OMI total bromine monoxide (OMBRO) data product: Algorithm, retrieval and measurement comparisons

OMI total bromine monoxide (OMBRO) data product: Algorithm, retrieval and measurement comparisons Raid M. Suleiman, Kelly Chance, Xiong Liu, Gonzalo González Abad, Thomas P. Kurosu, Francois Hendrick, and Nicolas Theys Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA 5 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Royal Belgian Institute for Space Aeronomy, Brussels, Belgium

The fitting uncertainties of BrO VCDs typically vary between 4 and 7×10 12 molecules cm -2 (~10-20% of the measured BrO VCDs). Additional fitting uncertainties can be caused by the interferences from O2-O2, and H2CO and their correlation with BrO. AMF uncertainties are estimated to be around 10% with the used single stratospheric only BrO profile. However,under 25 conditions of high tropospheric concentrations, AMFs errors due to this assumption of profile can be as high as 50%.
The retrievals agree well with GOME-2 observations at simultaneous nadir overpasses and with ground-based zenith-sky measurements at Harestua, Norway, with mean biases less than -30 0.22±1.13×10 13 molecules cm -2 and 0.12±0.76×10 13 molecules cm -2 , respectively. Global distribution and seasonal variation of OMI BrO are generally consistent with previous satellite observations. Finally, we confirm the capacity of OMBRO retrievals to observe enhancements of BrO over the U.S. Great Salt Lake despite the current retrieval set up considering a stratospheric profile in the AMF calculations. OMBRO retrievals also show significant BrO enhancements from the eruption of the Eyjafjallajökull volcano, although the BrO retrievals are affected under high SO2 loading conditions by the sub-optimum choice of SO2 cross sections.
Enhancement of BrO in the vicinity of salt lakes like the Dead Sea and the Great Salt Lake have been observed from ground-based measurements (Hebestreit et al., 1999;Matveev et al., 2001;10 Stutz et al., 2002;Tas et al., 2005;Holla et al., 2015). The active bromine compound release is due to the reaction between atmospheric oxidants with salt reservoirs. Satellite observation of salt lake BrO was first reported over the Great Salt Lake and the Dead Sea from OMI Hörmann et al. 2016). Seasonal variations of tropospheric BrO over the Rann of Kutch salt marsh have been observed using OMI from an independent research BrO product (Hörmann et al. 2016). 15 Bobrowski et al. (2003) made the first ground-based observations of BrO and SO2 abundances in the plume of the Soufrière Hills volcano (Montserrat) by multi-axis DOAS (MAX-DOAS). BrO and SO2 abundances as functions of the distance from the source were measured by MAX-DOAS in the volcanic plumes of Mt. Etna in Sicily, Italy and Villarica in Chile (Bobrowski et al., 2007).
The BrO/SO2 ratio in the plume of Nyiragongo and Etna was also studied (Bobrowski et al., 2015). 20 The first volcanic BrO measured from space was from the Ambrym volcano, measured by OMI . Theys et al. (2009) reported on GOME-2 detection of volcanic BrO emission after the Kasatochi eruption. Hörmann et al. (2013) examined GOME-2 observations of BrO slant column densities (SCDs) in the vicinity of volcanic plumes; it showed clear enhancements of BrO in ~1/4 of the volcanos, and revealed large spatial differences in BrO/SO2 ratios. 25 The purpose of this paper is to describe the OMI BrO operational algorithm and the data product, compare it with ground-based and other satellite measurements and analyze its spatiotemporal characteristics. This paper is organized as follows: Section 2 describes the OMI instrument and the data product. Section 3 gives a detailed description of the operational algorithm including 30 algorithm and product history, spectral fitting, AMF calculations, destriping, and fitting uncertainties. Section 4 presents results and discussion including comparison with GOME-2 and ground-based zenith-sky measurements at Harestua, Norway, global distribution, seasonality, enhanced BrO from the U.S. Great Salt Lake and Iceland's Eyjafjallajökull volcano. Section 5 concludes this study.
2 OMI instrument and OMBRO data product 5

