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# 1
Sili, Giulia • Urbani, Stefano • Acocella, Valerio
Abstract: This data publication includes movies and figures of twenty-six analogue models which are used to investigate what controls sill emplacement, defining a hierarchy among a selection of the proposed factors: compressive stresses, interface strength between layers, rigidity contrast between layers, density layering, ratio of layer thickness, magma flow rate and driving buoyancy pressure (Sili et al., 2019). Crust layering is simulated by pig-skin gelatin layers and magma intrusions is simulated by colored water. The experimental set-up is composed of a 40.5 X 29 X 40 cm3 clear-Perspex tank where a mobile wall applies a deviatoric compressive stress (C, in Table 1) to the solid gelatin (Figure 1). In each experiment is imposed two layers with different density and rigidity, separated by a weak or strong interface, excluding two experiments characterized by homogeneous gelatin (experiment 4 and 12). Three different rigidity contrast (1, 1.3, 1.8) between the two layers are imposed, defined as the ratio between the Young’s moduli of the upper (Eu) and lower (El) layer. By using NaCl and gelatin concentration, two layers with same rigidity but different densities are obtained, investigating the influence of the density contrasts on sill emplacement. The effects of the ratio between layer thicknesses (i.e. the ratio between upper and lower layer thickness: Thu/Thl) was simulated by changing only the thickness of the upper layer; while magma flow rate are studied changing the flow rate of peristaltic pump. Water density was increased by adding NaCl to analyze the effect of changing driving buoyancy pressure (Pm) that depends on the density difference between host rock and magma (Δρ), gravitational acceleration (g) and intrusion length (H). In the table different colors indicate the experiment result: black = dike; red = sill and blue = sheet. The here provided material includes time-lapse movies showing intrusion propagation of the twenty-six models with a velocity of 5 times higher compared to the real time (1 second in the movie is 25 real seconds). These visualizations are side (XZ or YZ plane in Figure 1) and/or top views (XY plane in Figure 1).
# 2
Zingerle, Philipp • Brockmann, Jan Martin • Pail, Roland • Gruber, Thomas • Willberg, Martin
Abstract: TIM_R6e is an extended version of the satellity-only global gravity field model TIM_R6 (Brockmann et al., 2019) which includes additional terrestrial gravity field observations over GOCE's polar gap areas. The included terrestrial information consists of the PolarGap campaign data (Forsberg et al., 2017) augumented by the AntGG gravity data compilation (Scheinert et al., 2016) over the southern polar gap (>83°S) and the ArcGP data (Forsberg et al. 2007) over the northern polar gap (>83°N). The combination is performed on normal equation level, encompassing the terrestrial data as spectrally limited geographic 0.5°x0.5° grids over the polar gaps.
Processing procedures: (extending TIM_R6) Gravity from orbits (SST): (identical to TIM_R6)- short-arc integral method applied to kinematic orbits, up to degree/order 150- orbit variance information included as part of the stochastic model, it is refined by empirical covariance functions Gravity from gradients (SGG): (identical to TIM_R6)- parameterization up to degree/order 300- observations used: Vxx, Vyy, Vzz and Vxz in the Gradiometer Reference Frame (GRF)- realistic stochastic modelling by applying digital decorrelation filters to the observation equations; estimated separately for individual data segments applying a robust procedure Gravity from terrestrial observations (TER):- collocation of the original terrestrial data sources onto 30'x30' geographic gravity disturbance grids (in the polar gap areas above 83° southern/northern latitude, thus forming a pair of polar caps)- spectral limitation of the data to D/O 300 within the collocation process- the chosen grid is fully compatible with the grid of the zero observation constraints of the original TIM_R6 model. In its function it replaces the original constraints- from the collocated polar caps, a partial normal equation system, up to D/O 300 is derived Combined solution:- addition of normal equations (SST D/O 150, SGG D/O 300, TER D/O 300)- Constraints: * Kaula-regularization applied to coefficients of degrees/orders 201 - 300 (constrained towards zero, fully compatible with TIM_R6)- weighting of SST and SGG is identical to TIM_R6. All TER observations are weighted with 5 mGal. Specific features of resulting gravity field: - Gravity field solution is (mostly) independent of any other gravity field information (outside the polar gap region)- Constraint towards zero starting from degree/order 201 to improve signal-to-noise ratio- Related variance-covariance information represents very well the true errors of the coefficients (outside the polar gap region)- Solution can be used for independent comparison and combination on normal equation level with other satellite-only models (e.g. GRACE), terrestrial gravity data, and altimetry (outside the polar gap region)- Since in the low degrees the solution is based solely on GOCE orbits, it is not competitive with a GRACE model in this spectral region (outside the polar gap region)- In comparison to TIM_R6, TIM_R6e should deliver more accurate results, especially towards the polar gaps. However, as it uses additional data sources it cannot be seen as totally independent anymore: even outside the polar gap regions correlations (introduced by the holistic nature of spherical harmonics) may be found.
