Articles | Volume 2, issue 2
https://doi.org/10.5194/amt-2-779-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-2-779-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
04 Dec 2009
Response of the Nevzorov hot wire probe in clouds dominated by droplet conditions in the drizzle size range
A. Schwarzenboeck
Clermont Université, Université Blaise Pascal, LAMP, 63177 Aubière, France
CNRS, UMR 6016, LAMP, 63173 Aubière, France
G. Mioche
Clermont Université, Université Blaise Pascal, LAMP, 63177 Aubière, France
CNRS, UMR 6016, LAMP, 63173 Aubière, France
A. Armetta
Clermont Université, Université Blaise Pascal, LAMP, 63177 Aubière, France
CNRS, UMR 6016, LAMP, 63173 Aubière, France
A. Herber
Alfred Wegener Institute for Polar and Marine Research (AWI), Columbusstrasse, 27568 Bremerhaven, Germany
J.-F. Gayet
Clermont Université, Université Blaise Pascal, LAMP, 63177 Aubière, France
CNRS, UMR 6016, LAMP, 63173 Aubière, France
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging
Revealing halos concealed by cirrus clouds
In situ observations of supercooled liquid water clouds over Dome C, Antarctica by balloon-borne sondes
Quantifying riming from airborne data during the HALO-(AC)3 campaign
Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network
Neural network processing of holographic images
Ice crystal images from optical array probes: classification with convolutional neural networks
Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar
The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning
Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning
Application of cloud particle sensor sondes for estimating the number concentration of cloud water droplets and liquid water content: case studies in the Arctic region
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
A convolutional neural network for classifying cloud particles recorded by imaging probes
Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice
Analysis algorithm for sky type and ice halo recognition in all-sky images
Study of the diffraction pattern of cloud particles and the respective responses of optical array probes
A method for computing the three-dimensional radial distribution function of cloud particles from holographic images
A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system
Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras
Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques
From pixels to patches: a cloud classification method based on a bag of micro-structures
Evaluation of cloud base height measurements from Ceilometer CL31 and MODIS satellite over Ahmedabad, India
Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC
Block-based cloud classification with statistical features and distribution of local texture features
Assessment of the performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probe measurements
Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements
Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements
Flor Vanessa Maciel, Minghui Diao, and Ching An Yang
Atmos. Meas. Tech., 17, 4843–4861, https://doi.org/10.5194/amt-17-4843-2024, https://doi.org/10.5194/amt-17-4843-2024, 2024
Short summary
Short summary
The partition between supercooled liquid water and ice crystals in mixed-phase clouds is investigated using aircraft-based in situ observations over the Southern Ocean. A novel method is developed to define four phases of mixed-phase clouds. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols and thermodynamic and dynamical conditions on ice nucleation and phase partitioning are examined.
Jingxuan Luo, Yubing Pan, Debin Su, Jinhua Zhong, Lingxiao Wu, Wei Zhao, Xiaoru Hu, Zhengchao Qi, Daren Lu, and Yinan Wang
Atmos. Meas. Tech., 17, 3765–3781, https://doi.org/10.5194/amt-17-3765-2024, https://doi.org/10.5194/amt-17-3765-2024, 2024
Short summary
Short summary
Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Yuji Ayatsuka
Atmos. Meas. Tech., 17, 3739–3750, https://doi.org/10.5194/amt-17-3739-2024, https://doi.org/10.5194/amt-17-3739-2024, 2024
Short summary
Short summary
Many types of halos appear in the sky. Each type of halo reflects the state of the atmosphere; therefore observing them from the ground greatly helps in understanding the state of the atmosphere. However, halos are easily obscured by the contrast of the cloud itself, making it difficult to observe them. This study describes the construction of a sky-color model for halos and a new effective algorithm to reveal halos in images.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-8, https://doi.org/10.5194/amt-2024-8, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Clouds in Antarctica are key elements affecting climate evolution. Some clouds are constituted of supercooled liquid water (SLW, water held in liquid form below 0 °C) and are difficult to forecast by models. We performed in-situ observations of SLW clouds at Concordia station using SLW sondes attached to meteorological balloons in summer 2021–2022. The SLW clouds were observed in a saturated layer at the top of the Planetary Boundary Layer in agreement with ground-based LIDAR observations.