OMI instrument
OMI was launched on the NASA Earth Observing System (EOS) Aura satellite into a sunsynchronous orbit on 15 July 2004. It is a push-broom imaging spectrometer that observes solar backscattered radiation in the visible and ultraviolet from 270-500 nm in three channels (UV1: 270-310 nm, UV2: 310-365 nm, visible: 350-500 nm) at spectral resolution of 0.42-0.63 nm and 10 spatial resolution in the normal (global sampling) mode ranging from 13×24 km 2 at direct nadir to about 28×150 km 2 at the swath edges. The global mode (GM) has 60 ground pixels with a total cross-track swath of 2600 km.
Since June 2007, certain cross-track positions of OMI data have been affected by the row anomaly 15 (http://projects.knmi.nl/omi/research/product/rowanomaly-background.php): some loose thermal insulating material likely appeared in front of the instrument's entrance slit, which can block and scatter the light thus causing errors in level 1b data and subsequently the level 2 retrievals (Kroon et al., 2011). Initially, the row anomaly only affected a few positions and the effect was small. But since January 2009, the anomaly has become more serious, spreading to ~1/3 of the positions and 20 retrievals at those positions are not recommended for scientific use. A flagging field has been introduced in the OMI level 1b data to indicate whether an OMI pixel is affected by this instrument anomaly.

OMBRO data product
The current operational BrO product, OMBRO version 3, contains BrO vertical column densities (VCDs), slant column densities (SCDs), effective air mass factors (AMFs) and ancillary 10 information retrieved from calibrated OMI radiance and irradiance spectra. Each BrO product file contains a single orbit of data, from pole to pole, for the sunlit portion of the orbit. The data product from 26 August 2004 through the present is available at GES DISC. Data used in this study cover the period from 1 January 2005 to 31 December 2014.

Algorithm and product history
OMBRO Version 1.0 was released on 1 February 2007, based on a spectral fitting window of 338-357 nm. Version 2.0 was released on 13 April 2008. It included major adjustments for Collection 3 Level 1b data, improved destriping measures, change of the fitting window to 340-357.5 nm, improvements to radiance wavelength calibration, and several improvements for processing near-20 real-time data. In both Versions 1 and 2, total BrO VCDs were retrieved in two steps: first performing spectral fitting using the basic optical absorption spectroscopy (BOAS) method to derive SCDs from OMI radiance spectra, and then converting from SCDs to VCDs by dividing AMFs. This is similar to current SAO H2CO, H2O and C2H2O2 as mentioned previously. The latest Version 3.0.5, released on 28 April 2011, includes major algorithm changes: the fitting window 25 was moved to 319.0-347.5 nm, and BrO cross sections are multiplied by wavelength-dependent AMFs, which are a function of albedo, before fitting, for a direct retrieval of BrO VCDs. SCDs are similarly retrieved in a separate step by fitting BrO cross sections that have not been multiplied with wavelength-dependent AMFs, and an effective AMF = SCD/VCD is computed. Diagnostic cloud information from the OMCLDO2 product (Acarreta et al., 2004) was added, and the rowanomaly indicating flags were carried over from the level 1b product. We recommend not to use pixels affected by the row anomaly despite being processed by the retrieval algorithm.
The current algorithm is described in detail in the rest of this section, with spectral fitting in Section 5 3.2, AMF calculation prior to spectral fitting in Section 3.3, post-processing de-stripping to remove cross-track dependent biases in Section 3.4, and fitting uncertainties and error estimates in Section 3.5.

Spectral fitting
Most aspects of the algorithm physics for the direct fitting of radiances by the BOAS method were 10 developed previously at SAO for analysis of GOME and SCIAMACHY satellite spectra (Chance, 1998, Chance et al., 2000, OMI, 2002Martin et al., 2006) and in the various algorithm descriptions of other SAO OMI products (Wang et al., 2014;Chan Miller et al., 2014;Gonzalez Abad et al., 2015).

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The spectral fitting in the SAO OMI BrO retrieval is based on a Gauss-Newton NLLS fitting procedure, the CERN ELSUNC procedure (Lindström and Wedin, 1987), which provides for bounded NLLS fitting. Processing begins with wavelength calibration for both irradiance and radiance. In each case the wavelength registration for the selected fitting window is determined independently for each cross-track position by cross-correlation of OMI spectra with a high 20 spectral resolution solar irradiance (Caspar and Chance, 1997;Chance, 1998; using the preflight instrument slit functions (Dirksen et al., 2006). Radiance wavelength calibration is performed for a representative swath line of radiance measurements (usually in the middle of the orbit) to determine a common wavelength grid for reference spectra.

