bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-based anomaly detection system for SAR data
Authors: Vinholi João Gabriel , Arbenina Maria
Published: February 2023

Anomaly detection in synthetic aperture radar (SAR) images is an important task with numerous applications, including damage assessment, oil spill detection, and land use classification. It is also a crucial pre-task for the detection and forecast of natural catastrophes (NatCat), as faulty data can lead to inaccurate predictions and potentially dangerous situations. Traditional methods for anomaly detection in SAR data often rely on manual inspection, which can be laborious and subjective. An AI-based approach to anomaly detection has the potential to quickly and automatically detect issues in SAR data, improving efficiency and accuracy. One advantage of using AI for anomaly detection is the ability to identify patterns and anomalies that may be difficult for humans to discern, including those that are subtle or not immediately apparent. In addition, an AI-based approach can be more objective and less prone to human bias compared to manual inspection. An AI-based anomaly detection system can also be highly scalable and adaptable, making it a flexible and valuable tool for anomaly detection in SAR images. We propose to present how an AI-based approach can be used to automatically detect different types of anomalies in SAR data to avoid faulty data being used in critical applications.  Overall, an AI-based anomaly detection system has the potential to significantly improve the efficiency and accuracy of anomaly detection in this domain and could have a wide range of applications in other fields also.
Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference
Authors: Moss Guy , Višnjević Vjeran , Schröder Cornelius , Macke Jakob , Drews Reinhard
Published: February 2023

The ice shelves buttressing the Antarctic ice sheet determine its stability. Over half of all mass loss in Antarctica occurs due to ice melting at the water-ice boundary at the base of ice shelves. Different contemporary methods of estimating the spatial distribution of the melting rates do not produce consistent results, and provide no information about decadal to centennial timescales. We explore a new method to infer the spatial distribution of the basal mass balance (BMB) using the internal stratigraphy which may contain additional information not present in other sources such as ice thickness and surface velocities alone. The method estimates the Bayesian posterior distribution of the BMB,  and provides us with a principled measure of uncertainty in our estimates.  Our inference procedure is based on simulation-based inference (SBI) [1], a novel machine learning inference method. SBI utilizes artificial neural networks to approximate probability distributions which characterize those parameters that yield data-compatible simulations, without the need of an explicit likelihood function. We demonstrate the validity of our method on a synthetic ice shelf example, and then apply it to Ekström ice shelf, East Antarctica, where we have radar measurements of the internal stratigraphy. The inference procedure relies on a simulator of the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack [2], and a time-discretized layer tracing scheme [3].  These detailed simulations, along with available stratigraphic data and the SBI methodology, allows us to compute a spatially-varying posterior distribution of the melting rate. This distribution corroborates existing estimates and extends upon them by quantifying the uncertainty in our inference. This uncertainty should be incorporated in future forecasting of ice shelf dynamics and stability analysis. [1] Lueckmann et al.: Benchmarking simulation-based inference (2020).[2] Shapero et al.: icepack: a new glacier flow modeling package in Python, version 1.0. (2021).[3] Born: Tracer transport in an isochronal ice-sheet model (2017).
Mobile Gamma-ray spectrometry for soil texture mapping
Authors: Said Hami , Toloza Arsenio , Rab Gerhard , Brunner Thomas , Heng Lee Kheng , Strauss Peter , Mbaye Modou , Dercon Gerd
Published: February 2023

Particle size distribution in soil, or texture, is a property essential for understanding processes driving soil water dynamics, fertility and conservation.  However, soil texture mapping, using traditional soil sampling and analytical techniques, is labor intensive, time consuming and hence expensive.  Having a more rapid and low-cost proximal sensing technique to accurately map soil texture would be a big step forward, in particular at a sufficiently detailed spatial scale for providing advice on soil management at field scale.For the development of such proximal sensing techniques assisting in soil texture monitoring, a study was carried out by the Joint FAO/IAEA Centre at the Petzenkirchen catchment of the Hydrological Open-Air Laboratory (HOAL), located 100 km west from Vienna in Lower Austria. As sensing technique for determining texture, with emphasis on the topsoil (0-30 cm), Gamma-Ray Sensor (GRS) technology was selected.  A Medusa MS-350 portable GRS was used to measure the spatial activity concentrations (Bq.kg-1) of 40K (potassium), 238U (uranium), and 232Th (thorium) over 20 points across the studied catchment. These activity concentrations were then linked with soil texture parameters of interest, such as silt, clay, and sand, at the same positions. In total 200 soil samples (10 soil samples for each of the 20 points) were taken for soil texture determination.Preliminary results showed the best correlation between 40K radionuclide concentrations and clay (R2 = 0.51), and silt (R2 = 0.46). Thus, spatial monitoring of 40K with mobile GRS shows potential for the monitoring of clay and silt. However, correlations with other radionuclides concentrations such as 238U and 232Th were weak with R2 coefficients less than 0.16.Further studies are now required to assess ways to improve 40K based predictability of soil texture and validate the applicability of this approach in a more generic way, i.e. a wide range of soil textures. This validation will then enable the further development of this nuclear technology for effective and efficient ground based and air-borne soil texture determination.
The role of stream heterogeneity in gas emissions from headwater streams
Authors: Durighetto Nicola , Carozzani Anna , Peruzzo Paolo , Botter Gianluca
Published: February 2023

