Christoph Kern Christine Sealing Patricia A. Nadeau Michael J. Cappos Tamar Elias Steven Fuke Thomas-Jon Hoomanawanui Keith Horton Kevan Kamibayashi Matthew Patrick A. Jeff Sutton William Tollett S. Miki Warren E. Frank Younger Michael Zoeller 20260318 The data are both tabular digital data (SO2 emission rates) and individual *.std ASCII files (spectra). Hilo, HI U.S. Geological Survey https://doi.org/10.5066/P1ZEDLNJ https://www.sciencebase.gov/catalog/item/6994db14b66b01dab3ca92d3 In 2012, the U.S. Geological Survey’s Hawaiian Volcano Observatory (HVO) installed an array of zenith-pointing ultraviolet spectrometers to track gas emissions from summit of Kīlauea (Businger et al., 2015; Elias et al., 2018). The instruments are arranged in an arc perpendicular to the dominant trade-wind direction approximately 2 kilometers (km) southwest of the actively degassing summit vents. When volcanic plumes emitted from these vents pass overhead during daytime hours, the spectrometers record the spectral radiance of ultraviolet scattered skylight passing vertically through these plumes at 11 distinct locations. The HVO performed updates to the original spectrometer array in 2024. An additional spectrometer was installed on the northern end of the fenceline to better capture plumes emitting from the southwest side of Kīlauea’s summit crater which became more active after sections of the southwest rim collapsed in 2018. Also, the automated spectral processing routines were switched from the correlation spectroscopy approach employed by FLYSPEC instruments (Elias et al., 2018; Horton et al, 2006) to a differential optical absorption spectroscopy (DOAS) retrieval (Platt and Stutz, 2008). A variable-wavelength fit window (Elias et al., 2018; Kern, 2025) was implemented to avoid strong absorption effects stemming from very high overhead SO2 concentrations and make the derived emission rates consistent with those measured by the HVO using vehicle-based Mobile DOAS traverses (Nadeau et al., 2023). Since switching to the DOAS analysis methodology in 2024, we refer to the spectrometer array as the DOAS Fenceline Array (DFence). The raw data contained in this release consist of measured spectral radiances. During daylight hours, each node along the fenceline independently records the spectrum of scattered skylight overhead. Each spectrometer’s exposure time is automatically adjusted depending on illumination conditions, and spectra are co-added for a total integration time of 10 seconds. The spectra are stored in an ASCII format (*.std), the details of which are described in the metadata. The purpose of these measurements is to determine the emission rate of sulfur dioxide from active vents at Kīlauea’s summit. The recorded spectra are analyzed using DOAS retrievals which target the characteristic absorption of SO2 between 306 and 335 nanometers (nm) and yield the overhead column density of SO2 at each station as a function of time. Integrating across the entire array yields the cross-sectional gas burden (e.g., in molecules/centimeter). Finally, multiplication with the wind speed yields the gas emission rate, e.g. in kilograms/second (kg/s) or metric tons per day (t/d). The wind speeds reported in this dataset stem from an anemometer located near the center of the array but are scaled by a factor of 1.2 to account for ground effects (Elias et al., 2018). The measured SO2 emission rates represent an important volcano monitoring parameter as they track the volume of magma reaching shallow depths in the volcano’s plumbing system and erupting at the surface (Kern et al., 2020; Lerner et al., 2021). The measurements are also used by the University of Hawaiʻi’s Vog Measurement and Prediction project (VMAP, Businger et al., 2015; Holland et al., 2020, http://weather.hawaii.edu/vmap/new/) to provide air quality forecasts for the Hawaiian Islands. Note that successful calculation of the emission rate depends on the entire plume being captured by the nearly 4-km long fenceline array. Analysis shows that this requires wind directions between about 35 and 