Ad Hoc Ceilometer Evaluation Study: Mixing Layer Heights Network

The Ad-Hoc Ceilometer Evaluation Study (ACES) is a joint venture spawned from the National Academy of Science reports “Observing Weather from the Ground up: Network of Networks” and “The Future of Atmospheric Boundary Layer Observing, Understanding, and Modeling: Proceedings of a Workshop” that identified lower tropospheric profiling of trace gases, aerosol and thermodynamic quantities as a cross-cutting need for air quality, weather, climate, energy and other national priority economic areas. ACES is led by the University of Maryland, Baltimore County (UMBC), a university partner in the National Oceanic Atmospheric Administration (NOAA) Office of Education Cooperative Science Centers: Center for Earth System Sciences & Remote Sensing Technologies (ESSRST) and NOAA Center for Atmospheric Sciences & Meteorology (NCAS-M); and it involves the participation of federal (Environmental Protection Agency (EPA) and National Weather Service (NWS)), and state (Maryland Department of the Environment) stakeholders.

The research activities discussed here deal with the need of a “network of networks” that builds new and integrates already existing radiosonde launch sites, wind profilers, and lidars/ceilometer into a national network to address the current inadequacies in determining the mixing layer layer height (MLH) and help guide the EPA’s Photochemical Assessment Monitoring Sites program new hourly MLH requirement and supplement the NWS ceilometer testbed. The MLH 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 mixing layer.

Several commercial lidars/ceilometers have been identified and evaluated. Comparison of their ease of operation, impact of challenging environments (clean air to hazy days) to Signal-to Noise Ratio (SNR) and commercial MLH retrieval, mathematical methods considered for automated detection of MLH are discussed in order 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.