2.1 Sky radiometer observation of aerosol optical properties

The sky radiometer (POM-02; Prede Co., Ltd., Tokyo, Japan), a sun–sky photometer measuring direct and diffuse solar irradiances, is the main instrument of the international ground-based remote-sensing network SKYNET (e.g., Takamura and Nakajima, 2004; Nakajima et al., 2007). Measurements of the direct solar and diffuse irradiances within 160^∘ of the center of the Sun were conducted every 10 min. Values of aerosol optical depth (AOD), single-scattering albedo (SSA), and refractive index at 340, 380, 400, 500, 675, and 870 nm were retrieved using the sky radiometer analysis package from the Center for Environmental Remote Sensing (SR-CEReS) version 1 (Mok et al., 2018), in which SKYRAD.pack version 5 (Hashimoto et al., 2012) is implemented to retrieve aerosol properties, along with all pre- and post-processing programs for the purpose of near-real-time data delivery. Data at 1020 nm were not used in this study to avoid possible impact by low AAOD and interference by water vapor (H₂O) on the estimate of the AAE. Cloud screening was carried out using the method of Khatri and Takamura (2009) but without using global irradiance data from a pyranometer.

The SKYNET sky radiometer has on-site calibration methods, namely the improved Langley (IL) method determining the calibration constant (F₀) (e.g., Campanelli et al., 2007) and the solar disk scan (SDS) method determining the solid view angle (SVA) (e.g., Nakajima et at., 1996; Uchiyama et al., 2018). Recently, Mok et al. (2018) used retrievals with SR-CEReS to compare SKYNET sky radiometer AOD and SSA data with those derived from a combination of Aerosol Robotic Network (AERONET), multifilter rotating shadow-band radiometer (MFRSR), and Pandora observations in Seoul, Korea, during and after the NASA KORUS-AQ (Korea–US Air Quality) campaign in 2016 (Mok et al., 2018, and references therein). For most cases, their agreements were found to be within ±0.01 and ±0.05 for AOD and SSA data, respectively, at all wavelengths from 340 to 870 nm, supporting the ability of the on-site calibration methods using IL and SDS.

Since the importance of accurate SVA determination was particularly pointed out to better interpret the difference seen in previous SSA comparisons between SKYNET and AERONET (Khatri et al., 2016), sensitivity analysis was performed in the present study by conducting additional retrievals using SVAs offset by ±0.01 msr ($\sim \pm \mathrm{4}$ %), which is likely to correspond to the uncertainty in SVA determined by a single SDS. Both positive and negative offsets were tested but only the positive offset is discussed here because the negative offset tended to show only little or no impact on SSA, when SSA was close to unity. This is because a smaller SVA leads to a larger SSA (Hashimoto et al., 2012). The impacts by the SVA offset of +0.01 msr on SSAs were estimated to be as small as $-\mathrm{0.010}\pm \mathrm{0.005}$, $-\mathrm{0.010}\pm \mathrm{0.005}$, $-\mathrm{0.010}\pm \mathrm{0.005}$, $-\mathrm{0.010}\pm \mathrm{0.006}$, $-\mathrm{0.012}\pm \mathrm{0.007}$, and $-\mathrm{0.011}\pm \mathrm{0.008}$ at 340, 380, 400, 500, 675, and 870 nm, respectively. Thus, overestimation (underestimation) in SVA leads to underestimation (overestimation) in SSA, but the magnitude was found to be very small at about ±0.01, when the uncertainty in SVA was $\sim \pm \mathrm{0.01}$ msr. The small impact on SSAs should be a result of compensation by the associated change in F₀ values; using SVA values offset by +0.01 msr as an input, the IL method employed in SR-CEReS yields smaller F₀ values by about 2.1±0.1 %, 1.8±0.2 %, 1.7±0.2 %, 1.2±0.2 %, 0.7±0.2 %, and 0.5±0.1 % for 340, 380, 400, 500, 675, and 870 nm, respectively. The resulting smaller F₀ leads to a larger SSA (Hashimoto et al., 2012), which is an opposite trend in the direct impact that a larger SVA leads to a smaller SSA (Hashimoto et al., 2012). Results from these sensitivity analyses support the agreement of SSAs within ±0.05 seen in recent comparisons by Mok et al. (2018) during and after the NASA KORUS-AQ campaign.

Using the AOD and SSA data retrieved, AAOD and AAE values were derived as follows. First, for each measurement and for respective wavelengths from 340 to 870 nm, the AAOD value and its uncertainty (ε_AAOD) were calculated with the following equations.

