Special Issue "Remote Sensing of Clouds"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editor

Dr. Filomena Romano
E-Mail Website
Guest Editor
National Research Council of Italy (CNR) - Institute of Methodologies for Environmental Analysis (IMAA), C.da S. Loja, 85050 Potenza, Italy
Interests: infrared/microwave remote sensing; clouds and precipitation
Special Issues and Collections 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 papers will be 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 2000 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 (5 papers)

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Open AccessArticle
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
Remote Sens. 2019, 11(24), 3045; https://doi.org/10.3390/rs11243045 - 17 Dec 2019
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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Open AccessArticle
Applications of QC and Merged Doppler Spectral Density Data from Ka-Band Cloud Radar to Microphysics Retrieval and Comparison with Airplane in Situ Observation
Remote Sens. 2019, 11(13), 1595; https://doi.org/10.3390/rs11131595 - 04 Jul 2019
Cited by 2
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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Open AccessArticle
Optical and Geometrical Properties of Cirrus Clouds over the Tibetan Plateau Measured by LiDAR and Radiosonde Sounding during the Summertime in 2014
Remote Sens. 2019, 11(3), 302; https://doi.org/10.3390/rs11030302 - 02 Feb 2019
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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Open AccessArticle
Algorithms for Doppler Spectral Density Data Quality Control and Merging for the Ka-Band Solid-State Transmitter Cloud Radar
Remote Sens. 2019, 11(2), 209; https://doi.org/10.3390/rs11020209 - 21 Jan 2019
Cited by 3
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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Other

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Open AccessLetter
Cloud Effective Emissivity Retrievals Using Combined Ground-Based Infrared Cloud Measuring Instrument and Ceilometer Observations
Remote Sens. 2018, 10(12), 2033; https://doi.org/10.3390/rs10122033 - 14 Dec 2018
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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