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

Hye Won,Myoung Ahn

doi:10.3390/rs10071049

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 °C.

Highlights

The roles that clouds play in the atmosphere are ubiquitous, in the atmospheric processes such as the Earth’s energy budget, precipitation, and chemical, dynamical, and optical phenomena, and in the atmospheric measurements, by interfering with various types of remote sensing
The deviation increases in the quadratic relationship, and shows as large as 35 ◦C at −100 ◦C of TbS, which implies that the Control and EXP2 could be better positioned for cold environments such as the winter time or cold clouds
Since the two important characteristics of infrared thermometer (IRT)—dynamic range and sampling rate—are directly related to the formulation of the empirical equation for the detection algorithm, the algorithm performance is highly dependent on the characteristics of IRT data

Summary

Introduction

The roles that clouds play in the atmosphere are ubiquitous, in the atmospheric processes such as the Earth’s energy budget, precipitation, and chemical, dynamical, and optical phenomena, and in the atmospheric measurements, by interfering with various types of remote sensing. The measured radiances of approximately 10 μm correspond to the atmospheric window region and are quite sensitive to cloud presence, resulting in the measured radiance in cloudy conditions showing a warmer (spectral) and more variable (temporal) characteristic compared with clear-sky radiation. As it uses the emitted radiation from the atmosphere, cloud information could be obtained during both day and night. 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. 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]

Methods

Findings

Discussion

Conclusion