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Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: implications for observation network design

Warren, E. ORCID:, Charlton‐Perez, C., Lean, H. ORCID:, Kotthaus, S. and Grimmond, S. ORCID: (2022) Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: implications for observation network design. Quarterly Journal of the Royal Meteorological Society, 148 (744). ISSN 0035-9009

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To link to this item DOI: 10.1002/qj.4253


Sensors that measure the attenuated backscatter coefficient (e.g. automatic lidars and ceilometers, ALC) provide information on aerosols which can impact urban climate and citizen health. To design an observational network of ALC sensors for supporting data assimilation, and improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio-temporal patterns of aerosol attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model, LM), for 28 clear-sky days and nights. Initially, attenuated backscatter coefficient data are analysed using S-mode principal component analysis with VARIMAX rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level with common EOFs found across different heights (day and night) for both NWP models. EOFs relate strongly to orography, wind and location of aerosol emissions sources highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban-rural differences are largest when wind speeds are low and vertical boundary layer dynamics can more effectively distribute near-surface aerosol emissions vertically. In several night-time EOFs, gravity wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. Principal component analysis (PCA) output, with agglomerative Ward cluster analysis (CA), minimises intra-group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA-CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here the CA is used with wind roses to suggest the optimised placement of ALC deployment is one in the city to observe the urban plume, and others surrounding the city, with priority given to cluster size and frequency of upwind advection.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:103328
Publisher:Royal Meteorological Society


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