Headwater streams as hotspots of carbon dioxide evasion from surface water, and therefore represent a key component of the global carbon cycle. The gas transfer velocity at the water-air interface, k, modulates gas emissions from rivers and streams and is physically related to the energy dissipated by the flow field, ε. Here, we developed mathematical tools for quantifying the fraction of carbon emissions that can be related to localized height drops in the riverbed (e.g. in steps or step-pool formations, which constitute localized energy losses). Direct measures of stream CO2 outgassing in an Italian headwater catchment and numerical simulations are also part of the study. Our results show that high energy heterogeneous streams are characterized by significantly higher gas transfer velocities than that of an homogeneous stream. The empirical data also suggests the presence of a pronounced heterogeneity of outgassing along a river network. In particular, in many settings the total gas evasion may be dominated by localized gas emissions in correspondence of hydraulic discontinuities. These results offer a clue for the interpretation of empirical data about stream outgassing in heterogeneous reaches, and provides insight into the development of more advanced models for the large-scale estimation of CO2 outgassing from mountain rivers.
Irrigation management with a time-continuous two-source modelling of tree crops calibrated with satellite LST data
Authors: Paciolla Nicola , Corbari Chiara , Mancini Marco
Published: February 2023

Agriculture will progressively require more and more attention as changing climatic conditions and reduced water availability threaten food security worldwide. The optimization of the agricultural production is obtained with constant monitoring of the plant health (in terms of e.g., soil moisture, leaf temperature or evapotranspiration), which can be challenging if crop fields are too extensive.Thermal observations from remote sensing are extensively used in agricultural monitoring to power (mostly-residual) energy balance model that provide evapotranspiration estimates. Two main issues hinder the quality of the results from these models: (a) sub-pixel heterogeneity, in particular related to mixed crops (e.g. row and tree crops), which can be captured only partially by the available LST information and (b) temporal frequency of the information, which for most freely-available products is usually at odds with spatial resolution (e.g., 1 km data from MODIS is available daily, whereas 90 m data from Landsat only once every 7-8 days). Furthermore, tree crops draw water from deep layers of soil, further disconnecting the satellite information from the biophysical processes involved in plant growth.In this work, the use of a continuous, two-source, double-soil-layer coupled energy-water balance model is displayed as a solution of these issues. The link between the two balances allows to compute surface temperature internally, meaning that satellite LST observations are used, only when available, for the calibration process. Furthermore, the use of a double source in the energy exchanges allows to properly address the intra-pixel heterogeneity. Finally, the double soil layer allows to address the soil water and energy vertical gradient in complex systems, properly framing the surface observation from remote sensing within the overall environment.Two pear tree fields in the Po Valley have been chosen as focus to study the effectiveness of this model, via a monitoring of the 2022 irrigation season, employing Sentinel 2 observations for the vegetation data and Landsat 8 LST for the calibration process. ET estimates are evaluated against flux tower observations. The increased accuracy of these estimates is key to enforce a more precise and effective irrigation and optimize the use of the water resource.
Modeling and analysis of Distributed Acoustic Sensing (DAS) data in Geothermal environments
Authors: Pecci Davide , Porras Juan , De Solda Michele , Grigoli Francesco , Stucchi Eusebio , Iannelli Renato
Published: February 2023

DAS technology is particularly suitable for microseismic monitoring application in geothermal environments. This instrumentation can resist to high temperatures (up to about 100°C or more) higher than the operational temperature of standard acquisition instruments (e.g., geophones), allowing the fiber to be located very close to the reservoir. For this reason, DAS is particularly useful for induced seismicity monitoring of Enhanced Geothermal System (EGS). Being of recent development, this acquisition technology still lacks appropriate modeling and analysis tools able to handle such a large amount of data without losing efficiency. Furthermore, open-access DAS datasets are still a rarity, if compared to other geophysical datasets (e.g., seismological data). Therefore, we aim to generate an open-access synthetic (but realistic) DAS dataset that may help the geophysical community to develop “ad hoc” data analysis methods suitable for this kind of data. In the presented work we make use of the spectral element modeling software 'Salvus', developed by Mondaic, which also allows the simulation of DAS data. In particular, it outputs a strain measurement between all points defined as receivers in the simulation. Using the repositories of DAS data collected at the geothermal test site Frontier Observatory for Research in Geothermal Energy (FORGE) located in Utah (USA), we tried to simulate realistic DAS acquisition conditions of seismic events related to low-magnitude natural seismic activity from the nearby Mineral Mountains and microseismic events related to hydraulic stimulation operations for the generation of an EGS.In order to obtain realistic synthetic data, we first analyze the spectral properties of real noise waveforms by using the Power Spectral Density (PSD) Analysis. Starting from observed PSDs we model the synthetic noise waveforms using a stochastic approach. Then we add it to the synthetic event traces and compare them with the observed ones. We finally test a semblance-based event detector on a 1-hour continuous waveforms of synthetic data to evaluate the performance of the detector in different operational conditions (e.g., different noise levels and inter-event times).
Post-glacial catchment denudation rates from 14C concentrations in Glen Feshie, Scotland
Authors: Towers Anya , Mudd Simon , Attal Mikael , Binnie Steven , Clubb Fiona , Dunai Tibor
Published: February 2023

