This paper demonstrates a method for quantifying wind speed uncertainties in remote emissions surveys that specifically accounts for how wind errors are correlated across time and space. Using independent weather station data, models are presented for oil and gas regions in Canada, the U.S., and Colombia, along with a Python tool to enable broader use. This work enables robust accounting of uncertainties in emissions inventories and provides guidance to minimize uncertainties in remote surveys.