# 3
Hyde, Emilee • Kyba, Christopher
Abstract: This dataset is related to the question of whether communities inside of certified "International Dark Sky Places" have different levels of lighting change in comparison to communities of similar size that are located further away. It is a supplement to Coesfeld et al. (2019), in which this question was examined for the time period 2012-2018. This dataset contains the boundaries of the analysis areas (i.e. community boundaries) in the directory "Coordinates and Graphs". These boundaries are stored as polygons in plain text format. Additional data related to the publication (e.g. Excel tables containing summary data of measured lighting trends for each community) are also included. Details of the data are available in the data description file.
# 4
Lühr, Birger • Ibanez, Jesus M. • Dahm, Torsten
Abstract: The TOMO-ETNA experiment was focused on the base of generation and acquisition of seismic signal (active and passive) at Mt. Etna volcano and surrounding area. The terrestrial campaign consists in the deployment of 80 short-period three-component seismic stations (June 15 to July24), 17 Broadband seismometers (June 15 to October 30) provided by Helmholtz Centre Potsdam (GFZ) German Research Centre for Geosciences using the German Geophysical Instrument Pool Potsdam (GIPP Gerätepool Geophysik), and the coordination with 133 permanent seismic station belonging to the “Istituto Nazionale di Geofisica e Vulcanologia” (INGV) of Italy. This temporary seismic network recorded active and passive seismic sources. Active seismic sources were generated by an array of air-guns mounted in the Spanish Oceanographic vessel “Sarmiento de Gamboa” with a power capacity of up to 5.200 cubic inches. In total more than 26.000 shots were fired and more than 450 local and regional earthquakes were recorded. Until July the Oceanographic Vessel “Sarmiento de Gamboa” and the hydrographic vessel “Galatea” were responsible for the offshore activities, that included deployment of OBSs, and several marine activities. The vessel “Aegaeo” performed additional seismic, magnetic and gravimetric experiments until the end of November 2014. This experiment was part of the “Task 5.3 - Mt. Etna structure” of the “EU MED-SUV Project” concerned with the investigation of Mt. Etna volcano (seismic tomography experiment - TOMO-ETNA) by means of passive and active refraction/reflection seismic methods. It focused on the investigation of Etna’s roots and surrounding areas by means of passive and active seismic methods. Therefore, this experiment included activities both on-land and offshore with the main objective to obtain a new high-resolution tomography in order to improve the 3D image of the crustal structures existing beneath the Etna volcano and the northeast Sicily (Peloritani - Nebrodi chain) up to the Aeolian Islands. Waveform data are open and available from the GEOFON data centre, under network code 1T.