Inferring erosion rates from 10Be or 26Al concentrations in stream sediments has become standard practice in geomorphology. In formerly glaciated landscapes, however, this technique is problematic because repeated phases of shielding and exposure during the past glacial and interglacial periods can lead to 10Be or 26Al concentrations that are difficult to interpret. Cosmogenic in-situ 14C has a short half-life (~5,730 years) that means 14C atoms in stream sediments cannot be inherited from before the glacial period and inferred erosion rates will reflect post-glacial, Holocene erosion. Using cosmogenic in-situ 14C, we report the first millennial-scale erosion rates in the post-glacial landscapes of Glen Feshie, within the Cairngorm mountains of Scotland.The River Feshie contains active gravel reaches that cut through glacial outwash terraces. We counterintuitively find the lowest inferred erosion rates (0.06 mm/yr) in the steepest side tributary and the highest inferred erosion rates at the low-relief outlet of Glen Feshie near the confluence with the River Spey (0.21 mm/yr). Based on field observations, we interpret that hillslopes have been largely inactive and contributed limited sediment fluxes. To provide further insight into the highest erosion rate documented furthest downstream, we consider the hypothesis that sediment from the hillslopes with higher concentrations of 14C has been diluted with lower concentration material from the terraces. Further, we hypothesise that if terraces that border the channel increase in height downstream, their incision could have remobilised an increasing amount of sediment with lower 14C concentrations downstream, leading to increased dilution and the observed concentrations. Results show terrace height above the channels does not increase downstream and averages approximately 2 meters. We therefore suggest terrace height does not account for higher erosion rates, and present a cosmogenic-nuclide mixing model to explore the degree to which the input of sediment that has been shielded from cosmic rays in terraces can explain the observed concentrations in stream sediments.   
The installation and operation of a multi-parameter volcano monitoring network on the islands of Saba and St. Eustatius in the Caribbean Netherlands
Authors: de Zeeuw-van Dalfsen Elske , Sleeman Reinoud , Krietemeyer Andreas
Published: February 2023

In the Caribbean Netherlands, the islands of Saba and St. Eustatius host the active but quiescent volcanoes Mt. Scenery and The Quill. To mitigate volcanic risk to the islands, robust monitoring is essential. Therefore in the past five years the Royal Netherlands Meteorological Institute (KNMI) significantly expanded the volcano monitoring network on both islands.The seismic monitoring network was expanded from seven to 11 broadband seismometers located across the islands. Seismic data are sent to and stored at KNMI and Observatories and Research Facilities for European Seismology (ORFEUS). Eight permanent continuous Global Navigation Satellite System (GNSS) stations were newly installed, where possible co-located with the broadband seismometers. GNSS data are sent to and stored at KNMI and UNAVCO. On a daily basis we run an automatic earthquake detection system and coincidence trigger to identify seismic events and create GNSS time series using both network and Precise Point Positioning (PPP) solutions.The installation of new instruments was challenging due to the remoteness of the envisioned locations which were needed to monitor all sides of the volcanoes.  Local governmental and military assistance was key to the success of the mission. At the most remote locations instruments are operated on solar power and data are transmitted using  Very-Small-Aperture Terminal (VSAT) technology. Ensuring the operability of the monitoring network remains demanding due to the harsh tropical conditions (hurricanes, UV-radiation, sea spray, lightning) as well as network and power outages. Apart from seismic and GNSS instruments, we also deploy three temperature sensors and four cost-effective GNSS units to extend our monitoring network. Furthermore, in collaboration with Delft University of Technology (TU Delft) we test the feasibility of the use of Interferometric Synthetic Aperture Radar (InSAR) for the monitoring of these islands.
Aspects of the geomorphology of the Late Palaeozoic glaciated landscape of Namibia as revealed by photogrammetry
Authors: Le Heron Daniel , Kettler Christoph , Dietrich Pierre , Griffis Neil , Montañez Isabel , Wohlschlägl Ricarda
Published: February 2023

