Effects of Dynamic Range and Sampling Rate of an Infrared Thermometer to the Accuracy of the Cloud Detection

Abstract

Cloud detection using downwelling radiation measured by infrared thermometer (IRT) has been utilized for many applications. The current study investigates the effects of disparate IRT specifications, including the dynamic range and sampling rates on the performance of cloud detection, which utilizes the spectral and temporal characteristics of cloudy radiation. To analyze the effects, the detection algorithm that was prepared with and applied to the IRT data with different specifications is compared with reference data, a ceilometer, and micro-pulse lidar (MPL). The comparison results show that the low-altitude clouds are detected with a sufficient accuracy: better than 97% probability of detection (POD). This is due to the much warmer brightness temperature (Tb) of the low clouds compared with the clear sky in the atmospheric window region where the IRT measurement was made. Conversely, the high-altitude cold clouds are hard to detect with the spectral test due to the much-reduced Tb contrast between cloudy and clear sky. Thus, the algorithm performance is largely dependent on the performance of the temporal test. Since the lower measurement noise provides a better estimation of the temporal variability of clear sky Tb with less estimation uncertainty, the IRT data having a better noise performance shows a better POD value by as much as 52.2% compared with the MPL result. However, the improvement is realized only when the dynamic range of IRT covers sufficiently cold Tb, such as -100 degrees C.