Nina Maherndl, Manuel Moser, Johannes Lucke, Mario Mech, Nils Risse, Imke Schirmacher, and Maximilian Maahn
Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
Short summary
Short summary
In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee
Atmos. Meas. Tech., 16, 5403–5413, https://doi.org/10.5194/amt-16-5403-2023, https://doi.org/10.5194/amt-16-5403-2023, 2023
Short summary
Short summary
A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne
Atmos. Meas. Tech., 15, 5793–5819, https://doi.org/10.5194/amt-15-5793-2022, https://doi.org/10.5194/amt-15-5793-2022, 2022
Short summary
Short summary
We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.
Louis Jaffeux, Alfons Schwarzenböck, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 15, 5141–5157, https://doi.org/10.5194/amt-15-5141-2022, https://doi.org/10.5194/amt-15-5141-2022, 2022
Short summary
Short summary
Optical array probes are instruments used aboard research aircraft to capture 2D images of ice or water particles in clouds. This study presents a new tool using innovative machine learning, called convolutional neural networks, designed to identify the shape of imaged ice particles for two of these imagers, namely 2DS and PIP. Such a tool will be a very strong asset for understanding cloud microphysics. Beyond traditional evaluation metrics, human inspections were performed of unknown data.
Yun Yuan, Huige Di, Yuanyuan Liu, Tao Yang, Qimeng Li, Qing Yan, Wenhui Xin, Shichun Li, and Dengxin Hua
Atmos. Meas. Tech., 15, 4989–5006, https://doi.org/10.5194/amt-15-4989-2022, https://doi.org/10.5194/amt-15-4989-2022, 2022
Short summary
Short summary
We put forward a new algorithm for joint observation of the cloud boundary by lidar and Ka-band millimetre-wave cloud radar. Cloud cover and boundary distribution characteristics are analysed from December 2020 to November 2021 in Xi'an. More than 34 % of clouds appear as a single layer every month. The maximum and minimum normalized cloud cover occurs in summer and winter, respectively. The study can provide more information on aerosol–cloud interactions and forecasting numerical models.
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, https://doi.org/10.5194/amt-14-7079-2021, 2021
Short summary
Short summary
Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.
Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
Atmos. Meas. Tech., 14, 6695–6710, https://doi.org/10.5194/amt-14-6695-2021, https://doi.org/10.5194/amt-14-6695-2021, 2021
Short summary
Short summary
This study investigates a method for 24 h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channels of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
Short summary
Short summary
A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020, https://doi.org/10.5194/amt-13-5595-2020, 2020
Short summary
Short summary
In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related to ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland, and we discuss the advantages and shortcomings of the algorithm.
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, https://doi.org/10.5194/amt-13-2219-2020, 2020
Short summary
Short summary
Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, https://doi.org/10.5194/amt-13-1757-2020, 2020
Short summary
Short summary
In the changing Arctic, quantifying the resulting variability of incoming solar radiation is important to better elucidate the net radiative effect of clouds. As part of a multidisciplinary expedition in the central Arctic held in early summer 2017, a novel network of pyranometers was deployed over an ice floe to investigate the spatiotemporal variability of solar radiation under different sky conditions. This study presents the collected data and an analysis of the spatiotemporal variability.
Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit
Atmos. Meas. Tech., 12, 4241–4259, https://doi.org/10.5194/amt-12-4241-2019, https://doi.org/10.5194/amt-12-4241-2019, 2019
Short summary
Short summary
How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, and fragments. Ice halos form if ice particles in these clouds are in a smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, as gathered by total sky imagers (TSIs). Such an analysis will allow one to explore geographical and seasonal variations in cirrus cloud particle types.
Thibault Vaillant de Guélis, Alfons Schwarzenböck, Valery Shcherbakov, Christophe Gourbeyre, Bastien Laurent, Régis Dupuy, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 12, 2513–2529, https://doi.org/10.5194/amt-12-2513-2019, https://doi.org/10.5194/amt-12-2513-2019, 2019
Michael L. Larsen and Raymond A. Shaw
Atmos. Meas. Tech., 11, 4261–4272, https://doi.org/10.5194/amt-11-4261-2018, https://doi.org/10.5194/amt-11-4261-2018, 2018
Short summary
Short summary
A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.
