Impact of the choice of a reanalysis dataset on statistical downscaling of precipitation

Statistical downscaling techniques based on a perfect prognosis approach often rely on reanalyses to infer the statistical relationship between synoptic predictors and the local variable of interest, here daily precipitation. Nowadays, multiple global reanalysis datasets are available. These are generated by different atmospheric models with different assimilation techniques and present various spatial resolutions. The context of the application of statistical downscaling might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. The present work focused on the analogue method, which is a statistical downscaling technique. It relies on analogy in terms of synoptic-scale predictors with situations in the past to provide a probabilistic prediction for the day of interest. In order to quantify the impact of the datasets, ten different global reanalyses (NCEP Reanalysis I and II, ERA-Interim, NCEP CFSR, JMA JRA-55 and JRA-55C, NASA MERRA-2, NOAA-CIRES 20CR-2c, ERA-20C, and CERA-20C) were compared in seven variants of the analogue method, over 301 precipitation stations in Switzerland. Although all reanalysis datasets are usually assumed very good over Europe, significant differences in terms of performance of precipitation prediction were identified. The choice of the dataset can have a larger impact than the choice of the predictor variables. The impact of the reanalyses was found to increase with the complexity of the analogue method, when local variables are added, such as moisture, as compared to synoptic predictors, such as the geopotential height. As expected, the output spatial resolution of the reanalyses was found to have larger impact on local variables as well. Output resolutions below one degree were found to have generally limited to no benefit. Reanalyses with longer archives allow increasing the pool of potential analogues resulting in higher performances. However, when adding variables affected by errors in a more distant past, the skill decreased again

Comparison of present global reanalysis datasets in the context of a statistical downscaling method for precipitation prediction

The analogue method is a statistical downscaling method for precipitation prediction. It uses similarity in terms of synoptic-scale predictors with situations in the past in order to provide a probabilistic prediction for the day of interest. It has been used for decades in a context of weather or flood forecasting, and is more recently also applied to climate studies, whether for reconstruction of past weather conditions or future climate impact studies. In order to evaluate the relationship between synoptic scale predictors and the local weather variable of interest, e.g. precipitation, reanalysis datasets are necessary. Nowadays, the number of available reanalysis datasets increases. These are generated by different atmospheric models with different assimilation techniques and offer various spatial and temporal resolutions. A major difference between these datasets is also the length of the archive they provide. While some datasets start at the beginning of the satellite era (1980) and assimilate these data, others aim at homogeneity on a longer period (e.g. 20th century) and only assimilate conventional observations. The context of the application of analogue methods might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies, a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. In order to fill this gap and to advise on the choice of appropriate datasets, nine different global reanalysis datasets were compared in seven distinct versions of analogue methods, over 300 precipitation stations in Switzerland. Significant differences in terms of prediction performance were identified. Although the impact of the reanalysis dataset on the skill score varies according to the chosen predictor, be it atmospheric circulation or thermodynamic variables, some hierarchy between the datasets is often preserved. This work can thus help choosing an appropriate dataset for the analogue method, or raise awareness of the consequences of using a certain dataset

Three-dimensional pore structure and ion conductivity of porous ceramic diaphragms

The ion conductivity of two series of porous ceramic diaphragms impregnated with caustic potash was investigated by electrochemical impedance spectroscopy. To understand the impact of the pore structure on ion conductivity, the three-dimensional (3-D) pore geometry of the diaphragms was characterized with synchrotron x-ray absorption tomography. Ion migration was calculated based on an extended pore structure model, which includes the electrolyte conductivity and geometric pore parameters, for example, tortuosity (τ) and constriction factor (β), but no fitting parameters. The calculated ion conductivities are in agreement with the data obtained from electrochemical measurements on the diaphragms. The geometric tortuosity was found to be nearly independent of porosity. Pore path constrictions diminish with increasing porosity. The lower constrictivity provides more pore space that can effectively be used for mass transport. Direct measurements from tomographs of tortuosity and constrictivity opens new possibilities to study pore structures and transport properties of porous materials

Defect detection in glass fabric reinforced thermoplastics by laboratory-based X-ray scattering

Glass fabric reinforced thermoplastic (GFRT) constitutes a class of composite materials that are especially suited for automobile construction due to their combination of low weight, ease of production and mechanical properties. However, in the manufacturing process, during forming of prefabricated laminates, defects in the glass fabric as well as in the polymer matrix can occur, which may compromise the safety or the lifetime of components. Thus, the detection of defects in GFRTs for production monitoring and a deep understanding of defect formation/evolution is essential for mass production. Here, we experimentally demonstrate that local fiber shifts in the fabric, a type of defect difficult to image with other techniques, can be detected reliably by X-ray scattering based on the edge-illumination principle. This implies applications for research on mechanism of defect formation as well as for industrial application in production monitoring

