The EGU General Assembly最新文献

For more than three years now, the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve the total, tropospheric and stratospheric  column abundance of  NO₂.   The S5P Mission Performance Centre  performs continuous QA/QC of these data products enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in the S5P Automated Validation Server (AVS), and globally in more in-depth analyses.  Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons.  After three years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO₂ data products, with particular attention paid to the impact of the various processor improvements, especially in the latest version (v1.4), activated on 2 December 2020, which introduces an updated cloud retrieval resulting in higher NO₂ columns in polluted regions. Also the upcoming v2, due in April 2021 but already used to produce a Diagnostic Data Set, is discussed.

S5P NO₂ data are compared to ground-based measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Measurements from the Pandonia Global Network (PGN) serve as a reference for total NO₂ validation, Multi-Axis DOAS data for tropospheric  NO₂ validation, and NDACC zenith-scattered-light DOAS data for stratospheric NO₂ validation.  Comparison methods are optimized to limit spatial and temporal mismatch errors (co-location strategy, photochemical adjustment to account for local time difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements.  This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and mismatch errors, and (3) the dependence of these on key influence quantities (surface albedo, cloud cover…)

Overall, the MPC quality assessment of S5P NO₂ data concludes to an excellent performance for the stratospheric data (bias<5%, dispersion<10%). The tropospheric data show a negative bias of -30% and a dispersion of 3Pmolec/cm² vs. ground-based data. This dispersion is larger than the mission requirement on data precision, but it can partly be attributed to comparison errors such as those due to differences in resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4

The highest average annual rainfall in Croatia is in the Northern Adriatic, with some parts of the region receiving more than 2000 mm per year. Characteristics of the region’s weather are periods of intense rain alternating with dry periods in which the amount of precipitation can be negligible for more than a month. The area's water supply relies on karst groundwater sources that are primarily fed by Mediterranean precipitation. The aforementioned precipitation regime results in high groundwater yields in the cold part of the hydrological year and substantially decreased water quantities in the summer months. Under such specific conditions, it is of considerable importance to find out about the potential for climate change in order to ensure timely adjustment of the management and use of natural sources of water.

We present a comparison of the isotopic composition of precipitation collected on the mountain Učka in periods 2008-2011 and 2019-2020. Rain gauges were located on a vertical gradient from sea level up to nearly 1400 m. Unlike the isotopic altitude effect that did not change significantly compared to the one reported for the first period (Roller-Lutz et al, 2013), the weighted means of isotopic values were more positive in the second period.  For the cold part of the hydrological year, local meteoric water line has recently moved to higher values, indicating the sources of precipitation from drier Mediterranean regions. Local meteoric water line for the warm part of the last hydrological year, indicates presence of increased evaporation and thus confirms lower precipitation amounts.

Roller-Lutz Zvjezdana, Mance Diana, Hunjak Tamara, Lutz Hans O. (2013) On the isotopic altitude effect of precipitation in the Northern Adriatic (Croatia), Isotopes in Hydrology, Marine Ecosystems and Climate Change Studies. Vol. I. Proceedings of an International Symposium

This work was supported by the University of Rijeka as part of the research project uniri-pr-prirod-19-24.

Dhaka city with an area of about 306 Km² and a population of more than 20 million is located in the central part of Bangladesh. Immense and prolonged groundwater abstraction due to rapid unplanned urbanization and population blast in this city have led to significant decline in groundwater level in the last three decades. 78% of the supplied water comprises groundwater from the Dupi Tila Sandstone aquifer system. Hydrogeological and geophysical data aided to the delineation of three different aquifers (based on lithology): Upper Dupi Tila aquifer (UDA), Middle Dupi Tila aquifer (MDA) and Lower Dupi Tila aquifer (LDA).  The evaluation of long-term hydrographs, piezometric maps and synthetic graphical overviews of piezometric trends in both the UDA and MDA depicts that the rate of dropping of groundwater level (GWL) is very substantial. Massive pumping in the city has altered its natural hydrologic system. The groundwater level has dropped on average 2.25 m/year and 2.8 m/year in UDA and MDA, respectively, in the whole city in 2018, whereas the average rate of decline in the center of the depression cone during this time was 4.0 m/year and 5.74 m/year respectively. Presently, the groundwater level elevation has declined to levels lower than -85 and -65 m PWD in UDA and MDA, respectively. The changes in pattern and magnitude of depression cones in UDA and MDA are directly associated with the city expansion and number of deep tube wells installed over a certain period in particular parts of the city. The depletion of GWL from 1980 to 2018 is very notable. There is only limited vertical recharge possible in the UDA and MDA as they are semi-confined aquifers, and only lateral flow mostly in the UDA and MDA from the surroundings is to be expected. In this regard the long-term management of groundwater resources in Dhaka city is urgently needed, otherwise the condition may go beyond control.

Key words: Groundwater abstraction, city expansion, hydrographs, piezometric maps, GWL decline, depression cone.

