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Atmosphere
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Remote Sensing of Clouds
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Remote Sensing of Clouds

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (15 October 2019) | Viewed by 17983

Special Issue Editor

Dr. Filomena Romano

E-Mail Website
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

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Published Papers (5 papers)

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Editorial

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2 pages, 156 KiB  
Editorial
Remote Sensing of Clouds
by Filomena Romano
Atmosphere 2019, 10(12), 814; https://doi.org/10.3390/atmos10120814 - 15 Dec 2019
Viewed by 2104
Abstract
This special issue collects four original and review articles dealing with different cloud aspects, from microphysical properties to macrophysical features [...] Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)

Research

Jump to: Editorial

15 pages, 3709 KiB  
Article
Characteristics of Oceanic Warm Cloud Layers within Multilevel Cloud Systems Derived by Satellite Measurements
by Yuhao Ding, Qi Liu and Ping Lao
Atmosphere 2019, 10(8), 465; https://doi.org/10.3390/atmos10080465 - 14 Aug 2019
Cited by 5 | Viewed by 3531
Abstract
Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data [...] Read more.
Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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11 pages, 1416 KiB  
Article
Comparison of SAFNWC/MSG Satellite Cloud Type with Vaisala CL51 Ceilometer-Detected Cloud Base Layer Using the Sky Condition Algorithm and Vaisala BL-View Software
by Milan Šálek and Beáta Szabó-Takács
Atmosphere 2019, 10(6), 316; https://doi.org/10.3390/atmos10060316 - 7 Jun 2019
Cited by 5 | Viewed by 4270
Abstract
Ceilometer detection can be used to determine cloud type based on cloud layer height. Satellite observations provide images of clouds’ physical properties. During the summer and winter of 2017, Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) cloud [...] Read more.
Ceilometer detection can be used to determine cloud type based on cloud layer height. Satellite observations provide images of clouds’ physical properties. During the summer and winter of 2017, Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) cloud type was compared to cloud base layers based upon a sky condition algorithm of Vaisala CL51 ceilometer and the BL-View applied range-variant smoothing backscatter profile at the National Atmospheric Observatory in Košetice, Czech Republic. This study investigated whether the larger measurement range of CL51 improved high cloud base detection and the effect of the range-variant smoothing on cloud base detection. The comparison utilized a multi-category contingency table wherein hit rate, false alarm ratio, frequency of bias, and proportion correct were evaluated. The accuracy of low-level and high cloud type detection by satellite was almost identical in both seasons compared to that using the sky condition algorithm. The occurrence of satellite high cloud detection was greatest when the ceilometer detected high cloud base above low and/or medium cloud base. The hit rate of high cloud detection increased significantly when the BL-View-produced cloud base layer was applied as a reference. We conclude that BL-View produces more accurate high cloud base detection. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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18 pages, 4233 KiB  
Article
Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models
by Keith D. Hutchison and Barbara D. Iisager
Atmosphere 2019, 10(4), 177; https://doi.org/10.3390/atmos10040177 - 3 Apr 2019
Cited by 3 | Viewed by 3232
Abstract
Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and [...] Read more.
Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and climate models. A possible explanation for this apparent omission involves the difficulty in developing cloud ground truth databases for the verification of large-scale numerical simulations. Therefore, the process of developing highly accurate cloud cover fraction truth data from manually generated cloud/no-cloud analyses of multispectral satellite imagery is the focus of this article. The procedures exploit the phenomenology to maximize cloud signatures in a variety of remotely sensed satellite spectral bands in order to create accurate binary cloud/no-cloud analyses. These manual analyses become cloud cover fraction truth after being mapped to the grids of the target datasets. The process is demonstrated by examining all clouds in a NAM dataset along with a 24 h WRF cloud forecast field generated from them. Quantitative comparisons with the cloud truth data for the case study show that clouds in the NAM data are under-specified while the WRF model greatly over-predicts them. It is concluded that highly accurate cloud cover truth data are valuable for assessing cloud model input and output datasets and their creation requires the collection of satellite imagery in a minimum set of spectral bands. It is advocated that these remote sensing requirements be considered for inclusion into the designs of future environmental satellite systems. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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16 pages, 21936 KiB  
Article
A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images
by Ling Han, Tingting Wu, Qing Liu and Zhiheng Liu
Atmosphere 2019, 10(2), 44; https://doi.org/10.3390/atmos10020044 - 23 Jan 2019
Cited by 5 | Viewed by 4031
Abstract
The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud [...] Read more.
The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud detection and allow an efficient use of satellite images. Firstly, we selected thick and thin clouds from high resolution Sentinel-2 images and applied a matched filter. Secondly, the fractal digital number-frequency (DN-N) algorithm was applied to detect clouds associated with anomalies. Thirdly, spatial analyses, particularly spatial overlaying and hotspot analyses, were conducted to eliminate false anomalies. The results indicate that the method is effective for detecting clouds with various cloud covers over different areas. The resulting cloud detection effect possesses specific advantages compared to classic methods, especially for satellite images of snow and brightly colored ground objects with spectral characteristics similar to those of clouds. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
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