bluuebunny/crossref_metadata_2025 huggingface dataset:
Combined vector network analyzer and impedance analyzer for broadband determination of complex permittivity spectrum of glass beads with talc
Authors: Szerement Justyna , Saito Hironobu , Furuhata Kahori , Yagihara Shin , Szypłowska Agnieszka , Kafarski Marcin , Lewandowski Arkadiusz , Wilczek Andrzej , Woszczyk Aleksandra , Skierucha Wojciech
Published: March 2020

Soil complex dielectric permittivity is frequency dependent. At low frequencies soil dielectric spectrum exhibits relaxation effects mainly due to interfacial phenomena caused by water strongly bounded to solid phase particles surfaces, double-layer effects and Maxwell-Wagner effect. At frequencies of several GHz and above, the influence of dielectric dispersion of free water dipoles can be observed.  Since dielectric soil moisture meters operate at frequencies from kHz up to several GHz, their output can be affected by these phenomena.

Currently, there is a variety of commercial sensors that operate at various frequencies from kHz up to several GHz. Most popular are TDR sensors with frequency band up to 1-2 GHz and capacitance/impedance sensors that operate at a single frequency usually from the range
1-150 MHz. Therefore, the knowledge of the broadband complex dielectric permittivity spectrum can help to improve the existing and develop new methods and devices for soil moisture and salinity estimation. Also, accurate characterization of complex dielectric permittivity spectrum of porous materials in the broadband frequency range is required for modeling of dielectric properties of materials in terms of moisture, salinity, density, mineralogy etc.

The aim of the study was to measure the complex dielectric permittivity of glass beads with 5% talc moistened with distilled water and saline water (electrical conductivity of 500, 1000, 1500 mS/m). The experiment was carried out using a seven-rod probe connected to an impedance analyzer (IA) and a vector network analyzer (VNA) using a multiplexer in the frequency range from 40Hz to 110MHz (IA) and 10MHz to 500MHz (VNA). The glass beads (90-106 µm, Fuji Manufacturing Industries, Japan) with 5% talc (Sigma Aldrich) in 4 different moisture and 4 different salinity values were examined. The results obtained from the IA and the VNA were combined and modeled with complex conductivity and dielectric permittivity model. The influence of water content and electrical conductivity on broadband complex dielectric spectra and the fitted model parameters was examined.

 

The work has been supported by the National Centre for Research and Development, Poland, BIOSTRATEG3/343547/8/NCBR/2017.
Exposure dating of the giant fossil landslide in the upper reaches of the Yellow River, China
Authors: Li Danhui , Bai Shibiao
Published: March 2020

Abstract: The upper reaches of the Yellow River is located in the transition zone between the Tibetan Plateau and the Loess Plateau, of which a large area is covered by extensive loess deposits. The Tibetan Plateau uplift has resulted in a high geomorphic activity. One landslide inventory of this region is compiled, which includes about 100 giant ancient landslides. Furthermore, their positions, boundaries, area, volume and so on are managed in geographic information system (GIS). The determinations of those giant ancient ages are an important step towards understanding the causes, frequency, hazards, the earth surface uplift and landscape-lowering rate. Development of OSL techniques has provided another alternative means of dating landslide and colluvial sediment. There are many challenges and some problems of luminescence dating of landslide and colluvial deposits because of the insufficiently bleached sediments condition. There are also some controversial issues existing in present studies of landslide dating by using Cosmic Ray Exposure (CRE) method. The study use the landslide pond sediments and the dammed lake deposits to dating the giant ancient landslide using OSL techniques, the surface of landslide scarp and boulders to dating the giant ancient landslide using CRE. The two dating results based on different datable landslide elements were be cross-validated using the typical giant ancient landslides in the upper reaches of the Yellow River, China.

Keywords: Giant fossil landslide; cosmogenic nuclides chronology; luminescence dating, the upper reaches of the Yellow River

 
Design and experimental production of organic fertilizers from biogas digestates and secondary materials for horticultural purposes
Authors: Repmann Frank , Dietrich Nils , Hirsch Florian , Raab Thomas
Published: March 2020

An ever growing quantity of digestates produced from agricultural biogas facilities puts alternative use options, other than spreading those residues on agricultural land, into focus, particularly to protect the ground water from nitrogen leaching in intensively used regions. Within the framework of the FNR funded project Skarabäus, Brandenburg University of Technology (BTU) together with partners from the Institute of Agricultural and Urban Ecological Projects affiliated to Humboldt University Berlin (IASP) and the Humboldt University Berlin (HU) investigate whether biogas digestates could be converted to fertilizer products of defined composition to be used outside agricultural production particularly for gardening and landscaping. The project rationale is to agglomerate the separated digestates to produce a fertilizer which is flexible in the design of properties, easy to handle for application and  effective to plant’s growth. Basically the tumble agglomeration was considered as the main process. The properties of the fertilizer product, particularly the nutrient content with respect to nitrogen, phosphorus and potassium, was thought to be adjusted by adding nutrient rich secondary materials like meat and bone meal, replaced powders from fire extinguishers and recycled material originating from waste water treatment during the agglomeration process.

