Journal article

Assimilation of ground versus lidar observations for PM10 forecasting

Wang Y, Sartelet K, Bocquet M, Chazette P ...see all

Atmospheric Chemistry and Physics, vol. 13, issue 1 (2013) pp. 269-283

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Abstract

This article investigates the potential impact of future ground-based
lidar networks on analysis and short-term forecasts of particulate
matter with a diameter smaller than 10 mu m (PM10). To do so, an
Observing System Simulation Experiment (OSSE) is built for PM10 data
assimilation (DA) using optimal interpolation (OI) over Europe for one
month from 15 July to 15 August 2001. First, using a lidar network with
12 stations and representing the ``true{''} atmosphere by a simulation
called ``nature run{''}, we estimate the efficiency of assimilating the
lidar network measurements in improving PM10 concentration for analysis
and forecast. It is compared to the efficiency of assimilating
concentration measurements from the AirBase ground network, which
includes about 500 stations in western Europe. It is found that
assimilating the lidar observations decreases by about 54% the root
mean square error (RMSE) of PM10 concentrations after 12 h of
assimilation and during the first forecast day, against 59% for the
assimilation of AirBase measurements. However, the assimilation of lidar
observations leads to similar scores as AirBase's during the second
forecast day. The RMSE of the second forecast day is improved on average
over the summer month by 57% by the lidar DA, against 56% by the
AirBase DA. Moreover, the spatial and temporal influence of the
assimilation of lidar observations is larger and longer. The results
show a potentially powerful impact of the future lidar networks.
Secondly, since a lidar is a costly instrument, a sensitivity study on
the number and location of required lidars is performed to help define
an optimal lidar network for PM10 forecasts. With 12 lidar stations, an
efficient network in improving PM10 forecast over Europe is obtained by
regularly spacing the lidars. Data assimilation with a lidar network of
26 or 76 stations is compared to DA with the previously-used lidar
network. During the first forecast day, the assimilation of 76 lidar
stations' measurements leads to a better score (the RMSE decreased by
about 65%) than AirBase's (the RMSE decreased by about 59%).

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