Long Term Monitoring and Connection between Topography and Cloud Cover Distribution in Serbia

The use of weather satellite recordings has been growing rapidly over the last three decades. Determining the patterns between meteorological and topographical features is an important scientific job. Cloud cover analysis and properties can be of the utmost significance for potential cloud seeding. Here, the analysis of the cloud properties was conducted by means of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite recordings. The resolution of used data was 1 km2 within the period of 30 years (1989–2019). This research showed moderate changing of cloudiness in the territory of Serbia with a high cloudiness in February, followed by cloudiness in January and November. For the past three decades, May has been the month with the highest cloudiness. The regions in the east and south-west, and particularly in the west, have a high absolute cloudiness, which is connected with the high elevation of the country. By means of long term monitoring, the whole territory of Serbia was analyzed for the first time, in terms of cloudiness. Apart from the statistical and numerical results obtained, this research showed a connection between relief and clouds, especially in the winter season. Linear regression MK (Mann–Kendall test) has proven this theory right, connecting high elevation sides with high absolute cloudiness through the year.

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Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3824 ◽

2020 ◽

Author(s):

Xu Yang ◽

Zhaohui Shang ◽

Keliang Hu ◽

Yi Hu ◽

Bin Ma ◽

...

Keyword(s):

Cloud Cover ◽

Emission Lines ◽

Austral Winter ◽

Site Testing ◽

Dome A ◽

Astronomical Observations ◽

Auroral Emission ◽

Long Term Monitoring ◽

Term Monitoring

Abstract Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz. Quantitative site testing is still needed to confirm the site’s properties. In this paper, we present a statistical analysis of cloud cover and aurora contamination from the Kunlun Cloud and Aurora Monitor (KLCAM). KLCAM is an automatic, unattended all-sky camera aiming for long-term monitoring of the usable observing time and optical sky background at Dome A. It was installed at Dome A in January 2017, worked through the austral winter, and collected over 47,000 images over 490 days. A semi-quantitative visual data analysis of cloud cover and auroral contamination was carried out by five individuals. The analysis shows that the night sky was free of clouds for 83 per cent of the time, which ranks Dome A highly in a comparison with other observatory sites. Although aurorae were detected somewhere on an image for nearly 45 per cent of the time, the chance of a point on the sky being affected by an aurora is small. The strongest auroral emission lines can be filtered out with customized filters.

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Examination of POLDER/PARASOL and MODIS/Aqua Cloud Fractions and Properties Representativeness

Journal of Climate ◽

10.1175/2011jcli3857.1 ◽

2011 ◽

Vol 24 (16) ◽

pp. 4435-4450 ◽

Cited By ~ 18

Author(s):

Shan Zeng ◽

Frédéric Parol ◽

Jérôme Riedi ◽

Céline Cornet ◽

François Thieuleux

Keyword(s):

Cloud Cover ◽

Temporal Variations ◽

Cloud Fraction ◽

Atmospheric Sciences ◽

Cloud Parameters ◽

Cloud Properties ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Level 2

Abstract The Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) and Aqua are two satellites on sun-synchronous orbits in the A-Train constellation. Aboard these two platforms, the Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) provide quasi simultaneous and coincident observations of cloud properties. The similar orbits but different detecting characteristics of these two sensors call for a comparison between the derived datasets to identify and quantify potential uncertainties in retrieved cloud properties. To focus on the differences due to different sensor spatial resolution and coverage, while minimizing sampling and weighting issues, the authors have recomputed monthly statistics directly from the respective official level-2 products. The authors have developed a joint dataset that contains both POLDER and MODIS level-2 cloud products collocated on a common sinusoidal grid. The authors have then computed and analyzed monthly statistics of cloud fractions corresponding either to the total cloud cover or to the “retrieved” cloud fraction for which cloud optical properties are derived. These simple yet crucial cloud statistics need to be clearly understood to allow further comparison work of the other cloud parameters. From this study, it is demonstrated that on average POLDER and MODIS datasets capture correctly the main characteristics of global cloud cover and provide similar spatial distributions and temporal variations. However, each sensor has its own advantages and weaknesses in discriminating between clear and cloudy skies in particular situations. Also it is shown that significant differences exist between the MODIS total cloud fraction (day mean) and the “retrieved” cloud fraction (combined mean). This study found a global negative difference of about 10% between POLDER and MODIS day-mean cloud fraction. On the contrary, a global positive difference of about 10% exists between POLDER and MODIS combined-mean cloud fraction. These statistical biases show both global and regional distributions that can be driven by sensors characteristics, environmental factors, and also carry potential information on cloud cover structure. These results provide information on the quality of cloud cover derived from POLDER and MODIS and should be taken into account for the use of other cloud products.

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