# 5
Dietze, Michael • Polvi, Lina
Abstract: Our understanding of the effects of ice on channel morphodynamics and bedload transport in northern rivers, frozen for several months, are hindered by the difficulties of ‘seeing’ through the ice. We use continuous seismic records of a small network at the Sävar River in northern Sweden to interpret processes and quantify water and sediment fluxes. We apply a seismic inversion approach to determine seasonal differences in hydraulics and bedload sediment transport under ice-covered vs. open-channel flow conditions and provide a first-order estimation of sediment flux in that Fennoscandian river. Analysis of seismic signals of ice-cracking support our visual interpretation of ice break-up timing and the main ice break-up mechanism as thermal rather than mechanical. Waveform data are available from the GEOFON data centre, under network code 8E, and are available under CC-BY 4.0 license.
# 6
Rabbel, W. • Thorwart, M.
Abstract: The Villarrica Volcano is one of the most active volcanoes in South America and is located in a major tourism region. A dense seismological network is used to investigate the seismic characteristics of the volcano and its seismic structure tomographically with high spatial resolution. The network was in operation for 2 week from 01.03.2012 to 14.03.2012. It consisted of 30 3-component and 45 1-component short period seismographs covering an area of 2000 km*2. The covered area has a diameter of 45 km and includes the volcanic building.
# 7
Förster, Matthias • Doornbos, Eelco
Abstract: This dataset comprises global upper thermospheric cross-track neutral wind measurements obtained from accelerometer data of the CHAMP satellite during its almost ten year’s lifetime from 2001 to 2009. One key scientific instrument on-board CHAMP was a sensitive triaxial accelerometer. It was located at the spacecraft's centre of mass and sampled effectively accelerations due to non-gravitational forces with an accuracy of ~3×10^-9 ms^-2 (Doornbos et al., 2010). The along-track air drag measurements resulted in thermospheric mass density estimations, while the instrument was sensitive enough to deduce also the horizontal neutral wind component from the cross-track accelerations. The CHAllenging Minisatellite Payload (CHAMP) spacecraft circled the Earth from July 2000 to September 2010 on a near-polar orbit (inclination 87.3°). Each orbit period took about 93 minutes at an altitude of initially 455 km, and decaying to about 320 km in 2009. Due to CHAMP's precession, the satellite achieved full coverage of all local times within about 131 days in each case. This work was part of a study in 2007-2009 (Doornbos et al., 2009) funded by the European Space Agency’s General Studies Program which aimed at a more precise estimation of the non-gravitational forces, considering the precise satellite geometry and its optical and mechanical surface properties. To obtain the actual air drag forces, the modelled accelerations due to radiation pressure forces from the sun, the Earth's albedo, and the Earth's infrared radiation had to be computed and removed from the calibrated and edited accelerometer data to get the observed aerodynamic acceleration vector. The modelling of the radiation pressure forces comprised several nontrivial components like the modelling of eclipse and semi-shadow conditions for solar radiation pressure, values for the reflectivity and infrared emissivity of Earth surface elements, and models of the geometry and optical properties of the satellite surfaces (Doornbos et al., 2010). The detailed description of supersonic flow of the neutral gas particles across the satellite's surface and its reflection requires a model of the gas–surface interaction, which specifies the angular distribution and energy flux of the reflected particles. One has to make assumptions and educated guesses, because information on the gas–surface interaction, as well as in situ observations of aerodynamic model parameters like air temperature and neutral gas species' concentrations should be measured by independent instruments on the accelerometer-carrying satellite. Here, we relied on the empirical atmosphere model NRLMSISE-00 (Picone et al., 2002) and the rarefied aerodynamic equations for flat panels, derived by Sentman (1961). These equations take into account the random thermal motion of the incident particles and assume a completely diffuse distribution of the reflected particle flux. The energy flux accommodation coefficient alpha (Moe et al., 2004), which determines whether the particles retain their mean kinetic energy (alpha = 0) or acquire the temperature of the spacecraft surface wall (alpha = 1), was found to be optimally chosen with alpha = 0.8 for this data set. This thermospheric cross-track neutral wind data set consists of a series of annual CDF data files for both CHAMP wind measurements (subfolder: CH_PN_R03_denswind_iter2_Sentman_alpha08) and CHAMP orbital data (subfolder: CH_orbit_GEO_RSO). The CDF data files are documented in the header. The complete dataset contains more than 25 million data points with a temporal cadence of 10 sec. In addition to the data, we are providing supplementary Figures to Aruliah et al. (2019, subfolder: 2019-001_Foerster-Doornbos_Figures). They are complementary, in particular, to Figs. 1-4 of this paper, but additionally show the original data as “cloud” of data points in the background of the statistical averages. Each figure plot (png-format) has an accompanying txt-file of the same name (except the extension) with ASCII tables of the hourly statistical averages and their standard deviations. The data were used in various previous publications mainly with respect to high-latitude upper thermosphere studies (Förster et al., 2008, 2011) and investigations of the interhemispheric coupling processes of the magnetosphere, ionosphere, and thermosphere (Förster et al., 2017). Actually, this data publication serves as supplement to Aruliah et al. (2019).