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Following wavelength correction, an undersampling correction spectrum is computed to partially correct for spectral undersampling (lack of Nyquist sampling: Chance, 1998;Slijkhuis et al., 1999;Chance et al., 2005). The calculation of the corrections for the undersampling is accomplished by convolving the preflight slit functions with the high-resolution solar spectrum and differencing its fully-sampled and undersampled representations . 30 To process each OMI orbit it is split into blocks of 100 swath lines. Spectral fitting is then performed for each block by processing the 60 cross-track pixels included in each swath line sequentially before advancing to the next swath line. The spectra are modeled as follows: where 0 is the solar irradiance (used in our operational BrO retrieval) or radiance reference measurement, is the Earthshine radiance (detected at satellite), is albedo, i, j, k, are the coefficients to the reference spectra of Ai, Bj, Ck, (for example, trace gas cross sections, Ring effect,  (Dirksen et al., 2006) after correcting for the solar I0 effect (Aliwell et 15 al., 2002). Fig. 1 shows the trace gas cross sections and Ring spectra used in the current operational algorithm. The black lines are the original high-resolution reference spectra, and the color lines show the corresponding spectra convolved with OMI slit function, which are used in the fitting.
For improved numerical stability, radiances and irradiances are divided by their respective 20 averages over the fitting window, renormalizing them to values of ~1. BrO is fitted in the spectral window 319.0-347.5 nm, within the UV-2 channel of the OMI instrument. The switch from the previous fitting window of 340-357.5 nm to this shorter and wider fitting window is based on extensive sensitivity analysis following the method described by Vogel et al., 2013. This new fitting window aims at reducing the fitting uncertainty by including more BrO spectral structures 25 as shown in Fig. 1 and reducing retrieval noise while preserving the stability of the algorithm. An analysis of the retrieval sensitivity to different windows is included in section 3.5.
The rotational Raman scattering (Chance and Spurr, 1997; and undersampling correction spectra, Ai, are first added to the albedo-adjusted solar irradiance aI0, 30 with coefficients i as shown in Eq. 1. Radiances I are then modeled as this quantity attenuated by absorption from BrO, O3, NO2, H2CO, and SO2 with coefficients j fitted to the reference spectra Bj as shown in Eq. 1. A common mode spectrum Ck, computed on line, is added by fitting coefficient k after the Beer-Lambert law contribution terms. For each cross-track position, an initial fit of all the pixels along the track between 30 o N and 30 o S is performed to determine the 5 common mode spectra, derived as the average of the fitting residuals. The common mode spectra include any instrument effects that are uncorrelated to molecular scattering and absorption. This is done to reduce the fitting root-mean-square (RMS) residuals, and the overall uncertainties. These are then applied as reference spectra in fitting of the entire orbit. The fitting additionally contains additive (Polybaseline) and multiplicative closure polynomials (Polyscale), parameters for spectral 10 shift and, potentially, squeeze (not normally used). The operational parameters and the cross sections used are provided in Table 1.
As part of the development of the OMBRO retrieval algorithm, a significant amount of effort was dedicated to algorithm "tuning", i.e., the optimization of elements in the retrieval process, 15 including interfering absorbers like O2-O2. The spectral region of 343 nm, where O2-O2 has an absorption feature larger than the BrO absorption, essentially is impossible to avoid in BrO retrievals: the fitting window would have to either terminate at shorter wavelengths or start past this feature, and both approaches yield to unacceptable low information content for the BrO retrievals to succeed. During the tuning process, we investigated the effects of, among many other 20 things, including or excluding O2-O2, the use of different spectroscopic data sets (Greenblatt et al., 1990 andHermans et al., 1999 cross-sections), shorter or longer wavelength windows for the retrieval, and even extending the retrieval window beyond the O2-O2 absorption feature but excluding the approximate wavelength slice of the feature itself. The only approach that provided quantitatively satisfactory results -i.e., stability of the retrieval under a wide range of conditions, 25 minimized correlation with clouds, low fitting uncertainties, consistency of OMI global total column BrO with published results, and low noise in pixel-to-pixel retrievals -was to exclude O2-O2 from the OMBRO V3. It is difficult to quantify O2-O2 atmospheric content from the absorption feature around 343 nm alone, and its correlation with absorption bands of BrO and H2CO leads to spectral correlations in the course of the non-linear least squares minimization process that are 30 detrimental to the OMI BrO retrievals. Lampel et al., (2018) provides spectrally resolved O2-O2 cross sections not only at 343 nm, but also at 328 nm (see Fig. 1) which is about 20% of the absorption at 343 nm and has not been shown in previous O2-O2 cross sections. Future updates to the operational OMBRO algorithm will investigate the effect of including Lampel et al., (2018) O2-O2 cross sections on the fitting.

