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Remote Sensing of Clouds

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 50362

Special Issue Editor

Dr. Filomena Romano

E-Mail
Guest Editor
Institute of Methodologies for Environmental Analysis, National Research Council (IMAA/CNR), 85050 Tito Scalo, Potenza, Italy
Interests: cloud remote sensing; cloud radiative forcing; cloud detection and classification; cloud microphysical properties; surface solar irradiance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing of clouds is a hot topic of modern atmospheric remote sensing studies. Clouds largely modify the radiation budget, both in the solar and thermal spectral ranges, playing a fundamental role in the Earth’s climate state and making adjustments to climate forcing. Global changes in surface temperature are highly sensitive to cloud amount and type; hence, it is not surprising that the largest uncertainty in model estimates of global warming is due to clouds. Their properties could change with time, leading to planetary energy imbalance on a global scale. Optical and thermal infrared remote sensing of clouds is a mature research field with a long history. Great progress has been achieved using both ground-based and satellite instrumentation in retrieval of microphysical clouds parameters.

The Special Issue is aimed at the presentation of recent results in ground-based and satellite remote sensing of clouds, including innovative applications for meteorology and atmospheric physics and validation of retrievals based on independent measurements.

Being at the boundary between atmospheric and remote sensing sciences, the “Remote Sensing of Clouds” Special Issue is jointly organized between “Atmosphere” and “Remote Sensing” journals. According to the Aims & Scope of these journals, articles showing the exploitation of remote sensing data in cloud physics and meteorology can be submitted to “Atmosphere”, while articles presenting cloud remote sensing technology and methodology can be submitted to “Remote Sensing”.

Dr. Filomena Romano
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clouds
  • satellite
  • ground-based
  • remote sensing
  • meteorology
  • microphysical clouds parameters

Published Papers (15 papers)

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Editorial

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3 pages, 166 KiB  
Editorial
Editorial for the Special Issue “Remote Sensing of Clouds”
by Filomena Romano
Remote Sens. 2020, 12(24), 4085; https://doi.org/10.3390/rs12244085 - 14 Dec 2020
Cited by 1 | Viewed by 1665
Abstract
Remote sensing of clouds is a subject of intensive study in modern atmospheric remote sensing [...] Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)

