Advection plays a fundamental role in the transport of heat and moisture within the Atmospheric Boundary Layer (ABL). Recently, horizontal advection has been quantified using ground-based remote sensing networks deployed at multiple locations. This study presents a novel single-site methodology to estimate horizontal temperature and humidity advection. Horizontal gradients are derived from a microwave radiometer (MWR) by exploiting its azimuthal scanning capability, while wind components are obtained from a Doppler wind lidar (DWL). Advection is estimated and a comprehensive uncertainty quantification framework is developed, based on standard error propagation and a Monte Carlo approach that accounts for correlated uncertainties in the MWR's observations. The resulting uncertainties are evaluated relative to the magnitude of advection over a range of horizontal gradients and wind speeds.</p> <p>For typical ABL conditions, the uncertainty associated with humidity advection is approximately 60 % of its estimated value, while temperature advection uncertainties reach 140 %. However, under conditions characterized by strong horizontal gradients and high wind speeds, the observed magnitude of advection exceeds its uncertainties, enabling the detection of significant events such as frontal passages. As proof of concept, two strong advection episodes are analyzed. On 19 June 2022, a pronounced cold advection event was observed, and the current estimation captured intense cooling throughout the ABL, with vertically averaged values reaching &minus;10 K h^-1, with relative uncertainties generally below 40 %. A second case, on 8 October 2021, highlights humidity advection, with dominant zonal contribution at around 2300 UTC. The height-averaged estimated humidity advection reached -10 g kg^-1h^-1, with uncertainties typically below 30 %. The present study provides a novel advection estimate and its robust assessment of capabilities and limitations, highlighting the clear identification of strong advection events in the ABL, reducing the logistic complexity of capturing this variable.