davis
Atmos. Meas. Tech., 13, 5259–5275,Short summary
Cloud-top pressure (CTP) is an important cloud property for climate and weather studies. Based on differential oxygen absorption, both oxygen A-band and B-band pairs can be used to retrieve CTP. However, it is currently very challenging to perform a CTP retrieval accurately due to the complicated in-cloud penetration effect. To address this issue, we propose an analytic transfer inverse model for DSCOVR EPIC observations to retrieve CTP considering in-cloud photon penetration.
Frédéric Tridon, Alessandro Battaglia, and Stefan Kneifel
Atmos. Meas. Tech., 13, 5065–5085,Short summary
The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961,Short summary
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563,Short summary
CALIOP data users will find more cloud layers detected in V4, with edges that extend further than in V3, for an increase in total atmospheric cloud volume of 6 %–9 % for high-confidence cloud phases and 1 %–2 % for all cloudy bins, including cloud fringes and unknown cloud phases. In V4 there are many fewer cloud layers identified as horizontally oriented ice, particularly in the 3° off-nadir view. Depolarization at 532 nm is the predominant parameter determining cloud thermodynamic phase.
Vladimir S. Kostsov, Dmitry V. Ionov, and Anke Kniffka
Atmos. Meas. Tech., 13, 4565–4587,Short summary
Previously, observations from satellites provided evidence for systematic differences between the values of the cloud liquid water path over land and water areas in northern Europe. An attempt is made to detect such differences by means of ground-based microwave measurements performed near the coastline of the Gulf of Finland. The results demonstrate the existence of the cloud liquid water path gradient, which is positive as in the case of the satellite measurements (larger values over land).
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech., 13, 4219–4245,Short summary
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
Yue Li, Bryan A. Baum, Andrew K. Heidinger, W. Paul Menzel, and Elisabeth Weisz
Atmos. Meas. Tech., 13, 4035–4049,Short summary
Use of VIIRS+CrIS fusion products, which provide VIIRS with MODIS-like IR sounding channels, improves cloud mask, cloud phase, and cloud top height retrievals when compared to those using VIIRS data only. NOAA CLAVR-x cloud retrievals for both S-NPP and NOAA-20 data are evaluated through comparisons to the CALIPSO v4 and MODIS Collection 6.1 cloud products. Cloud height retrievals show significant improvement for semitransparent ice clouds, with a reduction in retrieval uncertainties.
Florian Tornow, Carlos Domenech, Howard W. Barker, René Preusker, and Jürgen Fischer
Atmos. Meas. Tech., 13, 3909–3922,Short summary
Clouds reflect sunlight unevenly, which makes it difficult to quantify the portion reflected back to space via satellite observation. To improve quantification, we propose a new statistical model that incorporates more satellite-inferred cloud and atmospheric properties than state-of-the-art models. We use concepts from radiative transfer theory that we statistically optimize to fit observations. The new model often explains past satellite observations better and predicts reflection plausibly.
Rocco Sedona, Lars Hoffmann, Reinhold Spang, Gabriele Cavallaro, Sabine Griessbach, Michael Höpfner, Matthias Book, and Morris Riedel
Atmos. Meas. Tech., 13, 3661–3682,Short summary
Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared spectra to classify PSC types. ML methods have proved to reach results in line with those obtained using well-established approaches. Among the considered ML methods, random forest (RF) seems to be the most promising one, being able to produce explainable classification results.
Alain Protat and Ian McRobert
Atmos. Meas. Tech., 13, 3609–3620,Short summary
Three-dimensional (3D) wind motions play a major role in driving the life cycle of clouds. In this pilot study we have developed a technique to measure the 3D winds in clouds, using a shipborne Doppler cloud radar on a stabilized platform. The stabilized platform is driven to point in a series of predefined directions to collect the required measurements. Comparisons with radiosondes demonstrate that accurate 1 min resolution 3D wind motions can be obtained from this instrumental setup.
Daniel J. Miller, Michal Segal-Rozenhaimer, Kirk Knobelspiesse, Jens Redemann, Brian Cairns, Mikhail Alexandrov, Bastiaan van Diedenhoven, and Andrzej Wasilewski
Atmos. Meas. Tech., 13, 3447–3470,Short summary
A neural network (NN) is developed and used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. The NN is applied to research scanning polarimeter (RSP) observations obtained during the ORACLES field campaign and compared to other co-located remote sensing retrievals of cloud effective radius and optical thickness. A NN approach can advance more complex iterative search retrieval algorithms by providing a quick initial guess.
Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277,Short summary
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
Brent A. McBride, J. Vanderlei Martins, Henrique M. J. Barbosa, William Birmingham, and Lorraine A. Remer
Atmos. Meas. Tech., 13, 1777–1796,Short summary
Clouds play a large role in the way our Earth system distributes energy. The measurement of cloud droplet size distribution (DSD) is one way to connect small-scale cloud processes to scattered radiation. Our small satellite instrument, the Airborne Hyper-Angular Rainbow Polarimeter, is the first to infer DSDs over a wide spatial cloud field using polarized light. This study improves the way we interpret cloud properties and shows that high-quality science does not require a large taxpayer cost.
Badrinarayanan, V., Kendall, A., and Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for scene segmentation, IEEE T. Pattern Anal., 39, 2481–2495 https://doi.org/10.1109/TPAMI.2016.2644615, 2017.
Bao, S., Letu, H., Zhao, C., Tana, G., Shang, H., Wang, T., Lige, B., Bao, Y., Purevjav, G., He, J., and Zhao, J.: Spatiotemporal Distributions of Cloud Parameters and the Temperature Response Over the Mongolian Plateau During 2006–2015 Based on MODIS Data, IEEE J. Sel. Top. Appl., 12, 549–558, https://doi.org/10.1109/JSTARS.2018.2857827, 2019.
Carslaw, K.: Atmospheric physics: cosmic rays, clouds and climate, Nature, 460, 332–333, 2009.
Dev, S., Lee, Y. H., and Winkler, S.: Color-based segmentation of sky/cloud images from ground-based cameras, IEEE J. Sel. Top. Appl., 10, 231–242, 2017.
Feister, U. and Shields, J.: Cloud and radiance measurements with the vis/nir daylight whole sky imager at lindenberg (germany), Meteorol. Z., 14, 627–639, 2005.
Garrett, T. J. and Zhao, C.: Ground-based remote sensing of thin clouds in the Arctic, Atmos. Meas. Tech., 6, 1227–1243, https://doi.org/10.5194/amt-6-1227-2013, 2013.
Genkova, I., Long, C., Besnard, T., and Gillotay, D.: Assessing cloud spatial and vertical distribution with cloud infrared radiometer cir-7, P. Soc. Photo.-Opt. Ins., 5571, 1–10, 2004.
Heinle, A., Macke, A., and Srivastav, A.: Automatic cloud classification of whole sky images, Atmos. Meas. Tech., 3, 557–567, https://doi.org/10.5194/amt-3-557-2010, 2010.
Hinton, G. E.: Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair, International Conference on International Conference on Machine Learning, 21–24 June 2010, Haifa, Israel, Omnipress, 2010.
Huang, D., Zhao, C., Dunn, M., Dong, X., Mace, G. G., Jensen, M. P., Xie, S., and Liu, Y.: An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies, Atmos. Meas. Tech., 5, 1409–1424, https://doi.org/10.5194/amt-5-1409-2012, 2012.
Ioffe, S. and Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, International Conference on International Conference on Machine Learning JMLR.org, July 2015, Lille, France, 2015.
Kreuter, A., Zangerl, M., Schwarzmann, M., and Blumthaler, M.: All-sky imaging: a simple, versatile system for atmospheric research, Appl. Optics, 48, 1091–1097, 2009.
LeCun, Y. and Bengio, Y.: Convolutional networks for images, speech, and time series, the handbook of brain theory and neural networks, MIT Press, 1998.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel L. D.: Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541–551, 2014.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Li, J., Lv, Q., Jian, B., Zhang, M., Zhao, C., Fu, Q., Kawamoto, K., and Zhang, H.: The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau, Atmos. Chem. Phys., 18, 7329–7343, https://doi.org/10.5194/acp-18-7329-2018, 2018.
Li, Q., Lu, W., and Yang, J.: A hybrid thresholding algorithm for cloud detection on ground-based color images, J. Atmos. Ocean. Tech., 28, 1286–1296, 2011.
Liang, Y., Cao, Z., and Yang, X.: Deepcloud: ground-based cloud image categorization using deep convolutional features, IEEE T. Geosci. Remote, 55, 5729-5740, 2017.
Liu, S., Zhang, L., Zhang, Z., Wang, C., and Xiao, B.: Automatic cloud detection for all-sky images using superpixel segmentation, IEEE Geosci. Remote S., 12, 354–358, 2014.
Liu, S., Zhang, Z., Xiao, B., and Cao, X.: Ground-based cloud detection using automatic graph cut, IEEE Geosci. Remote S., 12, 1342–1346, 2015.
Long, C., Slater, D., and Tooman, T.: Total sky imager model 880 status and testing results, Office of Scientific and Technical Information Technical Reports, 2001.
Long, C. N. and Charles, N.: Correcting for circumsolar and near-horizon errors in sky cover retrievals from sky images, Open Atmospheric Science Journal, 4, 45–52, 2010.