Experiments revealed that separated digestates could hardly be agglomerated due to the high amount of relatively large and inflexible fiber contained. The addition of binding agents like clay minerals strongly improved the agglomeration process. However, so far best results were achieved when separated digestates were composted prior to the agglomeration process. In this way no binding agents were necessary. Agglomerates produced from composted digestates showed a reasonable particle size distribution and nutrient- and organic matter content generally suitable for application in horticulture, given that future greenhouse and field experiments could also demonstrate the beneficial application.
Basin subsidence and Miocene/Pliocene sedimentary change in the Browse Basin, NW Australia
Authors: Thronberens Sebastian , Back Stefan , Reuning Lars , Bourget Julien
Published: March 2020

The upper Miocene to Pliocene interval of the Browse Basin on the Australian North West Shelf (NWS) records a significant paleo-environmental change in its sedimentary record concerning the decay of middle to late Miocene tropical reefs. Seismic observations towards the Pliocene show a clear landward migration of carbonate build-ups in the eastern part of the basin, and very high subsidence rates seem to have outpaced most reef growth in distal shelf-edge positions. Nevertheless, the Scott Reef and the Seringapatam Reef were able to withstand shelf-edge drowning, which indicates a significant contribution of inversion-related uplift for reef survival. The contribution of basin subsidence as a driving factor for this reef decay and survival is still discussed and has not been studied in detail. This study provides an estimate for the laterally and through time changing late Miocene/Pliocene subsidence pattern. A 3D paleo-environmental reconstruction was generated by 3D quantitative backstripping, integrating 3D paleo-waterdepth information derived from seismic-based depositional system interpretation. The base of this analysis is a giant 2D and 3D seismic-reflection data set (>130.000 km²) integrated with borehole data (logs, cores, cuttings), new Sr-isotope dating, X-ray diffractometry (XRD) and microfacies analyses, supporting paleo-bathymetric correction and ties to global sea-level data. The seismic-reflection data is covering a study area extending over 130.000 km² and is supported by industry borehole data (logs, cores, and cuttings), SR-Isotope dating, X-Ray diffractometry (XRD) and microfacies analysis.
Do local urban emissions influence ambient meteorology?
Authors: Karlický Jan
Published: March 2020

The WRF-Chem model was used to analyze three different 14-day periods during 2016–2017 in Prague, Czech Republic. Specifically a summertime high-level ozone episode, a summertime convective episode and a wintertime episode with high concentrations of aerosol pollutants have been analyzed in great detail. Simulations were run on a 2 km grid domain covering the center of the Czech Republic with the capital Prague, which was nested into a 10 km domain covering Central Europe. For the analysis of the meteorological impact of the reduction of urban induced emissions, two model simulations were performed for each episode; one simulation with full anthropogenic emissions and a second idealized simulation where emissions over the Prague urban area were reduced to the background level. In this presentation we discuss the differences and similarities between these simulations for chemical species (gas and particle pollutants) but also meteorological variables (e.g., downward solar radiation, temperature, boundary layer height).
Precipitation Nowcasting using Deep Neural Network
Authors: Bakey Mohamed Chafik , Serrurier Mathieu
Published: March 2020

Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts
DUSTER: a multi-sensor instrument to study dust transport and electrostatic removal for exploration missions
Authors: Ranvier Sylvain , Hess Sebastien , Mateo Velez Jean-Charles , Alvaro Sanchez Angel , De Keyser Johan
Published: March 2020

One major environmental constraint during exploration missions is the presence of charged dust-like particles, which are present on the Moon, Mars, comets and asteroids. From an analysis of the effects of lunar dust on Extra-Vehicular Activity (EVA) systems during the six Apollo missions that landed on the lunar surface, it was found that these effects can take many forms such as external vision obscuration, false instrument readings, dust coating and contamination, loss of traction, clogging of mechanisms, abrasion, thermal control problems and seal failures. One of the most serious effects is the compromising of astronaut health by irritation and inhalation of lunar dust.

Therefore, it is of utmost importance to characterise the properties of the dust particles present on the exploration sites and their transportation mechanisms to enable efficient mitigation techniques to be put in place.