The geometry of unconformities carved by deep time ice sheets is often obscured and restricted by discontinuous exposure, or outcrop conditions that do not readily permit the examination of glacial unconformities (for example, steeply dipping strata). Here, we present new uncrewed aerial vehicle (UAV) data from selected outcrops across northern, central and southern Namibia to shed new light on the nature of the basal Dwyka unconformity. This includes the onlap relationship of basal diamictites onto the Gomatum palaeo-fjord system in northern Namibia, highly complex mapped ice flow orientations elsewhere in the northern Kaokoveld, previously undiscovered grooves along the Fish River area, and a spectacular set of subglacial grooves along the border with South Africa along the Orange River. In the latter two cases, photogrammetric methods integrating orthophotos and digital elevation models reveal the presence of subglacial grooves for the first time, since the features are too subtle to observed using conventional approaches at outcrop. Furthermore, subglacial grooves often show different orientations to striations and fabrics measured in overlying diamictites, raising fresh questions about the nature of small-scale flow variations beneath Late Palaeozoic ice sheets.
High but variable CH4 emissions post-fire is associated with stochastic recruitment and assembly of methanogens
Authors: Bandla Aditya , Akhtar Hasan , Lupascu Massimo , S. Sukri Rahayu , Swarup Sanjay
Published: February 2023

Frequent fires in tropical Peat Swamp Forests (PSFs) that have been repurposed for forestry and agriculture result in substantial emissions of locked carbon. Fire-affected PSFs emit double the amount of CH4 compared to intact ones over extended periods of time. CH4 production is largely driven by communities of microorganisms (microbiomes – archaea in particular), thus, our ability to reduce emissions hinges on (i) identifying those with the capacity to generate CH4, (ii) ecological processes that shape their composition and functioning, and (iii) environmental variables which drive them. Ecological processes are particularly important as they determine our ability to predict the trajectory of communities and their functioning. Trajectories of communities shaped by deterministic processes can be predicted based on environmental variables as opposed to those shaped by stochastic processes whose trajectories are difficult to predict. We fill this knowledge gap by sequencing and comparing the ecological processes shaping peat microbiomes from a fire-impacted PSF to an intact PSF that occur within the same peat dome in Brunei. The composition of archaeal communities were significantly different between the fire-impacted and intact PSFs and strongly stratified by depth. The largest difference was observed between communities from the surface (0-5 cm) and those from below the water table (95-100 cm). In the fire-impacted PSF, archaea doubled in abundance in the anoxic zone compared to the surface, while, no such change was detected in the intact PSF. Archaeal communities occurring in the anoxic layers of the fire-impacted PSF were dominated by methanogens from the class Methanomicrobia and Bathyarchaeia, both of which occur in high-methane flux habitats. We determined ecological processes shaping the assembly of these methanogenic populations using bin-based phylogenetic null models. This showed that methanogenic populations in the fire-impacted PSF were largely shaped by stochastic processes, whereas, similar populations, albeit at lower abundances, were shaped by deterministic processes in the intact PSF. Changes in pH and dissolved oxygen correlated strongly with differences in assembly processes. Our work shows that changes in the environment resulting from fires can set methanogenic communities on unpredictable trajectories, which in turn correlate strongly with both increased and non-homogeneous CH4 emissions. Altering these key environmental correlates could form the basis for developing nature-based solutions for reducing emissions from fire-impacted peatlands.
Analysing ozone variability at northern polar latitude during sudden stratospheric warming events using ground-based microwave radiometer
Authors: Shi Guochun , Stober Gunter
Published: February 2023

Sudden stratospheric warmings (SSWs) have significant impacts on the Arctic ozone. In this study, MERRA-2 provides the characteristics of the zonal-mean zonal wind and temperature influenced by the planetary waves during major SSWs. We present an analysis of ozone variations in the stratosphere over Ny-Ålesund, Svalbard (79°N, 12°E) based on the ground-based microwave radiometer GROMOS-C during the major SSW events that occurred from 2015 to 2022. The results are compared with Aura-MLS observations and MERRA-2 simulations. GROMOS-C captures the high variability of stratospheric ozone fluctuations during SSWs at polar latitudes very well. The stratospheric ozone dramatically increases after SSW onset day, which lasts up to two months. The polar vortex is disturbed or weakened by SSW resulting in the meridional transport of ozone from the mid-latitude into the polar regions. Therefore, this study assists in understanding the relationship between the interannual variability of stratospheric ozone and the occurrence of SSWs and has significant implications for stratospheric ozone trends in the northern polar regions.
A new view of a 1970s radar dataset from Greenland
Authors: Karlsson Nanna Bjørnholt , Schroeder Dustin , Sørensen Louise S. , Chu Winnie , Teisberg Thomas , Tarzano Angelo , Skou Niels , Dall Jørgen
Published: February 2023

