Atmospheric Attenuation for Lidar Systems in Adverse Weather Conditions

Abstract: In this study, the weather impact on lidar signals has been researched. A lidar system was placed with a target at approximately 90 m and has together with a weather station collected data for about a year before this study. By using the raw detector data from the lidar, the full waveform can be obtained and the amplitude of the return pulse can be calculated. Atmospheric attenuation of lidar signals is often modeled using the lidar equation, which predicts an exponential decrease in energy over the distance. The factor in the exponent is referred to as the extinction coefficient and it is the main property studied in this thesis. By utilizing models for the extinction coefficient under different weather conditions, it is possible to simulate the performance of the lidar.  The extinction coefficient was calculated using different empirical models. The empirical models investigated in this thesis are the Kim and Kruse models for known visibility, the Al Naboulsi model for different types of fog with known visibility, the Carbonneau model for known precipitation amount in rainy conditions, and a similar model for snowy conditions. For the case of rain, a physical model was also used, which is derived through Mie theory. The physical model requires a particle size distribution, which is the number of particles of a certain radius per unit volume. A particle size distribution for rain was generated using the Ulbrich raindrop size distribution, using the precipitation amount recorded by the weather station. Particle size distributions for radiation and advection fog were also simulated.  The measured attenuation in lidar signals was compared to the predicted attenuation that was calculated using different models for the extinction coefficient in the lidar equation. Generally, the models tend to underestimate the amplitude of the return pulse. This can partially be explained by the assumptions used to derive the lidar equation, which neglects all augmentation of the beam. The visibility models gave more accurate results compared to the precipitation models. This was expected, since visibility is defined as a measure of attenuation and precipitation amount is not.  When a lidar signal is emitted, the light will be reflected from optical surfaces within the lidar and cause a pulse to be detected. This pulse is referred to as the zeropulse. In the first couple of meters of the transmission, we expect to see some backscattered light from adverse weather, since the detector has a larger solid angle at shorter distances. This returned light will be combined with the zeropulse and cause it to expand in width. By examining the zeropulse, it was possible to observe a difference between the average zeropulse under some different weather conditions. This leads to the conclusion that it may be possible to extract some information about current weather conditions from the zeropulse data, given that there is little ambient light and snowy weather conditions.  By integrating the zeropulse, variations in the shape of the zeropulse could be described by a single value. Then by separating the data into low and high visibility populations, the zeropulse integral could be used to predict the visibility. The conclusion was that the zeropulse integral can accurately predict whether visibility is above or below a threshold value, given that there is little ambient light and the visibility is known to be below 19950 m.