Remote Sensing of Urban Mixed Layer Structure in Los Angeles, with Applications to Greenhouse Gas Emissions Quantification

Abstract:

Urban areas are responsible for a large and increasing part of global greenhouse gas emissions. Effective policy for controlling urban emissions depends on an accurate understanding of those emissions, including their trends in time, distribution across source types, and the effects of regulatory, economic, or technological changes on the rate of emissions. The level of detail required will demand input both from bottom-up inventories and from top-down atmospheric concentration measurements, which must then be translated into emissions fluxes. Among the meteorological information needed for that translation, the mixing dynamics of the lower atmosphere are crucial; an error in the depth of the mixed layer produces a proportional error in the flux estimate. Ground-based remote sensing can provide continuous information about the mixed layer. We present results highlighting the importance of such continuous information.

A Sigma Space mini-Micro Pulse Lidar (miniMPL) and a Vaisala ceilometer were deployed in the Los Angeles megacity, California over a period of several years. A locally appropriate, wavelet-based algorithm was devised to estimate the depth of the mixed layer on the basis of this remote sensing data. A voting scheme, novel for this application, was used to establish confidence. While a seasonal trend in mixed layer depth does exist, that trend is masked by very high day-to-day variability. Daily maximum mixed layer depths often differ by a factor of two from monthly or seasonal averages. We use a simplified model to demonstrate the impact of this variability on top-down approaches to greenhouse gas emissions quantification, demonstrating the value of continuous observations.