The ML height is an important meteorological parameter that affects near-surface atmospheric pollutant concentrations since it determines the volume of air into which pollutants and their precursors are emitted, serving as a diagnostic to improve air quality forecasting and dispersion models. In addition, is important in determining the relationship between atmospheric column measurements of gases and aerosols, and their surface concentrations since pollutants are frequently created and contained within the ML, and serves as a diagnostic to improve air quality forecasting and dispersion models.
Several commercial lidars and ceilometers have been identified and evaluated, as part of NWS ASOS Ceilometer Testbed, for time-height profiling of the ML. Comparison of their ease of operation, performance for determination of ML height in network operations is discussed. The ML height estimation, is based on the detection of a sharp decrease in aerosol backscatter at the top of the mixing layer. Methods considered for automated detection of the ML height from commercial units will be compared with such obtained with Covariance Wavelet Transform (Haar function), Cluster Analysis, Fuzzy Logic and Peak-based threshold. Results are also compared to ML heights obtained from microwave radiometers, Doppler wind lidars and radiosondes. The main goal of this study is to determine the best suited instrumentation and methodology that will satisfy the spatial and temporal requirements necessary to improve the next generation forecast models used in the United States.