Volcanoes emit different gaseous species, SO₂ and in particular halogen species especially bromine and chlorine compounds. In general, halogens play an important role in the atmosphere by contributing to ozone depletion in the stratosphere (WMO Ozone assessment, 2018) and by modifying air composition and oxidizing capacity in the troposphere (Von Glasow et al. 2004). The halogen species emitted by volcanoes are halides. The chemical processing occurring within the plume leads to the formation of BrO from HBr following the ‘bromine explosion’ mechanism as evidenced from both observations and modelling (e.g., Bobrowski et al. Nature, 2003; Roberts et al., Chem. Geol. 2009). Oxidized forms of chlorine and bromine are modelled to be formed within the plume due to the heterogenous reaction of HOBr with HCl and HBr, forming BrCl and Br₂ that photolyses and produces Br and Cl radicals. So far, modelling studies were mainly focused on the very local scale and processes occurring within a few hours after eruption.

In this study, the objective is to go a step further by analyzing the impact at the regional scale over the Mediterranean basin of a Mt Etna eruption event. For this, we use the MOCAGE model (Guth et al., GMD, 2016), a chemistry transport model run with a resolution of 0.2°x 0.2°, to quantify the impacts of the halogens species emitted by the volcano on the tropospheric composition. We have selected here the case of the eruption of Mount Etna around Christmas 2018 characterised by large amounts of emissions over several days (Calvari et al., remote sensing 2020; Corrdadini et al., remote sensing 2020). The results show that MOCAGE represents rather well the chemistry of the halogens in the volcanic plume because it established theory of plume chemistry. The bromine explosion process takes place on the first day of the eruption and even more strongly the day after, with a rapid increase of the in-plume BrO concentrations and a corresponding strong reduction of ozone and NO2 concentrations.

We also compared MOCAGE results with the WRF-CHEM model simulations for the same case study. We note that the tropospheric column of BrO and SO₂ in the two models have the same order of magnitude with more rapid bromine explosion occurring in WRF-CHEM simulations. Finally, we compared the MOCAGE results to tropospheric columns of BrO and SO₂ retrieved from TROPOMI spaceborne instrument.

Data publications with digital object identifiers (DOI) are best practice for FAIR sharing data. Originally developed with the purpose of providing permanent access to (static) datasets described in scholarly literature, DOI today are more and more assigned to dynamic data. These DOIs are providing a citable and traceable reference of various types of sources (data, software, samples, equipment) and means of rewarding the originators and institutions. As a result of international groups, like the Coalition on Publishing Data in the Earth, Space and Environmental Sciences (COPDESS) and the Enabling FAIR Data project, data with assigned DOIs are fully citable in scholarly literature and many journals require the data underlying a publication to be available – even before accepting an article. Initial metrics for data citation allows data providers to demonstrate the value of the data collected by institutes and individual scientists.

This is especially relevant for the geodesy, because, geodesy researchers are often much more involved in operational aspects and data provision than researchers in other fields might be. Therefore, compared to other scientific disciplines, geodesy researchers appear to be producing less “countable scientific” output. Consequently, geodesy data and equipment require a structured and well-documented mechanism which will enable citability, scientific recognition and reward that can be provided by assigning DOI to data and data products.

To address these challenges and to identify opportunities for improved coordination and advocacy within the geodetic community, the International Association of Geodesy’s (IAG) Global Geodetic Observing System (GGOS) has established a Working Group on “Digital Object Identifiers (DOIs) for Geodetic Data Sets” in 2019. This Working Group is designated to establish best practices and advocate for the consistent implementation of DOIs across all IAG Services and in the greater geodetic community.

The main objectives and activities of this working group are:

• (1) to identify what the community needs from consistent usage of DOIs for data in terms of being able to discover data, permanently cite data, and acknowledge the data providers
• (2) to develop recommendations for DOI minting strategies for different geodetic data types and granularity across IAG Services (static, dynamic, observational data, data products, combination products, networks)
• (3) to develop recommendations for a consistent method for data citation across all IAG Services, to support data providers, and to provide quantitative support detailing the use of geodetic datasets and other resources.
• (4) to develop recommendations for connecting metadata standards for data discovery (e.g. DataCite, ISO19115) with community metadata standards (GeodesyML, Station Logs)

This presentation will provide an update on recent topics and

The Eastern Iranian Orocline provides us several opportunities to study magmatism in relation to tectonic events. The buckling of this orocline is accompanied by an extreme extension in its Khorasan outer arc during which a calc-alkaline dike swarm, generally andesite to dacite, intruded in a radial pattern into the Paleocene-Eocene volcano-sedimentary units, belonging to the platform of the Lut block. The azimuth of these dikes shows a declination of 30 degrees, from N300^o to N330^o. The U²³⁵/Pb²⁰⁷ age of ~41±74 Ma from zircon crystals taken from the dikes represents a considerable buckling with an extension occurred during the middle-upper Eocene. In fact, this time refers to the buckling in the boundary of the inner- and outer-arc of the orocline. This could be a noticeable document of syn-orocline magmatism in the Tethyan realm in the east of the Iranian plateau. The dikes and their host rocks are also sampled for AMS analysis and paleomagnetic measurements to test the amount of the oroclinal buckling in the Qayen area.

