Reduced visibility and adverse cloud cover is a major issue for aviation, road traffic, and military activities. Synoptic meteorological stations and LIDAR measurements are common tools to detect meteorological conditions. However, a low density of meteorological stations and LIDAR measurements may limit a detailed spatial analysis. While geostationary satellite data is a valuable source of information for analyzing the spatio-temporal variability of fog and clouds on a global scale, considerable effort is still required to improve the detection of atmospheric variables on a local scale, especially during the night.
In this study we propose to use thermal camera images to (1) improve cloud detection and (2) to study visibility conditions during nighttime. For this purpose, we leverage FLIR A320 and FLIR A655sc Stationary Thermal Imagers installed in the city of Bern, Switzerland. We find that the proposed data provides detailed information about low clouds and the cloud base height that is usually not seen by satellites. However, clouds with a small optical depth such as thin cirrus clouds are difficult to detect as the noise level of the captured thermal images is high.
The second part of this study focuses on the detection of structural features. Predefined targets such as roof windows, an antenna, or a small church tower are selected at distances of 140m to 1210m from the camera. We distinguish between active targets (heated targets or targets with insufficient thermal insulation) and passive structural features to analyze the sensor's visibility range. We have found that a successful detection of some passive structural features highly depends on incident solar radiation. Therefore, the detection of such features is often hindered during the night. On the other hand, active targets can be detected without difficulty during the night due to major differences in temperature between the heated target and its surrounding non-heated objects. We retrieve response values by the cross-correlation of master edge signatures of the targets and the actual edge-detected thermal camera image. These response values are a precise indicator of the atmospheric conditions and allows us to detect restricted visibility conditions.
This study presents an automatic visibility retrieval of a FLIR A320 Stationary Thermal Imager installed on a measurement tower on the mountain Lagern located in the Swiss Jura Mountains. Our visibility retrieval makes use of edges that are automatically detected from thermal camera images. Predefined target regions, such as mountain silhouettes or buildings with high thermal differences to the surroundings, are used to derive the maximum visibility distance that is detectable in the image. To allow a stable, automatic processing, our procedure additionally removes noise in the image and includes automatic image alignment to correct small shifts of the camera. We present a detailed analysis of visibility derived from more than 24000 thermal images of the years 2015 and 2016 by comparing them to (1) visibility derived from a panoramic camera image (VISrange), (2) measurements of a forward-scatter visibility meter (Vaisala FD12 working in the NIR spectra), and (3) modeled visibility values using the Thermal Range Model TRM4. Atmospheric conditions, mainly water vapor from European Center for Medium Weather Forecast (ECMWF), were considered to calculate the extinction coefficients using MODTRAN. The automatic visibility retrieval based on FLIR A320 images is often in good agreement with the retrieval from the systems working in different spectral ranges. However, some significant differences were detected as well, depending on weather conditions, thermal differences of the monitored landscape, and defined target size.