The overall objective of the DUSTER project is to develop instrumentation and technologies to study dust particles and electrostatic transportation for planetary and small body exploration missions. Specifically, the aim is to design, manufacture and test in a relevant environment a compact multi-sensor instrument for in situ analysis of dust properties (mechanical and electrical) and electrostatic transportation that can be used on a small lunar lander. To that end, the instrument includes:

- A dust collector: electrodes biased at high potential to attract/collect dust particles, coupled to an electrometer

- Langmuir probes

- E-field probes

Using this instrument, the following parameters will be derived:

- Charging level of dust as a function of the environmental parameters (illumination, plasma density and temperature)

- (gravity + cohesive forces)/charge ratio distribution of dust layer

These two parameters will allow the determination of the electric field needed to attract/collect dust according to the environmental conditions (illumination, plasma density and temperature), which, among other applications, will allow designing electrostatic dust mitigation devices and dust sample collectors.
High-resolution size distributions of organic aerosol sources at two sites in Delhi National capital region (NCR) in Indo-Gangetic Plain
Authors: M. Thamban Navaneeth , Lalchandani Vipul , Kumar Varun , Mishra Suneeti , Bhattu Deepika , Gates Jay Slowik , Prevot Andre , Tripathi Sachchida Nand
Published: March 2020

Delhi National Capital Region (NCR) is a well-known aerosol hotspot in the world, located in the western Indo-Gangetic Plain (IGP). Understanding the size and evolution of organic aerosol (OA) sources at NCR in winter period is very important due to their complexity in origin and processing.  High-Resolution Particle Time of Flight (HR-PToF) size distribution analysis is performed on the HR-ToF aerosol mass spectrometer (AMS) derived data at two sites, i.e., Indian Institute of Technology, Delhi (IITD) and Manav Rachna University, Faridabad (MRU) in NCR region to understand the size distribution and evolution of OA. Proxies of UMR sources of Hydrocarbon OA (m/z 57), Biomass burning OA (m/z 60), Semi volatile OA (m/z 43) and Low-volatile oxygenated OA (m/z 44) are selected to understand the size distribution of the isobaric high-resolution fragments at these proxies. The HR size distribution of primary and secondary fragments at these organic proxies shows relatively distinct mass distributions at lower and higher size bins. The diurnal variation of the mean modal diameters (MMD) of the HR fragments indicates that the C4H9+ (Hydrocarbon OA proxy) shows much diurnal variation in both the sites than the other proxies, where CO2+ shows the least variation.
A new approach for arriving at higher frequencies through stochastic modelling: application to site attenuation (kappa)
Authors: Pikoulis Erion-Vasilis , Ktenidou Olga-Joan , Psarakis Emmanouil , Abrahamson Norman
Published: March 2020

We propose a framework for stochastically modelling the Fourier spectrum of the noisy seismic recording, considering that a seismic signal is a random rather than a deterministic quantity. We show that under this assumption, the noisy recording’s periodogram can be modelled as independent Exponential random variables with a frequency-dependent mean. With this model, estimating seismological parameters can be tackled through Maximum Likelihood (ML), allowing a fast, accurate and robust solution. This new approach constitutes a general estimation framework applicable to any parameter estimation that uses spectral analysis. Here we apply it to the high-frequency decay parameter kappa, which is particularly important for estimating and adjusting ground motion on rock. The improved ML performance is shown through a series of experiments on synthetic and recorded seismograms. The biggest advantage of the new method is its ability to account for the noise in the recording instead of just trying to avoid it, as is typically done when any ‘acceptable’ frequency range is isolated through signal-to-noise (SNR) criteria. As a result, our proposed technique can achieve acceptable results even for what would be typically considered very low and often unusable SNR, pushing the boundary of what is considered usable quality in seismic recordings.
Comparison of the performance of different Territorial Landslide Early Warning Systems
Authors: Cepeda Jose , Luca Piciullo , Davide Tirante , Gaetano Pecoraro , Michele Calvello
Published: March 2020

Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim for such systems is to forecast the increased probability of landslides occurrence in a given warning zone. Nowadays, there are around 30 Te-LEWS operational worldwide. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. Often, a contingency matrix 2x2, usually employed for rainfall thresholds validation purposes, is used. Recently an original method has been proposed by Calvello and Piciullo, 2016: the EDuMaP.

This paper describes the new excel user-friendly tool for the application of the method. Moreover, a description of different indicators used for the performance evaluation of different Te-LEWS is provided. Subsequently, the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the SMART warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need thresholds refinement. The SMART performance has been evaluated with both the EDuMaP and a 2x2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed analysis, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. Moreover, a comparison of the performance of different Te-LEWS with the SMART model has been carried out highlighting critical issues and positive aspects. Finally, the weakness aspects and the future developments of the SMART warning model are described.

