bluuebunny/crossref_metadata_2025 huggingface dataset:
Constraining gradient-based inversion with a variational autoencoder to reproduce geological patterns
Authors: Lopez-Alvis Jorge , Laloy Eric , Hermans Thomas , Nguyen Frédéric
Published: March 2020

Given the sparsity of geophysical data it is useful to rely on prior information on the expected geological patterns to constrain the inverse problem and obtain a realistic image of the subsurface. By using several examples of such patterns (e.g. those obtained from a training image), deep generative models learn a low-dimensional latent space that can be seen as a reparameterization of the original high-dimensional parameters and then inversion can be done in this latent space. Examples of such generative models are the variational autoencoder (VAE) and the generative adversarial network (GAN). Both usually include deep neural networks within their architecture and have shown good performance in reproducing high-dimensional structured subsurface models. However, they both use a highly nonlinear function to map from latent space to the original high-dimensional parameter space which hinders the optimization of the objective function during inversion. Particularly, such nonlinearity may give rise to local minima where gradient-based inversion gets trapped and therefore fails to reach the global minimum. GAN has been previously used with gradient-based inversion in a linear traveltime tomography synthetic test where it was shown to often fail in reaching a consistent RMSE (compared to the added noise) because optimization converges to local minima. On the other hand, inversion with MCMC and GAN was shown to reach acceptable RMSE values. When applicable, however, a gradient-based inversion is preferred because of its lower computational demand. We propose using VAE together with gradient-based inversion and show that optimization reaches lower RMSE values on average compared to GAN in a linear traveltime tomography synthetic case. We also compare the subsurface models that are generated during the iterations of the optimization to explore the effect of the different latent spaces used by GAN and VAE. We identify a trade-off between a strict following of the patterns and getting trapped in local minima during optimization, i.e. VAE seems to be able to break some continuous channels in order to not get trapped in local minima whereas GAN does not break channels. Finally, we perform some synthetic tests with nonlinear traveltime tomography and show that gradient-based inversion with VAE is able to recover a similar global structure to the true model but its final RMSE values are still far from the added noise level.
Reconstruction of anthropogenic environmental changes from a Cuban coral over the last 175 years
Authors: Harbott Marie , Wu Henry C. , Kuhnert Henning , Kasemann Simone A. , Jimenez Carlos , Gonzales Diaz Patricia , Rixen Tim
Published: March 2020

Ocean warming and ocean acidification (OA) are increasingly influencing marine life. Parts of the increasing amount of CO2 in the atmosphere will eventually get absorbed by the ocean, which changes the oceans carbonate chemistry and threatens the ecological competitiveness of calcareous marine organisms. Currently,  the global coverage of studies on the development of pH since preindustrial times is sparse. An important region to study environmental and climate variations is the northwestern coastal part of Cuba where the Loop Current (LC) joins the Florida Current and contributes to the Gulf Stream. The tropical Atlantic is a primary region for the formation of warm surface water of the thermohaline ocean circulation and the Caribbean in particular as a habitat for coral reefs in the Atlantic making them susceptible to changes in water temperatures and carbonate chemistry. This provides a unique chance to study multiple aspects of the implications of anthropogenic activities such as changes in SST, ocean pH, and carbonate chemistry using the coral skeletal geochemistry as an archive of climate and environmental changes. Here we present results from a multi-proxy approach for the reconstruction of environmental change and natural climate variability from a North Cuban Siderastrea siderea coral. The sub-seasonally resolved records indicate interannual to decadal changes in SST and seawater carbonate chemistry since 1830 CE. The comparison with pH will provide clues on whether the regional climate variability has been directly affected by atmospheric CO2 forcing.
Faults distribution and seismogenic potential in the Calabrian back-arc domain, SE Tyrrhenian Sea
Authors: Palmiotto Camilla , Loreto Maria Filomena , Muto Francesco , Ferrante Valentina , Pettenati Franco , Sandron Denis , Tripodi Vincenzo
Published: March 2020

The Western Calabrian margin (Italy) is the most active segment of the Apennine back-arc system, formed in response to the slow Africa – Eurasia convergence. The offshore area represents the transitional region between the arc and the back-arc: it is affected by several fault systems, most of them able to trigger highly destructive earthquakes. Indeed, the Calabria and its western offshore are characterized by the highest seismic moment release of the entire Apennines, also evidenced by historical seismicity catalogue, the most accurate over the world. During last decades, scientific community invested huge resources in assessment of seismic and tsunami hazards. Furthermore, during last years several local-scale works allowed of improving knowledge of the faults geometry, magmatism, seismogenic and tsunamigenic potential along the western offshore region (Loreto et al., 2017; Brutto et al., 2016; De Ritis et al., 2019). Some active faults, belonging to NE-SW-trending normal fault systems accommodating the inner-arc collapse related to slab-decupling, are also responsible of the most destructive historical sequences, still to be adequately characterized. Using vintage SPARKER 30 Kj acquired in the seventies and recent multichannel seismic profiles together with middle resolution morpho-bathymetric data we produced a new tectonic map of the Calabria back-arc system. Further, we characterized some before-unknown faults and linked them with shallow structures, as ridges and slumps / slides. This area seemingly less populated of faults compared to the peri-Tyrrhenian margin, where several faults belong to different systems, i.e. (i) the rifting system active that allowed the opening of the Tyrrhenian Basin and (ii) the slab-decupling related normal faults system currently active. The comparison with historical and instrumental seismicity allowed us to highlight possible seismic gaps that, if considered, could strongly improve the map of seismogenic potential of the Tyrrhenian back-arc system.