Long, C. N., Sabburg, J. M., Calbó, J., and Pagès, D.: Retrieving cloud characteristics from ground-based daytime color all-sky images, J. Atmos. Ocean. Tech., 23, 633–652, 2006.
Ma, Z., Liu, Q., Zhao, C., Shen, X., Wang, Y., Jiang, J. H., Li, Z., and Yung, Y.: Application and evaluation of an explicit prognostic cloud-cover scheme in GRAPES global forecast system, J. Adv. Model. Earth Sy., 10, 652–667, https://doi.org/10.1002/2017MS001234, 2018.
Otsu, N.: A threshold selection method from gray-level histograms, IEEE T. Syst. Man Cyb., 9, 62–66, 1979.
Papin, C., Bouthemy, P., and Rochard, G.: Unsupervised segmentation of low clouds from infrared meteosat images based on a contextual spatio-temporal labeling approach, IEEE T. Geosci. Remote, 40, 104–114, 2002.
Rossow, W. B. and Schiffer, R. A.: ISCCP Cloud Data Products, B. Am. Meteorol. Soc., 72, 2–20, 1991.
Shi, C., Wang, C., Yu, W., and Xiao, B.: Deep convolutional activations-based features for ground-based cloud classification, IEEE Geosci. Remote S., 14, 816–820, 2017.
Shi, M., Xie, F., Zi, Y., and Yin, J.: Cloud detection of remote sensing images by deep learning, IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 10–15 July 2016.
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition, Proc. Int. Conf. Learn. Representat., 1–14, 2015
Souzaecher, M. P., Pereira, E. B., Bins, L. S., and Andrade, M. A. R.: A simple method for the assessment of the cloud cover state in high-latitude regions by a ground-based digital camera, J. Atmos. Ocean. Tech., 23, 437–447, https://doi.org/10.1175/jtech1833.1, 2004.
Stephens, G. L.: Cloud feedbacks in the climate system: a critical review, J. Climate, 18, 237–273, 2005.
Sutskever, I., Martens, J., Dahl, G., and Hinton, G.: On the importance of initialization and momentum in deep learning, International Conference on International Conference on Machine Learning, JMLR.org, June 2013, Atlanta, GA, USA, 2013.
Taigman, Y., Ming, Y., Ranzato, M., and Wolf, L.: DeepFace: Closing the Gap to Human-Level Performance in Face Verification, IEEE Conference on Computer Vision and Pattern Recognition, 23–28 June 2014, Columbus, OH, USA, 2014.
Tapakis, R. and Charalambides, A. G.: Equipment and methodologies for cloud detection and classification: a review, Sol. Energy, 95, 392–430, 2013.
Tao, F., Xie, W., Wang, Y., and Xia, Y.: Development of an all-sky imaging system for cloud cover assessment, Appl. Optics, 58, 5516–5524, 2019.
Wang, D., Khosla, A., Gargeya, R., Irshad, H., and Beck, A.: Deep Learning for Identifying Metastatic Breast Cancer, 2016.
Wang, Y. and Zhao, C.: Can MODIS cloud fraction fully represent the diurnal and seasonal variations at DOE ARM SGP and Manus sites, J. Geophys. Res.-Atmos., 122, 329–343, 2017.
Yang, H., Kurtz, B., Nguyen, D., Urquhart, B., Chow, C. W., Ghonima, M., and Kleissl, J.: Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego, Sol. Energy, 103, 502–524, 2014.
Yuan, F., Lee, Y. H., and Meng, Y. S.: Comparison of cloud models for propagation studies in Ka-band satellite applications, International Symposium on Antennas and Propagation, 2–5 December 2014, Kaohsiung, Taiwan, https://doi.org/10.1109/ISANP.2014.7026691, 2015.
Yuan, K., Yuan, K., Meng, G., Cheng, D., Bai, J., and Pan, X. C.: Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy, IEEE International Conference on Image Processing, 17–20 September 2017, Beijing, China, https://doi.org/10.1109/ICIP.2017.8296243, 2018.
Zhang, J. L., Liu, P., Zhang, F., and Song, Q. Q.: CloudNet: Ground-based cloud classification with deep convolutional neural network, Geophys. Res. Lett., 45, 8665–8672, https://doi.org/10.1029/2018GL077787, 2018.
Zhao, C., Wang, Y., Wang, Q., Li, Z., Wang, Z., and Liu, D.: A new cloud and aerosol layer detection method based on micropulse lidar measurements, J. Geophys. Res.-Atmos., 119, 6788–6802, https://doi.org/10.1002/2014JD021760, 2014.
Zhao, C., Chen, Y., Li, J., Letu H., Su, Y., Chen, T., and Wu, X.: Fifteen-year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations, Int. J. Climatol., 39, 2612–2629, https://doi.org/10.1002/joc.5975, 2019.