# 8
Pick, Leonie
Abstract: The software package “ClassifyStorms” performs a classification of geomagnetic storms according to their interplanetary driving mechanisms based exclusively on magnetometer measurements from ground. In the present version two such driver classes are considered for storms dating back to 1930. Class 0 contains storms driven by Corotating or Stream Interaction Regions (C/SIRs) and Class 1 contains storms driven by Interplanetary Coronal Mass Ejections (ICMEs). The properties and geomagnetic responses of these two solar wind structures are reviewed, e.g., by Kilpua et al. (2017, http://doi.org/10.1007/s11214-017-0411-3). The classification task is executed by a supervised binary logistic regression model in the framework of python's scikit-learn library. The model is validated mathematically and physically by checking the driver occurrence statistics in dependence on the solar cycle phase and storm intensity. A detailed description of the classification model is given in Pick et al. (2019) to which this software is supplementary material. Under "Files" you can download ClassifyStorms_1.0.0.zip, which contains "StormsClassified.csv". This table lists the Date (Year-Month-Day) and Time (Hour:Minutes:Seconds) of 7546 classified geomagnetic storms together with the predicted interplanetary driver class label (0 or 1) and its probability (between 0 and 1). The directory also includes an ASCII file ("ReferenceEvents.txt"), which lists the "reference" events, i.e., a compilation of three published lists with identified ICMEs and C/SIRs (Jian et al., 2006a, 2006b, 2011; Shen et al., 2017; Turner et al., 2009). If you want to execute the code yourself, run the jupyter notebook “ClassifyStorms.ipynb” (https://jupyter.org/). The notebook accesses the python modules “Imports.py”, “Modules.py” and “Plots.py” as well as the input data set “Input.nc”, an xarray Dataset (http://xarray.pydata.org/en/stable) saved in NetCDF format (https://www.unidata.ucar.edu/software/netcdf). The latter contains a temporally smoothed version of the long-term stable Hourly Magnetospheric Currents index (HMC index, http://doi.org/10.5880/GFZ.2.3.2018.006) and the underlying geomagnetic observatory measurements. The files provided here correspond to the software version discussed in the corresponding paper (see above). An up-to-date version of the software is available on GitLab via https://gitext.gfz-potsdam.de/lpick/ClassifyStorms. The “Readme.md” file provides all information needed to run or modify “ClassifyStorms” from the GitLab source.