Air mass factors
Due to significant variation in O3 absorption and Rayleigh scattering in the fitting window AMFs vary with wavelength by 10-15% as shown in Fig. 2. At large solar and viewing zenith angles it is difficult to identify a single representative AMF ad hoc. The wavelength dependent AMFs are introduced to take into account for such strong variation within the BrO fitting window. They are 10 applied pre-fit to the BrO cross sections, and the spectral fit retrieves VCDs directly. This direct fitting approach is a major departure from the commonly employed 2-step fitting procedure (OMI, 2002). It was first developed for retrievals of trace gases from SCIMACHY radiances in the shortwave infrared (Buchwitz et al., 2000) and has been demonstrated for total O3 and SO2 retrievals from GOME/SCIAMACHY measurements in the ultraviolet (Bracher et al., 2005;15 Coldewey-Egbers et al., 2005;Weber et al., 2005;Lee et al., 2008).
The albedo-and wavelength-dependent AMFs were pre-computed with the Linearized Discrete Ordinate Radiative Transfer code (LIDORT, Spurr, 2006) using a single mostly stratospheric BrO profile (Fig. 3, left panel). The BrO profile, based on the model of Yung et al. (1980), has ~30% 20 BrO below 15 km, ~10% BrO below 10 km, and ~2% BrO below 5 km. It should be noted that a fixed profile is inconsistent with the varying tropopause height (both with latitude and dynamically e.g. Salawitch et al. 2010) and therefore with the profile shape in the stratosphere, but the impact on the AMF is typically small as the scattering weight does not change much in the stratosphere.
For conditions with enhanced BrO in the lower troposphere, using this profile will overestimate 25 the AMFs and therefore underestimate the BrO VCDs as discussed in Section 3.5. Surface albedos are based on a geographically varying monthly mean climatology derived from OMI (Kleipool et al., 2008). Although AMFs based on this BrO profile only slightly depend on surface albedo, albedo effects can be significant over highly reflective snow/ice surfaces, reducing VCDs by 5-

10%. 30
In order to provide the AMF in the data product for consistency with previous versions based on a two-step approach, a second fitting of all OMI spectra is performed with unmodified BrO cross sections, which yields SCDs. An effective AMF can then be computed as AMF = SCD/VCD. However, the correlation with O3 becomes noticeable (~0.10) only at SZAs above ~80 o .

Destriping
OMI L1b data exhibit small differences with cross-track position, due to differences in the 20 dead/bad pixel masks (cross-track positions are mapped to physically separate areas on the CCD), dark current correction, and radiometric calibration, which lead to cross-track stripes in Level 2 product (Veihelmann and Kleipool, 2006). Our destriping algorithm employs several methods to reduce cross-track striping of the BrO columns. First, we screen outliers in the fitting residuals.
This method, originally developed to mitigate the effect of the South Atlantic Anomaly in SAO 25 OMI BrO, H2CO, and OClO data products, is now also being employed for GOME-2 . Screening outliers is done through computing the median, rmed, and the standard deviation σ of residual spectra r(λ) and in subsequent refitting excluding any spectral points for which ( ) ≥ | ± 3 |. This can be done repeatedly for every ground pixel, which makes the processing slow. However, we do it once for a reference swath line, recording the positions of the 30 bad pixels, and excluding them by default in each subsequent fit. Second, after the completion of the spectral fitting process for all ground pixels in the granule, a post-processing cross-track bias correction is performed: an average cross-track pattern is calculated from the along-track averages of all BrO VCDs for each cross-track position within a ±30° latitude band around the equator, to which a low-order polynomial is fitted. The differences between the cross-track pattern and the 5 fitted polynomial is then applied as a cross-track VCD correction (or "smoothing") factor. The smoothed VCDs are provided in a separate data field, ColumnAmountDestriped. Smoothed SCDs are derived in an analogous fashion and are also included in the data product.