The progressive failure of a snow layer deposited on a stiff substrate is at the base of the comprehension of several physical processes that can be found both in natural and artificial conditions. For instance, glide avalanches often originate from the reduction of the basal friction between the snowpack and the underlying ground due to the presence of liquid water film or depth hoar at the snow-ground interface. Moreover, the interaction between snow and construction materials relates to many other applications such as the study of new and more efficient snow removal techniques, the safety of travelers along snow covered roads, the snow redistribution from roofs and buildings, etc. 

Despite this large number of application fields, laboratory investigations are still limited. We performed cold room tests on artificially made snow-mortar interface specimens through a direct shear test device. The effects of confinement pressure, temperature and dry snow hardness (due to sintering times) were taken into account. The tests were carried out in displacement-controlled conditions in order to study the entire failure process at the interface and the following irreversible sliding. The results show some interesting and encouraging aspects for understanding the shear strength of the interface. From a micromechanical point of view we recorded the tests with a high-definition video camera and analyzed the data with the Particle Image Velocimetry technique to obtain the motion fields on the external side of the specimens. Here, we present and discuss some preliminary results of the experimental activity and suggest some future implementations and further developments of the studied topic.       

Graph neural networks are a newly established category of machine learning algorithms dealing with relational data. They can be used for the analysis of both spatial and/or temporal data. They are capable of modeling how time series of nodes, which are located at different spatial positions, change by the exchange of information between nodes and their neighbors. As a result, time series can be predicted to future epochs.

GNSS networks consist of stations at different locations, each producing time series of geodetic parameters, such as changes in their positions. In order to successfully apply graph neural networks to predict time series from GNSS networks, the physical properties of GNSS time series should be taken into account. Thus, we suggest a new graph neural network algorithm that has both a physical and a mathematical basis. The physical part is based on the fundamental concept of information exchange between nodes and their neighbors. Here, the temporal correlation between the changes of time series of the nodes and their neighbors is considered, which is computed by geophysical loading and/or climatic data. The mathematical part comes from the time series prediction by mathematical models, after the removal of trends and periodic effects using the singular spectrum analysis algorithm. In addition, it plays a role in the computation of the impact of neighboring nodes, based on the spatial correlation computed according to the pair-wise node-neighbor distance. The final prediction is the simple weighted summation of the predicted values of the time series of the node and those of its neighbors, in which weights are the multiplication of the spatial and temporal correlations.

In order to show the efficiency of the proposed algorithm, we considered a global network of more than 18000 GNSS stations and defined the neighbors of each node as stations that are located within the range of 10 km. We performed several different analyses, including the comparison between different machine learning algorithms and statistical methods for the time series prediction part, the impact of the type of data used for the computation of temporal correlation (climatic and/or geophysical loading), and comparison with other state-of-the-art graph neural network algorithms. We demonstrate the superiority of our method to the current graph neural network algorithms when applied to time series of geodetic networks. In addition, we show that the best machine learning algorithm to use within our graph neural network architecture is the multilayer perceptron, which shows an average of 0.34 mm in prediction accuracy. Furthermore, we find that the statistical methods have lower accuracies than machine learning ones, as much as 44 percent.

The North Andean Sliver (hereinafter NAS) lies at the northwestern end of the South American plate (hereinafter SOAM). This extensive area exhibits a complex deformation process controlled by the interactions of Nazca, Caribbean, South America plates, and Panama block, producing crustal seismicity, arc-continental collision, and subduction processes. Previous models based on partial GPS data sets have estimated the NAS kinematics as a single rigid block moving towards northeast  at 8-10 mm/yr (Nocquet et al. 2014, Mora-Paez et al 2019). By contrary, geologic interpretations as well as seismotectonic data propose more complex kinematic models based on the interaction of several blocks (Audemard et al 2014, Alvarado et al 2016).  Here, we present an updated and most extensive interseismic horizontal velocity field derived from continuous and episodic GPS data between 1994 and 2019 that encompasses the whole North Andean Sliver.  We then interpret it, developing a kinematic elastic block model in order to simultaneously estimate rigid block rotations, consistent slip rates at faults and the spatial distribution of interseismic coupling at the Nazca/NAS megathrust interface. Our model is not constrained either by a priori information derived from geologic slip rates or by a priori information of creeping faults. In contrast with previous simplest models, our model will allow us to estimate the degree of slip partitioning more precisely along the NAZCA/SOAM convergence as well as an improved model of interseismic coupling. We will discuss our coupling distribution with respect to previous models, and our block geometry quantifying the goodness of fit, resolution,  and considering its consistency with geological interpretations.