 

Bibliography

Brutto, F. et al. (2016). The Neogene-Quaternary geodynamic evolution of the central Calabrian Arc: A case study from the western Catanzaro Trough basin. Journal of Geodynamics, 102, 95-114.

Loreto, M. F. (2017). Reconstructed seismic and tsunami scenarios of the 1905 Calabria earthquake (SE Tyrrhenian sea) as a tool for geohazard assessment. Engineering geology, 224, 1-14.

Tripodi, V. et al. (2018). Neogene-Quaternary evolution of the forearc and backarc regions between the Serre and Aspromonte Massifs, Calabria (southern Italy). Marine and Petroleum Geology, 95, 328-343.
Glaciological setting and subglacial conditions at Little Dome C, the future site for Beyond Epica – Oldest Ice Core
Authors: Rix Julius , Mulvaney Robert , Martin Carlos , Ritz Catherine , Frezzotti Massimo
Published: March 2020

Ice domes in the interior of East Antarctica are ideal candidates in the quest for the longest continuous record of climate in polar ice. They are in areas with low surface precipitation, low horizontal advection and large ice thickness. However, the age of the ice near the bottom of the column is very sensitive to subglacial thermal conditions as they can promote basal melting and the loss of the bottommost and oldest ice. Here we report the main findings from a geophysical survey and a shallow, 460m depth, rapid access drilled borehole. We use a low frequency radar, DELORES, to survey the area and detect subglacial melting; a phase sensitive radar, ApRES, to obtain englacial vertical strain-rates and crystal orientation fabrics in selected sites; and, at the drilling site, borehole temperature and water isotope data in the top 460m. Our main findings are: 1) The subglacial topography is characterized by topographic highs criss-crossed by deep valley troughs with typically 0.5-1km difference in height. There is evidence of subglacial melting in the troughs. However the ice stratigaphy, that we survey in detail with DELORES system with 500m grid, drapes over the rough topography and the topographic highs are presently melt-free. 2) The optical birefringence, observed in ApRES polarimetry, shows two aligned crystal orientation fabrics that are typical for glacial periods. This indicate uniform ice-flow conditions during, at least, the last two glacial-interglacial periods and is consistent with the polarimetry from EPICA Dome C. 3) Using the borehole temperature, englacial strain-rates and temperature records from EPICA Dome C we estimate that the geothermal heatflux in the area is 55 +/- 1 mW/m2. Also we find that, due to the delay between basal and surface temperatures, the basal temperature at Little Dome C is currently the coldest and was 0.5 C warmer 80 kyrs ago. We estimate that any topographic high where the ice thickness is below 2810 +/- 10 m was melt-free during the warmest conditions. This information, together with other evidence, lead to choosing the site for the future Beyond Epica – Oldest Ice Core project.
Assessment of the relationship between pre-fire fuel estimates and Fire Radiative Power in Portugal
Authors: Alonso Catarina , Gouveia Célia M. , Páscoa Patrícia
Published: March 2020

Forest fires are recurrent in Portugal, either due to climate conditions, to land use change, or to a combination of both. Wet and mild winters, together with dry and warm summers, favour the growth of vegetation and its subsequent low moisture content, increasing fuel availability. The assessment and management of fuel loads is essential to understand and minimize fire risk. The structural risk depends on the type of available fuel and on the age of vegetation. Therefore, reducing fuel loads is often required to mitigate fire severity.  

Active fire observations of fire radiative power (FRP) have been shown to be correlated to rates of biomass combustion. The Meteosat FRP-PIXEL product is delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), since 2004 with 15-min temporal resolution. We propose to do the first assessment, for Portugal, of the relationship between Fire Radiative Energy (FRE) per fire and pre-fire fuel load estimates, as obtained from Dry Matter Productivity (DMP), disseminated by Copernicus Global Land Service (CGLS) at 1km spatial resolution since 1999. The analysis is performed for the main land cover types in Portugal that show high sensitivity to wildfires. The severest wildfire events in Portugal since 2004 are also analysed with detail, namely the fires of 2005, 2012 and 2017 and the obtained results related with soil moisture, fuel type and fire size.