Research

Jump to: Editorial, Other

25 pages, 12959 KiB  
Article
An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones
by Zheng Wang, Jun Du, Junshi Xia, Cheng Chen, Qun Zeng, Liqiao Tian, Lihui Wang and Zhihua Mao
Remote Sens. 2020, 12(18), 3003; https://doi.org/10.3390/rs12183003 - 15 Sep 2020
Cited by 7 | Viewed by 2922
Abstract
Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds [...] Read more.
Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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30 pages, 7285 KiB  
Article
A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery
by Adrián E. Yuchechen, S. Gabriela Lakkis, Agustín Caferri, Pablo O. Canziani and Juan Pablo Muszkats
Remote Sens. 2020, 12(18), 2991; https://doi.org/10.3390/rs12182991 - 14 Sep 2020
Cited by 6 | Viewed by 2657
Abstract
An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was [...] Read more.
An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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18 pages, 5319 KiB  
Article
Convective Initiation Proxies for Nowcasting Precipitation Severity Using the MSG-SEVIRI Rapid Scan
by Donatello Gallucci, Maria Pia De Natale, Domenico Cimini, Francesco Di Paola, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Teodosio Nilo, Elisabetta Ricciardelli, Mariassunta Viggiano and Filomena Romano
Remote Sens. 2020, 12(16), 2562; https://doi.org/10.3390/rs12162562 - 09 Aug 2020
Cited by 5 | Viewed by 3494
Abstract
In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than [...] Read more.
In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than 10 mm and greater than 30 mm. The analysis is conducted on a representative dataset containing 92 severe and weak precipitation events collected over the Italian peninsula in the period 2016–2019 over June-September. The events are selected to be short-lived (i.e., less than 12 h) and localized (i.e., less than 50×50km2). Italian National Radar Network products, namely the Vertical Maximum Intensity (VMI) and the Surface Rain Total (SRT) variables (from Dewetra Platform by CIMA, Italian Civil Protection Department), are used as indicators of convection (i.e., VMI greater than 35 dBZ echo intensity) and cumulated rainfall, respectively. The considered predictors are linear combinations of spectral infrared channels measured with the Rapid Scan Service (RSS) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) geostationary satellites. We select a 5×5 SEVIRI pixel-box centered on the storm core and perform a statistical analysis of the predictors up to 2.5 h around the event occurrence. We demonstrate that some of the proxies—describing growth and glaciation storm properties—show few degrees contrast between severe and nonsevere precipitation cases, hence carrying significant information to help discriminate the two types. We design a threshold scheme based on the three most informative predictors to distinguish weak and strong precipitation events. This analysis yields accuracy higher than 0.6 and the probability of false detection lower than 0.26; in terms of reducing false alarms, this method shows slight better performances compared to related works, at the expense of a lower probability of detection. The overall results, however, show limited capability for these infrared proxies as stand-alone predictors to distinguish severe from nonsevere precipitation events. Nonetheless, these may serve as additional tools to reduce the false alarm ratio in nowcasting algorithms for convective orographic storms. This study also provides further insight into the correlation between early infrared fields signatures prior to convection and subsequent evolution of the storms, extending previous works in this field. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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34 pages, 33158 KiB  
Article
Detection and Avoidance of Atmospheric Aviation Hazards Using Infrared Spectroscopic Imaging
by Fred Prata
Remote Sens. 2020, 12(14), 2309; https://doi.org/10.3390/rs12142309 - 18 Jul 2020
Cited by 5 | Viewed by 3501
Abstract
Atmospheric aviation hazards due to turbulence, poor visibility, high-altitude ice crystals and volcanic ash and gases are known problems for aviation and can cause both economic damage to engines and airframes as well as having the potential to cause the engines to stall [...] Read more.
Atmospheric aviation hazards due to turbulence, poor visibility, high-altitude ice crystals and volcanic ash and gases are known problems for aviation and can cause both economic damage to engines and airframes as well as having the potential to cause the engines to stall in flight with possible loss of the aircraft. Current space- and ground-based assets allow observations of some of these hazards and their detection and movement can be forecast using modern computer weather forecasting and dispersion models. These largely strategic resources have proved very valuable but somewhat limited in the tactical sense, where commercial aviation must make rapid decisions in order to avoid an undetected or un-forecast hazardous cloud or atmospheric condition. Here we investigate the use of multi-spectral (two channels or more) infrared imaging from an aircraft perspective, and show that it is possible to use this information to provide tactical awareness tools for use by aviators and other stakeholders. This study has a strong focus on volcanic ash as an aviation hazard but also includes applications to some forms of clear air turbulence (CAT), to high-altitude ice crystals (HAIC) and windblown desert dust. For volcanic ash detection, the research shows that current two-channel satellite-based infrared techniques provide acceptable discrimination and quantification, but two-channel infrared imaging airborne solutions have significant drawbacks. Because of the limitation of two-channel methods, infrared spectroscopic techniques are investigated and it is shown they can significantly reduce the confusion caused by meteorological hydrometeors and potentially provide information on other atmospheric hazards to aviation, such as HAIC and some forms of turbulence. Not only are these findings important for on-going efforts to incorporate IR imaging onto commercial aircraft, but they also have relevance to the increasing use of drones for hazard detection, research and monitoring. Uncooled infrared bolometric imaging cameras with spectroscopic capabilities are available and we describe one such system for use on airborne platforms. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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22 pages, 9960 KiB  