Robot Assisted THz Imaging with a Time Domain Spectrometer

THz-Time domain spectroscopic imaging is demonstrated combining a robotic scanning method with continuous signal acquisition and holographic reconstruction of the object to improve the imaging resolution. We apply the method to a metallic Siemens star in order to quantify resolution and to wood samples to demonstrate the technique on a non-metallic object with an unknown structure

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.ISSN:2375-254

Interpretation and Utility of the Moments of Small-Angle X-Ray Scattering Distributions

Small angle x-ray scattering has been proven to be a valuable method for accessing structural information below the spatial resolution limit implied by direct imaging. Here, we theoretically derive the relation that links the subpixel differential phase signal provided by the sample to the moments of scattering distributions accessible by refraction sensitive x-ray imaging techniques. As an important special case we explain the scatter or dark-field contrast in terms of the sample’s phase signal. Further, we establish that, for binary phase objects, the n th moment scales with the difference of the refractive index decrement to the power of n . Finally, we experimentally demonstrate the utility of the moments by quantitatively determining the particle sizes of a range of powders with a laboratory-based setup

Klimawandel und Jahreszeiten

Der Klimawandel hat unsere Aufmerksamkeit vermehrt auf Veränderungen in unserer Umwelt gelenkt. Extreme Ereignisse wie Stürme, Überschwemmungen, Trocken- und Hitzeperioden haben direkte Auswirkungen auf unsere Lebenswelt und unseren Alltag. Weniger offensichtlich aber von grosser Tragweite sind die Einflüsse, die der Klimawandel auf die Jahreszeiten ausübt. Die Verschiebung von Blüh- und Ernteterminen, der Rückgang von Schneetagen in höheren Lagen und gar das Ausbleiben von Winterschnee im Mittelland zeigt vor allem mit Blick über mehrere Jahre und Jahrzehnte die gravierenden Folgen für die Umwelt aber auch für Landwirtschaft, Tourismus und Raumplanung. In phänologischen Beobachtungen – breit gefasst definiert als jahreszeitlich wiederkehrende Erscheinungsformen in der Umwelt – lassen sich sinnlich und alltäglich die Veränderungen erfahren, die der Klimawandel mit sich bringt. Darüber hinaus bilden die Überlieferungen dieser Beobachtungen von Generation zu Generation wichtige Brücken in Familiengeschichten und wichtigen Ortsbeschreibungen. Vor allem aber bringen uns die Beobachtungen näher, wie eng Mensch und Natur schon immer verbunden waren. Am Geographischen Institut der Universität Bern hat phänologische Forschung eine lange Tradition. Was in den späten 1960er-Jahren als Beobachtungsnetz und Datengrundlage für die Raum- und Agrareignungsplanung begann, mündete 1970 in die erste komplette Saison des Beobachtungsprogramms BernClim und bildet heute zusammen mit dem Datenschatz des Schweizer Phänologie Beobachtungsnetzes von MeteoSchweiz das Rückgrat für raum-zeitliche Beschreibungen seit Mitte des 20. Jahrhunderts. Beobachtungen seit dem Spätmittelalter bieten darüber hinaus die einmalige Möglichkeit, auch langfristige Veränderungen des Klimas zu zeigen. Zum 50-jährigen Bestehen des Beobachtungsprogramms BernClim entstand die vorliegende Broschüre. Über die Aktivitäten in Bern hinaus kommen auch Forschende zu Wort, die sich mit ebenso viel Herzblut und Ausdauer für die phänologischen Beobachtungen und Auswertungen an anderen Institutionen engagieren. Sie forschen mit Pflanzen- und Tierbeobachtungen, im Wald und auf dem Feld und ziehen Schlüsse aus Wetterdaten, Schnee- und Gewässermessungen. Die Grundlage für viele der hier präsentierten Jahreszeiten-Geschichten bilden Beobachtungen, die zum grossen Teil von Freiwilligen und oft über Jahre und Jahrzehnte gemacht wurden. Phänologie ist ohne dieses Engagement nicht möglich und bringt die Herausforderung mit sich, aus vielen individuellen Beobachtungen ein systematisches, grösseres Bild zu zeichnen. Die Publikation dieser Broschüre wurde durch die Sebastiana-Stiftung, das Oeschger-Zentrum für Klimaforschung (OCCR) und die Kommission für Phänologie und Saisonalität (KPS) der Akademie der Naturwissenschaften Schweiz (SCNAT) unterstützt. Gewidmet ist sie allen, die sich dafür einsetzen, das Zusammenspiel zwischen Mensch und Natur aus jahreszeitlicher Perspektive zu dokumentieren und besser zu verstehen