Multi-component vertical profile retrievals for MAX-DOAS observations during CINDI

1. Multi-component vertical profile retrievals for MAX-DOAS observations during CINDI Hitoshi Irie (JAMSTEC) H. Takashima, Y. Kanaya, (JAMSTEC) R. van der Hoff (RIVM), M. Van Roozendael (BIRA) Folkerd Wittrock (IUP Bremen), and Ankie Piters (KNMI)

2. Exploring the potential of MAX-DOAS- multi-component retrievals - • The CINDI campaign has provided the first opportunity for an intensive MAX-DOAS intercomparison study. Most of MAX-DOAS instruments covered UV and/or VIS regions, in which significant absorptions by not only NO2 but also other trace gases are contained. • In principle, the MAX-DOAS NO2 vertical profile retrieval method utilizing AMFs derived through the aerosol profile retrieval can be applied to other trace gases, where O4 absorptions are available for both UV and VIS regions. • However, this potential has been poorly explored, as the MAX-DOAS development is at an early stage. • It is expected that exploring this using CINDI data will extract the MAX-DOAS’s latent potential and hence would accelerate the MAX-DOAS development over a wide research area. • Here, we present our multi-component retrieval method applied to CINDI data and show its preliminary results.

3. JAMSTEC's MAX-DOAS in CINDI The JAMSTEC‘s MAX-DOAS has been placedon the roof of the porte cabin #4.

4. Windows chosen for “8-component “ retrievals O3 SO2 CHOCHO H2O HCHO Aerosol (VIS) NO2 Aerosol (UV)

5. Overview of the profile retrieval algorithm

6. Special parameterization of profile Lidar aerosol extinction coefficient data at Tsukuba in Nov. 2006 • In my intensive development period 2005-2007, Frieβ et al. (2006) suggested that the aerosol retrieval can strongly depend on the a priori, if the absolute value of AEC is used. • Indeed, lidar data at Tsukuba showed large temporal variations, but most aerosols were, in general, near the surface(70% of AOD in 0-1 km). • At that time, some groups (incl. Heidelberg group) was using a simplified profile, represented by AOD and the mixing height. • I decided to use a parameterization consisting of: • AOD (total column) • profile shape (e.g., F1 is the fraction of AOD in 0-1 km) How do we set a-priori values?

7. General characteristic of the retrieval, in terms of the a priori parameters used • Aerosol retrieval • A-priori is based on lidar data at Tsukuba for 2006-2007. • The retrieval is not subject to the a priori of AOD, because the area* for AOD is almost unity. (*a rough measure of the fraction of the retrieval that comes from the measurements) • The retrieval of AEC at 0-1 km is less subject to the a priori of F1, but the AECs above 1 km could be influenced to some extent. • NO2 (and other trace gases) retrieval • Profile shape factors are based on lidar aerosol data at Tsukuba for 2006-2007. • The a priori NO2 column used is the 20% of the largest value in a set of NO2 DSCDs for each retrieval.  The a priori varies with time. • The retrieval is not subject to the a priori of NO2 column, because the area is almost unity. • The retrieval of NO2 at 0-1 km is less subject to the a priori of F1, but the NO2 above 1 km could be influenced to some extent. • Profile is retrieved by scaling the given a-priori profile first, followed by changing the profile shape if enough information is contained. • Identical a priori setups have been used for all the sites of our observations.

8. Advantages/disadvantages • Advantages • The same a-priori assumptions for different sites are valid as the retrieval is not much dependent on the choice of a-priori.  better at development stage! • A thick vertical layer for the lowest layer would reduce the variation of F1, making the use of constant F1 valid for many cases. • Disadvantages • Profile above ~2 km may not properly be represented. • Vertical resolution and averaging kernels (on altitude grid) are unavailable for each profile.

9. Initial data screening • Data screening is performed, • if retrieved AOD is greater than 3 (the largest value in the LUTs) • Cloud screening to some extent • If the measured and fitted DSCDs do not agree to within 20% • Screening out poorly-converged cases • If the degrees of freedom for signal (DOFS) is less than 1.0 • Screening out cases containing insufficient information • Obviously, we need an additional cloud screening method for better statistics of data analysis. • Color index and water vapor, both of which can be derived from MAX-DOAS, were found to be very useful (Takashima et al., 2009).

10. Results

11. Time series plotsfor the 8 components at 0-1 km • Both aerosol extinction coefficients at VIS and UV reveal consistent temporal variations. • For NO2, not only day-to-day variations but also diurnal variations were large, as expected. • Correlations between HCHO and CHOCHO  later • Correlations with NCEP-derived H2O  later • On July 3, aerosols, NO2, HCHO, CHOCHO, and H2O concentrations were enhanced. • On July 5-6 and 11-14, SO2 concentrations were high. • Qualitative explanation from trajectory analysis possible?

12. Backward trajectory analysis Cases with high aerosols, NO2, HCHO, CHOCHO, and H2O Cases with high SO2 • The observed air masses were below 1 km in past 5 days, suggesting strong surface influences. • Air masses with enhancements in HCHO, etc., came from north-east directions. • Air masses with SO2 enhancements came from west, through Straits of Dover.

13. Statistics of 10-day backward trajectories • Air masses with enhancements in HCHO, etc. have traversed mainly over land.  influenced by biological activities over land? • Air masses with enhancements in SO2 have traversed mainly over ocean.  influenced by emissions from ships passing through Straits of Dover and/or facilities near the coast? Duration over land Duration over ocean Cabauw Cabauw Your comments will be greatly appreciated.

14. Correlation analysis • Positive correlations between CHOCHO and HCHO were found, as expected. • The CHOCHO/HCHO ratio was generally less than 0.05. • MAX-DOAS H2O VMRs agreed very well with NCEP-derived surface values.

15. NO2 comparisons with in-situ observations Correlations against RIVM NO2 at 3 m Median diurnal variations for June 11-July 9 • Both in-situ and MAX-DOAS data show diurnal variations with high concentration in early morning. Minimum concentrations are found in early afternoon (around 2 pm). • When the boundary layer is well mixed, excellent agreement is found. In early morning, MAX-DOAS data is deviated from the surface value, but the same tendency occurs in in situ data at 200 m  the observed differences were most likely caused by vertical inhomogeneity in NO2 below 1 km.

16. Summary • We have presented our multi-component retrieval method. • AEC357nm, AEC476nm, NO2, H2O, HCHO, CHOCHO, SO2, O3. • In general, temporal variations in mean values below 1 km look reasonable. • On July 3, HCHO, aerosols, NO2, CHOCHO, and H2O were enhanced, due probably to emissions from biogenic sources over land. • On July 5-6 and 11-14, SO2 was enhanced, due probably to emissions from ships passing through Straits of Dover and/or facilities near the coast. • Positive CHOCHO-HCHO correlations were found. The CHOCHO/HCHO ratio was generally less than 0.05. • MAX-DOAS H2O VMRs agreed very well with NCEP-derived surface values. • All these results support the capability of MAX-DOAS observations applicable to various air quality studies. • For quantitative studies, more investigations are needed. In particular, we plan an improvement for a-priori profile shapes. Comparisons with CHIMERE (with the help of Folkert) and ozone lidar would be very much interested.