This paper presents a new application of assimilating
lidar signals to aerosol forecasting. It aims at investigating
the impact of a ground-based lidar network on
the analysis and short-term forecasts of aerosols through a
case study in the Mediterranean basin. To do so, we employ
a data assimilation (DA) algorithm based on the optimal
interpolation method developed in the POLAIR3D chemistry
transport model (CTM) of the POLYPHEMUS air quality
modelling platform. We assimilate hourly averaged normalised
range-corrected lidar signals (PR2) retrieved from
a 72 h period of intensive and continuous measurements
performed in July 2012 by ground-based lidar systems
of the European Aerosol Research Lidar Network (EARLINET)
integrated into the Aerosols, Clouds, and Trace
gases Research InfraStructure (ACTRIS) network and an additional
system in Corsica deployed in the framework of
the pre-ChArMEx (Chemistry-Aerosol Mediterranean Experiment)/
TRAQA (TRAnsport à longue distance et Qualité
de l’Air) campaign. This lidar campaign was dedicated to
demonstrating the potential operationality of a research network
like EARLINET and the potential usefulness of assimilation
of lidar signals to aerosol forecasts. Particles with an
aerodynamic diameter lower than 2.5 µm (PM2.5) and those
with an aerodynamic diameter higher than 2.5 µm but lower
than 10 µm (PM10-2.5) are analysed separately using the lidar
observations at each DA step. First, we study the spatial
and temporal influences of the assimilation of lidar signals
on aerosol forecasting. We conduct sensitivity studies on algorithmic
parameters, e.g. the horizontal correlation length
(Lh) used in the background error covariance matrix (50 km,
100 km or 200 km), the altitudes at which DA is performed
(0.75–3.5 km, 1.0–3.5 km or 1.5–3.5 km a.g.l.) and the assimilation
period length (12 h or 24 h). We find that DA with
Lh = 100 km and assimilation from 1.0 to 3.5 km a.g.l. during
a 12 h assimilation period length leads to the best scores
for PM10 and PM2.5 during the forecast period with reference
to available measurements from surface networks. Secondly,
the aerosol simulation results without and with lidar
DA using the optimal parameters (Lh = 100 km, an assimilation
altitude range from 1.0 to 3.5 km a.g.l. and a 12 h
DA period) are evaluated using the level 2.0 (cloud-screened
and quality-assured) aerosol optical depth (AOD) data from
AERONET, and mass concentration measurements (PM10 or
PM2.5) from the French air quality (BDQA) network and
the EMEP-Spain/Portugal network. The results show that the
simulation with DA leads to better scores than the one without
DA for PM2.5, PM10 and AOD. Additionally, the comparison
of model results to evaluation data indicates that the
temporal impact of assimilating lidar signals is longer than
36 h after the assimilation period.
