Atmospheric concentrations of CH4 have tripled since the Industrial Revolution. One culprit of this increase is animal agriculture, contributing 8 to 10% of global greenhouse gas emissions primarily in the form of CH4. According to US Environmental Protection Agency greenhouse gas inventory estimates, the majority of the manure emissions are from manure management on dairy farms (53%). Most of these manure emissions are generated from liquid manure in anaerobic lagoons. Thus, accurate estimates of the emissions from these lagoons are essential for developing management strategies to reduce CH4 emissions. Emissions of methane from two manure lagoons, one in Southern California and the other in Central California, were estimated by fitting results from a state-of-the-art dispersion model to CH4 concentrations measured with a mobile monitor. The sampling was conducted by stationing the mobile monitors at several locations (29-42) around the lagoons for time intervals ranging from 10 to 15 minutes. A sonic anemometer provided micrometeorological measurements used by the dispersion model. Emissions were computed by fitting the time-averaged methane concentrations to model estimates. The 95% confidence intervals for the emissions were computed by bootstrapping pseudo observations created by adding residuals between model estimates and corresponding observations to the best fit model estimates. The coefficient of determination, r2, between model and measurements made at the Southern California dairy was over 0.86 and the geometric standard deviation (sg) was 1.1; the steady westerly wind direction was a major factor for this result. At the Central California dairy, the winds were light and variable resulting in an r2 of about 0.9 and a high sg of 1.4. The sensitivity of the emission estimates to wind direction was determined by running the dispersion model for different wind sectors. We found that the emission estimates were within 1.5 times of each other under all wind conditions. The dispersion model was cross-validated by estimating the emissions using only half the total receptors and then predicting the concentration at other receptors using this emission rate. This technique can be used to improve methane emission estimates in manure management and to assess the effectiveness of the different strategies to reduce emissions.