The short observational record is one of the main obstacles to improving the present understanding of the future of the Polar ice sheets. While the quantity and quality of observations presently are increasing observations from before the 1990s are scarce. Here, we present the first results from a newly digitized ice-penetrating radar dataset acquired over the Greenland Ice Sheet in the 1970s. The data consist of more than 170,000 km of radar flight lines. While the ice thickness information from the data has been digitized by previous studies, the data itself (notably the z-scopes) were until recently only available as 35-mm films, microfiche copies of the films, and enlarged positives: Formats that are not usable for digital analysis.In 2019, the film rolls were scanned by a digital scanner and subsequently, a large effort has been directed at carrying out quality control of the data with the view of making them publicly available.  Here we present the first results from this digitization. The overall data quality is good, and we are able to retrieve valuable information on layer stratigraphy and ice-flow dynamics.
High spatio-temporal rainfall algorithm over Galápagos archipelago using multispectral GOES-16 infrared brightness temperatures: the GRR product
Authors: Turini Nazli , Delgado Maldonado Byron , Bendix Jörg
Published: February 2023

Due to its geographic location and unique climatic circumstances, the Galápagos archipelago is renowned for its exceptional and high endemic biodiversity. Nevertheless, due to limited access to the permanent freshwater body in the archipelago, the freshwater budget is almost exclusively dependent on precipitation. However, with the lack of spatial and temporal distribution of rainfall information, it is not easy to understand the short- and long-term dynamics of rainfall in the Galápagos.The poster presents the new satellite-based rainfall retrieval algorithm, the Galápagos Rainfall Retrieval (GRR), which offers the potential for a high spatio-temporal resolution (2 km, 10 min) rainfall product in near real-time for the Galápagos archipelago.The algorithm is proposed to combine physical methods with machine learning in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to retrieve both cold season Garua drizzle and warm season convective rainfall.In the first step, a threshold technique and spectral differences are used to identify the cloudy regions and distinguish the low, middle, and high clouds. Next, the cloud-covered region will go under a different entity-based classification method (e.g., slope test/ML algorithm) for each cloud type to detect the low stratus/Garua drizzle and potentially convective core regions. The next test follows for all detected potentially convective core regions based on cloud formation over time and space to examine whether they are likely to be decaying. If the convective core is classified as decaying, it is labelled stratiform rain; otherwise, it is labelled as the active convective core.Finally, the rainfall assignment will be performed by training the random forest regression models. The convective and stratiform cells will be trained based on microwave-only IMERG-V06 rainfall data separately, meanwhile the cloudsat would be used to train the rainfall rate for the Garua detected regions.  By the end, all of these steps are combined together as GRR product. The algorithm of GRR product will be developed for the time period 1/1/2022-1/1/2023 and then it would be applied to the entire available GOES-16 dataset.The validation will be conduct by: i) independent microwave-only IMERG-V06 rainfall data/cloudsat not used for model training ii) recently installed automated weather stations (AWS) network with high temporal resolution of 10 minutes covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal).
Evaluation of GRACE Follow-On Accelerometer Transplant Based on High-Precision Environment Modelling
Authors: Huckfeldt Moritz , Wöske Florian , Rievers Benny , List Meike
Published: February 2023

The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellites are equipped with high-precision three-axis accelerometers to measure all non-gravitational accelerations acting on the satellites. Unfortunately, after only one month in orbit the accelerometer on one of the two satellites produced decreasingly accurate measurements. Due to this, the GRACE-D accelerometer data have to be replaced by artificial data for the use in the Gravity Field Recovery (GFR) process. This artificial GRACE-D Science Data System (SDS) data product is generated by a so called transplant of GRACE-C data.We are developing a GFR procedure for GRACE-FO that utilizes modelled GRACE-D accelerometer data including our own transplant approach. Here we present the performance evaluation of our modelled data when compared to gravity fields from other processing centres. This includes the comparison of different models for non-gravitational perturbations, the transplant procedure and the influence of varying calibration methods. This work is part of the Collaborative Research Center 1464 TerraQ and funded by DFG.
Trends and characteristics of warm and dry extreme compound event in Southeastern Europe
Authors: Orihan Maja , Živaljević Branislav
Published: February 2023