Acknowledgements: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under projects FIRECAST (PCIF/GRF/0204/2017) and IMPECAF (PTDC/CTA-CLI/28902/2017).
Reconstructing orogen dynamics from detrital geo-thermochronological signals in pro- and retro-foreland basins: A case sudy of the Pyrenees, France
Authors: Ternois Sébastien , Léger Julien , Pik Raphaël , Ford Mary
Published: March 2020

A bivergent orogenic belt and its foreland basins form an intimately coupled system for which cause-effect relationships have generated much debate. This is the case in the Pyrenees, where the contemporaneity of crustal thermal events (cooling) with foreland tectonic events (subsidence) during convergence has, until recent years, remained unclear due to little quantification of processes during early orogenesis. Over the last 5 years, the development of new tools for generating, manipulating and modelling data and processes in geo-thermochronology has been stimulating new research that challenged established paradigms in both Pyrenean pro- and retro-foreland systems. Results obtained for these systems must now be compared and integrated into a reconstruction of the Pyrenean orogen history.

In this study we first review published detrital geo-thermochronology data in the eastern to central Pyrenees, harmonizing the way by which they are inverted for the inference of sediment provenance with use of current statistical modelling methods. Second, we re-interpret these detrital data in terms of provenance for both Pyrenean pro- and retro-foreland systems. Last, we compare the geo-thermochronological signals in both foreland basins to reconstruct orogen dynamics in the Pyrenees. We critically assess the implications of this newly proposed reconstruction for the validity of conceptual models for orogenic evolution and of plate kinematic models in the northwestern Mediterranean region.
Insensitivety to initial condition/prior in data assimilation for the case of the optimal filter and deterministic model
Authors: Oljaca Lea , Broecker Jochen , Kuna Tobias
Published: March 2020

Data assimilation is a term used to describe efforts to improve our knowledge
of a system by combining incomplete observations with imperfect models.
This is more generally known as filtering, which is ’optimal’ estimation of
the state of a system as it evolves over time, in the mean square sense. In
a Bayesian framework, the optimal filter is therefore naturally a sequence of
conditional probabilities of a signal given the observations and can be up-
dated recursively with new observations with Bayes’ formula. When, the
dynamics and observations errors are linear, this is equivalent to the Kalman
filter. In the nonlinear case, deriving an explicit form for the posterior dis-
tribution is in general not possible.
One of the important difficulties with applying the nonlinear filter in practice
is that the initial condition, the prior, is required to initialise the filtering.
However we are unlikely to know the correct initial distribution accurately
or at all. A filter is called stable if it is insensitive with respect to the
prior, that is, it converges to the same distribution, regardless of the initial
condition.
A body of work exists showing stability of the filter which rely on the stochas-
ticity of the underlying dynamics. In contrast, we show stability of the op-
timal filter for a class of nonlinear and deterministic dynamical systems and
our result relies on the intrinsic chaotic properties of the dynamics. We build
on the considerable knowledge that exists on the existence of SRB measures
in uniformly hyperbolic dynamical systems and we view the conditional prob-
abilities as SRB measures ‘conditional on the observation’ which are shown
to be absolutely continuous along the unstable manifold. This is in line with
the result of Bouquet, Carrassi et al [1] regarding data assimilation in the
“unstable subspace”, where they show stability of the filter if the unstable
and neutral subspaces are uniformly observed.

[1] M. Bocquet et al. “Degenerate Kalman Filter Error Covariances and
Their Convergence onto the Unstable Subspace”. In: SIAM/ASA Jour-
nal on Uncertainty Quantification 5.1 (2017), pp. 304–333. url: https:
//doi.org/10.1137/16M1068712.
From source to sink - Sustainable and reproducible data pipelines with SaQC
Authors: Schäfer David , Palm Bert , Schmidt Lennart , Lünenschloß Peter , Bumberger Jan
Published: March 2020

The number of sensors used in the environmental system sciences is increasing rapidly, and while this trend undoubtedly provides a great potential to broaden the understanding of complex spatio-temporal processes, it comes with its own set of new challenges. The flow of data from a source to its sink, from sensors to databases, involves many, usually error prone intermediate steps. From the data acquisition with its specific scientific and technical challenges, over the data transfer from often remote locations to the final data processing, all carry great potential to introduce errors and disturbances into the actual environmental signal.

Quantifying these errors becomes a crucial part of the later evaluation of all measured data. While many large environmental observatories are moving from manual to more automated ways of data processing and quality assurance, these systems are usually highly customized and hand written. This approach is non-ideal in several ways: First, it is a waste of resources as the same algorithms are implemented over and over again and second, it imposes great challenges to reproducibility. If the relevant programs are made available at all, they expose all problems of software reuse: correctness of the implementation, readability and comprehensibility for future users, as well as transferability between different computing environments. Beside these problems, related to software development in general, another crucial factor comes into play: the end product, a processed and quality controlled data set, is closely tied to the current version of the programs in use. Even small changes to the source code can lead to vastly differing results. If this is not approached responsibly, data and programs will inevitably fall out of sync.