This paper has been conceived in the context of the research-based innovation project Klima 2050 - "Risk reduction through climate adaptation of buildings and infrastructure" http://www.klima2050.no/.
Use of Unmanned Aerial Systems for Hydrological Monitoring
Authors: Manfreda Salvatore ,   HARMONIOUS TEAM
Published: March 2020

Unmanned Aerial Systems (UAS) are offering an extraordinary opportunity to improve our ability to monitor river basins. The wide use of UAS leaded to a significant grow of the number of applications and methodologies developed for specific scopes of environmental monitoring. For this reason, there is a serious challenge to harmonise and provide standardised guidance applicable across a broad range of environments and conditions. In this context, a network of scientists is cooperating within the framework of a COST (European Cooperation in Science and Technology) Action named “Harmonious - Given the wide use of UAS within environmental studies”. The intention of “Harmonious” is to promote monitoring strategies, establish harmonised monitoring practices, and transfer most recent advances on UAS methodologies to others within a global network. The working groups of Harmonious are currently working on the definition of practical guidance for environmental studies identifying critical processes and the interconnection of each step for a successful workflow. Given the number of environmental constraints and variables, it is impractical to provide a protocol that can be applied universally under all possible conditions, but it is possible to systematise the fragmented knowledge on this topic identifying the best-practices to improve the overall quality of the final products. Preliminary results of the HARMONIOUS COST Action will be given.
Effects of momentum and enthalpy exchange on the typhoon intensity in the atmospheric boundary layer considering sea spray and rainfall
Authors: Okachi Hiroki , Yamada Tomohito
Published: March 2020

   Typhoon intensity changes according to the momentum and enthalpy flux supplied from the boundary layer. MPI theory uses the ratio between a drag coefficient and an enthalpy exchange coefficient, which are indexes that indicate how much momentum or enthalpy is exchanged between the air and the sea. Each is a coefficient depending on wind speed, temperature and SST.

However, Lighthill (1999) is shown that latent heat exchange varies because sea spray generated from the sea surface evaporates in the boundary layer. In addition, Barenblatt (2005), inspired by Lighthill (1999), showed that the Karman constant changes according to the Froude number and the drag coefficient changes. Since both changes can change the MPI theory, it is necessary to quantitatively evaluate the effect of the droplets generated from the sea surface in order to grasp both accurately. In addition, it is necessary to consider the effects of rainfall in actual storms, which often involve rainfall.

In this study, to evaluate the flux exchange in the boundary layer quantitatively, we show the drag coefficient and the enthalpy exchange coefficient taking into account sea spray and rain. In addition, we show the results of observation of sea spray and rain using disdrometer and X-band radar.
Urban hydrology and flood mapping using UAV imagery
Authors: Nolet Corjan , ten Veldhuis Marie-Claire , van Til Jan , de Klerk Martijn
Published: March 2020

Unmanned Aerial Vehicles (UAVs) are a very effective means to map river beds and flood extent accurately across a wide area, even while the flood is happening or shortly thereafter. Flood mapping information is also very valuable in a long-term context, for drainage infrastructure planning and management. Here, we will present three applications: UAV-based information for hydrologic modelling of the urban drainage system, for flood extent mapping and for identification of bottlenecks in the system that can cause urban flooding.

UAV flights were conducted in Kumasi, the second biggest and fastest growing city in Ghana, where urban flooding has become more frequent due to changes in the climate and have a more negative impact due to rapid urbanization and population growth. Not only are the natural flood plains increasingly being used for anthropogenic purposes, the increased population growth also brings along more solid (plastic) waste on the streets and into the riverbeds and riverways. This creates blockages in drains and riverways, which reduces its drainage capacity and adds to the flooding problems. UAVs were used to collect elevation information (DEM), river bed dimensions and land-use. This information was used to construct a hydrological model to predict river flows and flooding. In addition, using thermal imagery from UAV flights over partially flooded agricultural fields near the town of Kianjai, Kenya, we will demonstrate that UAV imagery can identify flooded areas even when cross-cut by vegetation or other obstacles.

We will present the three applications and discuss the promises and challenges of deploying UAVs for the purpose of urban hydrological modelling and flood mapping.
Retrieving soil-water retention curve at the wet part by remote sensing
Authors: Pittaki-Chrysodonta Zampela , Moldrup Per , V. Iversen Bo , Knadel Maria , W. de Jonge Lis
Published: March 2020

The soil water retention curve (SWRC) at the wet part is important for understanding and modeling the water flow and solute transport in the vadose zone. However, direct measurements of SWRC is often laborious and time consuming processes. The Campbell function is a simple method to fit the measured data. The parameters of the Campbell function have been recently proven that can be predicted using visible-near-infrared spectroscopy. However, predicting the SWRC using image spectral data could be an inexpensive and fast method. In this study, 100-cm3 soil samples from Denmark were included and the soil water content was measured at a soil-water matric potential from pF 1 [log(10)= pF 1] up to pF 3. The anchored Campbell soil-water retention function was selected instead of the original. Specifically, in this function the equation is anchored at the soil-water content at pF 3 (θpF3) instead at the saturated water content. The image spectral data were correlated with the Campbell parameters [θpF3, and the pore size distribution index (Campbell b). The results showed the potential of remote sensing to be used as a fast and alternative method for predicting the SWRC in a large-scale.
Drying Development – The Causal Effect Droughts on (Long-Term) Economic Growth
Authors: Marbler Alexander
Published: March 2020

