Articles | Volume 5, issue 9
Research article 25 Sep 2012
Research article | 25 Sep 2012
Ice hydrometeor profile retrieval algorithm for high-frequency microwave radiometers: application to the CoSSIR instrument during TC4
K. F. Evans et al.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information RetrievalImproving 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 study of polarimetric noise induced by satellite motion: Application to the 3MI and similar sensorsA kernel-driven BRDF model to inform satellite-derived visible anvil cloud detectionCloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observationsAnalysis of 3D Cloud Effects in OCO-2 XCO2 RetrievalsTwo-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurementsEstimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radarsA robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radarA 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 fusionRadiative transfer simulations and observations of infrared spectra in the presence of polar stratospheric clouds: Detection and discrimination of cloud typesUsing 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 algorithmMICRU background map and effective cloud fraction algorithms designed for UV/vis satellite instruments with large viewing anglesA machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observationsSegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observationSpatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurementsTowards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imageryCloud detection over snow and ice with oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC)Ground-based observations of cloud and drizzle liquid water path in stratocumulus cloudsIncreasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: evaluation of candidate approaches with MODIS observationsEstimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in PayerneTowards an operational Ice Cloud Imager (ICI) retrieval productIce crystal number concentration from lidar, cloud radar and radar wind profiler measurementsRetrieval of cloud properties from spectral zenith radiances observed by sky radiometersA new approach to estimate supersaturation fluctuations in stratocumulus cloud using ground-based remote-sensing measurementsELIFAN, an algorithm for the estimation of cloud cover from sky imagersEstimating solar irradiance using sky imagersToward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical propertiesUse of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud productsThe importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snowCalibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSatAll-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloudpeakTree: a framework for structure-preserving radar Doppler spectra analysisDevelopment and validation of a supervised machine learning radar Doppler spectra peak-finding algorithmFootprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievalsEstimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3ECorrelated observation error models for assimilating all-sky infrared radiancesCloud identification and classification from high spectral resolution data in the far infrared and mid-infraredInvestigating the liquid water path over the tropical Atlantic with synergistic airborne measurements
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,
Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
This research estimates the magnitude of a motion-induced noise in the measurement of polarimetric state of light by a planned instrument on a future satellite. We discovered that the motion-induced noise can not be cancelled out by spatio-temporal 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.
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.
Steven T. Massie, Heather Cronk, Aronne Merrelli, K. Sebastian Schmidt, Hong Chen, and David Baker
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort 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, is 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.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort 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.
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.
Xiaoyu Hu, Jinming Ge, Jiajing Du, Qinghao Li, Jianping Huang, and Qiang Fu
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutters. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutters by considering their different features in several variables. We applied this method to the radar observations at SACOL site and found the results have a good agreement with lidar detections.
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.
Christoph Kalicinsky, Sabine Griessbach, and Reinhold Spang
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
For an airborne viewing geometry radiative transfer simulations of infrared limb emission spectra in the presence of PSCs (NAT, STS, ice, and mixtures) were used to develop a size sensitive NAT detection algorithm. Characteristic size dependent spectral features in the region 810–820 cm−1 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).
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.
Holger Sihler, Steffen Beirle, Steffen Dörner, Marloes Gutenstein-Penning de Vries, Christoph Hörmann, Christian Borger, Simon Warnach, and Thomas Wagner
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
MICRU is an algorithm for the retrieval of effective cloud fractions (CF) from satellite measurements. CF 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 CF 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.
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.
Wanyi Xie, Dong Liu, Ming Yang, Shaoqing Chen, Benge Wang, Zhenzhu Wang, Yingwei Xia, Yong Liu, Yiren Wang, and Chaofang Zhang
Atmos. Meas. Tech., 13, 1953–1961,
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.
Jason M. Apke, Kyle A. Hilburn, Steven D. Miller, and David A. Peterson
Atmos. Meas. Tech., 13, 1593–1608,Short summary
Objective identification of deep convection outflow boundaries (OFBs) in next-generation geostationary satellite imagery is explored here using motion derived from a tuned advanced optical flow algorithm. Motion discontinuity preservation within the derivation is found crucial for successful OFB tracking between images, which yields new meteorological data for objective systems to use. These results provide the first step towards a fully automated satellite-based OFB identification algorithm.