The presented software, the 'System for automated Quality Control (SaQC)' (www.ufz.git.de/rdm-software/saqc), helps to either solve, or massively simplify the solution to the presented challenges. As a mainly no-code platform with a large set of implemented functionality, SaQC lowers the entry barrier for the non-programming scientific practitioner, without sacrificing the possibilities to fine-grained adaptation to project specific needs. The text based configuration allows the easy integration into version control systems and thus opens the opportunity to use well established software for data lineage. We will give a short overview of the program's unique features and showcase possibilities to build reliable and reproducible processing and quality assurance pipelines for real-world data from a spatially distributed, heterogeneous sensor network.
CTBT IMS - International Cooperation at its finest
Authors: Meral OZel Nurcan , Jepsen David
Published: March 2020

The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty is one of the most ambitious global projects ever undertaken by more than 183 states. It’s establishment exemplifies international cooperation through the huge undertaking, coordination and massive investment by all Member States. The IMS monitors the whole earth, atmosphere and undersea for any potential nuclear test but this extraordinary network can also detect and record traces of natural and anthrogenic disasters that are ever present.

The IMS network sets a precedent for reliability, quality and requirements on a global scale. The network is comprised of 4 technologies (seismic, infrasound, hydroacoustic and radionuclide) that monitor the earth’s environments to an incredibly low level and hence is an invaluable resource for monitoring and understanding natural hazards.To date, for example, the CTBTO has signed 14 agreements with tsunami warning organizations for the usage of CTBT data to assist with the timeliness and accuracy of tsunami warnings.

The effectiveness of plans to deal with natural disasters depends on a country’s level of resources and readiness. Member States can rely and call upon the CTBTO and the IMS network to assist them at a time of crisis. The IMS is truly a global network that has no borders.
Investigating the dynamics of an Antarctic coastal polynya using a regional climate model
Authors: Huot Pierre-Vincent , Fichefet Thierry , Kittel Christoph , Jourdain Nicolas , Fettweis Xavier
Published: March 2020

Coastal polynyas of the Southern Ocean, such as the Mertz Glacier Polynya, are paramount features of the polar climate. They allow for exchanges of heat, momentum and moisture between the atmosphere and ocean where sea ice usually prevents such interactions. Polynyas are believed to have a profound impact on polar and global climate, thanks to their sustained sea ice production and the associated formation of Dense Shelf Waters. Less is known, however, about the impact of polynyas on the atmosphere. Changes in air properties and winds induced by heat and moisture flux could for instance affect precipitation regime over the ice sheet or sea ice. As the formation and evolution of coastal polynyas are tied to the state of the atmosphere, such changes can also induce important feedbacks to polynyas dynamics. Such processes have almost never been studied, whether on the field or with the help of coupled models. Here, we propose to describe the behavior of a coastal polynya and its relationship with the ocean and atmosphere. To do so, we developed a regional coupled model of the ocean, sea ice and atmosphere (including interactive basal melt of ice shelves) and applied it to the Adélie Land area, in East Antarctica. The dynamics of the Mertz Glacier Polynya is described, together with its impact on the atmosphere, sea ice growth, dense water production and ice shelf melt. To assess the importance of potential feedbacks, we compare the dynamics of the polynya from the coupled model to a forced ocean-sea ice model. We then use the regional coupled model to investigate the implications of the Mertz ice tongue calving in early 2010 which led to a drastic decrease of the Mertz Glacier Polynya extent. This experiment aims at investigating the sensitivity of the atmosphere to the activity of the polynya and to evaluate the impact of the calving on regional climate. This work improves the understanding of the Mertz Glacier Polynya dynamics, and of the impact of coastal polynyas on polar climate. It also constitutes an additional step in the modelling of the polar regions in Earth System Models.
Modelling of nonlinear processes in deforming and reacting porous saturated rocks: different regimes for reaction front propagation
Authors: Khakimova Lyudmila , Alkhimenkov Yury , Cheremisin Alexey , Podladchikov Yury
Published: March 2020