85 degrees azimuth (winds out of the northeast) measured at the location of the anemometer. Note that this does not necessarily accurately reflect the direction that the plume is traveling overhead, as ground effects can skew the wind direction to higher values near the ground when compared to conditions several hundred meters higher. The array must not only capture the plume center but also detect either edge to ensure that the plume is fully accounted for. For this purpose, we report a plume completeness parameter with each emission rate measurement (see the metadata for a detailed description). The reporting period encompasses episodes of high lava fountaining at Kīlauea summit. These episodes are associated with prodigious gas emissions and plumes that can reach many kilometers above the active vents. In these conditions, the fenceline array is often overwhelmed as SO2 clouds drift over all nodes in the system, and we are therefore often not able to reliably track the emission rate during such episodes. While the spectral radiances measured by the individual DOAS nodes represent the raw data, we additionally include a few derived products in this release to demonstrate the utility of the measurements. FilteredDFenceEmissionRates.csv provides a table of derived SO2 emission rates and auxiliary data. Details of the data processing steps followed to produce this table are given in the metadata file (DFence.xml). We also include three figures depicting example time series of SO2 emission rates. Figure1.png shows the emission rate between December 23, 2024, and December 31, 2025, which includes 39 episodes of lava fountaining (indicated by the shaded regions) from Kīlauea summit vents occurring during the 2024 – present eruption. Figure2.png shows emission rates derived leading up to and during episode 25. Although DFence was unable to capture the highest emissions due to the gas plume spreading over the entire array, the existing data show that emission rates exceeded 2,000 kg/s during the intense lava fountaining period. Finally, Figure3.png shows an example of gas pistoning (Patrick et al., 2016) occurring in the morning hours of June 11, 2025, leading up to episode 25. Gas pistoning behavior often preceded lava fountaining episodes, with SO2 emission rates typically fluctuating between about 0 and several 10 kg/s on minute time scales. Also shown is real-time seismic amplitude measured at a location near the volcano’s summit (data available at https://doi.org/10.7914/SN/HV). SO2 emissions and seismicity show notable correlation, measured peaks in degassing lagging behind the seismic observations by about 5 minutes – the time it takes for the gas plume to drift from the point of emission to the fenceline array 2 km downwind. Figure4.png shows a map of the DFence network, with overhead SO2 column densities mapped to the color scale on the right. References Businger, S., Huff, R., Pattantyus, A., Horton, K.A., Sutton, A.J., Elias, T., Cherubini, T., 2015. Observing and Forecasting Vog Dispersion from Kilauea Volcano, Hawaii. Bull. Amer. Meteor. Soc. 96, 1667–1686. https://doi.org/10.1175/BAMS-D-14-00150.1 Elias, T., Kern, C., Horton, K.A., Sutton, A.J., Garbeil, H., 2018. Measuring SO2 Emission Rates at Kīlauea Volcano, Hawaii, Using an Array of Upward-Looking UV Spectrometers, 2014–2017. Front. Earth Sci. 6, 1–20. https://doi.org/10.3389/feart.2018.00214 Holland, L., Businger, S., Elias, T., Cherubini, T., 2020. Two Ensemble Approaches for Forecasting Sulfur Dioxide Emissions From Kīlauea Volcano. Weather Forecast 35, 1923–1937. https://doi.org/10.1175/WAF-D-19-0189.1 Horton, K.A., Williams-Jones, G., Garbeil, H., Elias, T., Sutton, A.J., Mouginis-Mark, P., Porter, J.N., Clegg, S., 2006. Real-time measurement of volcanic SO2 emissions: validation of a new UV correlation spectrometer (FLYSPEC). Bull. Volcanol. 68, 323–327. https://doi.org/10.1007/s00445-005-0014-9 Kern, C., Lerner, A.H., Elias, T., Nadeau, P.A., Holland, L., Kelly, P.J., Werner, C.A., Clor, L.E., Cappos, M., 2020. Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements. Bull Volcanol 82. https://doi.org/10.1007/s00445-020-01390-8 Kern, C., 2025. Ultraviolet and visible remote sensing of volcanic gas emissions. J. Volcanol. Geotherm. Res. 468, 108423. https://doi.org/10.1016/j.jvolgeores.2025.108423 Lerner, A.H., Wallace, P.J., Shea, T., Mourey, A.J., Kelly, P.J., Nadeau, P.A., Elias, T., Kern, C., Clor, L.E., Gansecki, C., Lee, R.L., Moore, L.R., Werner, C.A., 2021. The petrologic and degassing behavior of sulfur and other magmatic volatiles from the 2018 eruption of Kīlauea, Hawaiʻi: melt concentrations, magma storage depths, and magma recycling. Bull Volcanol 83, 1–32. https://doi.org/10.1007/s00445-021-01459-y Nadeau, P., Kern, C., Cappos, M., Elias, T., Warren, S., Lerner, A., Sealing, C., Slagle, C., Moisseeva, N., Holland, L., Clor, L., Werner, C., 2023. Sulfur dioxide emission rates from Hawaiian volcanoes, 2018-2022. USGS Data Release. https://doi.org/10.5066/P9SNW2B7 Patrick, M.R., Orr, T., Sutton, A.J., Lev, E., Thelen, W., Fee, D., 2016. Shallowly driven fluctuations in lava lake outgassing (gas pistoning), Kilauea Volcano. Earth Planet Sci Lett 433, 326–338. https://doi.org/10.1016/j.epsl.2015.10.052 Platt, U., Stutz, J., 2008. Differential Optical Absorption Spectroscopy - Principles and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75776-4 The data were collected to track emission rates of sulfur dioxide at volcanoes in Hawaii as a means of monitoring and better understanding volcanic activity. 20241201 20260131 ground condition Complete As needed -155.5 -155.2 19.5 19.3 ISO 19115 Topic Category geoscientificInformation None Volcano Sulfur dioxide SO2 emission rate DOAS Spectroscopy USGS Thesaurus volcanology USGS Metadata Identifier USGS:6994db14b66b01dab3ca92d3 Geographic Names Information System Kīlauea None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Christoph Kern U.S. Geological Survey, ALASKA REGION Research Physicist mailing and physical

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Vancouver WA 98683 USA 1-360-993-8922 ckern@usgs.gov Spectral data has a signal to noise ratio of better than 250:1. Dark noise is less than 50 counts (root-mean-square) per co-added spectrum. Stray light is specified to less than 0.1% at 435 nm. Corrected linearity of response is better than 99%. SO2 emission rates data have been filtered for outliers that are not representative of likely true emission rates from the volcano, as with instrumental issues or poor atmospheric conditions. Data are considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted Step 1: Spectral acquisition These are the spectral radiance data acquired by the DOAS spectrometers. Aside from the on-board linearity correction which occurs prior to transfer to disk, no additional processing was applied to the raw spectra provided in this dataset. Unknown Step 2: Plume speed estimation Derivation of SO2 emission rates from the DFence spectrometer array relies on an accurate estimation of plume propagation speed, with calculated emission rates being linearly dependent on the assumed plume speed. At Kīlauea’s summit, emission rates were calculated using ground-based wind speeds measured by a telemetered anemometer (HRSDH) stationed 3 m above ground level near Kīlauea’s summit. The wind speed data from this instrument had previously been determined to underestimate plume speed by ~20% [Elias and others, 2018b]; accordingly, raw wind speed data from that station were multiplied by a factor of 1.2, and the adjusted data were entered into the table (FilteredDFenceEmissionRates.csv) and used in determining SO2 emission rates. References: Elias, T., Kern, C., Horton, K. A., Sutton, A. J., & Garbeil, H. (2018b). Measuring SO2 Emission Rates at Kīlauea Volcano, Hawaii, Using an Array of Upward-Looking UV Spectrometers, 2014–2017. Frontiers in Earth Science, 6(214). https://doi.org/10.3389/feart.2018.00214 Unknown Step 3: Emission rate calculation The retrieval of SO2 column densities from spectra of downwelling scattered solar radiation collected while driving traverses beneath the volcanic