BrO VCD Error Analysis 10
Estimated fitting uncertainties are given as = √ where C is the covariance matrix of the standard errors. This definition is strictly true only when the errors are normally distributed. In the case where the level 1 data product uncertainties are not reliable estimates of the actual uncertainties, spectral data are given unity weight over the fitting window, and the 1σ fitting error in parameter i is determined as 15 where is the root mean square of the fitting residuals, npoints is the number of points in the fitting window, and nvaried is the number of parameters varied during the fitting.
The fitting uncertainties for single measurements of the BrO VCDs typically vary between 4×10 12 20 and 7×10 12 molecules cm -2 , consistently throughout the data record. The uncertainties vary with cross-track positions, from ~7×10 12 at nadir positions to ~4×10 12 at edge positions due to the increase of photon path length through the stratosphere. Relatively, the VCD uncertainties typically range between 10-20% of individual BrO VCDs, but could be as low as 5% over BrO hotspots. This is roughly 2-3 times worse that what was achieved from GOME-1 data. 25 The BrO VCD retrieval uncertainties listed in the data product only include random spectral fitting errors. Error sources from AMFs (i.e., BrO climatology), atmospheric composition and state (pressure/temperature vertical profiles, total O3 column, etc.) and other sources of VCD uncertainty are not included. We provide here error estimates for these additional error sources.
Uncertainties in the AMFs, used to convert slant to vertical columns, are estimated to be 10% or less except when there is substantially enhanced tropospheric BrO. Hence the total uncertainties 5 of the BrO vertical columns typically range within 15-30%. To estimate the AMF error associated with enhanced tropospheric concentrations we have studied the difference between AMFs calculated using the stratospheric only BrO profile and a stratospheric-tropospheric profile as shown in the right panel of Fig. 3. Fig. 4 shows the dependency of the relative AMF difference with respect to wavelength (top panel), albedo (middle panel) and VZA (bottom panel) as a 10 function of the SZA between calculations performed using these two profiles. The use of stratospheric only BrO profile can lead to AMF errors up to 50% depending on albedo and viewing geometry. On average, using the stratospheric only BrO profile overestimates AMF and underestimates VCD by 41%. 15 We have performed sensitivity analysis of OMI BrO VCD with respect to various retrieval settings using orbit 26564 on 13 July 2009. Table 2 shows the median VCDs, median fitting uncertainties and the number of negative VCD pixels for each configuration. Table 3  Including the interference of O2-O2 leads to a decrease of the median VCD by ~12% and an increase of the median fitting uncertainty by ~10% with respect to the operational set up. Excluding H2CO from the fitting significantly reduces the retrieved BrO columns by ~37%, given that the 30 strong anticorrelation between both molecules is not taken into account. Fitting the mean residual (common mode) has a small impact in the retrieval results, the median VCD only changes ~3%, but reduces the median fitting uncertainty by ~30% with respect to the exclusion of the common mode. To study the impact of the slit functions we have performed the retrieval using both online slit functions, modelled as a Gaussian, and the preflight instrument slit functions. The median difference between these two retrievals is 27% for orbit number 26564. We have investigated the 5 impacts of the order of scaling and baseline polynomials; it can cause uncertainties of ~10% as shown in Table 3.
To study the impact of the radiative transfer effects of the O3 absorption in our retrieval we have adopted the correction method described by Pukite et al., 2010. We find that between 60 south 10 and 60 north the average difference is smaller than 10% with values around 2% near the equator.
However, as we move near the poles with solar zenith angles above 60 the differences start to be bigger arriving to mean values around 30%.

Results and discussions
Comparisons of the OMI OMBRO product with GOME-2 satellite retrievals and remote sensing 15 ground based measurements over Harestua, Norway as well as monthly mean averages illustrate the quality of the retrieval on a global scale. On a local scale, recent scientific studies looking at BrO enhancements in volcanic plumes and over salt lakes are pushing the limits of the current OMBRO setups. In the following sections, we provide details of these comparisons (section 4.1) and discuss OMI OMBRO global distribution (section 4.2) and local enhancements over salt lakes 20 and volcanic plumes observations (section 4.3), and their applicability and strategies to correctly use the publicly available OMBRO product.