Developing new numerical reactive transport models is essential for predicting and describing natural and technogenic petroleum and geological processes at different scales. Examples of such processes are pore fluid migration in subduction zones, causing seismic and volcanic activity, chemical and thermal enhanced oil recovery activities, etc. New numerical reactive transport models must be validated against analytical or semi-analytical solutions to ensure its correct numerical implementation. In this study, we construct thermo-hydro-chemo-mechanical model which takes into account multi-phase fluid flow in porous matrix associated with inter- and intra-phase chemical reactions with significant temperature and volume effect and treats porosity and permeability evolution. All equations are derived from basic principles of conservation of mass, energy, and momentum and the thermodynamic admissibility of all equations is verified. We solve the proposed system of equations both with a finite difference approach on a staggered grid and characteristic-based Lax-Friedrichs different order schemes to treat the disintegration of discontinuities.  Resolving the problem of large discrepancies during the time evolution of coupled physical processes is challenging. For that, we use pseudo-iterations which force slow modes to attenuate quickly. Furthermore, we perform dimensionless analysis of the proposed model which allows us to detect proper dimensionally independent, dimensionally dependent and non-dimensional parameters. A new semi-analytical is derived which is based on a relaxation method of defining the stationary solution of system of partial differential equations, so detection specific regimes for reaction front propagation are possible. As a result, reaction front velocity dependence on Peclet, Damkohler and Lewis nondimensional parameters is obtained.
Do hazard maps mirror loss data? – A vulnerability assessment based on loss data and hazard maps
Authors: Mertin Mirjam , Brughelli Mattia , Zischg Andreas , Röthlisberger Veronika , Schlögl Matthias , Keiler Margreth
Published: March 2020

Implementing effective flood risk strategies is an essential task for policy-makers which will gain in importance as flood losses are expected to increase due to socio-economic and climatic drivers in near future. Flood risk mitigation incorporates structural and non-structural measures such as the declaration of flood hazard zones, both of which are associated with high financial expenses. Essential information to ensure maximum effectiveness and cost efficiency of flood protection measures is provided by quantitative flood loss analyses based, for example, on data from insurance claims.

This project aims to model the expected flood damage, thus the vulnerability to buildings by examining country-wide, empirical flood loss data of Switzerland of the past 35 years. The developed method includes several steps: First, the loss data are statistically analysed, second the spatial distribution of the loss data in the different hazard zones is assessed and third, vulnerability models for each hazard zone are developed including further parameters such as building values or building zones. A further objective is to provide an overview of possible methods which differ in complexity and data requirement and can be adapted for other applications outside of Switzerland. First results show that the extent of loss increases as the degree of hazard rises. In contrast, however, the number of damage events is highest in flood zones with a lower degree of hazard. Further possibilities how risk adaptation strategies can be supported or complemented by flood loss data are presented within this project.
Evaluating the performance of a max-stable process for estimating intensity-duration-frequency curves
Authors: Jurado Oscar E. , Ulrich Jana , Rust Henning W.
Published: March 2020

A recent development in the modeling of intensity-duration-frequency (IDF) curves involves the use of a spatial max-stable process to explicitly account for asymptotic dependence between durations. To accomplish this, we use a duration-space instead of a geographic-space, following Tyralis and Langousis (2018). The resulting IDF curves can then be used to estimate extreme rainfall for any arbitrary rainfall duration. We aim to determine whether the use of a model that explicitly accounts for the dependence between durations could improve the estimates of extreme rainfall. The performance of the max-stable process is compared to the duration dependent GEV (d-GEV) approach for IDF-curve estimation proposed by Koutsoyiannis et al. (1998). The max-stable approach explicitly models the dependence via a parametric model, while the d-GEV approach assumes that the durations are independent. The performance of both approaches is assessed for two scenarios, in a controlled simulation experiment, and for observations from a rain gauge. A Brown-Resnick max-stable process and a duration-dependent GEV was fitted to the data in both scenarios. The performance is measured using the Quantile Skill Score (QSS) with the d-GEV as the reference model. The resulting skill scores show that correctly specifying the dependence structure leads to the max-stable model perfomring similarly to the d-GEV. This pattern was observed also for low and high levels of dependence.
Influence of external forcings on the hydroclimate conditions in the Europe-Mediterranean Region over the Common Era : a model/data approach
Authors: Khodri Myriam , Feng Yang , Li Laurent , Sicre Marie-Alexandrine , Lebas Nicolas
Published: March 2020

The climate system has been largely influenced by emerging anthropogenic forcing effects during the last decades of the historical period. Hence, the historical simulations may not be the most appropriate ones to constrain the internal climate variability at such long time scales. The last 2000 years provide a promising time frame constrained by climate reconstructions to explore the interactions between external forcings and the internal dynamics of climate. The Common Era is indeed relatively long and forcing are reasonably well reconstructed and physical processes modelled. In this contribution, we use IPSL-CM6A-LR model simulations covering the last 1500 years (500AD to Present Day) and available paleo-proxy reconstructions to study the influence of the internal variability and external forcing on climate variability in the North Atlantic at decadal-to-multi-decadal time scales and the impacts on the hydro-climate conditions evolution over Europe-Mediterranean sector.
Wave data prediction and reconstruction by recurrent neural networks at the nearshore area of Norderney
Authors: Jörges Christoph , Berkenbrink Cordula , Stumpe Britta
Published: March 2020