# 9
Neuper, Malte • Ehret, Uwe
Abstract: The dataset consist of time series of hourly rain rates and mean radar reflectivity factor (herein after referred to as reflectivity) near the ground, 100 meter and 1500 meter above the ground at six locations in the Attert catchment in Luxembourg. The time series cover a time span of 4 years (from the 1st of October 2012 tor the 30th of September 2016). The dataset was derived from drop size measurements we conducted at six stations with six laser optical disdrometers and two micro rain radars (MRR) within the CAOS Project (DFG Research Group: From Catchments as Organized Systems to Models based on Functional Units (FOR 1598). The time series of rain rates and radar reflectivity factors (reflectivities) were calculated (derived) via the 3.5th and 6th statistical moments of the drop size distributions using the particular raw data of drop sizes and fall velocities. The primary reason for the measurements was to improve radar based quantitative precipitation estimation in general and the conversion of the reflectivity Z (measured by operational weather radar) to a rain rate R at the ground via the so-called Z-R relation within a mesoscale catchment. GENERAL CONVENTIONS:• Time extent: 1.10.2012 00:00 – 30.09.2016 23:00 (35064 values)• Time reference: UTC • Time stamp: end• Time resolution: 1h• Time series are equidistant and gapless• Missing values: NaN• Delimiter: ; (semicolon)• decimal separator: . (point) STATION LOCATIONS:Name; Abbreviation; Latitude (WGS-84); Longitude(WGS-84); height a.s.l; InstrumentationOberpallen;OPA; 49.73201°; 5.84712°;287 m; disdrometer Useldange;USL; 49.76738°; 5.96756°; 280 m; disdrometer and MRR Ell;ELL; 49.76558°; 5.84401°; 290 m; disdrometer Post;POS; 49.75394°; 5.75481°; 345 m; disdrometer Petit-Nobressart;PIN; 49.77938°; 5.80526°; 374 m; disdrometer and MRR Hostert-Folschette;HOF; 49.81267°; 5.87008°; 435 m; disdrometer HEADER – VARIABLES DESCRIPTION:Name - description:Date-UTC – Date as yyyy-mm-dd HH:MM (4 digit year-2 digit month – 2 digit day 2 digit hour: 2 digit minute)Time Zone: UTC. Decade – tenner day of the year (that is 1st to 10th of January = 1 ; 11th to 20th of January = 2 ; 21th to 30th of January = 3 ; … 21st to 31st of December = 36.Month – Month of the year (1: January, 2: February, 3:March,…, 12: December).dBZ0_DIS_ELL – reflectivity at ground level (in dBZ) at the station Ell derived from disdrometer measurements.dBZ0_DIS_HOF – reflectivity at ground level (in dBZ) at the station Hostert-Folschette derived from disdrometer measurements.dBZ0_DIS_OPA – reflectivity at ground level (in dBZ) at the station Oberpallen derived from disdrometer measurements.dBZ0_DIS_PIN – reflectivity at ground level (in dBZ) at the station Petit-Nobressart derived from disdrometer measurements.dBZ0_DIS_POS – reflectivity at ground level (in dBZ) at the station Post derived from disdrometer measurements.dBZ0_DIS_USL – reflectivity at ground level (in dBZ) at the station Useldange derived from disdrometer measurements.dBZ100_MRR_PIN – reflectivity 100 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ100_MRR_USL – reflectivity 100 m above ground (in dBZ) at the station Useldange derived from MRR measurements.dBZ1500_MRR_PIN – reflectivity 1500 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ1500_MRR_USL – reflectivity 1500 m above ground (in dBZ) at the station Useldange derived from MRR measurements.RR0_DIS_ELL – rain rate at ground level (in mm/h) at the station Ell derived from disdrometer measurements.RR0_DIS_HOF – rain rate at ground level (in mm/h) at the station Hostert-Folschette derived from disdrometer measurements.RR0_DIS_OPA – rain rate at ground level (in mm/h) at the station Oberpallen derived from disdrometer measurements.RR0_DIS_PIN– rain rate at ground level (in mm/h) at the station Petit-Nobressart derived from disdrometer measurements.RR0_DIS_POS – rain rate at ground level (in mm/h) at the station Post derived from disdrometer measurements.RR0_DIS_USL – rain rate at ground level (in mm/h) at the station Useldange derived from disdrometer measurements.RR100_MRR_PIN – rain rate 100 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR100_MRR_USL – rain rate 100 m above ground (in mm/h) at the station Useldange derived from MRR measurements.RR1500_MRR_PIN – rain rate 1500 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR1500_MRR_USL – rain rate 1500 m above ground (in mm/h) at the station Useldange derived from MRR measurements. The instruments were maintained and cleaned monthly. The data was quality checked. Cases with solid precipitation were excluded using the output form the Pasivel² present weather sensor software, which especially was needed since disdrometer data was contaminated by cobwebs. But since the present weather analyzer classified these (due to their slow movement within the wind) as snow, these then could easily be eliminated.