Comparisons with GOME-2 and ground-based observations
To assess the quality of the OMBRO product, we first compared OMI BrO VCDs with BIRA/GOME-2 BrO observations (Theys et al., 2011). GOME-2 has descending orbit with a local 25 equator crossing time (ECT) of 9:30 am and OMI has ascending orbit with an ECT of 1:45 pm. To minimize the effects of diurnal variation especially under high SZAs (e.g., McLinden et al., 2006; on the comparison, we conduct the comparison using simultaneous nadir overpasses (SNOs) within 2 minutes between GOME-2 and OMI predicted by NOAA National Calibration Center's SNO prediction tool (https://ncc.nesdis.noaa.gov/SNOPredictions). Given Aura and Metop-A satellite orbits, all these SNOs occur at high latitudes around 75 o S/N. Fig. 5 shows the time series of comparison of individual OMI/GOME-2 BrO retrievals from February 2007 through November 2008. The temporal variation of BrO at the SNO locations is captured 5 similarly by OMI and GOME-2 BrO. The scatter plot in Fig. 6 quantifies the comparison between OMI and GOME-2 BrO. OMI BrO shows excellent agreement with GOME-2 BrO with a correlation of 0.74, and a mean bias of -0.216 ± 1.13×10 13 molecules cm -2 (mean relative bias of -2.6 ± 22.1%). Considering very different retrieval algorithms including different cross sections and BrO profiles, such a good agreement is remarkable. GOME-2 retrievals use the BrO cross 10 sections of Fleischmann et al. (2004) while our BrO retrievals use the BrO cross sections of Wilmouth et al. (1999). According to the sensitivity studies by Hendrick et al. (2009), using the Fleischmann cross section increases BrO by ~10%. So, accounting for different cross sections, OMI BrO underestimates the GOME-2 BrO by ~10%. In addition, the GOME-2 algorithm uses a residual technique to estimate tropospheric BrO from measured BrO SCDs by subtracting a 15 dynamic estimate of stratospheric BrO climatology driven by O3 and NO2 concentrations and by using two different tropospheric BrO profiles depending on surface albedo conditions. This is very different from the approach of using a single BrO profile in the OMI BrO algorithm, and can contribute to some of the BrO differences. Furthermore, additional algorithm uncertainties in both algorithms and different spatial sampling can also cause some differences. Fig. 7 shows the VCDs 20 monthly averages of GOME-2 data (green) and OMBRO (black) from February 2007 to December 2009 where the seasonal variations are clearly seen. Our study shows that OMI has negative mean biases of 0.35×10 13 molecules cm -2 (12%), 0.33×10 13 molecules cm -2 (10%), 0.25×10 13 molecules cm -2 (17%), and 0.30×10 13 molecules cm -2 (10%) for Alaska, Southern Pacific, Hudson Bay, and Greenland, respectively. 25 We also used ground-based zenith-sky measurements of total column BrO at Harestua, Norway (Hendrick et al., 2007) to estimate the quality of the OMI BrO. We compared daily mean total BrO at Harestua with the mean OMI BrO from individual footprints that contain the location of Harestua site. Fig. 8 shows the time series of the comparison between OMI total BrO and Harestua 30 total BrO from February 2005 through August 2011 with the scatter plot shown in Fig. 9. Ground-based BrO shows an obvious seasonality with high values in the winter/spring and low values in the summer/fall. Such seasonality is well captured by OMI BrO. OMI BrO shows a reasonable good agreement with Harestua BrO with a correlation of 0.46 and a mean bias of 0.12±0.76×10 13 molecules cm -2 (mean relative bias of 3.18±16.30%, with respect to individual Harestua BrO). Sihler et al. (2012) compared GOME-2 BrO to ground-based observations at Utqiagvik (Barrow) 5 finding the correlation to be weaker (r = 0.3), likely due to both elevated and shallow surface layers of BrO. However, their correlation between GOME-2 BrO and ground-based measurements made from the Icebreaker Amundsen, in the Canadian Arctic Ocean (r = 0.4) is closer to our correlation here. From the Harestua data, tropospheric BrO typically consists of 15-30% of the total BrO, larger than what we have assumed in the troposphere. The use of a single BrO profile in the OMI 10 BrO algorithm will likely underestimate the actual BrO. Accounting for the uncertainty due to profile shape, OMI BrO will have a larger positive bias relative to Harestua measurements, which can be caused by other algorithm uncertainties and the spatiotemporal differences between OMI and Harestua BrO.