Extreme compound events defined as the simultaneous occurrence of multiple natural hazards such as floods, droughts, and heatwaves, have become increasingly frequent in South East Europe in recent years. These events can have significant impacts on the region's social, economic, and environmental systems and can lead to significant human losses. Because South East is identified as a climate hot spot for future climate change and occurrence of extreme compound events, it is very important to identify and study these hot spots. This information could be useful for decision-makers and practitioners in the water management and agriculture sector, especially in the Balkan region. Here we present the first results of the study of the compound warm and dry over the Western Balkan area since compound events have not been studied over the territory of Eastern Europe since 1950 until today. Using daily data on maximum temperature and precipitation, we calculated the frequency and trends of the warm/dry (WD) indices. Trends were calculated using the Mann-Kendall trend test in R and the resolution that was used is ERA5 1950-now. Presented results are annual and also seasonal variations. The index that we used shows cold and dry events per year or season, the results indicate the rising trend over the whole territory of South East Europe, where trends were statistically significant by over 95 percent. We investigated years with recorded heat waves and the most severe droughts in the observed region in the years: 2007, 2012, 2015, and 2017. In 2007 there were more than 140 warm and dry events in the Western Balkans area, in 2012 there were between 160 and 200 warm and dry events, in 2015 more than 140 and in 2017 around 140 warm and dry events. Studying extreme compound events is very important because it helps us to better understand the underlying causes of extreme weather and other natural disasters, especially in this area of high agricultural potential. This knowledge can help farmers to make informed decisions about how to identify potential risks and develop strategies to mitigate them to maximize their yields and minimize losses.Our results highlight the need for targeted and effective risk management strategies to reduce the negative impacts of extreme compound events in South East Europe.  KEY WORDSCompound events, temperature, precipitation, Eastern Europe, trends   Funding: This research was supported by the EXtremeClimTwin project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No952384.
Impact assessment of future wind farm characteristics on cluster-scale wake losses in the North Sea
Authors: van Lipzig Nicole , Borgers Ruben
Published: February 2023

Offshore wind zones are reaching sizes at which they start to affect each other and potentially also alter mesoscale weather systems, impacting the energy production. Here, we assess the impact of future wind farm characteristics, like turbine type and capacity density, on cluster-scale wake losses. For this we use the mesoscale model COSMO-CLM at the km-scale resolution, which skillfully models frequency distributions of wind speed and wind direction at turbine level compared to measurement masts, wind lidars and satellite data. It was found that inter-farm wakes can reduce the long-term capacity factor at the inflow edge of wind farms from 59% to between 55% and 40% depending on the degree of clustering and the size of the upwind farms, for a layout equipped with 5MW turbines at a capacity density of 8.1 MW / km². Moving to next-generation wind turbines (15MW) partly mitigates this degradation, as the total generation over all windfarms (TWh) is increased by 19% under the same wind farm capacity density. On the other hand, increases in the capacity density in this future layout lead to a less than proportional (0.8 to 1) increase in the basin-integrated, total generation as a consequence of more intense intra- and inter-farm wake effects. Generally, wind farm characteristics play an essential role in inter-farm wake losses, which should be included in future wind farm planning.
Relations between coronal shock waves properties and acceleration of solar energetic particles
Authors: Jarry Manon , Rouillard Alexis , Plotnikov Illya , Kouloumvakos Athanasios , Warmuth Alexander
Published: February 2023

The mechanisms that produce solar energetic particles (SEPs) are still highly debated but coronal shock waves have been proposed as efficient particle accelerators that may be implicated in the production of SEPs.An analysis of 32 Coronal Mass Ejections (CMEs) that produced strong pressure waves in the solar corona during their eruption has been done. For each event Kouloumvakos et al. (2019) exploited remote-sensing observations from multiple vantage points to reconstruct their 3-D ellipsoidal shapes. This catalogue of shock waves provides important statistical information on their kinematic evolution that we report in Jarry et al. (2023) together with their relation to X-ray flaring activitiy.Different SEPs exhibit significant spectral and compositional variability. We looked for links between the composition of SEPs including abundance ratios (such as Fe/O) and shock parameters (Mach number, shock geometry, ..) that typically evolves rapidly along the magnetic field lines connected to the spacecraft recording SEPs.This work was funded by the H2020 SERPENTINE project.
The temperature effect on the Intrinsic quantum yield at the ecosystem level
Authors: Sandoval David , Flo Victor , Morfopoulos Catherine , Prentice Iain Colin
Published: February 2023

The intrinsic quantum yield (φ0) is a measure of the efficiency of photosynthesis at low light levels and it is a crucial parameter for modelling gross primary productivity using “light use efficiency” (LUE) models. These models often assume that φ0 is constant, but there is evidence retrieved at leaf level in the lab, that it may depend on temperature in a bell-shaped curve, with a peak around 30°C. This temperature dependence of φ0(T) is still not fully understood, thus, it is still unknown if the shape of φ0(T) is universal or if the responses at the leaf and ecosystem levels widely differ. Here we derived φ0(T) at the ecosystem level for different sites during their growing season. We used sub-daily above and below-canopy measurements of photosynthetic flux density, long-wave radiation measurements to derive surface canopy temperature, and eddy covariance measurements of CO2 exchange. We then compared our estimations with empirical models found in the literature and propose a new empirical equation. We found that φ0(T) at the ecosystem level also follows a bell-shaped curve, with a rapid increase after 5 °C to peak around 20 °C to 25 °C, and a slight decrease with further increasing temperature. Overall, our estimations show lower values than previous leaf-level observations reported in the literature.  The results suggest that this new formulation for φ0(T) may improve the predictions of current LUE models, but further testing is needed.
The Derivation of Irish Water Quality via Sentinel-2 Imagery
Authors: Zhao MinYan , O'Loughlin Fiachra
Published: February 2023

