Despite recent technical advances, adverse weather still constitutes an important decision factor in the efficient use of IR sensors. The presence of fog, clouds or precipitation affects both the IR transmission and background properties of the atmosphere. Taking these effects into account requires the knowledge of the optical parameters of fog, clouds or precipitation which, in general, fluctuate too much on a scale of a few kilometers to be predictable with acceptable accuracy. Therefore, systems performance calculations based on modeling alone cannot provide all the necessary information for real time, on-site decision making. A promising alternative is continuous monitoring of atmospheric aerosol properties with a lidar. The method use in this study is the multiple-field- of-view technique which takes advantage of the information contained in the multiple scattering contributions to solve for both the droplet concentration and effective diameter. We can then use these solutions to derive the atmospheric radiance and transmittance, and calculate from there the contrast-to-noise ratio of IR images of small targets. Using actual lidar probings, examples of performance curves of a generic surveillance sensor are obtained for two types of targets. Results show that performance can drastically change over an interval as short as one minute, which emphasizes the need for real time, on site monitoring in adverse weather.