Sea level rise, a possible increase in frequency and intensity of storms and other effects of global warming exert pressure on the coastal regions of the North Sea. Also storm surges threaten the basis of existence for many people in the affected areas. As well as for building coastal protection or offshore structures, detailed knowledge of wave data, especially the wave height, is of particular interest. Therefore, the nearshore wave climate at the island Norderney is measured by buoys since the early 1990s. Caused by crossing ships or weather impacts, these buoys can be damaged. This leads to a huge amount of missing data in the wave data time series, which are the basis for numerical modelling, statistical analysis and developing coastal protection.
Artificial neural networks are a common method to reconstruct and forecast wave heights nowadays. This study shows a new technique to reconstruct and forecast significant wave height measured by buoys in the nearshore area of the Norderney coastline. Buoy data of the period 2004 to 2017 from the NLWKN – Coastal Research Station at Norderney were used to train three different statistical and machine learning models namely linear regression, feed-forward neural network and long short-term memory (LSTM), respectively. An energy density spectrum was tested against calculated sea state parameter as input. The LSTM – a recurrent neural network – is the proposed algorithm to reconstruct wave height data. It is especially designed for sequential data, but was performed on wave spectral data in this study for the first time. Depending on the input parameter of the respectively model, the LSTM can reconstruct and forecast time series of arbitrary length.
Using information about wind speed and direction and water depth, as well as the wave height of two neighboring buoy stations, the LSTM reconstructs the wave height with a correlation coefficient of 0.98 between measured and reconstructed data.
Unfortunately, the forecasting and reconstruction error of extreme events is highly underestimated, though these events are of great interest for climate and ocean science. Currently, this error is being specifically attempted to improve. Compared to numerical modeling, the machine learning approach requires less computational effort. Results of this study can be used to complete spatial and temporal wave height datasets, providing a better basis for trend analysis in relation to climate change and for validating numerical models for decision making in coastal protection and management.
Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project
Authors: Alessandri Andrea , Catalano Franco , De Felice Matteo , Nielsen Kristian , Troccoli Alberto , Formenton Marco , Piccioni Gaia
Published: March 2020

A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.

The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).

All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.

Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.
Use of FT ICR MS to characterize seasonal and spatial variability of dissolved organic matter in a small forested catchment
Authors: da Silva Maria Paula , Reemtsma Thorsten , Blaurock Katharina , Beudert Burkhard , Lechtenfeld Oliver
Published: March 2020

Inland water process large amounts of dissolved organic matter (DOM), representing an important component in the global carbon cycle. Locally, DOM has an important ecological and biogeochemical role that may vary according to its quality (e.g. composition). Land use, season and hydrologic regime are some factors that possibly will influence the changes in DOM composition. State-of-the art technique to study the molecular chemical composition of DOM is Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS). The analysis of changes in DOM quality by FT ICR MS allows conclusions to be drawn about the sources and mobilization processes of the organic material. In this study we investigate the changes in DOM quality over a period of 1.5 year in a small forested catchment composed of two different zones: wetland (zone A) and steep slope areas (zone B). The catchment is located in the National Park Bayerischer Wald in Southern Germany. This offers a natural environment unaffected by direct anthropogenic influence. Therefore, only indirect anthropogenic effects and natural vegetation disturbances and possible interactions between them can affect DOM dynamics. Monthly samples were taken along the rivers (1st and 2nd order) from September 2018-November 2018 and from April 2019-November 2019 with a total of 124 samples. The samples were analyzed by FT ICR MS, total organic carbon analyzer and UV/VIS spectrometer. Our results showed that the concentration of dissolved organic carbon between the sampling points is similar, but differs over the year at normal discharge conditions. FT ICR MS analysis indicated that the main molecular composition of DOM was CHO (38-47%), with the majority of the composition consisting of highly unsaturated compounds. Conversely, samples in zone A had more aliphatic compounds and nitrogen formulas than the ones sampled in zone B during the year. UV/VIS data also indicated that DOM is more aromatic in the zone B. The results suggest that DOM coming mainly from ground water is the dominant pool of organic matter in the wetland during the year, while in the steep zone a contribution from fresh-plant derived DOM is expected. We also found predominantly low averaged molecular weight DOM in summer in the catchment, suggesting that biological activity plays an important role during this season in DOM quality. We conclude that understanding the dynamic and mobilization mechanisms of DOM in catchments with low human impact are important for the conceptual understanding of natural DOM regulation mechanisms.
Development of a nonlinear ensemble data assimilation method with global state-space covariance localization
Authors: Zupanski Milija
Published: March 2020

High-dimensional ensemble data assimilation applications require error covariance localization in order to address the problem of insufficient degrees of freedom, typically accomplished using the observation-space covariance localization. However, this creates a challenge for vertically integrated observations, such as satellite radiances, aerosol optical depth, etc., since the exact observation location in vertical does not exist. For nonlinear problems, there is an implied inconsistency in iterative minimization due to using observation-space localization which effectively prevents finding the optimal global minimizing solution. Using state-space localization, however, in principal resolves both issues associated with observation space localization.