Article
Elucidating the Life Cycle of Warm-Season Mesoscale Convective Systems in Eastern China from the Himawari-8 Geostationary Satellite
by Dandan Chen, Jianping Guo, Dan Yao, Zhe Feng and Yanluan Lin
Remote Sens. 2020, 12(14), 2307; https://doi.org/10.3390/rs12142307 - 18 Jul 2020
Cited by 18 | Viewed by 3029
Abstract
The life cycle of mesoscale convective systems (MCSs) in eastern China is yet to be fully understood, mainly due to the lack of observations of high spatio-temporal resolution and objective methods. Here, we quantitatively analyze the properties of warm-season (from April to September [...] Read more.
The life cycle of mesoscale convective systems (MCSs) in eastern China is yet to be fully understood, mainly due to the lack of observations of high spatio-temporal resolution and objective methods. Here, we quantitatively analyze the properties of warm-season (from April to September of 2016) MCSs during their lifetimes using the Himawari-8 geostationary satellite, combined with ground-based radars and gauge measurements. Generally, the occurrence of satellite derived MCSs has a noon peak over the land and an early morning peak over the ocean, which is several hours earlier than the precipitation peak. The developing and dissipative stages are significantly longer as total durations of MCSs increase. Aided by three-dimensional radar mosaics, we find the fraction of convective cores over northern China is much lower when compared with those in central United States, indicating that the precipitation produced by broad stratiform clouds may be more important for northern China. When there exists a large amount of stratiform precipitation, it releases a large amount of latent heat and promotes the large-scale circulations, which favors the maintenance of MCSs. These findings provide quantitative results about the life cycle of warm-season MCSs in eastern China based on multiple data sources and large numbers of samples. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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19 pages, 5488 KiB  
Article
Cirrus Cloud Identification from Airborne Far-Infrared and Mid-Infrared Spectra
by Davide Magurno, William Cossich, Tiziano Maestri, Richard Bantges, Helen Brindley, Stuart Fox, Chawn Harlow, Jonathan Murray, Juliet Pickering, Laura Warwick and Hilke Oetjen
Remote Sens. 2020, 12(13), 2097; https://doi.org/10.3390/rs12132097 - 30 Jun 2020
Cited by 8 | Viewed by 3214
Abstract
Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the [...] Read more.
Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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18 pages, 5496 KiB  
Article
Falling Mixed-Phase Ice Virga and their Liquid Parent Cloud Layers as Observed by Ground-Based Lidars
by Chong Cheng and Fan Yi
Remote Sens. 2020, 12(13), 2094; https://doi.org/10.3390/rs12132094 - 30 Jun 2020
Cited by 13 | Viewed by 2500
Abstract
Falling mixed-phase virga from a thin supercooled liquid layer cloud base were observed on 20 occasions at altitudes of 2.3–9.4 km with ground-based lidars at Wuhan (30.5 °N, 114.4 °E), China. Polarization lidar profile (3.75-m) analysis reveals some ubiquitous features of both falling [...] Read more.
Falling mixed-phase virga from a thin supercooled liquid layer cloud base were observed on 20 occasions at altitudes of 2.3–9.4 km with ground-based lidars at Wuhan (30.5 °N, 114.4 °E), China. Polarization lidar profile (3.75-m) analysis reveals some ubiquitous features of both falling mixed-phase virga and their liquid parent cloud layers. Each liquid parent cloud had a well-defined base height where the backscatter ratio R was ~7.0 and the R profile had a clear inflection point. At an altitude of ~34 m above the base height, the depolarization ratio reached its minimum value (~0.04), indicating a liquid-only level therein. The thin parent cloud layers tended to form on the top of a broad preexisting aerosol/liquid water layer. The falling virga below the base height showed firstly a significant depolarization ratio increase, suggesting that most supercooled liquid drops in the virga were rapidly frozen into ice crystals (via contact freezing). After reaching a local maximum value of the depolarization ratio, both the values of the backscatter ratio and depolarization ratio for the virga exhibited an overall decrease with decreasing height, indicating sublimated ice crystals. The diameters of the ice crystals in the virga were estimated based on an ice particle sublimation model along with the lidar and radiosonde observations. It was found that the ice crystal particles in these virga cases tended to have smaller mean diameters and narrower size distributions with increasing altitude. The mean diameter value is 350 ± 111 µm at altitudes of 4–8.5 km. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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Graphical abstract

