Ad-Hoc Ceilometer Evaluation Study (ACES): Lidar/Ceilometer Mixing Layer Heights and Network
Abstract
National Academy of Science reports in the last decade, such as "Observing Weather from the Ground up: Network of Networks" and "The Future of Atmospheric Boundary Layer Observing,Understanding, and Modeling: Proceedings of a Workshop", 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. The reports discuss the need of a "network of networks" which builds new and integrates already existing radiosonde launch sites, wind profilers, and lidars into a national network to address the current inadequacies in determining the mixing layer layer height (MLH). 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.
The Ad-Hoc Ceilometer Evaluation Study (ACES) is a joint research venture between the University of Maryland, Baltimore County (UMBC), Environmental Protection Agency and National Weather Service to help guide Photochemical Assessment Monitoring Sites program new hourly MLH requirement and supplement the ceilometer testbed, respectively. UMBC is a University partner in 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). ACES consists of a heterogeneous lidar/ceilometer network along Baltimore-Washington DC metropolitan-area and the Chesapeake Bay. Several commercial lidars and 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.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A13I2568D
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0394 Instruments and techniques;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 3394 Instruments and techniques;
- ATMOSPHERIC PROCESSES