We present an analysis of methane (CH4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) predictions based on seasonally varying California‐specific CH4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH4 emissions are 2.42 ± 0.49 Tg CH4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64 Tg CH4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model‐measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH4 emissions, in agreement with CARB's inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of California's CH4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).

No abstract

The utility of aggregating data from near-surface meteorological networks for initiating dispersion models is examined by using data from the ''WeatherBug'' network that is operated by Earth Networks, Inc. WeatherBug instruments are typically mounted 2-3 m above the eaves of buildings and thus are more representative of the immediate surroundings than of conditions over the broader area. This study focuses on subnetworks of WeatherBug sites that are within circles of varying radius about selected stations of the DCNet program. DCNet is a Washington, D.C., research program of the NOAA Air Resources Laboratory. The aggregation of data within varying-sized circles of 3-10-km radius yields average velocities and velocitycomponent standard deviations that are largely independent of the number of stations reporting-provided that number exceeds about 10. Given this finding, variances of wind components are aggregated from arrays of WeatherBug stations within a 5-km radius of selected central DCNet locations, with on average 11 WeatherBug stations per array. The total variance of wind components from the surface (WeatherBug) subnetworks is taken to be the sum of two parts: the temporal variance is the average of the conventional windcomponent variances at each site and the spatial variance is based on the velocity-component averages of the individual sites. These two variances (and the standard deviations derived from them) are found to be similar. Moreover, the total wind-component variance is comparable to that observed at the DCNet reference stations. The near-surface rooftop wind velocities are about 35% of the magnitudes of the DCNet measurements. Limited additional data indicate that these results can be extended to New York City.

Data from six urban areas in a nationwide network of sites within the surface roughness layer are examined. It is found that the average velocity variances in time, derived by averaging the conventional variances from a network of n stations, are nearly equal to the velocity variances in space, derived as the variances among the n average velocities. This similarity is modified during sunlit hours, when convection appears to elevate the former. The data show little dependence of the ratio of these two variances on wind speed. It is concluded that the average state of the surface roughness layer in urban and suburban areas like those considered here tends toward an approximate equality of these two measures of variance, much as has been observed elsewhere for the case of forests.

Inverse atmospheric dispersion models are used to provide measurement-based, or "top down", estimates of greenhouse gas (GHG) emissions for comparison with input-based, or "bottom-up", estimates. To minimize uncertainty, inverse estimates require accurate measure ments of GHG concentrations and meteorological data, and are improved when networks of sensor sites are used in concert. To closely approximate free stream mixing ratio values, it is widely-used practice to mount measurement equipment on isolated open-lattice towers to reduce the potential influences of a boundary layer formed by the supporting structure. However, this is often not possible when GHG concentrations are measured in urban environments, where open locations are unavailable or the use of such towers would be prohibitively expensive. In these environments, networks of rooftop-mounted sensors are more likely to be cost-effective and simpler to implement. Unfortunately, the flat-topped buildings that are typical of urban settings generate wind recirculation zones and turbulence that may interfere with rooftop trace gas mixing ratio measurements. In this study, large eddy simulations (LES) of wind flow over a large office building were performed to estimate the effect and potential error introduced by performing measurements on building rooftops as compared with free stream or tower-based measurements. Time dependent concentrations of carbon dioxide were computed at a number of locations and heights above the roof of a tall building and compared with the original in put signal. Simulation results are used to develop guidelines for optimum placement of sensors on rooftop for accurate measurement of GHG mixing ratio that are necessary for atmospheric inversion models.