Do droughts have a causal effect on economic development? Based on meteorological observations, I construct a global data set of drought events occurring in 183 countries over the period 1960-2018. Identifying the drought exposure for each of earth’s 0.5° x 0.5° grid cell, I maintain the local information of drought shocks which economic agents experience. To identify the causal impact of droughts on long-run economic growth, I model economic growth as an impulse-response function in contemporaneous and historical area-averaged drought exposure. Exploiting the random natural variation in weather realizations as the source for exogenous within-country variation in drought exposure, I find that higher drought exposure causes slower economic development in poor, but not in rich, countries. National incomes in poor countries decline and keep declining 16 years following a drought. Specifically, a one standard deviation increase in drought exposure lowers GDP p.c. by 1.1 percentage points 16 years later. To understood why populations successfully adapt to climatic hazards in some dimensions and fail in others and which factors contribute to (successful) adaptation, I examine through which channels droughts affect economic development and estimate the extent of adaptation to drought.
Greenland ice sheet surface mass balance response to high CO2 forcing: threshold and mechanisms for accelerated surface mass loss
Authors: Sellevold Raymond , Vizcaino Miren
Published: March 2020

We use the Community Earth System model 2.1 to investigate the response of the Greenland Ice sheet (GrIS) surface mass balance (SMB) to an idealized high CO2 forcing scenario (1% per year increase to four-times-preindustrial). The SMB calculation is coupled with the atmospheric model, using a physically-based surface energy balance scheme for melt, explicit calculation of snow albedo, and a realistic treatment of polar snow and firn compaction. The SMB becomes negative for a global mean temperature increase of 2.7 K compared to pre-industrial temperature, and the surface mass loss accelerates. Longwave radiation is the primary contributor to melt energy before acceleration. A decrease of the albedo due to ablation area expansion together with turbulent heat flux increase due to the surface of the ice sheet nearing melting point, are the main contributors at/after acceleration. Further, trends towards more positive North Atlantic Oscillation and more negative Greenland Blocking Index partially reduces future melt increase.
Building a modelling framework to simulate ecosystem processes under changing climate: the long road from Biome-BGC to Biome-BGCMAg
Authors: Hidy Dóra , Fodor Nándor , Hollós Roland , Barcza Zoltán
Published: March 2020

During the past 15 years, our research group was developing the Biome-BGCMAg (formerly known as Biome-BGCMuSo) biogeochemical model to improve its ability to simulate carbon and water cycle in different ecosystems, with options for managed croplands, grasslands, and forests. We made various model improvements based on the results of model validation and benchmarking. Our goal is to have a model that is suitable for estimating and predicting greenhouse gas fluxes of different ecosystems at various scales under changing management and climate conditions.

The current, most recent model is called Biome-BGCMAg which is a process-based, biogeochemical model that simulates the storage and flux of water, carbon, and nitrogen in the soil-plant-atmosphere system. Biome-BGCMAg was derived from the widely known Biome-BGC v4.1.1 model developed by the Numerical Terradynamic Simulation Group (NTSG), University of Montana, USA. One of the most important model developments is the implementation of a multilayer soil module with water, carbon, nitrogen, and soil organic matter profiles. We implemented drought and anoxic soil state-related plant mortality. Alternative calculation methods for various processes were implemented to support possible algorithm ensemble modelling approach. Optional dynamic allocation algorithm was introduced using predefined phenophases based on growing degree day method. We implemented optional temperature dependence of allocation and possible assimilation downregulation as a function of temperature. Nitrogen budget simulation was improved. Furthermore, human intervention modules were developed to simulate cropland management (e.g. planting, harvest, ploughing, and application of fertilizers) and forest thinning. Dynamic whole plant mortality was implemented in the model to enable more realistic simulation of forest stand development. Last (but not least) conditional management (irrigation and mowing) was introduced to analyze the effect of different management strategies in the future. We started to build a sophisticated R based software to increase the visibility of the model and enable its use by the wider scientific community.