 

In this work we present a new nonlinear ensemble data assimilation method that employs covariance localization in state space and finds an optimal analysis solution. The new method resembles “modified ensembles” in the sense that ensemble size is increased in the analysis, but it differs in methodology used to create ensemble modifications, calculate the analysis error covariance, and define the initial ensemble perturbations for data assimilation cycling. From a practical point of view, the new method is considerably more efficient and potentially applicable to realistic high-dimensional data assimilation problems. A distinct characteristic of the new algorithm is that the localized error covariance and minimization are global, i.e. explicitly defined over all state points. The presentation will focus on examining feasible options for estimating the analysis error covariance and for defining the initial ensemble perturbations.
Drivers of Extreme Wind Events in Mexico for Wind Power Applications
Authors: Thomas Simon , Martinez-Alvarado Oscar , Drew Dan , Bloomfield Hannah
Published: March 2020

In this talk, we investigate the causes of the strongest and weakest winds observed across Mexico and explore the consequences of these to current and future wind energy production in the country. Using 40 years of the ERA-5 atmospheric reanalysis data, we find that the strongest winds in this region are caused by cold surges, where an anticyclone moves South from the Central United States of America resulting in strong Northerly winds across the Gulf of Mexico which channel through the gap in the mountains to the South of Mexico. Other regions have different drivers for high and low wind speed events. The strongest winds across the East coast of Mexico originate from Easterly trade winds propagating across the Gulf of Mexico, whereas those in Baja California Sur are influenced by the proximity of the North Pacific High. These regions in Mexico have peak (and sustained low) wind speeds at different times of year which suggests that wind farms in different regions could compliment one another to optimise wind power generation. However, all stations but Baja California Sur see the same weather patterns associated with weak wind events, meaning that low wind power production may be unavoidable at these times. The conditions that proceed these sustained periods of strong and weak winds are explored to gain some predictability for wind power applications. The El Nino Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal time-scales.
Precipitation partitioning by vegetation: A global synthesis
Authors: Van Stan John , Gutman Ethan , Friesen Jan
Published: March 2020

We aim to discuss topics and questions raised by the recent book published under this presentation's title. The book presents research on precipitation partitioning processes in vegetated ecosystems, putting them into a global context. It describes the processes by which meteoric water comes into contact with the vegetation's canopy, typically the first surface contact of precipitation on land. It also discusses how precipitation partitioning by vegetation impacts the amount, patterning, and chemistry of water reaching the surface, as well as the amount and timing of evaporative return to the atmosphere. Although this process has been extensively studied, this is the first review of the global literature on the partitioning of precipitation by forests, shrubs, crops, grasslands and other less-studies plant types.  
Faster Than Real Time tsunami simulations – challenges and solutions towards High Performance Exascale Computing
Authors: Macias Jorge , Castro Manuel J. , de la Asunción Marc , González-Vida José Manuel , Sánchez-Linares Carlos , Lovholt Finn , Lorito Stefano
Published: March 2020

Tsunami simulation in the framework of Tsunami Early Warning Systems (TEWS) is a quite recent achievement, but still limited regarding the size of the problem and restricted to tsunami wave propagation. Faster Than Real Time (FTRT) tsunami simulations require greatly improved and highly efficient computational methods to achieve extremely fast and effective calculations. HPC facilities have the role to bring this efficiency to a maximum possible and drastically reducing computational times. Putting these two ingredients together is the aim of Pilot Demonstrator 2 (PD2) in ChEESE project. This PD will comprise both earthquake and landslide sources. Earthquake tsunami generation is to an extent simpler than landslide tsunami generation, as landslide generated tsunamis depend on the landslide dynamics which necessitate coupling dynamic landslide simulation models to the tsunami propagation. In both cases, FTRT simulations in several contexts and configurations will be the final aim of this pilot.

Among the objectives of our work in ChEESE project are achieving unprecedented FTRT tsunami computations with existing models and investigate the scalability limits of such models; increasing the size of the problems by increasing spatial resolution and/or producing longer simulations while still computing FTRT, dealing with problems and resolutions never done before; developing a TEWS including inundation for a particular target coastal zone, or numerous scenarios allowing PTHA (PD7) and PTF (PD8), an aim unattainable at present or including more physics in shallow water models for taking into account dispersive effects.