plume followed the basic DOAS approach [Platt and Stutz, 2008]. This method follows the Beer-Lambert-Bouguer Law of Absorption which describes the absorption of radiation while passing through a medium. The DOAS technique takes advantage of the fact that the absorption cross-sections of atmospheric traces gases depend on the wavelength of incident radiation and attempts to model differences between the clear sky spectrum and measurements collected under the plume with a linear combination of trace gas absorption cross-sections. In the DOAS fit process, each measurement spectrum is analyzed by adjusting the fit parameters until a best fit between the model and the measurement is achieved. This accounts for broadband scattering and absorption processes occurring in the plume and background atmosphere leaving only the differential absorption of the trace gases to be fit by their respective absorption cross-sections. Once the best fit is found, the values of the fit parameters correspond to the best estimate of trace gas column densities. In our analyses, we included the absorption cross-sections of SO2 [Vandaele and others, 2009] and O3 [Bogumil and others, 2003], as well as a pseudo Ring-correction cross-section to account for inelastic scattering processes in the atmosphere [Grainger and Ring, 1962]. Our model contained a fifth order polynomial to account for broadband spectral features and allowed for accounting for any slight changes in spectrometer calibration due to possible thermal expansion of the optical bench over time. The DOAS fits were performed using the non-linear Levenberg-Marquardt implementation [Levenberg, 1944; Marquardt, 1963] contained in the DOASIS spectral retrieval software package [Kraus, 2006]. The fit routines were called by a custom MATLAB code (“DFence”) specifically written for analysis of DOAS measurements [Kern and others, 2020a]. Due to the high variability of SO2 column densities encountered during measurements at Kīlauea, a variable wavelength fit window was used [Elias and others, 2018a,b] for many of the measurements. In order to avoid saturation and wavelength-dependent light-path effects that occur when trace gas absorption reduces spectral radiance of incident light by more than ~10% [Platt and Stutz, 2008; Kern and others, 2010, 2012], the variable fit algorithm adjusted the lower end of our fit range between 306 and 327 nm while the upper end was kept constant at 340 nm. Because the differential absorption cross-section of SO2 decreases by about two orders of magnitude when moving from 306 to 327 nm, strong SO2 absorption can generally be avoided by moving the lower end of the fit region towards longer wavelengths until the <10% absorption threshold is satisfied. On the other hand, when low SO2 burdens are encountered, the lower end of the fit region is moved towards shorter wavelengths to include stronger SO2 absorption bands, thus increasing the measurement sensitivity to low SO2 column densities. In our implementation, the adjustment of the lower fit bound is performed iteratively depending on the retrieved SO2 column density in a given fit region. The fit process ends when the fit region is found for which the sensitivity is maximized while at the same time not exceeding 10% absorption, and the result of this fit is selected as the best estimate of the SO2 column density. The DFence software utilized the derived gas column densities (and their location) for each traverse in conjunction with the input plume speed and gas source location to calculate the final emission rate. References Bogumil, K., Orphal, J., Homann, T., Voigt, S., Spietz, P., Fleischmann, O. C., Vogel, A., Hartmann, M., Kromminga, H., Bovensmann, H., Frerick, J., & Burrows, J. P. (2003, 2003/05/05/). Measurements of molecular absorption spectra with the SCIAMACHY pre-flight model: instrument characterization and reference data for atmospheric remote-sensing in the 230–2380 nm region. Journal of Photochemistry and Photobiology A: Chemistry, 157(2), 167-184. https://doi.org/https://doi.org/10.1016/S1010-6030(03)00062-5 