A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Abstract

Abstract. For a better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of the clear sky condition. It is designated as cloud free data only when both the spectral and temporal tests confirm a cloud free data. Overall, most of the thick and uniform clouds are successfully screened out by the spectral test, while the broken and fast-varying clouds are screened out by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for 6 months, from January 2013 to June 2013. The overall proportion correct is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of failures occur when the new algorithm detects clouds while the ceilometer does not detect, resulting in different values of the probability of detection with different cloud base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

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

“…Secondly, the data acquisition process is capable of reporting Tb with a higher sampling rate at 5 Hz [10], whereas many similar instruments have a longer sampling rate of 3 s. It is conceived to capture the temporal variability of clouds better with the increased sampling rate, which is an additional aspect that the current study attempts to analyze. Finally, the temperature resolution is slightly better than previous instruments such as used by Ahn et al [9] (also, see Figure 2). Here, the temperature resolutions are interpreted as the noise equivalent delta-temperature .0"W, and 318 m above sea level) (a) and its site map including the location of the infrared thermometer (IRT), ceilometer, and micro-pulse lidar (MPL) (b).…”

“…However, it is also notable that the dynamic range of the IRT is limited, especially regarding the measurement limit of −50 • C, which could degrade the algorithm performance when the measured brightness temperature is cooler than −50 • C [9]. This issue with cold Tb is further complicated by a limited capability of the reference data used for the algorithm validation.…”

“…Here, it should be emphasized that Tb CLR has a rather large variation, even at the atmospheric window region, which is mainly depending on the variability of water vapor and the temperature of the lower atmosphere [3,[12][13][14]. It has been shown that a sufficiently accurate Tb CLR is obtained with an empirical formula as a function of the real-time surface temperature (T SFC ) and humidity (e) (a detailed description for the derivation of the empirical formula is given elsewhere [9], while a summary is also given in Appendix A). Concerning the derivation of the empirical formulas for clear sky, the measured Tb from the IRT (hereafter Tb IRT ) is regressed with the simulated clear sky Tb to reflect the characteristics of a specific IRT.…”

“…The cloud detection algorithm using the IRT data [9] is based on the characteristics of the cloudy Tb against the clear sky Tb (hereafter Tb CLR ). When the measured Tb is warmer and/or more variable than the Tb CLR , the measurement is determined to be cloud-affected.…”

“…Furthermore, Brocard et al [6] tried to utilize a temporal fluctuation of brightness temperature (hereafter Tb) obtained from the infrared radiometer to detect cirrus cloud, which regulates outgoing longwave radiation. From these studies, it has been shown that the detection algorithm using the spectral and temporal characteristics of the cloud radiation provides quite satisfactory and reliable cloud detection [3,[6][7][8][9].…”