Assimilation of ground versus lidar observations for PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; forecasting

Y Wang,P Chazette,M Bocquet,K N Sartelet

doi:10.5194/acp-13-269-2013

Abstract

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 μm (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%).

Highlights

Aerosols have an impact on regional and global climates (Ramanathan et al, 2001; Leon et al, 2002; Sheridan et al, 2002; Intergovernment Panel on Climate Control, IPCC 2007) as well as on ecological equilibrium (Barker and Tingey, 1992) and human health by penetrating the respiratory system and leading to respiratory and cardiovascular diseases (Lauwerys et al, 2007; Dockery and Pope, 1996)
During the first forecast day, the assimilation of 76 lidar stations’ measurements leads to a better score than AirBase’s
The results show that the impact on PM10 forecast of assimilating data from a lidar network with 12 stations and data from a ground network AirBase with 488 stations are similar in terms of scores, AirBase data assimilation (DA) leads to slightly better scores for the first forecast day

Summary

Introduction

Aerosols have an impact on regional and global climates (Ramanathan et al, 2001; Leon et al, 2002; Sheridan et al, 2002; Intergovernment Panel on Climate Control , IPCC 2007) as well as on ecological equilibrium (Barker and Tingey, 1992) and human health by penetrating the respiratory system and leading to respiratory and cardiovascular diseases (Lauwerys et al, 2007; Dockery and Pope, 1996). Y. Wang et al.: Data assimilation high (Roustan et al, 2010), which leads to significant differences between model simulations and observations (Sartelet et al, 2007). Applications of DA to PM10 forecasts are still sparse They include Tombette et al (2009) and Denby et al (2008) over Europe and Pagowski et al (2010) over the United States of America. They demonstrated the feasibility and the usefulness of DA for aerosol forecasts

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Conclusion