A LiDAR instrument to monitor the evolution of the urban boundary layer capping inversion over Manchester has been developed from a previous instrument. This LiDAR uses a frequency-tripled Nd:YAG laser, operating at low pulse energy but high repetition frequency. Rotational Raman scattering of this laser light is parsed into two channels by narrowband interference filters, before detection by photomultiplier tubes operating in photon-counting mode. The receiving telescope was refocused to operate in the boundary layer, and an interference filter was replaced following modelling work. The calibrations of this instrument use locally-launched sondes to determine corrections due to operating in the near-field region of the receiving telescope. The LiDAR receiver was thoroughly calibrated under laboratory conditions to construct a lookup table. Locally-launched sondes were used to correct for mirror shading by instrument components, as well as constrain the overlap function of the BLT. A temperature resolution of better than 0.4K arising from Poisson noise was achieved for data collected for the mean temperature profile measured over the course of a night, with temperature inversions being identifiable down to a height of 500m. A total temperature error of less than 3K was achieved by taking the whole-night mean, which is significantly less than the size of the smallest identified temperature inversion (7.6Â±2K). The LiDAR instrument data was compared with locally-launched sondes to validate the collected data, agreeing with the sonde measurements to within the uncertainty of the instrument. A WRF model temperature output was compared to both the BLT and sonde data and found to poorly capture the boundary layer temperature profile. The inversion strength was always underestimated by several K, and when the inversion height is below 300m the model underestimates the inversion height by 100-500m.
|Date of Award||1 Aug 2018|
- The University of Manchester
|Supervisor||Geraint Vaughan (Supervisor) & Hugo Ricketts (Supervisor)|