ALBERT

Description: 〈div data-abstract-type="normal"〉〈p〉The new mineral stefanweissite, IMA2018-020, was discovered in sanidinite volcanic ejecta from the Laach Lake (Laacher See) paleovolcano, Eifel region, Rhineland-Palatinate, Germany. Associated minerals are sanidine, nosean, biotite, augite, titanite, ferriallanite-(La), magnetite, baddeleyite and a pyrochlore-group mineral. Stefanweissite is brown and reddish-brown, with adamantine lustre; the streak is light brown to yellow. It forms long-prismatic crystals up to 0.03 mm 〈span〉×〈/span〉 0.07 mm 〈span〉×〈/span〉 1.0 mm and acicular crystals up to 2 mm long and 0.02 mm thick typically combined in radiated aggregates in cavities in sanidinite. 〈span〉D〈/span〉〈span〉calc.〈/span〉 = 5.254 g/cm〈span〉3〈/span〉. The mean refractive index calculated from the Gladstone–Dale equation is 2.260. The Raman spectrum shows the absence of hydrogen-bearing groups. The chemical composition is (electron microprobe, wt.%): CaO 7.63, MnO 2.51, FeO 7.86, Al〈span〉2〈/span〉O〈span〉3〈/span〉 0.25, La〈span〉2〈/span〉O〈span〉3〈/span〉 2.28, Ce〈span〉2〈/span〉O〈span〉3〈/span〉 6.54, Pr〈span〉2〈/span〉O〈span〉3〈/span〉 1.01, Nd〈span〉2〈/span〉O〈span〉3〈/span〉 1.59, ThO〈span〉2〈/span〉 3.71, UO〈span〉2〈/span〉 1.09, TiO〈span〉2〈/span〉 17.32, ZrO〈span〉2〈/span〉 28.03, HfO〈span〉2〈/span〉 0.91, Nb〈span〉2〈/span〉O〈span〉5〈/span〉 19.96, total 99.69. The empirical formula based on 14 O atoms per formula unit is Ca〈span〉1.13〈/span〉(Ce〈span〉0.33〈/span〉La〈span〉0.12〈/span〉Nd〈span〉0.08〈/span〉Pr〈span〉0.05〈/span〉)〈span〉Σ0.58〈/span〉Th〈span〉0.12〈/span〉U〈span〉0.03〈/span〉Mn〈span〉0.29〈/span〉Fe〈span〉0.91〈/span〉Al〈span〉0.04〈/span〉Zr〈span〉1.89〈/span〉Hf〈span〉0.04〈/span〉Ti〈span〉1.80〈/span〉Nb〈span〉1.19〈/span〉O〈span〉14〈/span〉. The simplified formula is (Ca,〈span〉REE〈/span〉)〈span〉2〈/span〉Zr〈span〉2〈/span〉(Nb,Ti)(Ti,Nb)〈span〉2〈/span〉Fe〈span〉2+〈/span〉O〈span〉14〈/span〉. Stefanweissite is orthorhombic, with space group 〈span〉Cmca〈/span〉. The unit-cell parameters are: 〈span〉a〈/span〉 = 7.2896(4) Å, 〈span〉b〈/span〉 = 14.1435(5) Å, 〈span〉c〈/span〉 = 10.1713(4) Å and 〈span〉V〈/span〉 = 1048.68(7) Å〈span〉3〈/span〉. The crystal structure was solved using single-crystal X-ray diffraction data. Stefanweissite is an analogue of zirconolite-3〈span〉O〈/span〉 with Nb dominant over Ti in one of two octahedral sites. The strongest lines of the powder X-ray diffraction pattern [〈span〉d〈/span〉, Å (〈span〉I〈/span〉, %) (〈span〉hkl〈/span〉)] are: 2.983(100)(202), 2.897(71)(042), 1.828(38)(154, 400, 333), 1.793(25)(244), 1.767(16)(080), 1.517(10)(282), 1.187(19)(483, 1.11.3, 602). Type material is deposited in the collections of the Fersman Mineralogical Museum of the Russian Academy of Sciences, Moscow, Russia, with the registration number 5191/1.〈/p〉〈/div〉

Description: The new mineral stefanweissite, IMA2018-020, was discovered in sanidinite volcanic ejecta from the Laach Lake (Laacher See) paleovolcano, Eifel region, Rhineland-Palatinate, Germany. Associated minerals are sanidine, nosean, biotite, augite, titanite, ferriallanite-(La), magnetite, baddeleyite and a pyrochlore-group mineral. Stefanweissite is brown and reddish-brown, with adamantine lustre; the streak is light brown to yellow. It forms long-prismatic crystals up to 0.03 mm × 0.07 mm × 1.0 mm and acicular crystals up to 2 mm long and 0.02 mm thick typically combined in radiated aggregates in cavities in sanidinite. Dcalc. = 5.254 g/cm3. The mean refractive index calculated from the Gladstone–Dale equation is 2.260. The Raman spectrum shows the absence of hydrogen-bearing groups. The chemical composition is (electron microprobe, wt.%): CaO 7.63, MnO 2.51, FeO 7.86, Al2O3 0.25, La2O3 2.28, Ce2O3 6.54, Pr2O3 1.01, Nd2O3 1.59, ThO2 3.71, UO2 1.09, TiO2 17.32, ZrO2 28.03, HfO2 0.91, Nb2O5 19.96, total 99.69. The empirical formula based on 14 O atoms per formula unit is Ca1.13(Ce0.33La0.12Nd0.08Pr0.05)Σ0.58Th0.12U0.03Mn0.29Fe0.91Al0.04Zr1.89Hf0.04Ti1.80Nb1.19O14. The simplified formula is (Ca,REE)2Zr2(Nb,Ti)(Ti,Nb)2Fe2+O14. Stefanweissite is orthorhombic, with space group Cmca. The unit-cell parameters are: a = 7.2896(4) Å, b = 14.1435(5) Å, c = 10.1713(4) Å and V = 1048.68(7) Å3. The crystal structure was solved using single-crystal X-ray diffraction data. Stefanweissite is an analogue of zirconolite-3O with Nb dominant over Ti in one of two octahedral sites. The strongest lines of the powder X-ray diffraction pattern [d, Å (I, %) (hkl)] are: 2.983(100)(202), 2.897(71)(042), 1.828(38)(154, 400, 333), 1.793(25)(244), 1.767(16)(080), 1.517(10)(282), 1.187(19)(483, 1.11.3, 602). Type material is deposited in the collections of the Fersman Mineralogical Museum of the Russian Academy of Sciences, Moscow, Russia, with the registration number 5191/1.

