Spatial variability of forward modelled attenuated backscatter in clear-sky conditions over a megacity: Implications...

Area of Science:

• Atmospheric Science
• Environmental Monitoring
• Urban Climatology

Background:

• Automatic lidars and ceilometers (ALCs) measure aerosol-attenuated backscatter, crucial for understanding urban climate and air quality impacts.
• Designing effective ALC observational networks requires robust methodologies for data assimilation and improved urban weather/air quality predictions.

Purpose of the Study:

• To develop and apply a methodology for modelling spatio-temporal aerosol patterns using numerical weather prediction (NWP) models.
• To inform the optimal design of ALC sensor networks for urban air quality and weather monitoring.

Main Methods:

• Spatio-temporal modelling of aerosol-attenuated backscatter coefficient using Met Office NWP models (UKV at 1.5 km, London Model at 300 m).
• Analysis of model data using S-mode principal component analysis (PCA) with varimax rotation to identify empirical orthogonal functions (EOFs).
• Application of agglomerative Ward cluster analysis (CA) to PCA output for network design recommendations.

Main Results:

• EOFs revealed strong relationships between aerosol variability and orography, wind, and emission sources across the megacity.
• Urban-rural differences in aerosol distribution were most pronounced under low wind speeds.
• PCA-CA, combined with wind roses, indicated an optimal ALC network comprising sensors within the city and surrounding areas.

Conclusions:

• Principal component analysis combined with cluster analysis provides an adaptable methodology for designing observation networks.
• The study highlights the importance of orography, wind, and emission sources in controlling urban aerosol patterns.
• Optimized ALC sensor placement should balance urban plume monitoring with surrounding area coverage, considering wind patterns.