The water quality in Republic of Ireland is regulated under the water framework directive (WFD), which requires all EU countries achieve good ecological and chemical status before 2027. However, the Irish Environmental Protection Agency (EPA) reports in 2021 that just half of the rivers, lakes, estuaries, and coastal waters achieved satisfactory or higher status.Water quality in Ireland is monitored by traditional methods, which cannot provide timely spatiotemporal information. While remote sensing (RS)-based water quality monitoring work have been carried out in many EU countries in accordance with WFD directive, the use of RS for water quality estimation in Ireland has not been fully explored.To explore the feasibility of RS for Irish waters, Sentinel-2 surface reflectance has been used to assess several water quality parameters (chlorophyll-a, transparency, turbidity, suspended solids (SS), total nitrogen, total phosphorus, biological oxygen demand (BOD), dissolved oxygen, and chemical oxygen demand (COD)) from March 2017 to July 2022. These were compared with the Sentinel-2 surface reflectance data resulting in a total of 6509 corresponding data points.Initially, empirical algorithms were used to derive water quality concentrations in rivers, lakes, estuaries, and coastal waters separately. Initial results indicate that the combination of green and blue bands was correlated to coastal waters’ chlorophyll-a (R2 = 0.27). For chlorophyll-a in transitional waters, the combination of red and red edge was highly correlated. However, no single band or combination were suitable for deriving chlorophyll-a in lakes. For SS, red and near infrared band are useful in detecting changes in coastal and transitional waters. Whereas, for lakes and rivers, blue and shortwave infrared band were best to derive SS. In addition to empirical algorithms, multiple machine learning methods have been used to derive water quality parameters from Sentinel-2 reflectance, with the aim of exploring if machine learning approaches can improve estimates compared with the empirical approaches.
Quantifying the COS fluxes from plane, willow, beach and oak litter
Authors: Kitz Florian , Wachter Herbert , Wohlfahrt Georg
Published: February 2023

Flux partitioning, the quantification of photosynthesis and respiration, is a major uncertainty in modelling the carbon cycle and in times when robust models are needed to assess future global changes a persistent problem. A promising new approach is to derive gross primary production (GPP) from measurements of the carbonyl sulfide (COS) flux, the most abundant sulfur-containing trace gas in the atmosphere, with a mean concentration of about 500 pptv in the troposphere. The method is based on the observation that COS and CO2 enter the leaf via a similar pathway and are processed by the same enzyme (carbonic anhydrase), in case of COS a unidirectional process, allowing researchers to use COS uptake as a proxy for the gross CO2 uptake by plants. A prerequisite for using COS as a proxy for photosynthesis is a robust estimation of all non-living-leaf sources and sinks in an ecosystem. One major uncertainty in this regard is the contribution of soils and their respective litter layers to the overall ecosystem COS flux.COS and CO2 fluxes from litter were measured in real-time using a quantum cascade laser (QCL). The plant litter from four different broadleaf tree species (plane, willow, beech and oak), collected a maximum of one hour before measurements started in the lab (to retain in situ moisture and the microbial biome), was measured under alternating dark and light (UV-A) conditions.COS litter fluxes varied between the tree species, with plane primarily emitting COS, beech consuming COS and oak and willow being on average neutral (willow with a huge variance). COS litter fluxes within a species seem to correlate with litter moisture. The COS flux was ranging between -4 and 4 pmol kg DW-1 s-1, which is relevant in magnitude compared to the overall ecosystem COS flux and shouldn’t be neglected in future assessments of the global COS budget. 
Pore network modeling as a tool for determining gas diffusivity in peat
Authors: Kiuru Petri , Palviainen Marjo , Marchionne Arianna , Grönholm Tiia , Raivonen Maarit , Kohl Lukas , Laurén Annamari
Published: February 2023

Peatlands are significant modulators of biogeochemical cycles and important carbon stocks on a global scale, and they may become large sources of the greenhouse gases (GHG) carbon dioxide and methane because of their vulnerability to management practices and changes in climate. Because gas exchange between peat and the atmosphere occurs primarily via diffusion, a proper knowledge of the gas diffusion rate is essential for correct estimation of the amount of GHG emissions from peatlands. Diffusion is controlled by the structure and connectivity of peat pore space. Pore network modeling (PNM) is an efficient method for the pore-scale description and simulation of transport processes in porous matter and, therefore, a useful tool for the assessment of gas diffusivity in peat, as it explicitly illustrates the relationship between the peat microstructure and the gas transport properties on a macroscopic scale. PNM can also be used to simulate time-dependent soil biogeochemical processes, such as GHG production and consumption.We extracted interconnecting macropore (diameter greater than 0.1 mm) networks from three-dimensional X-ray micro-computed tomography (µCT) images of peat samples from three depths and simulated steady-state diffusion in the networks using PNM. We then compared the obtained soil gas diffusion coefficients to those determined experimentally from the same samples. The gas diffusivity measurements were made using the diffusion chamber method under different water contents adjusted in a pressure plate extractor. The measured soil gas diffusivity was lower in deeper layers because of decreased air-filled porosity and pore connectivity. Nevertheless, the diffusion rates were not extremely low close to saturation, which may imply that connected air-filled pathways for gas diffusion are present in peat even in wet conditions. The pore network simulations were able to reproduce the experimentally determined gas diffusion dynamics rather well. This also implies that the topology and the dimensions of the pore space of most of the peat samples were adequately represented by the network objects. Therefore, the combination of µCT and PNM can be considered a potential alternative to the assessment of soil gas diffusivity through traditional laboratory measurements. However, further research is needed on gas diffusivity in different peat types over a wide water content range. Furthermore, the presented approach provides a basis for mechanistic simulation of GHG cycling in soils.
Remote Sensing Deployable Analysis environmenT
Authors: Chandramouli Pranav , Nattino Francesco , Grootes Meiert , Ku Ou , Alidoost Fakhereh , Dzigan Yifat
Published: February 2023

