Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Abstract

Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) POLAIR3D of the air quality modelling platform POLYPHEMUS to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris onboard a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, POLYPHEMUS well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM 10 (particulate matter with diameter less than 10 µm) concentrations. Another analyses PM 2.5 (particulate matter with diameter less than 2.5 µm) and PM 2.5−10 (particulate matter with a diameter higher than 2.5 µm and lower than 10 µm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM 10 than PM 2.5 , probably because PM 2.5 is better modelled than PM 10 . Furthermore, the algorithm which analyses both PM 2.5 and PM 2.5−10 provides the best scores for PM 10 . The averaged root-mean-square error (RMSE) of PM 10 is 11.63 µg m −3 with DA (PM 2.5 and PM 2.5−10 ), compared to 13.69 µg m −3 with DA (PM 10 ) and 17.74 µg m −3 without DA on 1 July 2009. The averaged RMSE of PM 10 is 4.73 µg m −3 with DA (PM 2.5 and PM 2.5−10 ), against 6.08 µg m −3 with DA (PM 10 ) and 6.67 µg m −3 without DA on 26 July 2009.

Comparison of IASI water vapor retrieval with H<sub>2</sub>O-Raman lidar in the framework of the Mediterranean HyMeX and ChArMEx programs

“…Figure 5 illustrates this agreement: the IASI-derived WVIC exhibits a bias lower than previous IASI cross-comparisons, results are not degraded during the HyMeX fall period but significantly worse over the ChArMEx summer period where the slope of the linear fit is close to 0.70. Such discrepancy (underestimation) may be due to an incorrect consideration of the instrumental error in the variance/covariance matrix needed for the assimilation process (e.g., Wang et al, 2014). The error on the contribution to the IASI radiances may be linked to local heating associated with the presence of aerosol not being taken into account in the model, as for all spaceborne infrared sensors (e.g., Pierangélo et al, 2004).…”

Information constraints in variational data assimilation

“…(Schutgens et al , ; Liu et al , ); (Saide et al , ); (); (Chen et al , ; Pagowski et al , ; Rubin and Collins, )) or aerosol backscattering measurements (e.g. Wang et al , ; ; (); (Pagowski et al , ); Zhang et al , ) from remote‐sensing instruments in an aerosol transport model. A characteristic of such applications is that the number of observed parameters is often significantly smaller than the number of model variables.…”

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

“…The In order to be assimilated into an aerosol model, the raw aerosol signal can either be converted into aerosol concentrations using assumptions about their distribution (Raut et al, 2009a, b;Wang et al, 2013), or it can be assimilated directly into the model solving the lidar equation within the observation operator (Wang et al, 2014a). Note that even in the latter case, the redistribution over the aerosol size bins is carried out following the size distributions of the first guess used in the analysis.…”