Salt lakes and volcanic plumes enhancements of BrO
Following recent work by Hörmann et al. (2016) over the Rann of Kutch using OMI BrO retrievals 10 from an independent research product we have explored the capability of our OMBRO product to observe similar enhancements in other salt lakes. Fig. 11 shows monthly averaged OMI BrO over the Great Salt Lake for 06/2006, the corresponding surface albedo used in the retrieval, cloud cover (assuming a cloud filter of 40%) as well as the cloud pressure. Over the Great Salt Lake, BrO enhancement occurs predominantly over the lake bed with enhancements of ~5-10×10 12 molecules 15 cm -2 over background values (3-4×10 13 molecules cm -2 ). Despite observing these enhancements, the users of OMBRO for these kinds of studies should be aware of three limitations of the current retrieval algorithm. First, the BrO columns assume a mostly stratospheric BrO profile (Fig. 3 left panel) for the AMF calculation. Second, the OMI derived albedo climatology (Kleipool et al. 2008) used in OMBRO has a resolution of 0.5. At this resolution OMBRO retrievals can have 20 biases given the size of OMI pixels and the inherent sub-pixel albedo variability. Finally, high albedos inherent to salt lakes surface yield abnormally high cloud fractions and low cloud pressures over the salt lakes (Hörmann et al., 2016). All these factors should be considered in studies addressing the spatiotemporal distribution of BrO over salt lakes using OMBRO.

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During our analysis of volcanic eruption scenarios, it was discovered that the currently implemented SO2 molecular absorption cross sections (Vandaele et al., 1994) are a sub-optimum choice (see Fig. 12). Compared to more recent laboratory measurements (Hermans et al., 2009;Vandaele et al., 2009), the original SO2 cross sections implemented in OMBRO do not extend over the full BrO fitting window and exhibit the wrong behavior longward of 324 nm, 30 overestimating the most recent measurement by up to a factor of 3. As the correlation between BrO and both SO2 cross sections are very small (-0.03 for the current SO2 and 0.11 for the latest SO2 cross sections) over the spectral range of SO2 cross sections, interference by SO2 in BrO retrievals is usually not an issue at average atmospheric SO2 concentrations, but strong volcanic eruptions will render even small SO2 absorption features past 333 nm significant. Around 334 nm, 5 the Vandaele et al. (2009) data show an SO2 feature that correlates with BrO absorption when SO2 concentrations are significantly enhanced. As a consequence of this spectral correlation, SO2 may be partially aliased as BrO, since the implemented SO2 cross sections cannot account for it. Fig.   13 presents an example from the 2010 Eyjafjallajökull eruption to show that the BrO retrieval can be affected by the choice of SO2 cross sections. The next version of the OMBRO public release 10 will be produced using the updated SO2 absorption cross sections. Until then, caution is advised when using the OMI BrO product during elevated SO2 conditions. We recommend to use OMBRO product together with the operational OMI SO2 product (Li et al., 2013)  However, BrO enhancement around the volcano can still clearly be seen with the improved SO2 cross sections. This suggests that this BrO enhancement is not totally due to aliasing of SO2 as BrO, but potentially real BrO from the volcanic eruption.

Conclusions
This paper describes the current operational OMI BrO retrieval algorithm developed at SAO and the corresponding V3 OMI total BrO (OMBRO) product in detail. The OMI BrO retrieval algorithm is based on nonlinear least-squares direct fitting of radiance spectra in the spectral range 319.0-347.5 nm to obtain vertical column densities (VCDs) directly in one step. Compared to 5 previous versions of two-step algorithms, the fitting window was moved to shorter wavelengths and the spectral range was increased to reduce the fitting uncertainty. Because air mass factors (AMFs) vary significantly with wavelengths as a result of significant variation of O3 absorption, the wavelength and surface albedo dependent AMF, which is precomputed with the Linearized Discrete Ordinate Radiative Transfer (LIDORT) code using a single mostly stratospheric BrO 10 profile, is applied pre-fit to BrO cross sections for direct fitting of VCDs. Prior to the spectral fitting of BrO, wavelength calibration is performed for both irradiance and radiance at each crosstrack position and reference spectra are properly prepared at the radiance wavelength grid. Then radiances are modeled from the measured solar irradiance, accounting for rotational Raman scattering, undersampling, attenuation from BrO and interfering gases, and including additive and 15 multiplicative closure polynomials, and the average fitting residual spectrum. To maintain consistency with previous versions, a second fitting of all OMI spectra is performed with unmodified BrO cross sections to derive SCDs and the effective AMFs. Then a destriping step is employed to reduce the cross-track dependent stripes.