Jiangchuan Tao, Chunsheng Zhao, Ye Kuang, Gang Zhao, Chuanyang Shen, Yingli Yu, Yuxuan Bian, and Wanyun Xu
Atmos. Meas. Tech., 11, 895–906, https://doi.org/10.5194/amt-11-895-2018, https://doi.org/10.5194/amt-11-895-2018, 2018
Short summary
Short summary
Existing chamber technologies for direct measurements of number concentration of cloud condensation nuclei (NCCN) are sophisticated and expensive. In this paper, a new method is proposed to calculate NCCN based only on measurements of a humidified nephelometer system which have accounted for influences of both aerosol size and aerosol hygroscopicity on NCCN calculation. This new method makes NCCN measurements more convenient and is capable of obtaining NCCN at lower supersaturations.
Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017, https://doi.org/10.5194/amt-10-4587-2017, 2017
Short summary
Short summary
The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.
Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, Alice Grandin, John Walter Strapp, and Lyle Edward Lilie
Atmos. Meas. Tech., 10, 2239–2252, https://doi.org/10.5194/amt-10-2239-2017, https://doi.org/10.5194/amt-10-2239-2017, 2017
Short summary
Short summary
In this study we evaluate a method to estimate cloud water content (CWC) knowing cloud reflectivity. Ice hydrometeors are replace by ice oblate spheroids to simulate their reflectivity. There is no assumption on the relation between mass and their size. Then, a broad range of CWCs are compared with direct measurements of CWC. The accuracy of the method is ~ ±32 %. This study is performed in areas of convective clouds where reflectivity and CWC are especially high, what makes it unique.
Hsu-Yung Cheng and Chih-Lung Lin
Atmos. Meas. Tech., 10, 199–208, https://doi.org/10.5194/amt-10-199-2017, https://doi.org/10.5194/amt-10-199-2017, 2017
Short summary
Short summary
A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, https://doi.org/10.5194/amt-9-753-2016, 2016
Short summary
Short summary
This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
Short summary
Short summary
Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
Short summary
Short summary
Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
Short summary
Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 2014
Cited articles
Baumgardner, D., Strapp, W., and Dye, J. E.: Evaluation of the forward scattering spectrometer probe, Part II: Corrections for coincidence and dead-time losses. J. Atmos. Ocean. Tech., 2, 626–632, 1985.
Biter, C. J., Dye, J. E. Huffman, D., and King, W. D.: The drop response of the CSIRO liquid water content, J. Atmos. Ocean. Tech., 4, 359–367, 1987.
Emery, E., Miller, D., Plaskon, S., Strapp, J. W., and Lilie, L. E.: Ice Particle Impact on Cloud Water Content Instrumentation, 42nd AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2004-0731, 2004.
Gardiner, B. A. and Hallett, J.: Degradation of in-cloud forward scattering spectrometer probe measurements in the presence of ice particles, J. Atmos. Ocean. Tech., 2, 171–180, 1985.
Garrett, T. J., Hobbs, P. V., and Gerber, H.: Shortwave, single-scattering properties of arctic ice clouds, J. Geophys. Res., 106, D14, 15.155–15.172, 2001.
Gayet, J. F., Brown, P. R. A., and Albers, F.: A comparison of in-cloud measurements obtained with six PMS 2-D-C probes, J. Atmos. Ocean. Tech., 10, 180–194, 1993.
Gayet, J. F., Crepel, O., Fournol, J. F., and Oshchepkov, S.: A new airborne polar Nephelometer for the measurements of optical and microphysical cloud properties, Part I: Theoretical design, Ann. Geophys., 15(4), 451–459, 1997.
Gayet, J.-F., Stachlewska, I. S., Jourdan, O., Shcherbakov, V. N., Schwarzenboeck, A., and Neuber, R.: Microphysical and optical properties of precipitating drizzle and ice particles obtained from alternated lidar and in situ measurements, Ann. Geophys., 25(7), 1487–1497, 2007.
Gerber, H., Arends, B. G., and Ackerman, A. S.: New microphysics sensor for aircraft use, Atmos. Res., 31, 235–252, 1993
Hinds, W. C.: Aerosol technology: Properties, behaviour and measurement of airborne particles, John Wiley and Sons, ISBN 0-471-19410-7, 464 pp., New York, USA, 1999.
King, W. D., Parkin D. A., and Handsworth R. J.: A hot-wire water device having fully calculable response characteristics, J. Appl. Meteor., 17, 1809–1813, 1978.
Korolev, A. V., Nevzorov, A. N., Strapp, J. W., and Isaac, G. A.: The Nevzorov Airborne Hot-Wire LWC-TWC Probe : Principle of Operation and Performance Characteristics, J. Atmos. Ocean. Tech., 15, 1495–1510, 1998.