DISDROMETER:We deployed six second generation OTT Particle Size and Velocity (PARSIVEL², see Löffler-Mang and Joss, 2000 - https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2) optical disdrometers in the study area to measure drop size distributions at ground level in 1-minute resolution. Two were located at the same sites as the MRRs (Useldange and Petit-Nobressart). The others were placed such as to both capture the hydroclimatic variations in the study area and to cover it as uniformly as possible. We applied a quality control to the raw data as described by Friedrich et al. (2013 - https://doi.org/10.1175/JTECH-D-12-00254.1 and https://doi.org/10.1175/MWR-D-12-00116.1 ), converted the filtered data to drop-size concentrations per unit air volume to make them comparable to weather radar and MRR data, then converted them to reflectivity and rain rate using the 3.5th and 6th statistical moments of the drop size distributions and finally took 1-hour averages and sums thereof. MRR: From two vertical pointing K-band METEK micro rain radars (MRR) (Löffler-Mang et al. 1999 - https://doi.org/10.1175/1520-0426(1999)016<0379:OTPOAL>2.0.CO;2 and Peters et al. 2002 - Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Env. Res. 7: 353–362, 2002) measurements located at the sites Useldange and Petit-Nobressart drop size spectra were retrieved at 1500 meter and 100 meter above ground . We operated the MRR's at 100 vertical meters and 10 seconds temporal resolution, but for reasons of storage and processing efficiency did all further processing on 1-minute aggregations thereof. The raw Doppler spectra were transformed to drop size distributions via the drop size – fall velocity relation given in Atlas et al (1973 - https://doi.org/10.1029/RG011i001p00001). From the drop size distributions the rain rate and the reflectivity were calculated using the 3.5th and the 6th statistical moments of the drop size distributions. In doing so we assumed the vertical velocity of the air to be negligible.
# 10
Bentz, Stephan • Martínez-Garzón, Patricia • Kwiatek, Grzegorz • Dresen, Georg • Bohnhoff, Marco
Abstract: Preparatory mechanisms accompanying or leading to nucleation of larger earthquakes have been observed at both laboratory and field scale, but the precise conditions favoring extended nucleation processes are still largely unknown. In particular, it remains a matter of debate why earthquakes often occur spontaneously without noticeable precursors as opposed to an extended failure process accompanied by foreshocks. In this study, we have generated new high-resolution seismicity catalogs framing the occurrence of 20 ML > 2.5 earthquakes at The Geysers geothermal field in California. To this end, a seismicity catalog of the 11 days framing each large event was created. We selected 20 sequences in total from different tectonic settings within the field that sample the entire reservoir depth range and temporal periods with high or low injection rates. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b-values, while the southeastern part is dominated by decreased seismic activity and higher b-values. Periods of high injection coincide with high b-values, and vice versa. These observations potentially reflect differential stresses and damage varying across the field. About 50 % of analyzed sequences exhibit no change in seismicity rate in response to the large main event. Instead, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events.
Earthquake catalogs belonging to each of the 20 mainshock sequences. The catalogue is provided in tabular form (ASCII). The file names indate the NCEDC ID of the EQ Sequence mainshock (NCEDC = Northern California earthquake datacenter, Enhanced Geothermal Systems Earthquake Catalog, https://ncedc.org/egs/catalog-search.html). Definition of columns in the data tables (also in the header of the data): - X,Y are in local cartesian coordinates (m) [unlocated events are indicated by NaN]- Z (m) is in depth, negative values point downwards- Coordinate origin in Lat;Lon: 38.8042;-122.7822- dt refers to matlab serial time (i.e. the number of days from January 0, 0000, fractions of days are given as decimal digits)- Ml is local magnitude
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