Elias, T., Kern, C., Horton, K., Garbeil, H., and Sutton, A.J. (2018a). SO2 emission rates from Kilauea Volcano, Hawaii (2014-2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7794402 Elias, T., Kern, C., Horton, K. A., Sutton, A. J., & Garbeil, H. (2018b). Measuring SO2 Emission Rates at Kīlauea Volcano, Hawaii, Using an Array of Upward-Looking UV Spectrometers, 2014–2017. Frontiers in Earth Science, 6(214). https://doi.org/10.3389/feart.2018.00214 Grainger, J. F., & Ring, J. (1962, 1962/02/01). Anomalous Fraunhofer Line Profiles. Nature, 193(4817), 762-762. https://doi.org/10.1038/193762a0 Kern, C., Deutschmann, T., Vogel, L., Wöhrbach, M., Wagner, T., & Platt, U. (2010). Radiative transfer corrections for accurate spectroscopic measurements of volcanic gas emissions. Bulletin of Volcanology, 72(2), 233-247. https://doi.org/10.1007/s00445-009-0313-7 Kern, C., Deutschmann, T., Werner, C., Sutton, A. J., Elias, T., & Kelly, P. J. (2012). Improving the accuracy of SO2 column densities and emission rates obtained from upward-looking UV-spectroscopic measurements of volcanic plumes by taking realistic radiative transfer into account. Journal of Geophysical Research: Atmospheres, 117(D20), D20302. https://doi.org/10.1029/2012jd017936 Kern, C., Lerner, A. H., Elias, T., Nadeau, P. A., Holland, L., Kelly, P. J., Werner, C. A., Clor, L. E., & Cappos, M. (2020a). Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements. Bulletin of Volcanology, 82(7), 55. https://doi.org/10.1007/s00445-020-01390-8 Kraus, S. (2006). DOASIS—A Framework Design for DOAS. Ph. D. Thesis. University of Mannheim, Mannheim, Germany. Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2(2), 164-168. Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2), 431-441. Platt, U., Stutz, J. (2008). Differential absorption spectroscopy. Springer. Vandaele, A. C., Hermans, C., & Fally, S. (2009). Fourier transform measurements of SO2 absorption cross sections: II.: Temperature dependence in the 29000–44000 cm−1 (227–345 nm) region. Journal of Quantitative Spectroscopy and Radiative Transfer, 110(18), 2115-2126. https://doi.org/10.1016/j.jqsrt.2009.05.006 Unknown Step 4: Data quality filters The SO2 emission rate date in table FilteredDFenceEmissionRates.csv were filtered to ensure that contained data reflect only reliable measurements. The following filters were applied: (1) Emission rates of less than -10 kg/s were omitted; (2) Emission rates higher than 6000 kg/s were omitted; (3) Instances in which emission rates were greater than 10 kg/s but the relative error of the emission rate was greater than 1 were omitted; (4) Corrupt data from May 10, 2025, July 10, 2025, and November 10 to 15, 2025 were omitted; (5) Data reflecting wind speeds of less than 4 meters/second (m/s) were omitted, as low winds can lead to plume doubling back over the array; (6) Data reflecting wind speeds of greater than 30 m/s were omitted; as such data typically reflect errors in the wind speed measurement; (7) Data reflecting wind directions of less than 35 or greater than 85 deg were omitted, as such conditions lead to plumes missing the array; (8) Data with plume completeness less than 0.7 were omitted, as this indicates a significant portion of the plume was likely missed; (9) Measurements were limited to instances in which at least 5 of the 11 available nodes were reporting. Unknown Point Point 0.000001 0.000001 Decimal degrees FilteredDFenceEmissionRates.csv Comma Separated Value (CSV) file containing processed SO2 emission rates for data for Kīlauea summit and auxiliary data. Producer Defined DateTime_UTC Data acquisition date and time Producer Defined 1-Jan-2024 00:00:00 1-Jan-2100 00:00:00 DD-MMM-YYYY HH:MM:SS in UTC time TimeStamp_UTC Data acquisition time stamp Producer Defined 739252 767011 days since 0 January 0000 in UTC time SO2EmissionRate_kg_per_s Calculated SO2 emission rate Producer Defined -999999 999999 kilograms per second (kg/s) SO2EmissionRateError_kg_per_s Calculated uncertainty of the SO2 emission rate Producer Defined 0 999999 kilograms per second (kg/s) WindSpeed_m_per_s Wind speed used in calculation of SO2 emission rate Producer Defined 0 999999 meters per second (m/s) WindSpeedError_m_per_s Estimated uncertainty of the wind speed used in SO2 emission rate calculation Producer Defined 0 999999 meters per second (m/s) WindDirection_deg Wind direction used in SO2 emission rate calculation Producer Defined 0 359.9999999999999 degrees WindDirectionError_deg Estimated uncertainty of the wind direction used in SO2 emission rate calculation Producer Defined 0 359.9999999999999 degrees SO2XS_molec_per_m SO2 in cross-section of the overhead plume along the fenceline array Producer Defined -999999 999999 SO2 molecules per meter (molecules/m) SO2XSError_molec_per_m Uncertainty of SO2 in cross-section of the overhead plume along the fenceline array Producer Defined -999999 999999 SO2 molecules per meter (molecules/m) NumNodesReporting Number of nodes reporting and contributing to this measurement Producer Defined 1 999999 nodes PlumeCompleteness Data quality parameter indicating whether the full plume was recorded by the array; calculated as PC = 1 - Sme/Smax, where Sme is the mean SO2 column density recorded at the two edges of the array, and Smax is the maximum SO2 column density recorded anywhere along the array. Producer Defined 0 1 none Radiance spectra Radiance spectra from the individual nodes in the array are saved in the compressed *.zip file SpectralRadiance.zip. The zip file itself contains compressed directories with the naming convention YYYY-MM-DD, where YYYY is the 4-digit year, MM is the 2-digit month, and DD is the 2-digit day of the date of data collection in UTC. Each of these directories contains one or multiple subdirectories corresponding to the DFence nodes reporting data on the specified day. These subdirectories are named according to the serial number of the instrument with which the spectra are collected. Inside each of these subdirectories, individually acquired radiance spectra are saved in the *.std ASCII format. The file names adhere to the format XXX_YYYY-MM-DDThh-mm-ssZ, where XXX represents the instrument serial number, YYYY is the year of acquisition, MM is the month of acquisition, DD is the day of acquisition, hh is the hour of acquisition, mm is the minute of acquisition, and ss is the second of acquisition. Indicated dates/times are in UTC. The *.STD ASCII files begin with a 3-line header specifying the file format and the number of spectral channels. Then, 2048 individual upward-looking radiances are reported. The spectral data are followed by metadata associated with each spectrum, including Longitude (decimal degrees, WGS 84 datum), Latitude (decimal degrees, WGS 84 datum), Altitude (meters above sea level), ExposureTime of the spectral acquisition (milliseconds, ms), and NumScans which reports the number of individual spectra added together to form this reported measurement spectrum. The rest of the reported metadata is for internal use when opening the spectra in the freely available DOASIS software (https://novac-community.org/software) The spectra were measured by the authors with differential optical absorption spectroscopy instruments. Spectral radiance Relative intensity of incident radiation measured at numerous discrete wavelengths, in spectrometer 'counts' Producer Defined 0 65535 unitless measurement of relative intensity, often referred to as spectrometer 'counts' GS ScienceBase U.S. Geological Survey mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data https://doi.org/10.5066/P1ZEDLNJ ZIP files can be extracted with Microsoft Windows operating system; no additional software is required. However, use of a file manager is recommended when extracting spectral radiance data from multiple days. The free 7-zip software provides such functionality (https://www.7-zip.org/) None 20260318 Christoph Kern U.S. Geological Survey, ALASKA REGION Research Physicist mailing and physical

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Vancouver WA 98683 USA 1-360-993-8922 ckern@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998