Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

Abstract

Abstract. 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 (PR 2 ) retrieved from a 72 h period of intensive and continuous measurements 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 (PM 2.5 ) and those with an aerodynamic diameter higher than 2.5 µm but lower than 10 µm (PM 10−2.5 ) 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 (L h ) 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 L h = 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 PM 10 and PM 2.5 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 (L h = 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 (PM 10 or PM 2.5 ) 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 PM 2.5 , PM 10 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.

EARLINET: potential operationality of a research network

“…Although the persistence of forecast improvement of PM 10 is short when ground-based PM 10 measurements are assimilated, the assimilation of lidar measurements is expected to lengthen the time scale over which the forecast may be improved, by adding information on the vertical concentration of particles and constraining the transport. Indeed, the EARLINET 72 h measurement exercise already led to significant results in that field: Wang et al (2014) assimilated the SCC-1 products in the Eulerian chemistry transport model POLAIR3D of the air quality platform POLYPHEMUS (Mallet et al, 2007). Their findings indicate that a horizontal correlation length of 100 km, an assimilation altitude range of 1-3.5 km and an assimilation period length of 12 h give the best scores for PM 10 and PM 2.5 .…”

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…It is expected that the benefit of assimilating lidar signals will last longer (up to a few days) and should propagate farther than ground-based in situ measurements, owing to this height-resolved information but also owing to the smaller representativeness error in elevated layers. This has recently been demonstrated using lidar data from 3 days of intensive observations over the western Mediterranean Basin in July 2012 (Wang et al, 2014b).…”

EARLINET Single Calculus Chain – overview on methodology and strategy

“…The outputs of the SCC produced in that way can be used for a large variety of applications like the assimilation of lidar data in air-quality or dust transport models, model validation, or monitoring of special events like volcano eruptions. In particular, the SCC pre-processed data measured during the 72 h operationally exercise have been successfully assimilated in the air-quality model Polyphemus developed by the Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA) to improve the quality of PM 10 and PM 2.5 forecast on the ground (Wang et al, 2014).…”

“…Currently, it has been used by 20 different EARLINET stations which have submitted about 2600 raw data files covering a very large time period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Moreover, more than 5000 SCC optical products (about 3600 aerosol backscatter profiles and 1400 aerosol extinction profiles) have been calculated and used for different purposes like analysis of instrument intercomparisons (Wandinger et al, 2015), air-quality model assimilation experiment (Wang et al, 2014;Sicard et al, 2015), and ongoing long-term comparisons with manually retrieved products (Voudouri et al, 2015). The large usage and the long-term plan for the centralized processing system make the SCC the standard tool for the automatic analysis of EARLINET lidar data.…”