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The uncertainties of BrO VCDs included in the data product include only spectral fitting uncertainties, which typically vary between 4 and 7×10 12 molecules cm -2 (10-20% of BrO VCDs, could be as low as 5% over BrO hotspots), consistent throughout the data record. The uncertainties vary with cross-track positions, from ~7×10 12 at nadir positions to ~4×10 12 at edge positions. We have investigated additional fitting uncertainties caused by interferences from O2-O2, H2CO, O3, 25 and SO2, the impact of the choice of fitting window, the use of common mode, the orders of closure polynomials, and instrument slit functions. Uncertainties in the AMF calculations are estimated at ~10% unless the observation is made over a region with high tropospheric BrO columns. In this case, the use of a single stratospheric BrO profile is another source of uncertainty, overestimating AMFs (up to 50%) and therefore underestimating BrO VCDs. 30 We compared OMI BrO VCDs with BIRA/GOME-2 BrO observations at locations of simultaneous nadir overpasses (SNOs), which only occur around 75ºN and 75ºS. OMI BrO shows excellent agreement with GOME-2 BrO with a correlation of 0.74, and a mean bias of -0.216±1.13x10 13 molecules cm -2 (mean relative bias of -2.6 ± 22.1%). Monthly mean OMBRO VCDs during 2007-2009 show negative biases of 0.25-0.35×10 13 molecules cm -2 (10-17%) over 5 Alaska, Southern Pacific, Hudson Bay, and Greenland, respectively. We also compared OMI BrO with ground-based zenith-sky measurements of total BrO at Harestua, Norway. The BrO seasonality in Harestua is well captured by the OMI BrO and OMBRO retrieval showing a reasonable good agreement with the ground-based measurements. The correlation between both datasets is of 0.46 and the mean bias 0.12±0.76×10 13 molecules cm -2 (mean relative bias of 10 3.18±16.30%).
The global distribution and seasonal variation of OMBRO are generally consistent with previous satellite measurements. There are small values in the tropics with little seasonality, and large values at high latitudes with distinct seasonality. The seasonality is different between the northern 15 and southern hemisphere, with larger values in the hemispheric winter/spring (spring/summer) and smaller values in summer/fall (winter) for the northern (southern) hemisphere. This spatiotemporal variation is generally consistent from year to year and is hardly affected by the row anomaly, but does show some interannual variation. Finally, we have explored the feasibility of detecting enhanced BrO column over salt lakes and in volcanic plumes using OMBRO retrievals. We found 20 enhancement of the BrO with respect to the background levels of 5-10×10 12 molecules cm -2 over the U.S. Great Salt Lake. We also observed a significant enhancement from the eruption of Eyjafjallajökull volcano although BrO retrievals under high SO2 conditions can be affected by the current use of a sub-optimal choice of SO2 cross sections.

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Several important retrieval issues in the current operational algorithm that affect the quantitative use BrO VCDs have been raised in this paper such as the exclusion of O2-O2, nonoptimal SO2 cross sections, the neglect of radiative effect of O3 absorption, and the assumption of stratospheric only BrO profile. The users are advised to pay attention to these issues so that the product can be used properly. Future versions of OMBRO will include updated SO2 and O2-O2 cross sections, 30 corrections for the radiative transfer effect of the O3 absorption and reoptimize the spectral fitting windows to mitigate the interferences of other trace gases. We will also improve the AMF calculation accounting for clouds and O3 and will consider the use of model-based climatological BrO profiles. These updates will increase the capabilities of the OMBRO retrieval to quantitatively estimate enhancements over salt lakes and in volcanic plumes.

Acknowledgements
This study is supported by NASA Atmospheric Composition Program/Aura Science Team (NNX11AE58G) and the Smithsonian Institution. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. The 10 Dutch-Finnish OMI instrument is part of the NASA EOS Aura satellite payload. The OMI project is managed by NIVR and KNMI in the Netherlands. We acknowledge the OMI International Science Team for providing the SAO OMBRO data product used in this study. 2. Used for testing sensitivity to SO2 cross sections and will be used in the next version.
5        (Fig. 3, left panel) in the case there exists a significant tropospheric BrO column as shown in the stratospheric-tropospheric BrO profile (Fig. 3, right panel). 10