In our first attempt to simulate national scale greenhouse gas budget with Biome-BGCMAg 2.0, we executed the model at 10 x 10 km spatial resolution for Hungary, using eco-physiological parameterization and prescribed management for maize, winter wheat, forests and grassland. The first results revealed that the spatial pattern of net primary production and crop yield is not represented well by the model. Based on the first experiences we introduced new features within Biome-BGCMAg 2.1 that address soil water deficit related photosynthesis down-regulation. Missing stomatal conductance effect on C4 photosynthesis was also addressed by the new developments. 
AMOC step-wise inception during the present interglacial recorded by Nd-isotopes.
Authors: Menabreaz Lucie , Hillaire-Marcel Claude , Jenny Maccali , Poirier André , Ghaleb Bassam , Edinger Evan
Published: March 2020

The Atlantic Meridional Overturning Circulation (AMOC) and the production rate of the North Atlantic Deep Water (NADW) are major components of the North Atlantic climate-system, with important hemispheric climatic influences. The post-glacial history of the AMOC, as reconstructed from Nd-isotopes (εNd) in biogenic minerals and sediments, demonstrates its sensitivity to freshwater fluxes, leading to concerns about its near-future response to the ongoing accelerated Greenland/Arctic ice melting. Whereas the early Holocene inception of the deep NADW components originating from the Nordic Seas has been well documented from such εNd-data, information on the status of its western, shallower and most sensitive component, the Labrador Sea Water (LSW), is still missing. New εNd-measurements in corals from the Labrador Slope provide the means to fill this gap. These data demonstrate that convection in the Labrador Sea was fully implemented by ca. 4 ka BP only, i.e., well after the final demise of the Laurentide ice-sheet. The time- and space-transgressive pattern of the full AMOC inception implies more complex driving mechanisms than meltwater fluxes only. Whereas the late Holocene neo-glacial cooling trend could have played here a minor role, the penetration and strengthening of the Irminger Current into the Labrador Sea has likely been the driving force.
New petro-paleomagnetic data of Kandalaksha and Onega Bay Islands in the White Sea
Authors: Kosevich Natalia , Lebedev Ivan , Bagdasaryan Tanya
Published: March 2020

We have studied the AMS of metamorphic rocks (gneiss, granitoids, dykes) and soft sediments (mainly marine sediments or reworked diamicton) from the Kandalaksha and Onega Bay’s Islands of the White sea. The objects of research are located within the White sea mobile belt, represented by large tectonic nappes.

The magnetic susceptibility in soft sediment samples ranges from 78.6 E-6 to 1525E-6 (Km), and the degree (P) from 1.8% to 4.1%. Ellipsoids have a predominantly flattened type, such a distribution of AMS is typical for sedimentary rocks. At the same time, in a number of samples (from the Islands of Joker, Ipanchinikha and Olenevsky), the maximum axis is directed in a North-Westerly direction, which may indicate the flow direction. This is especially evident in flattened-triaxial ellipsoids (T=0.2-0.3). Values that have a T greater than 0.5 have a predominantly northerly direction and the orientation of minerals of the magnetic fraction and the direction of paleoflow is less pronounced.

The study of the anisotropy of the magnetic susceptibility of the Archean complexes composing the Islands of the Kandalaksha Bay of the White sea showed a high magnetic susceptibility-5E-6-1E-3 (Km), which confirms the change in the petrographic composition of gneiss. The degree of anisotropy (P) is 9% on average. It was found that the distribution of the main axes of the magnetic susceptibility ellipsoid coincides with shale and banding in the root outlets, while the maximum axis of the ellipsoid coincides with the West-North-West stretch of the regional fault. In the Onega Bay we sampled paleoproerozoic dykes, and there are AMS is coincided as contacts of studied dykes.

We done alternating field and thermal demagnetization of pilot collection which contains samples from all studied complexes. And it gives us not good results because of bad paleomagnetic record. Most of samples contain only low coercive or low temperature components and it mainly has modern direction.
Finding the drivers for a non-steady state and large-scale stresses acting on the Saturnian magnetosphere
Authors: Staniland Ned , Dougherty Michele , Masters Adam
Published: March 2020

In the inner region of Saturn’s rotationally-dominated magnetosphere, the governing magnetic field contributors are the internal magnetic field and the magnetodisc current sheet. The equatorially confined plasma sourced predominantly by the moon Enceladus stretches Saturn’s magnetic field lines into the characteristic ‘magnetodisc’ geometry. The extent of this effect varies due to both external and internal dynamical processes that perturb the system.

In this study, we use the complete dataset collected by the Cassini spacecraft to determine whether the magnetosphere is compressed, stretched or near some prescribed ground state. We find that there is an underlying dawn-dusk asymmetry in the ground state of Saturn’s magnetosphere, where the field is more compressed at dusk compared to dawn. Whilst Saturn spent a significant period of the Cassini mission near its ground state, we find evidence for large-scale stresses acting on the system, including large compression events that coincide with the declining phase of the solar cycle. These results are then compared to propagated solar wind data. In addition, approximately two thirds of our dataset is well described by the internal field and current sheet models, signifying the system was in steady-state during these passes. We further discuss the drivers for the non-steady state periods at Saturn and what this implies for the global dynamics of Saturn's magnetosphere.
Atmospheric Mission Data Packaging (AMiDA)
Authors: Natali Stefano , Rendl Clemens , Santillan Daniel , Hirtl Marcus , Scherllin-Pirscher Barbara , Hangler Andreas , Cede Alexander , Kreuter Axel , Retscher Christian
Published: March 2020

