Investigation of boundary layer structures with ceilometer using a novel robust algorithm

The vertical temperature and moisture distribution affect the layering of the atmospheric boundary layer and the xistence of inversions within this layer or on the top of it. These layers have a strong influence on the development of episodes of high concentrations of air pollutants which might be harmful to people and ecosystems. The height of the mixing layer is defined as the height up to which due to the thermal structure of the boundary layer vertical dispersion by turbulent mixing of air pollutants takes place. Most of the aerosol particles in an atmospheric column are usually confined to atmospheric layers below this height, the knowledge on the mixing layer height can thus be employed to convert column-mean optical depths measured from satellites into near-surface air quality information. Since several years, eye-safe lidar ceilometers are used for boundary layer monitoring. Comparison to temperature, humidity, and wind profiles reported by RASS, sodar, radio soundings, and weather mast in-situ sensors has confirmed their ability to detect convective or residual layers reaching up to heights exceeding 2500 m. Even more important for air quality applications is their near-range performance and the precise assessment of inversion layers and nocturnal stable layers below 200 m. This was one of the reasons to apply a single lens optical design for the Vaisala Ceilometer CL31 that has been chosen as standard cloud height indicator for the Automated Surface Observing System of the National Weather Service. One aspect readily put aside when dealing with measuring campaigns showing an ideal boundary layer diurnal evolution, is the influence of clouds, fog, and precipitation on the determination of the layer structure. An averaging procedure is required to suppress false layer hits generated by small fluctuations of the backscatter signal intensity. On the other hand, time averaging should be kept short in the presence of low patches of cloud or precipitation. The novel robust algorithm introduced in this paper applies averaging intervals in time and height depending on the current situation. It does not report a height of the mixing layer when it determines precipitation, fog, or a low cloud layer. The attached plot illustrates some features of this algorithm. Backscatter from clouds is excluded from the averaging procedure; there is only a small influence of the 00:00 cloud at 2000 m on mixing layer height (MLH) detection. Light drizzle starts at 04:00. This is detected by the algorithm; MLH reporting stops. More examples covering a variety of meteorological situations will be presented that demonstrate the quality of the algorithm and its application in the field of air quality forecasting.