Fog Measurements with IR Whole Sky Imager and Doppler Lidar, Combined with In Situ Instruments

This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote sensing with a thermal IR Whole Sky Imager and a Doppler Lidar. This work is the first extensive field campaign aimed to characterize fog properties in Israel and is a pioneer endeavor that encompasses simultaneous remote sensing measurements and analysis of a fog event with a thermal IR Whole Sky Imager. Radiation fog, as monitored by the sensor’s field of view, reveals three distinctive properties that make it possible to identify it. First, it exhibits an azimuthal symmetrical shape during the buildup phase. Second, the zenith brightness temperature is very close to the ground-level air temperature. Lastly, the rate of increase in cloud cover up to a completely overcast sky is very fast. Additionally, we validated the use of a Doppler Lidar as a tool for monitoring fog by proving that the measured backscatter-attenuation vertical profile agrees with the calculation of the Lidar equation fed with data measured by in situ instruments. It is shown that fog can be monitored by those two, off-the-shelf-stand-off-sensing technologies that were not originally designed for fog purposes. It enables the monitoring of fog properties such as type, evolution with time and vertical depth, and opens the path for future works of studying the different types of fog events.

Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory

In this study, we explored a new method of cloud detection called the Blue-Green (B-G) Color Difference, which is adapted from the widely used Red-Blue (R-B) Color Difference. The objective of this study was to test the effectiveness of these two methods in detecting daytime clouds. Three all-sky images were selected from a database system at PERMATApintar Observatory. Each selected all-sky image represented different sky conditions, namely clear, partially cloudy and overcast. Both methods were applied to all three images and compared in terms of cloud coverage detection. Our analysis revealed that both color difference methods were able to detect a thick cloud efficiently. However, the B-G was able to detect thin clouds better compared to the R-B method, resulting in a higher and more accurate cloud coverage detection.