The scientific and industrial communities are handling continuously increasing amounts of data from Earth Observation (EO) satellite missions and related instruments. This is in particular the case for the atmospheric sciences communities, with the recently launched Copernicus Sentinel-5 Precursor, the upcoming Sentinel-4, -5, and ESA’s Earth Explorers scientific satellites ADM-Aeolus and EarthCARE, but also heritage missions such as ENVISAT, MetOp and OMI Aura. However, the challenge is not only to manage the large volume of data generated by each mission / sensor, but also to manage the data variety. Tools are needed to be able to rapidly and trustfully identify, from all available datasets of a specific region for a specific timeframe, all available products for a selected field (e.g. ozone, trace gases) and prepare these data into a format that is ready to be extracted and used /analyzed (Analysis-Ready Data, ARD). Exploiting potential synergies to maximise the use of data from various sources will be key to harness the full potential of the available information. In summary, there is a need of an “intelligent” packaging of subsets of the available data tailored to the users’ needs.

The scope of the “Atmospheric Mission Data Packaging” (AMiDA) project is to design, implement, and demonstrate the functionalities of an infrastructure for access and distribution of a wide variety of EO data in the field of atmospheric sciences: heritage, current, and future missions will be managed by the platform, to allow the users accessing, visualizing, and downloading a meaningful subset of this growing data stream.

AMiDA (https://amida.adamplatform.eu/en/) makes use of the baseline functionalities provided by the TOP platform (http://top-platform.eu/) that already allows accessing and manipulating a large variety of satellite, model, and non-satellite remotely sensed data. TOP is empowered with spatial and temporal homogenization and packaging capabilities to create, from heterogeneous data sources (e.g., SO2 total column data from different satellites and numerical models) a single data structure (local data cube) for simultaneous exploitation of various data sources. The data cube can be exploited through the TOP tools (web application, Jupyter notebook and APIs) and downloaded by the user.

A comprehensive demonstration campaign will be performed through five main use cases to demonstrate the capability of AMiDA.

AMiDA is currently in its final development phase, thus the scope of the contribution is to present the initiative, preliminary results, and stimulate the discussion with potential users, analyzing their needs and see if and how they can use AMiDA to facilitate their everyday professional life.
Daily streamflow prediction using an LSTM neural network in Alpine catchments
Authors: Anand Mohit , Molnar Peter , Peleg Nadav
Published: March 2020

Prediction of rainfall-runoff response in Alpine catchments is complex because hydrological processes vary strongly in space and time, they are elevation and temperature dependent, subsurface water stores are heterogeneous, snow plays an important role, and runoff response is fast. As a result, the transformation of rainfall into runoff is highly nonlinear. Machine Learning (ML) methods are suitable for reproducing such nonlinearities between input and output data and have been used for streamflow prediction. Recurrent Neural Networks (RNNs) with memory states, such as Long and Short-Term Memory (LSTM) models, are particularly suitable for hydrological variables that are dependent in time. An example of a recent application of LSTM to the rainfall-runoff transformation in many catchments in the USA showed that the LSTM model can learn physically meaningful catchment embeddings from precipitation-temperature-streamflow data, and performs comparably to widely used conceptual hydrological models (Kratzert et al., 2019).

In this study, we tested the LSTM approach on high-quality daily data from 23 Alpine catchments in Switzerland with three goals in mind. First, the LSTM model was trained and validated using daily climate variables (precipitation, air temperature, sunshine duration) and streamflow data on all catchments individually and the performance was compared to a distributed hydrological model (PREVAH). The performance of the LSTM model was in many (but not in all) cases better than the hydrological model. Second, a single LSTM model was trained in all catchments simultaneously, embedding terrain attributes extracted from the Digital Elevation Model (DEM). In this way differences between catchments related to the elevation and temperature dependent hydrological processes, such as snow accumulation and melt, evapotranspiration, runoff generation, etc., can be captured. We show the performance of this model and evaluate the regionalization potential provided by it. Third, the LSTM model was applied in an ensemble forecasting context, and we discuss the benefits and limitations this application brings compared to forecasting with a process-based hydrological model.
Statistical properties of peak ground acceleration and their effect on results of probabilistic seismic hazard analysis
Authors: Pavlenko Vasily
Published: March 2020

