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Evaluating Ground-Based Remote Sensing Methods to Measure Planetary Boundary Layer Height

  • 15 July 2022
Evaluating Ground-Based Remote Sensing Methods to Measure Planetary Boundary Layer Height

 

The planetary boundary layer (PBL) the part of the atmosphere we live in - it is the lowest part of the troposphere that is directly influenced by surface forcings on short time scales. The height of the PBL plays an important role in local air quality and transport of heat and moisture. While, PBL height is a key parameter in models representing surface-atmosphere interactions, it is measured by different instruments which carry their own uncertainties under varying environmental conditions.To disentangle these uncertainties, a recent study, published in Atmospheric Measurement Techniques, reports the findings from a field campaign to understand the response of the planetary boundary layer to heterogeneous land surface forcing, comparing different observation-based methods. The study is led by researchers at NOAA Cooperative Institute for Research in Environmental Sciences with participation from NOAA Physical Sciences Laboratory, National Severe Storms Laboratory, Global Monitoring Laboratory, Cooperative Institute for Severe and High-Impact Weather Research and Operations, and University of Wisconsin-Madison. 

The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, used a variety of active and passive ground-based remote sensing instruments to estimate the height of the daytime PBL. When compared against radiosonde data, each instrument showed strengths and weaknesses under different conditions. For example, some instruments performed better when boundary layer clouds were present. These results from the CHEESEHEAD19 campaign provide useful information on the limitations of each instrument to measure PBL height and how they can be used appropriately for the evaluation of numerical weather prediction models.

The research was funded by the COM Program, as part of its initiative on developing surface-atmosphere interaction datasets to improve models, NOAA’s Atmospheric Science for Renewable energy (ASRE) program, as well as by the NOAA Cooperative Institute for Research in the Environmental Sciences.

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