19 pages, 6747 KiB  
Article
A Long-Term Cloud Albedo Data Record Since 1980 from UV Satellite Sensors
by Clark J. Weaver, Dong L. Wu, Pawan K. Bhartia, Gordon J. Labow and David P. Haffner
Remote Sens. 2020, 12(12), 1982; https://doi.org/10.3390/rs12121982 - 19 Jun 2020
Cited by 4 | Viewed by 2585
Abstract
Black-sky cloud albedo (BCA) is derived from satellite UV 340 nm observations from NOAA and NASA satellites to infer long-term (1980–2018) shortwave cloud albedo variations induced by volcano eruptions, the El Niño–Southern Oscillation, and decadal warming. While the UV cloud albedo has shown [...] Read more.
Black-sky cloud albedo (BCA) is derived from satellite UV 340 nm observations from NOAA and NASA satellites to infer long-term (1980–2018) shortwave cloud albedo variations induced by volcano eruptions, the El Niño–Southern Oscillation, and decadal warming. While the UV cloud albedo has shown no long-term trend since 1980, there are statistically significant reductions over the North Atlantic and over the marine stratocumulus decks off the coast of California; increases in cloud albedo can be seen over Southeast Asia and over cloud decks off the coast of South America. The derived BCA assumes a C-1 water cloud model with varying cloud optical depths and a Cox–Munk surface BRDF over the ocean, using radiances calibrated over the East Antarctic Plateau and Greenland ice sheets during summer. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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Graphical abstract

