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Editorial

Remote Sensing of Clouds

Institute of Methodologies for Environmental Analysis, National Research Council (IMAA/CNR), 85100 Potenza, Italy
Atmosphere 2019, 10(12), 814; https://doi.org/10.3390/atmos10120814
Submission received: 12 December 2019 / Revised: 13 December 2019 / Accepted: 13 December 2019 / Published: 15 December 2019
(This article belongs to the Special Issue Remote Sensing of Clouds)
This special issue collects four original and review articles dealing with different cloud aspects, from microphysical properties to macrophysical features. Clouds play a crucial role in modifying the energy budget of the Earth–Atmosphere system through their interaction with solar and longwave radiations [1,2,3]. Cloud properties (such as spatial location, temperature, horizontal and vertical distribution of liquid/ice water, optical thickness, particle size, and shape) are crucial for determining radiation and heat balance.
Paper one [4] presents an investigation on oceanic warm clouds embedded in multilayered structures, by using spaceborne radar data with fine vertical resolution. Wang et al. [5] conducted a comprehensive investigation on 20 years’ of radiosonde measurements and revealed that multilayered clouds account for about 42% of total cloud occurrences; therefore, studies on multilevel clouds are necessary both for the model parameterizations and in the retrieval algorithms [6].
In paper two [7] the authors investigated whether the larger measurement range of Vaisala CL51 ceilometer improves high cloud base detection and the effect of the range-variant smoothing on cloud base detection. Hutchison and D. Lisager in paper three [8] developed a cloud ground truth database for the verification of large-scale numerical simulations. Procedures to create manually-generated cloud analyses exploit phenomenological features to maximize cloud signatures in a variety of remotely-sensed satellite spectral bands, in order to facilitate scene interpretation and to create accurate cloud/no-cloud analyses.
Clouds and snow cover are spectrally-distinguishable, despite having similar reflectance spectra in the visible-light range [9]. The existence of clouds in remotely-sensed images obscures surface features. Therefore, accurate cloud cover detection is vital for earth observations using remote sensing data processing systems, which is the main topic in paper four [10].

Acknowledgments

I am very thankful to our colleagues for their invaluable contributions and to the reviewers for their constructive comments and suggestions that helped improving the manuscripts. We thank the editing office for their excellent support in processing and publishing this issue.

Conflicts of Interest

The author declares no conflict of interest.

References

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  4. Ding, Y.; Liu, Q.; Lao, P. Characteristics of Oceanic Warm Cloud Layers within Multilevel Cloud Systems Derived by Satellite Measurements. Atmosphere 2019, 10, 465. [Google Scholar] [CrossRef] [Green Version]
  5. Wang, J.; Rossow, W.B.; Zhang, Y. Cloud vertical structure and its variations from a 20-Yr global rawinsonde dataset. J. Clim. 2000, 13, 3041–3056. [Google Scholar] [CrossRef]
  6. Romano, F.D.; Cimini, D.; Rizzi, R.; Cuomo, V. Multilayered cloud parameters retrievals from combined infrared and microwave satellite observations. J. Geophys. Res. 2007, 112, D08210. [Google Scholar] [CrossRef] [Green Version]
  7. Šálek, M.; Szabó-Takács, B. 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. Atmosphere 2019, 10, 316. [Google Scholar] [CrossRef] [Green Version]
  8. Hutchison, K.D.; Iisager, B.D. Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models. Atmosphere 2019, 10, 177. [Google Scholar] [CrossRef] [Green Version]
  9. Romano, F.; Cimini, D.; Nilo, S.T.; Di Paola, F.; Ricciardelli, E.; Ripepi, E.; Viggiano, M. The Role of Emissivity in the Detection of Arctic Night Clouds. Remote Sens. 2017, 9, 406. [Google Scholar] [CrossRef] [Green Version]
  10. Han, L.; Wu, T.; Liu, Q.; Liu, Z. A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images. Atmosphere 2019, 10, 44. [Google Scholar] [CrossRef] [Green Version]

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MDPI and ACS Style

Romano, F. Remote Sensing of Clouds. Atmosphere 2019, 10, 814. https://doi.org/10.3390/atmos10120814

AMA Style

Romano F. Remote Sensing of Clouds. Atmosphere. 2019; 10(12):814. https://doi.org/10.3390/atmos10120814

Chicago/Turabian Style

Romano, Filomena. 2019. "Remote Sensing of Clouds" Atmosphere 10, no. 12: 814. https://doi.org/10.3390/atmos10120814

APA Style

Romano, F. (2019). Remote Sensing of Clouds. Atmosphere, 10(12), 814. https://doi.org/10.3390/atmos10120814

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