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech., 13, 1575–1591,Short summary
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499,Short summary
A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Frank Werner and Hartwig Deneke
Atmos. Meas. Tech., 13, 1089–1111,Short summary
The reliability of remotely sensed cloud variables from space depends on the horizontal resolution of the instrument. This study presents and evaluates several candidate approaches for increasing the spatial resolution of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) from the native 3 km scale to a horizontal resolution of 1 km. It is shown that uncertainties in the derived cloud products can be significantly mitigated by applying an appropriate downscaling scheme.
Christine Aebi, Julian Gröbner, Stelios Kazadzis, Laurent Vuilleumier, Antonis Gkikas, and Niklaus Kämpfer
Atmos. Meas. Tech., 13, 907–923,Short summary
Clouds are one of the largest sources of uncertainties in climate models. The current study estimates the cloud optical thickness (COT), the effective droplet radius and the single scattering albedo of stratus–altostratus and cirrus–cirrostratus clouds in Payerne, Switzerland, by combining ground- and satellite-based measurements and radiative transfer models. The estimated values are thereafter compared with data retrieved from other methods. The mean COT is distinct for different seasons.
Patrick Eriksson, Bengt Rydberg, Vinia Mattioli, Anke Thoss, Christophe Accadia, Ulf Klein, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 53–71,Short summary
The Ice Cloud Imager (ICI) will be the first operational satellite sensor operating at sub-millimetre wavelengths and this novel mission will thus provide important new data to weather forecasting and climate studies. The series of ICI instruments will together cover about 20 years. This article presents the basic technical characteristics of the sensor and outlines the day-one operational retrievals. An updated estimation of the expected retrieval performance is also presented.
Johannes Bühl, Patric Seifert, Martin Radenz, Holger Baars, and Albert Ansmann
Atmos. Meas. Tech., 12, 6601–6617,Short summary
In the present paper, we present a novel remote-sensing technique for the measurement of ice crystal number concentrations in clouds. The fall velocity of ice crystals measured with values from cloud radar and a radar wind profiler is used in order to derive information about ice crystal size and number concentration. In contrast to existing methods based on the combination of lidar and cloud radar, the present method can also be used in optically thick clouds.
Pradeep Khatri, Hironobu Iwabuchi, Tadahiro Hayasaka, Hitoshi Irie, Tamio Takamura, Akihiro Yamazaki, Alessandro Damiani, Husi Letu, and Qin Kai
Atmos. Meas. Tech., 12, 6037–6047,Short summary
In an attempt to make cloud retrievals from the surface more common and convenient, we developed a cloud retrieval algorithm applicable for sky radiometers. It is based on an optimum method by fitting measured transmittances with modeled values. Further, a cost-effective and easy-to-use calibration procedure is proposed and validated using data obtained from the standard method. A detailed error analysis and quality assessment are also performed.
Fan Yang, Robert McGraw, Edward P. Luke, Damao Zhang, Pavlos Kollias, and Andrew M. Vogelmann
Atmos. Meas. Tech., 12, 5817–5828,Short summary
In-cloud supersaturation is crucial for droplet activation, growth, and drizzle initiation but is poorly known and hardly measured. Here we provide a novel method to estimate supersaturation fluctuation in stratocumulus clouds using remote-sensing measurements, and results show that our estimated supersaturation agrees reasonably well with in situ measurements. Our method provides a unique way to estimate supersaturation in stratocumulus clouds from long-term ground-based observations.
Marie Lothon, Paul Barnéoud, Omar Gabella, Fabienne Lohou, Solène Derrien, Sylvain Rondi, Marjolaine Chiriaco, Sophie Bastin, Jean-Charles Dupont, Martial Haeffelin, Jordi Badosa, Nicolas Pascal, and Nadège Montoux
Atmos. Meas. Tech., 12, 5519–5534,Short summary
In the context of an atmospheric network of instrumented sites equipped with sky cameras for cloud monitoring, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images. ELIFAN is based on red-to-blue ratio thresholding applied on the image pixels and on the use of a blue-sky library. We present its principle and its performance and highlight the interest of combining several complementary instruments.
Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler
Atmos. Meas. Tech., 12, 5417–5429,Short summary
Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. In this paper, we derive a model for estimating solar irradiance using the pictures taken by those imagers. Unlike pyranometers, these sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term solar energy generation forecasting.
Penny M. Rowe, Christopher J. Cox, Steven Neshyba, and Von P. Walden
Atmos. Meas. Tech., 12, 5071–5086,Short summary
A better understanding of polar clouds is needed for predicting climate change, including cloud thickness and the sizes and amounts of liquid droplets and ice crystals. These properties can be estimated from an instrument (an infrared spectrometer) that sits on the surface and measures how much infrared radiation is emitted by the cloud. In this work we use model data to investigate how well such an instrument could retrieve cloud properties for different instrument and error characteristics.