27 pages, 4678 KiB  
Article
Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer
by Jiafeng Zheng, Liping Liu, Haonan Chen, Yabin Gou, Yuzhang Che, Haolin Xu and Qian Li
Remote Sens. 2019, 11(24), 3045; https://doi.org/10.3390/rs11243045 - 17 Dec 2019
Cited by 17 | Viewed by 4496
Abstract
The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal [...] Read more.
The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h−1, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by γ distributions N(D) = 1.49 × 104D−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 103D0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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18 pages, 10581 KiB  
Article
Applications of QC and Merged Doppler Spectral Density Data from Ka-Band Cloud Radar to Microphysics Retrieval and Comparison with Airplane in Situ Observation
by Liping Liu, Han Ding, Xiaobo Dong, Junwu Cao and Tao Su
Remote Sens. 2019, 11(13), 1595; https://doi.org/10.3390/rs11131595 - 04 Jul 2019
Cited by 10 | Viewed by 3100
Abstract
The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation [...] Read more.
The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation on the east side of Taihang Mountain in Hebei Province in 2018. To resolve the data quality problems caused by attenuation in the precipitation area; we focused on developing an algorithm for attenuation correction based on rain drop size distribution (DSD) retrieved from the merged Doppler spectral density data of the four operational modes following data quality control (QC). After dealiasing Doppler velocity and removal of range sidelobe artifacts; we merged the four types of Doppler spectral density data. Vertical air speed and DSD are retrieved from the merged Doppler spectral density data. Finally, we conducted attenuation correction of Doppler spectral density data and recalculated Doppler moments such as reflectivity; radial velocity; and spectral width. We evaluated the consistencies of reflectivity spectra from the four operational modes and DSD retrieval performance using airborne in situ observation. We drew three conclusions: First, the four operational modes observed similar reflectivity and velocity for clouds and low-velocity solid hydrometeors; however; three times of coherent integration underestimated Doppler reflectivity spectra for velocities greater than 2 m s−1. Reflectivity spectra were also underestimated for low signal-to-noise ratios in the low-sensitivity operational mode. Second, QC successfully dealiased Doppler velocity and removed range sidelobe artifacts; and merging of the reflectivity spectra mitigated the effects of coherent integration and pulse compression on radar data. Lastly, the CR observed similar DSD and liquid water content vertical profiles to airborne in situ observations. Comparing CR and aircraft data yielded uncertainty due to differences in observation space and temporal and spatial resolutions of the data. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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17 pages, 3970 KiB  
Article
Optical and Geometrical Properties of Cirrus Clouds over the Tibetan Plateau Measured by LiDAR and Radiosonde Sounding during the Summertime in 2014
by Guangyao Dai, Songhua Wu, Xiaoquan Song and Liping Liu
Remote Sens. 2019, 11(3), 302; https://doi.org/10.3390/rs11030302 - 02 Feb 2019
Cited by 9 | Viewed by 3867
Abstract
Optical and geometrical characteristics of the cirrus clouds over Naqu (4508 m a.s.l., 31.48° N, 92.06° E), in the Tibetan Plateau were determined from LiDAR and radiosonde measurements performed during the third TIbetan Plateau EXperiment of atmospheric sciences (TIPEX III) campaign from July [...] Read more.
Optical and geometrical characteristics of the cirrus clouds over Naqu (4508 m a.s.l., 31.48° N, 92.06° E), in the Tibetan Plateau were determined from LiDAR and radiosonde measurements performed during the third TIbetan Plateau EXperiment of atmospheric sciences (TIPEX III) campaign from July to August 2014. For the analysis of the temperature dependence, the simultaneous observations with LiDAR and radiosonde were conducted. Cirrus clouds were generally observed ranging from 5.2 km to 12 km above ground level (AGL) (i.e., 9.7 km to 16.5 km a.s.l.), with the midcloud temperatures ranging from −79.7 to −26.0 °C. The cloud thickness generally differed from 0.12 to 2.55 km with a mean thickness of 1.22 ± 0.70 km, and 85.7% of the measurement cases had thickness smaller than 1.5 km. The retrievals of linear particle depolarization ratio, extinction coefficient, and optical depth of cirrus clouds were provided. Moreover, the multiple scattering effect inside of cirrus clouds was corrected. The linear particle depolarization ratio of the cirrus clouds varied from 0.36 to 0.52, with a mean value of 0.44 ± 0.04. The optical depth of the cirrus clouds was between 0.01 and 3 following the scheme of Fernald-Klett method. Sub-visual, thin, and opaque cirrus clouds were observed at 4.76%, 61.90% and 33.34% of the measured cases, respectively. The temperature and thickness dependencies of the optical properties were studied in detail. A maximum cirrus thickness of around 2 km was found at temperatures between −60 and −50 °C. This study shows that the mean extinction coefficient of the cirrus clouds increases with the increase of temperature. Conversely, the measurements indicate that the linear particle depolarization ratio decreases with the increasing temperature. The relationships between the existence of cirrus clouds and the temperature anomaly (temperature difference from the mean value of the temperature during July and August 2014 over Naqu) and deep convective activity are also discussed. The formation of cirrus clouds is investigated and also its apparent relationship with the South Asia High Pressure, the dynamic processes of Rossby wave, and deep convective activity over the Tibetan Plateau. The outgoing longwave radiation of cirrus clouds is calculated with the Fu-Liou model and is shown to increases monotonously with the increase of optical depth. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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21 pages, 12122 KiB  
