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Bromine monoxide (BrO) is a key radical in the atmosphere, influencing the chemical state of the atmosphere, most notably the abundance of ozone (O3). O3 depletion caused by the release of bromine has been observed and modeled in polar regions, salt pans, and in particular inside volcanic plumes. Furthermore, the molar ratio of BrO and SO2 – which can be detected simultaneously via spectroscopic measurements using the differential optical absorption spectroscopy (DOAS) method – is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter. The detection of BrO in volcanic plumes from satellite spectroscopic observations is limited by the precision and sensitivity of the retrieval, which so far only allowed for the detection of BrO during major eruptions. The unprecedented spatial resolution of up to 3.5km×5.5km and the high signal-to-noise ratio of the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S-5P) enable observing and monitoring volcanic bromine release globally even for minor eruptions or even quiescent degassing. In this study, we investigate how far the BrO retrieval can be improved using TROPOMI data and how well BrO can be detected, even in small eruptions and during quiescent volcanic degassing. There are two steps for which improvements in accuracy are investigated and applied: the improvement and quantitative determination of (1) the detection limit of the DOAS BrO column retrieval and (2) the correction of the non-volcanic background BrO signal. First, the DOAS retrieval settings are varied, and their influence on accuracy and precision is investigated with respect to the detection limit and potential systematic influences. Based on these results, we propose a dedicated DOAS evaluation scheme optimized for the detection of BrO in volcanic plumes. For the DOAS retrieval, we propose the use of a large fit window from 323–360 nm , yielding a statistical uncertainty lower by a factor of 1.8 compared to previous BrO DOAS algorithms while not enhancing systematic influences. Second, the effect of the background BrO is reduced by a latitude-dependent empirical correction scheme correlated to cloud information as well as information on the O3 column. Via these improvements, the combined statistical and systematic uncertainties in the resulting BrO vertical column density is on the order of 7×1012moleculescm-2. We present a new and accurate retrieval algorithm of BrO columns from TROPOMI observations which allows for the detection of even slightly enhanced BrO amounts inside minor eruptive plumes of bromine-rich volcanoes. While designed specifically for TROPOMI observations, the retrieval algorithm is in general also applicable to other hyperspectral satellite observations. However, some parts might require adaptation. [ABSTRACT FROM AUTHOR]

The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2  which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study. [ABSTRACT FROM AUTHOR]