## 3 Results
### FRM4 DOAS Validation and Performance Assessment  
Validation of the processing system is complex and covers many different aspects, including validation of technical aspects such as the timelines of the processing of data, consistency in formats, feedback to users and proper reactions to error situations. These more technical points will not be covered here. A rigorous validation of the profiles retrieved by the system would consist of comparisons with independent profile information. Such a validation exercise is a major task and is limited by the availability of reference data. The approach taken here was therefore to apply the processing system to data from a subset of instruments operating during the CINDI-2 campaign [10], to evaluate the data for consistency, plausibility and coverage, and to compare the results to the validation data available from the campaign. This part is based on work performed in the framework of the CINDI-2 profile intercomparison exercise published in [50]. The subset of instruments used for the results of the CINDI-2 campaign is listed in [50] (Table 2). Instruments of different campaign performances were chosen to cover a broader range of input data quality. Instrument performances were assessed during the CINDI-2 semi-blind intercomparison exercise as described in [10]. The instrument subset for this validation includes two double-channel high signal-to-noise (SNR) scientific instruments (BIRA and IUP-Bremen), one scientific single-channel standard SNR instrument (AUTH) and one commercial two-channel Envines/Airyx instrument (DLR/USTC). For the analysis of this dataset, the processing system was applied to the level-1 data submitted during the CINDI-2 campaign. The version of the processing system was validation_01.0. All four options of the processing system (the optimal estimation retrieval MMF) and three flavours of the parametric MAPA retrieval using a fixed O4 scaling factor [51] of 0.8, a fixed scaling factor of 1.0 and a fitted O4 scaling factor were included. No O4 scaling is applied in the MMF implementation. Before validating the profile retrievals with independent data, a comparison was performed between the results from the different retrievals (MMF and MAPA with three different approaches to the O4 scaling factor). While overall, the retrievals showed good qualitative agreement, several interesting observations were made. As is often the case for retrievals based on optimal estimation, if no strong regularisation was used, there were cases where MMF retrievals found elevated layers, both for aerosols and for trace gases. In the case of aerosols, this was sometimes but not always linked to clouds at the given altitude. The differences between the two retrieval types were smaller for instruments witha good SNR than for those with more noise, in particular for UV retrievals. On average, the MAPA retrieved a larger AOD, often at higher altitudes. One important issue that became apparent during the comparisons was the large number of profiles flagged as invalid. Flagging between the retrievals was not consistent, and for one instrument with a lower SNR, all HCHO profiles were flagged as invalid. During the CINDI-2 campaign, surface concentrations of NO\({}_{2}\) and HCHO were determined with continuous LP-DOAS measurements taken in a measurement volume close to that observed by the MAX-DOAS instruments. These data can be used to validate the surface concentrations retrieved from the MAX-DOAS measurements. In Figure 11, results are shown for both the median of the datasets and for individual data points. For HCHO, the median of the retrievals fits excellently with the LP-DOAS values throughout the campaign, regardless of weather conditions. This is surprising, as HCHO absorption is small, and HCHO profiles are usually subject to noise. Possible explanations are less vertical variability making the a priori used more representative of the real situation or less horizontal variability when compared to NO\({}_{2}\).