Cloud detection and visibility estimation during night time using thermal camera images

2019 ◽  
Author(s):  
Céline Portenier ◽  
Beat Ott ◽  
Peter Wellig ◽  
Stefan Wunderle
Keyword(s):  
Cloud Detection ◽  
Thermal Camera ◽  
Night Time ◽  
Visibility Estimation

scholarly journals An empirical method to correct for temperature-dependent variations in the overlap function of CHM15k ceilometers

2016 ◽  
Vol 9 (7) ◽  
pp. 2947-2959 ◽  
Author(s):  
Maxime Hervo ◽  
Yann Poltera ◽  
Alexander Haefele
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Correction Method ◽  
Empirical Method ◽  
Cloud Base ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Temperature Dependent ◽  
Night Time ◽  
Gradient Based

Abstract. Imperfections in a lidar's overlap function lead to artefacts in the background, range and overlap-corrected lidar signals. These artefacts can erroneously be interpreted as an aerosol gradient or, in extreme cases, as a cloud base leading to false cloud detection. A correct specification of the overlap function is hence crucial in the use of automatic elastic lidars (ceilometers) for the detection of the planetary boundary layer or of low cloud. In this study, an algorithm is presented to correct such artefacts. It is based on the assumption of a homogeneous boundary layer and a correct specification of the overlap function down to a minimum range, which must be situated within the boundary layer. The strength of the algorithm lies in a sophisticated quality-check scheme which allows the reliable identification of favourable atmospheric conditions. The algorithm was applied to 2 years of data from a CHM15k ceilometer from the company Lufft. Backscatter signals corrected for background, range and overlap were compared using the overlap function provided by the manufacturer and the one corrected with the presented algorithm. Differences between corrected and uncorrected signals reached up to 45 % in the first 300 m above ground. The amplitude of the correction turned out to be temperature dependent and was larger for higher temperatures. A linear model of the correction as a function of the instrument's internal temperature was derived from the experimental data. Case studies and a statistical analysis of the strongest gradient derived from corrected signals reveal that the temperature model is capable of a high-quality correction of overlap artefacts, in particular those due to diurnal variations. The presented correction method has the potential to significantly improve the detection of the boundary layer with gradient-based methods because it removes false candidates and hence simplifies the attribution of the detected gradients to the planetary boundary layer. A particularly significant benefit can be expected for the detection of shallow stable layers typical of night-time situations. The algorithm is completely automatic and does not require any on-site intervention but requires the definition of an adequate instrument-specific configuration. It is therefore suited for use in large ceilometer networks.

Improved cloud detection in AVHRR daytime and night-time scenes over the ocean

2001 ◽  
Vol 22 (13) ◽  
pp. 2585-2615 ◽  
Author(s):  
J. J. Simpson ◽  
T. J. McIntire ◽  
J. R. Stitt ◽  
G. L. Hufford
Keyword(s):  
Cloud Detection ◽  
Night Time

scholarly journals Sky Quality Meter and satellite correlation for night cloud-cover analysis at astronomical sites

2020 ◽  
Vol 493 (2) ◽  
pp. 2463-2471 ◽  
Author(s):  
S Cavazzani ◽  
S Ortolani ◽  
A Bertolo ◽  
R Binotto ◽  
P Fiorentin ◽  
...  
Keyword(s):  
Real Time ◽  
Cloud Cover ◽  
Large Data ◽  
Temporal Analysis ◽  
Observation Time ◽  
Astronomical Observation ◽  
Cloud Detection ◽  
Night Time ◽  
Total Percentage ◽  
A Site

ABSTRACT The analysis of night cloud cover is very important for astronomical observations in real time, considering a typical observation time of about 15 minutes, and to provide statistics. In this article, we use the Sky Quality Meter (SQM) for high-resolution temporal analysis of the La Silla and Asiago (Ekar Observatory) sky: 3 and 5 minutes respectively. We investigate the annual temporal evolution of the natural contributions of the sky at a site not influenced by artificial light at night (ALAN) and at one highly influenced. We also make a correlation between GOES and Aqua satellite data and ground-based SQM data to confirm the relationship between the SQM data and cloud cover. We develop an algorithm that allows the use of the SQM for night cloud detection and reach correlations with the nighttime cloud cover detected by the GOES and Aqua satellites of 97.2 per cent at La Silla and 94.6 per cent at Asiago. Our algorithm also classifies photometric (PN) and spectroscopic nights (SN). We measure 59.1 per cent PN and 21.7 per cent SN for a total percentage of clear nights of 80.8 per cent at La Silla in 2018. The respective Ekar Observatory values are 31.1 per cent PN, 24.0 per cent SN and 55.1 per cent of total clear night time. Application to the SQM network would involve the development of long-term statistics and large data forecasting models for site testing and real-time astronomical observation.

scholarly journals Measurement of aerosol optical depth and sub-visual cloud detection using the optical depth sensor (ODS)

2015 ◽  
Vol 8 (9) ◽  
pp. 9611-9648 ◽  
Author(s):  
D. Toledo ◽  
P. Rannou ◽  
J.-P. Pommereau ◽  
A. Sarkissian ◽  
T. Foujols
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Diffuse Radiation ◽  
Dust Storms ◽  
Saharan Dust ◽  
Cloud Detection ◽  
Depth Sensor ◽  
Night Time ◽  
Data Set

Abstract. A small and sophisticated optical depth sensor (ODS) has been designed to work in the atmosphere of Earth and Mars. The instrument measures alternatively the diffuse radiation from the sky and the attenuated direct radiation from the sun on the surface. The principal goals of ODS are to retrieve the daily mean aerosol optical depth (AOD) and to detect very high and optically thin clouds, crucial parameters in understanding the Martian and Earth meteorology and climatology. The detection of clouds is undertaken at twilight, allowing the detection and characterization of clouds with opacities below 0.03 (sub-visual clouds). In addition, ODS is capable to retrieve the aerosol optical depth during night-time from moonlight measurements. In order to study the performance of ODS under Mars-like conditions as well as to evaluate the retrieval algorithms for terrestrial measurements, ODS was deployed in Ouagadougou (Africa) between November 2004 and October 2005, a sahelian region characterized by its high dust aerosol load and the frequent occurrence of Saharan dust storms. The daily average AOD values retrieved by ODS were compared with those provided by a CIMEL Sun-photometer of the AERONET (Aerosol Robotic NETwork) network localized at the same location. Results represent a good agreement between both ground-based instruments, with a correlation coefficient of 0.79 for the whole data set and 0.96 considering only the cloud-free days. From the whole dataset, a total of 71 sub-visual cirrus (SVC) were detected at twilight with opacities as thin as 1.10−3 and with a maximum of occurrence at altitudes between 14 and 20 km. Although further analysis and comparisons are required, results indicate the potential of ODS measurements to detect sub-visual clouds.

scholarly journals CLOUD DETECTION FOR NIGHT-TIME PANCHROMATIC VISIBLE AND NEAR-INFRARED SATELLITE IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 853-860
Author(s):  
L. Joachim ◽  
T. Storch
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Near Infrared ◽  
Cloud Detection ◽  
Night Time ◽  
Training Site ◽  
Multi Temporal ◽  
Land Covers ◽  
Scene Features

Abstract. Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites.

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