Article
Algorithms for Doppler Spectral Density Data Quality Control and Merging for the Ka-Band Solid-State Transmitter Cloud Radar
by Liping Liu and Jiafeng Zheng
Remote Sens. 2019, 11(2), 209; https://doi.org/10.3390/rs11020209 - 21 Jan 2019
Cited by 16 | Viewed by 4270
Abstract
The Chinese Ka-band solid-state transmitter cloud radar (CR) can operate in three different work modes with different pulse widths and coherent integration and non-coherent integration numbers to meet the requirement for long-term cloud measurements. The CR was used to observe cloud and precipitation [...] Read more.
The Chinese Ka-band solid-state transmitter cloud radar (CR) can operate in three different work modes with different pulse widths and coherent integration and non-coherent integration numbers to meet the requirement for long-term cloud measurements. The CR was used to observe cloud and precipitation data in southern China in 2016. In order to resolve the data quality problems caused by coherent integration and pulse compression, which are used to detect weak cloud in the cloud radar, this study focuses on analyzing the consistencies of reflectivity spectra using the three modes and the influence of coherent integration and pulse compression, developing an algorithm for Doppler spectral density data quality control (QC) and merging based on multiple-mode observation data. After dealiasing Doppler velocity and artefact removal, the three types of Doppler spectral density data were merged. Then, Doppler moments such as reflectivity, radial velocity, and spectral width were recalculated from the merged reflectivity spectra. Performance of the merging algorithm was evaluated. Three conclusions were drawn. Firstly, four rounds of coherent integration with a pulse repetition frequency (PRF) of 8333 Hz underestimated the reflectivity spectra for Doppler velocities exceeding 2 m·s−1, causing a large negative bias in the reflectivity and radial velocity when large drops were present. In contrast, two rounds of coherent integration affected the reflectivity spectra to a lesser extent. The reflectivity spectra were underestimated for low signal-to-noise ratios in the low-sensitivity mode. Secondly, pulse compression improved the radar sensitivity and air vertical speed observation, whereas the precipitation mode and coherent integration led to an underestimation of the number concentration of big raindrops and an overestimation of the number concentration of small drops. Thirdly, a comparison of the individual spectra with the merged reflectivity spectra showed that the Doppler moments filled in the gaps in the individual spectra during weak cloud periods, reduced the effects of coherent integration and pulse compression in liquid precipitation, mitigated the aliasing of Doppler velocity, and removed the artefacts, yielding a comprehensive and accurate depiction of most of the clouds and precipitation in the vertical column above the radar. The recalculated moments of the Doppler spectra had better quality than those merged from raw data. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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13 pages, 6344 KiB  
Letter
Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images
by Lukáš Krauz, Petr Janout, Martin Blažek and Petr Páta
Remote Sens. 2020, 12(11), 1902; https://doi.org/10.3390/rs12111902 - 11 Jun 2020
Cited by 8 | Viewed by 3220
Abstract
All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they [...] Read more.
All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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12 pages, 4196 KiB  
Letter
Cloud Effective Emissivity Retrievals Using Combined Ground-Based Infrared Cloud Measuring Instrument and Ceilometer Observations
by Lei Liu, Ting Zhang, Yi Wu, Zhencong Niu and Qi Wang
Remote Sens. 2018, 10(12), 2033; https://doi.org/10.3390/rs10122033 - 14 Dec 2018
Cited by 5 | Viewed by 2683
Abstract
In this paper, a new inversion procedure for cloud effective emissivity retrievals using a combined ground-based infrared cloud measuring instrument with ceilometer was developed. A quantitative sensitivity and performance analysis of the proposed method was also provided. It was found that the uncertainty [...] Read more.
In this paper, a new inversion procedure for cloud effective emissivity retrievals using a combined ground-based infrared cloud measuring instrument with ceilometer was developed. A quantitative sensitivity and performance analysis of the proposed method was also provided. It was found that the uncertainty of the derived effective emissivity was mainly associated with errors on the measurement radiance, the simulated radiance of clear sky and blackbody cloudy sky. Furthermore, the retrieval at low effective emissivity was most sensitive to the simulated clear sky radiances, whereas the blackbody cloudy sky radiance was the prevailing source of uncertainty at high emissivity. This newly proposed procedure was applied to the measurement taken in the CMA Beijing Observatory Station from November 2011 to June 2012 by the whole-sky infrared cloud-measuring system (WSIRCMS) and CYY-2B ceilometer. The cloud effective emissivity measurements were in good agreement with that of the MODIS/AQUA MYD06 Collection 6 (C6) cloud products. The mean difference between them was 0.03, with a linear correlation coefficient of 0.71. The results demonstrate that the retrieval method is robust and reliable. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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