Hye-Sil Kim, Bryan A. Baum, and Yong-Sang Choi
Atmos. Meas. Tech., 12, 5039–5054,Short summary
This study demonstrates that ice cloud emissivity uncertainties at 11, 12, and 13.3 µm can be used to provide a reasonable range of ice cloud layer boundaries. We test this methodology using MODIS Collection 6 cloud properties over the western North Pacific Ocean during August 2015. The cloud boundaries for single-layer optically thin ice clouds show good agreement with those from CALIOP version 4 products, with biases increasing for optically thick and multilayered clouds.
Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
Atmos. Meas. Tech., 12, 4993–5018,Short summary
The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.
Pavlos Kollias, Bernat Puigdomènech Treserras, and Alain Protat
Atmos. Meas. Tech., 12, 4949–4964,Short summary
Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based observatories. Calibrating these radars is important for establishing a long record of observations suitable for model evaluation and improvement. Here, the CloudSat CPR is used to assess the calibration of a record over 10 years long of ARM cloud radar observations (a total of 44 years). The results indicate that correction coefficients are needed to improve record reliability and usability.
Alan J. Geer, Stefano Migliorini, and Marco Matricardi
Atmos. Meas. Tech., 12, 4903–4929,Short summary
Satellite radiance observations have only recently become usable in conditions of cloud and precipitation for the initialization of weather forecasts. The move to
all-skyassimilation started with data from the microwave part of the spectrum, with substantial benefit to the quality of operational forecasts. The current work shows a framework in which cloudy infrared data, with its stronger and more non-linear sensitivity, can also benefit operational-quality forecasts.
Martin Radenz, Johannes Bühl, Patric Seifert, Hannes Griesche, and Ronny Engelmann
Atmos. Meas. Tech., 12, 4813–4828,Short summary
Clouds may be composed of more than one particle population even at the smallest scales. Cloud radar observations can contain information on multiple particle species, showing up as distinct peaks and subpeaks in the Doppler spectrum. We propose the use of binary tree structures to recursively structure these peaks. Two case studies from different locations and instruments illustrate how this approach can be used to disentangle particle populations in multilayered mixed-phase clouds.
Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke
Atmos. Meas. Tech., 12, 4591–4617,Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
Alexandre Guillaume, Brian H. Kahn, Eric J. Fetzer, Qing Yue, Gerald J. Manipon, Brian D. Wilson, and Hook Hua
Atmos. Meas. Tech., 12, 4361–4377,Short summary
A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar. The scale dependence of the cloud scenes is quantified. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity.
Jingjing Tian, Xiquan Dong, Baike Xi, Christopher R. Williams, and Peng Wu
Atmos. Meas. Tech., 12, 3743–3759,Short summary
Liquid water path (LWP) is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP) in stratiform precipitation systems. LWP partitioning is important but poorly understood. Here we estimate the RLWP and CLWP below the melting base simultaneously and separately using ceilometer and radar measurements. Results show that the occurrence of cloud particles below the melting base is low; however, when cloud particles exist, the CLWP value is much larger than the RLWP.
Alan J. Geer
Atmos. Meas. Tech., 12, 3629–3657,Short summary
Using more satellite data in cloudy areas helps improve weather forecasts, but all-sky assimilation is still tricky, particularly for infrared data. To allow the use of hyperspectral infrared sounder radiances in all-sky conditions, an error model is developed that, in the presence of cloud, broadens the correlations between channels and increases error variances. After fixing problems of gravity wave and bias amplification, the results of all-sky assimilation trials were promising.
Tiziano Maestri, William Cossich, and Iacopo Sbrolli
Atmos. Meas. Tech., 12, 3521–3540,Short summary
An innovative and flexible methodology for cloud identification and classification, CIC, is tested on a synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) mission. Results show that classification scores are greatly increased when far-infrared channels are accounted for and the identification of thin cirrus clouds is improved.
Marek Jacob, Felix Ament, Manuel Gutleben, Heike Konow, Mario Mech, Martin Wirth, and Susanne Crewell
Atmos. Meas. Tech., 12, 3237–3254,Short summary
Tropical clouds are a key climate component but are still not fully understood. Therefore, we analyze airborne remote sensing measurements that were taken in the dry and wet seasons over the Atlantic east of Barbados. From these we derive sub-kilometer resolution data of vertically integrated atmospheric water vapor and liquid water. Results show that although the humidity is lower in the dry season, clouds are more frequent, contain more water, and produce more rain than in the wet season.
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