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We estimate the amount of methane (CH₄) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic ^13∕12CH₄ measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). <br><br> The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH₄ and CO₂ (<i>X</i>_CH₄ and <i>X</i>_CO₂) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for <i>X</i>_CH₄ and <i>X</i>_CO₂, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. <br><br> Our top-down CH₄ emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s⁻¹ are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH₄ emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH₄ emissions of 2.2 to 3.5 ppt s⁻¹ at Chino.