Up to now, the two European tsunami flagship codes selected by ChESEE project (Tsunami-HySEA and Landslide-HySEA) have been audit and efficiency further improved. The improved code versions have been tested in three European 0-Tier HPC facilities: BSC (Spain), CINECA (Italy) and Piz Daint (Switzerland) using up to 32 NVIDIA Graphic Cards (P100 and V100) for scaling purposes. Computing times have been drastically reduced and a PTF study composed by around 10,000 scenarios (4 nested grids, 12 M cells, 8 hours simulations) have been computed in 6 days of wall-clock computations in the 64 GPUs available for us at the BSC.

 Acknowledgements. This research has been partially supported by the Spanish Government Research project MEGAFLOW (RTI2018-096064-B-C21), Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech and ChEESE project (EU Horizon 2020, grant agreement Nº 823844), https://cheese-coe.eu/
Geothermal Heat Flux in East Antarctica from HCA numerical modeling between 60-180°E Longitude
Authors: Salvini Francesco , Cianfarra Paola , Capponi Giovanni , Crispini Laura , Federico Laura , Rossi Costanza
Published: March 2020

Estimation of subglacial Geothermal Heat Flux (GHF) is of paramount importance to better understand the dynamics  of cryosphere and ice flow of the East Antarctica Ice Sheet (EAIS). Unfortunately, the GHF of East Antarctica is still poorly known and constrained, and direct measurements are still challenging. The EIAS is underlain by major subglacial mountain ranges and basins resulting from distinct geodynamic domains. These include Northern Victoria Land-Ross Sea, the Transantarctic Mountains, the Wilkes Subglacial Basin, the Gamburtsev Subglacial Mountains, the East Antarctic System and a major transpressional fault zone in between (e.g. Cianfarra & Maggi, 2017), which hosts clusters of subglacial lakes. The distribution of sedimentary basins and tectonic structures may affect the GHF in that it exhibits strong regional variations as testified by the presence of subglacial lakes at bedrock topographic elevation/depth with a range exceeding 1500 m, from deep subglacial basins to the flanking highlands. In the framework of the G-IDEA (Geo Ice Dynamics of East Antarctica) project, heat flow from the basement is quantified in key areas of East Antarctica between 60°E and 180°E, by an innovative application of the HCA (Hybrid Cellular Automata) method: the description of stationary conditions of the temperature field is used to replicate the observed distribution of wet vs dry ice-rock contacts in an ice-flowing environment. Evaluation of the geothermal flux is performed in key areas based on the numeric modeling of the ice-rock interaction, which can replicate the spatial distribution of wet contacts and subglacial lakes and is related to local dynamics of the ice sheet and its interaction with the atmosphere. The model takes into account the spatial distribution of the Curie temperature depth as derived from literature. The heat flux is estimated by modeling the stationary state of the ice-rock system with the HCA numerical method, and by its discretization into a large number of cells. Each cell is characterized by physical parameters such as density, enthalpy, thermal capacity and conductivity. By their interaction it is possible to compute their temperature evolution and to replicate the heat diffusion by conduction and convection (the ice movement) in the interfaces ice-rock and ice-atmosphere. The final resolution of the model is about 100 m. The presence of possible anomalous heath flow in the bedrock are identified by a stochastic approach that allow the estimation of the error in the computed heath flow values.
Rupture dynamics and fault mechanics of intraplate earthquakes in Brazil
Authors: Madden Elizabeth H. , Gabriel Alice-Agnes , Barros Lucas , Carvalho Juraci , França George
Published: March 2020

Mitigating intraplate earthquakes is necessary, as local populations are at risk both directly from strong shaking and indirectly from the threat to public infrastructure such as dams. However, the infrequency of these events and insufficient knowledge of how the ground will respond to passing seismic waves challenges mitigation. In Brazil, one M 5 earthquake occurs about every 5 years. M 4 earthquakes are more common and produce shaking intensities up to VI and VII on the Modified Mercalli scale. Brazilian earthquakes are shallower on average than events in other intraplate regions, which raises the possibility that fault mechanics and earthquake dynamics are different here. To work toward improving hazard mitigation and to better understand the physics of earthquakes in Brazil, we present 3D numerical models of the rupture process of two recent earthquakes using the open-source dynamic rupture and wave propagation software, SeisSol (www.seissol.org). Typically, sparse data prohibits the modeling of intraplate events. However, the 2010 Mara Rosa earthquake, the largest earthquake ever recorded in the Goiás-Tocantins Seismic Zone in central Brazil, and the 2017 Maranhão earthquake, which occurred in a previously aseismic region of northern Brazil, are relatively well studied and ample data is available. We report results within the range of uncertainty from the uncertainty in observations of stress drop, epicentral depth, fault geometry and regional stress state. The Mara Rosa earthquake occurred at an epicentral depth of ~2 km, while the Maranhão earthquake occurred between ~12-16 km depth. Modeling these two events allows us to contrast the influence of depth on the modeled earthquake source characteristics. We propose that fault cohesion dominates fault strength for the shallowest intraplate events, assuming a typical Mohr-Coulomb relationship.