Korolev, A. V., Strapp, J. W., Isaac, G. A., and Nevzorov, A. N.: In situ measurements of effective diameter and effective droplet number concentration, J. Geophys. Res., 27, 3993–4003, 1999.
Korolev, A. V. and Strapp, J. W.: Accuracy of Measurements of Cloud Ice Water Content by the Nevzorov Probe. 40th Aerospace Sciences Meeting and Exhibition, 14–17 January 2002, Reno, Nevada, AIAA, 2002.
Korolev, A. V., Isaac, G. A., Cober, S. G., Strapp, J. W., and Hallett, J.: Part A: Microphysical Characterization of Mixed Phase Clouds, Q. J. R. Meteorol. Soc., 129, 39–66, 2003.
Korolev, A. V., Strapp, J. W., Isaac, G. A., and Emery, E.: Improved airborne hot-wire measurements of ice water content in clouds, International Conference on Clouds and Precipitation, Cancun, Mexico, 2008.
Knollenberg, R. G.: Techniques for probing cloud microstructure. Clouds, their formation, optical properties and effects, edited by: Hobbs, P. V., Deepak, A., Academic Press, New York, 15–85, 1981.
Lawson, R. P., Korolev, A. V., Cober, S. G., Huang, T., Strapp, J. W., and Isaac, G. A.: Improved measurements of the drop size distribution of a freezing drizzle event, Atmos. Res., 48, 181–191, 1998.
Lawson, R. P., Baker B. A., Schmitt, C. G., and Jensen, T. L.: An overview of microphysical properties of Arctic clouds observed in May and July 1998 during FIRE ACE, J. Geophys. Res., 106, D14, 14989–15014, 2001.
Martin, G. M., Johnson D. W., and Spice A.: The measurement and parameterization of effective radius of droplets in stratocumulus clouds, J. Atmos. Sci., 51, 1823–1842, 1994.
Nevzorov, A. N.: "Aircraft cloud water content meter", (in Russian language), Transactions of Central Aerological Observatory (Trudi TsAO), 147, 19–26, 1983.
Nicholls, S., Leighton, J., and Barker, R. A. L.: A new fast-response instrument for measuring total water content from aircraft, J. Atmos. Oceanic Tech., 7, 706–718, 1990.
Ogren, J. A., Heintzenberg, J., and Charlson, R. J.: In situ sampling of clouds with a droplet to aerosol converter, Geophys. Res. Lett., 12(Eq. (2)), 121–124, 1985.
Ruskin, R. E.: Liquid water content devices, Atmos. Techn., 8, 38–42, 1976.
Stachlewska, I. S., Gayet, J.-F., Duroure, C., Schwarzenboeck, A., Jourdan, O., Shcherbakov, V., and Neuber, R.: Observations of mixed phase clouds using airborne lidar and in situ instrumentation, Revived and revised Papers Presented at the 23rd International Laser Radar Conference, Chikao Nagasava and Nobuo Sugimoto, 325–328, 2006.
Strapp, J. W., Oldenburg, J., Ide, R., Lilie, L., Bacic, S., Vukovic, Z., Oleskiw, M., Miller, D., Emery, E., and Leone, G.: Wind Tunnel Measurements of the Response of Hot-Wire Liquid Water Content Instruments to Large Droplets, J. Atmos. Ocean. Techn., 20, 791–806, 2003.
Strapp, J. W., Lilie, L. E., Emery, E. E., and Miller, D. R.: Preliminary Comparison of Ice Water Content as Measured by Hot Wire Instruments of Varying Configuration, 43rd Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA-2005-0860, 11–13 January 2005.
Ström, J., Busen, R., Quante, M., Guillemet, B., Brown, P., and Heintzenberg, J.: Pre-EUCREX Intercomparison of Airborne Humidity Measuring Instruments, J. Atmos. Ocean. Techn., 11, 1392–1399, 1994.
Voloshchuk, V. M.: The Introduction to the Hydrodynamics of Coarse-Dispersed Aerosols, Gidrometeoizdat, 208 pp., 1971.
Wendisch, M., Garrett, T. J., and Strapp, J. W.: Wind Tunnel Tests of the Airborne PVM-100A Response to Large Droplets, J. Atmos. Oceanic Techn., 19, 1577–1584, 2002.