The problem is considered of unrealistic ground motion estimates, which arise when the Cornell–McGuire method is used to estimate the seismic hazard for extremely low annual probabilities of exceedance. This problem stems from using the normal distribution in the modelling of the variability of the logarithm of ground motion parameters. In this study, the statistical properties of the logarithm of peak ground acceleration (PGA) are analysed by using the database of the strong-motion seismograph networks of Japan. The normal distribution and the generalised extreme value distribution (GEVD) models were considered in the analysis, with the preferred model being selected based on statistical criteria. The results indicate that the GEVD was a more appropriate model in eleven out of twelve instances. The estimates of the shape parameter of the GEVD were negative in every instance, indicating the presence of a finite upper bound of PGA. Therefore, the GEVD provides a model that is more realistic for the scatter of the logarithm of PGA, and the application of this model leads to a bounded seismic hazard curve.
Machine Learning of committor functions for predicting high impact climate events
Authors: Lucente Dario , Bouchet Freddy , Herbert Corentin
Published: March 2020

There is a growing interest in the climate community to improve the prediction of high impact climate events, for instance ENSO (El-Ni\~no--Southern Oscillation) or extreme events, using a combination of model and observation data. In this talk we present a machine learning approach for predicting the committor function, the relevant concept. 

Because the dynamics of the climate system is chaotic, one usually distinguishes between time scales much shorter than a Lyapunov time for which a deterministic weather forecast is relevant, and time scales much longer than a mixing times beyond which any deterministic forecast is irrelevant and only climate averaged or probabilistic quantities can be predicted. However, for most applications, the largest interest is for intermediate time scales for which some information, more precise than the climate averages, might be predicted, but for which a deterministic forecast is not relevant. We call this range of time scales \it{the predictability margin}. We stress in this talk that the prediction problem at the predictability margin is of a probabilistic nature. Indeed, such time scales might typically be of the order of the Lyapunov time scale or larger, where errors on the initial condition and model errors limit our ability to compute deterministically the evolution. In this talk we explain that, in a dynamical context, the relevant quantity for predicting a future event at the predictability margin is a committor function. A committor function is the probability that an event will occur or not in the future, as a function of the current state of the system. 

We compute and discuss the committor function from data, either through a direct approach or through a machine learning approach using neural networks. We discuss two examples: a) the computation of the Jin and Timmerman model, a low dimensional model proposed to explain the decadal amplitude changes of El-Ni\~no, b) the computation of committor function for extreme heat waves. We compare several machine learning approaches, using neural network or using kernel-based analogue methods.

From the point of view of the climate extremes, our main conclusion is that one should generically distinguish between states with either intrinsic predictability or intrinsic unpredictability. This predictability concept is markedly different from the deterministic unpredictability arising because of chaotic dynamics and exponential sensivity to initial conditions. 
Effects of geological heterogeneity on fluid distribution and pressure propagation in a shallow, stacked aquifer system at the ECCSEL Svelvik CO2 Field Lab, Norway
Authors: Sundal Anja , Grimstad Alv Arne , Weber Ulrich W. , Kurschner Wolfram , Hagby Klaus , RIngstad Cathrine
Published: March 2020

The ECCSEL Svelvik CO2 Field Lab is a test site for shallow CO2 injection operated by SINTEF, where the aim is to improve monitoring techniques and extend the knowledge base for storing CO2 underground in deep saline aquifers as a climate mitigation strategy. The test site is located in a Holocene ice contact deposit near Drammen in the Oslofjord. Test injection is possible at 65 m depth. There has been extensive research focused on increasing the understanding of monitoring methods for deep injection of CO2 and the (short term) migration of CO2, based on experiments performed in this shallow aquifer. To maximize the value of data collected in the shallow experiments a solid geological model is fundamental to enable prediction of how water and gas will behave in the reservoir. Various thicknesses of reservoir layers and degree of internal heterogeneity (clinoforms, unconformities, faults) are observed. Analysis of new data from wells (cuttings sediment samples, wire line logs) and comparison with existing data (e.g. seismic lines, georadar profiles) indicate upwards shallowing and upwards freshening trends through the stratigraphic succession, i.e. variation in palynomorph assemblages. Groundwater flux and aquifer connectivity was evaluated through comparison of water chemistry, noble gas content (the ICO2P project) as well as resistivity- and pressure-logging in upper (fresh) and lower (saline) parts. Analysis of the tidal pressure signal in the deep part of the aquifer gives an indication of the degree of communication between the layers of the aquifer. The areal extent of (semi-)sealing layers of mud, as well as intra-reservoir geological heterogeneity (inclined, graded sandy beds with thin, muddy lamina) affects CO2 distribution in the test reservoir, and is likely to lead buoyant fluids along preferential flow paths. Facies models include North-South progradational patterns and are represented in anisotropic property distributions (Petrel - Schlumberger) for fluid flow simulations (Eclipse - Schlumberger). Predicted CO2 flux is towards the North, below what appears to be locally extensive flow baffles. Integrated data analysis has improved the geological understanding of the Svelvik stacked aquifer system, which may be utilized in future applications to improve monitoring methods for safe large-scale CO2 storage.