Remote-sensing (RS) and Earth observation (EO) data have become crucial in areas ranging from science to policy, with their use expanding beyond the ‘usual’ fields of geosciences to encompass ‘green’ life sciences, agriculture, and even social sciences. Within this context, the RS-DAT project has developed and made available a readily deployable framework enabling researchers to scale their analysis of EO and RS data on HPC systems and associated storage resources. Building on and expanding the established tool stack of the Pangeo Community, the framework integrates tools to access, retrieve, explore, and process geospatial data, addressing common needs identified in the EO domain. On the computing side RS-DAT leverages Jupyter (Python), which provides users a web-based interface to access (remote) computational resources, and Dask, which enables to scale analysis and workflows to large computing systems. Both Jupyter and Dask are well-established tools in the Pangeo community and can be deployed in several ways and on different infrastructures. RS-DAT provides an easy-to-use deployment framework for two targets: the generic case of SLURM-based HPC systems (for example, Dutch Supercomputer Snellius/Spider) which offer flexibility in computational resources; and the special case of an ansible-based cloud-computing infrastructure (Surf Research Cloud (SRC)) which is more straight-forward for the user but less flexible. Both these frameworks enable the easy scale-up of workflows, using HPCs, to access, manipulate and process large-scale datasets as commonly found in EO. On the data access and storage side RS-DAT integrates two python packages, STAC2dCache and dCacheFS, which were developed to facilitate data retrieval from online STAC catalogs (STAC2dCache) and its storage on the HPC system or local mass storage, specifically dCache.  This ensures efficient computation for large-scale analyses where data retrieval and handling can cause significant bottlenecks. User-defined input/output to Zarr file format is also supported within the framework. We present an application of the tools developed to the calculation of leaf-spring indices for North America using the Daymet dataset at a 1km resolution for 42 years (~940 GiB, completed in under 5 hours using 60 cores on the Dutch supercomputing system) and look forward to on-going work integrating both deployment targets in the case of the Dutch HPC ecosystem.
Improving and evaluating the IPCC land ice emulator
Authors: Edwards Tamsin , Rougier Jonathan , Turner Fiona
Published: February 2023

The emulator used for projections of the land ice contribution to sea level by 2100 in the Intergovernmental Panel on Climate Change Sixth Assessment Report (Edwards et al., 2021) used a novel approach for incorporating structural uncertainty from the underlying computer models. This statistical (Gaussian Process) emulator represented entire multi-model ensembles at once -- for the Greenland and Antarctic ice sheets, and the world’s glaciers, under the model intercomparison projects ISMIP6 and GlacierMIP, respectively -- by using a noise (nugget) term to allow for multiple estimates of sea level contribution for a given set of model input values, analogous to kriging of spatial data.However, the emulator was rather simple in other respects: in particular, the sea level projection for each year from 2015-2100, and from each region of land ice (splitting Antarctica into 3 regions, and the glaciers into 19), were modelled independently, so temporal correlations emerged only upon smoothing the projections. The emulator was also not formally evaluated with observations, because the underlying simulations were only driven with meaningful climate forcings from 2015. These limitations have presented difficulties for users, who often need continuous time series projections and prefer, of course, these to be assessed with observations.Here the IPCC land ice emulator is improved for interpretation and use by decision-makers by estimating spatio-temporal correlations directly from the underlying simulations (Rougier, 2008; Rougier et al., 2009), to produce meaningful trajectories of sea level contribution from each land ice source. The extent to which the land ice emulator can be evaluated with data, now and in future, is also discussed. References:Edwards et al. (2021) Projected land ice contributions to twenty-first-century sea level rise, Nature, 593, 74–82.Rougier, J. (2008), Efficient Emulators for Multivariate Deterministic Functions, Journal of Computational and Graphical Statistics, 17(4):827–843.Rougier, J.C. et al. (2009), Expert Knowledge and Multivariate Emulation: The Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM), Technometrics, 51(4), 414-424.