For the estimate of ε_AAOD(λ), uncertainties for AOD(λ) and SSA(λ) (ε_AOD and ε_SSA) were assumed to be 0.01 and 0.05, respectively, based on comparisons by Mok et al. (2018). Since the comparisons by Mok et al. (2018) were made using independent measurements having uncertainties of the same order of magnitude, the actual uncertainties in sky radiometer AOD and SSA data would be smaller. AAE is calculated as the slope of the linear fit of ln [AAOD(λ)] versus ln (λ):

where a is an intercept. This equation is equivalent to expression using the power law:

where K is a constant. To exclude AAE data associated with large uncertainty, only the data that satisfy the criteria that (1) AAOD(λ) exceeds ε_AAOD(λ) for all wavelengths and (2) the correlation coefficient of the linear fit (R) is high ($-\mathrm{1.0}\le R\le -\mathrm{0.9}$) are used in the analysis below. To refine the data set of AOD, SSA, AAOD, and AAE with reduced uncertainty, the daily mean and its standard deviation with more than four data were calculated for 09:00–15:00 LT.

2.2 MAX-DOAS observation of trace gases

The MAX-DOAS is an instrument measuring UV–visible spectra of scattered sunlight at several elevation angles between the horizon and zenith (e.g., Hönninger and Platt, 2002; Hönninger et al., 2004; Irie et al., 2015). Its measurement is based on the well-established DOAS technique that quantitatively detects narrow band absorption by trace gases by applying the Lambert–Beer law (e.g., Platt and Stutz, 2008). Since the pioneering study by Hönninger and Platt (2002) and Hönninger et al. (2004), various types of instruments and algorithms for MAX-DOAS have been developed worldwide. One of the reasons for this is because ground-based MAX-DOAS observations at a low elevation angle provide enhanced signals of concentrations of important trace gases in the boundary layer (i.e., around the instrument altitude) and the concentrations can be interpreted as being the average over a distance, which is on the same order of or finer than the horizontal resolution of models and satellite observations but coarser than that of in situ observations (Irie et al., 2011).

From January to April 2016, our MAX-DOAS system (Prede Co., Ltd) (Irie et al., 2011; Hoque et al., 2018a, b) was operated continuously at the SKYNET Phimai site together with the sky radiometer. It was combined with the spectrometer Maya2000 Pro (Ocean Optics, Inc.) (with a slit of 25 µm) embedded in a temperature-controlled box to record high-resolution spectra (with the full width at half maximum of around 0.3–0.4 nm and the oversampling of 3–4) from 310 to 515 nm. Measurements were made at six elevation angles of 2, 3, 4, 6, 8, and 70^∘ every 30 min. Instead of 90^∘, the 70^∘ elevation angle was adopted as reference to reduce a variation range of signals measured at all the elevation angles, while the integration time was kept constant. MAX-DOAS off-axis elevation angle measurements were limited to below 10^∘ for minimizing the possible systematic error in oxygen collision complex fitting results but keeping the measurement sensitivity in the lowest layer of vertical profiles retrieved high (Irie et al., 2015).

Spectral analysis by the so-called DOAS method (Platt and Stutz, 2008) for spectral fitting using the nonlinear least-squares method and the subsequent vertical profile retrievals using the optimal estimation method were performed by our retrieval algorithm, JM2 (Japanese MAX-DOAS profile retrieval algorithm, version 2) (e.g., Irie et al., 2008, 2011, 2015). Using the recorded high-resolution UV–visible spectra from 310 to 515 nm, the JM2 allows us to retrieve lower-tropospheric vertical profile information for eight quantities, including HCHO, CHOCHO, nitrogen dioxide (NO₂), and H₂O concentrations, which are analyzed below. We used fitting windows and cross-section data identical to those described by Irie et al. (2011, 2015) and Hoque et al. (2018a). The residual for DOAS fitting was usually as low as below 10⁻³. In the vertical profile retrieval, the elevation angle setting was fully considered in the computation of differential air mass factors (e.g., Irie et al., 2011, 2015). The input parameters used for the vertical profile retrievals are the same as those used by Irie et al. (2015) for Cabauw, the Netherlands. The degrees of freedom for signal for trace gas vertical profiles retrieved here were usually 1–2. Of vertical profiles retrieved, the present study analyzed data for a layer of 0–1 km, which corresponds to the lowest layer with the highest sensitivity owing to the longest light path in profiles retrieved by JM2. The total uncertainties, including random and systematic errors, were estimated to be 24 % (HCHO), 19 % (CHOCHO), 15 % (NO₂), and 18 % (H₂O) (Irie et al., 2011). For HCHO (CHOCHO, NO₂, and H₂O) retrievals, the systematic error was estimated by conducting additional retrievals as JM2 aerosol retrieval uncertainties of 50 % (30 %), in which uncertainty due to assuming fixed SSA values should be included (Irie et al., 2008, 2011; Hoque et al., 2018a, b). Using the retrieved H₂O concentration, the relative humidity over water (RH_w) for the layer at 0–1 km was estimated using NCEP (National Centers for Environmental Prediction) pressure and temperature reanalysis data (2.5^∘ grid and 6-hourly). To be consistent with sky radiometer data, the daily mean values for 09:00–15:00 LT are analyzed in this study. More detailed descriptions about MAX-DOAS, including fitting windows and cross-section data, are given by Irie et al. (2011, 2015), Hoque et al. (2018a, b), and references therein.