Several studies have addressed the problem of optimizing field of view (FOV) size and sampling area of infrared sensors with the goal of achieving a higher percentage of cloud-free measurements. This study focuses on developing a tool to use global cloud analysis data in order to better understand the effects that different FOV sizes and satellite tracks have on the percentage of cloud-free measurements and the expected altitude of clouds that distort the signal of interest. This paper specifically discusses the situation of a satellite taking nadir measurements with a square FOV. The probability of a cloud contaminated measurement is estimated within 12-km grid boxes, making up a domain centered over the continental United States, using cloud fraction, cloud top altitude, and cloud base altitude values. The data confirms that the probability of a cloud contaminated FOV increases with an increase in FOV size. Compared to seasonal and diurnal variations, data suggests that FOV size has a relatively small effect on the expected value of cloud top and base altitudes. Increased understanding of factors effecting cloud contamination can improve scanning strategies and future satellite-based sensor designs.