Description: Synthetic mullite (Al2[Al2+2xSi2-2x]O10-x) is a very well characterised material. Due to its outstanding properties it is used for a vast range of technical applications representing one of the most important groups of ceramic materials. In contrast to the synthetic product, the mineral mullite is only poorly investigated. In this study crystal-structure refinements of natural mullites are presented for the first time. Three mullite single-crystals from the Ettringer Bellerberg, Eifel area, Germany (ME1 & ME2) and from Kladno, Czech Republic (MK1) have been examined by single crystal X-ray diffraction methods, Electron Microprobe Analyses (EMPA), and the spindle-stage method to determine the refractive indices. Chemical analyses revealed an incorporation of Fe and Ti cations and yielded the following compositions per formula unit with Z = 1: Al4.34Fe0.12Si1.49Ti0.05O9.77 for ME1 corresponding to an x-value of x = 0.23(4); ME2 consists of two different parts with compositions of Al4.406Fe0.103Si1.455Ti0.037O9.746 and Al4.15Fe0.107Si1.68Ti0.06O9.872 corresponding to x = 0.254(2) and x = 0.128(14), respectively; MK1 has a composition of Al4.40Fe0.018Si1.58O9.791 with x = 0.209(15). The crystal structure of natural mullite is similar to its synthetic counterpart with chains of edge-sharing (Al, Fe)O6 octahedra parallel c crosslinked by T2O7 groups with T = Al,Si and a number of oxygen vacancies corresponding to the x-value in the general composition. Mullite crystals belong to the orthorhombic space group Pbam with a = 7.5542(3) Å, b = 7.6998(3) Å, c = 2.8935(1) Å, V = 168.31(2) Å3 for ME1; a = 7.5353(3) Å, b = 7.7018(3) Å, c = 2.8928(3) Å, V = 167.89(4) Å3 for ME2 representing average values for the two parts; a = 7.5439(3) Å, b = 7.6958(3) Å, c = 2.8879(1) Å, V = 167.66(1) Å3 for MK1. The refinements give clear evidence that iron predominantly enters the octahedral sites, which is an explanation for the slight increase in lattice parameters c. It could be demonstrated that the incorporation of the foreign cations (Fe, Ti) increases the refractive indices (nx ≈ ny = 1.654(2) and nz = 1.668(2) for ME1; nx ≈ ny = 1.648(4) and nz = 1.662(4) for MK1). Our results confirm previous findings, that mullite minerals are commonly formed on the SiO2-rich side (x 〈 0.25) of the Al2O3–SiO2 system with compositions not yet observed for synthetic mullites, which usually range between about 0.25 ≤ x ≤ 0.4.

Description: Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

Description: Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent bottom up approach that uses satellite soil moisture observations for estimating rainfall through the SM2RAIN algorithm is suited to build long-term and consistent rainfall data record as a single polar orbiting satellite sensor is used. We exploit here the Advanced SCATterometer (ASCAT) on board three Metop satellites, launched in 2006, 2012 and 2018. The continuity of the scatterometer sensor on European operational weather satellites is ensured until mid-2040s through the Metop Second Generation Programme. By applying SM2RAIN algorithm to ASCAT soil moisture observations a long-term rainfall data record can be obtained, also operationally available in near real time. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN-ASCAT global daily rainfall dataset at 12.5 km sampling (2007–2018). The quality of SM2RAIN-ASCAT dataset is assessed on a regional scale through the comparison with high-quality ground networks in Europe, United States, India and Australia. Moreover, an assessment on a global scale is provided by using the Triple Collocation technique allowing us also the comparison with other global products such as the latest European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), the Global Precipitation Measurement (GPM) mission, and the gauge-based Global Precipitation Climatology Centre (GPCC) product. Results show that the SM2RAIN-ASCAT rainfall dataset performs relatively well both at regional and global scale, mainly in terms of root mean square error when compared to other datasets. Specifically, SM2RAIN-ASCAT dataset provides better performance better than GPM and GPCC in the data scarce regions of the world, such as Africa and South America. In these areas we expect the larger benefits in using SM2RAIN-ASCAT for hydrological and agricultural applications. The SM2RAIN-ASCAT dataset is freely available at https://doi.org/10.5281/zenodo.2591215.