ESSI1.6

Understanding human dynamics is of crucial importance for managing human activities for sustainable development. According to the United Nations, 68% of people will live in cities by 2050. Therefore, it is important to understand human footprints in order to develop policies that will improve the lives in urban and suburban areas. Our study aims at detecting spatial-temporal activity patterns from mobile phone data provided by a telecom service provider. To be more precise we used the activity data set which contains the amount of sent/received SMS, calls, as well as internet usage per radio-base station in defined time-stamps. The case study focus is on the capital city of Serbia, Belgrade, which has have nearly 2 million inhabitants and included the month of February 2020 in the analysis. We applied the biclustering (spectral co-clustering) algorithm on the telecom data to detect locations in the city that behave similarly in the specific time windows. Biclustering is a data mining technique that is being used for finding homogeneous submatrices among rows and columns of a matrix, widely used in text mining and gene expression data analysis.  Although, there are no examples in the literature of the algorithm usage on location-based data for urban application, we have seen the potential due to its ability to detect clusters in a more refined way, during a specific period of time that could not otherwise be detected with global clustering approach. To prepare the data for the algorithm appliance, we normalized each type of activity (SMS/Call In/Out and Internet activity) and aggregated the total activity on each antenna per hour. We transformed the data into the matrix, where rows were presenting the antennas, and columns the hours. The algorithm was applied for each day separately. On average number of discovered biclusters was 5, usually corresponding to regular based activities, such as work, home, commuting, and free time, but also to the city’s nightlife. Our results confirmed that urban spaces are the function of space and time. They revealed different functionalities of the urban and suburban parts in the city. We observed the presence of patterned behavior across the analyzed days. The type of day dictated the spatial-temporal activities that occurred. We distinguished different types of days, such as working days (Monday to Thursday), Fridays, weekends, and holidays. These findings showed the promising potential of the biclustering algorithm and could be utilized by policymakers for precisely detecting activity clusters across space and time that correspond to specific functions of the city.

How to cite: Grujić, N., Brdar, S., Novović, O., Obrenović, N., Govedarica, M., and Crnojević, V.: Biclustering for uncovering spatial-temporal patterns in telecom data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14423, https://doi.org/10.5194/egusphere-egu21-14423, 2021.

The SE-Carpathians produce significant geodynamic activity due to the current subduction process. The strong seismicity in the Vrancea-zone is its most important indicator. The focus area of these seismic events is relatively small, around 80*100 km and the distribution of their locations is quiet dense.

The authors have carried out cluster analyses of the focal mechanism solutions estimated from local and tele-seismic measurements and stress inversions to support the recent and previously published studies in this region. They have applied different pre-existing clustering methods – e.g. HDBSCAN (hierarchical density-based clustering for applications with noise) and agglomerative hierarchical analysis – considering to the geographical coordinates, focal depths and parameters of the focal mechanism solutions of the used seismic events, as well. Moreover, they have attempted to improve a fully-automated algorithm for the classification of the earthquakes for the estimations. This algorithm does not call for the setting of hyper-parameters, thus the affection of the subjectivity can be reduced significantly and the running time can be also decreased. In all cases, the resulted stress tensors are in close agreement with the earlier presented results.

How to cite: Czirok, L., Kuslits, L., and Gribovszki, K.: Cluster analysis in the studying of stress relation in the Vrancea-zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9749, https://doi.org/10.5194/egusphere-egu21-9749, 2021.

How to cite: Pacher, C., Schicker, I., DeWit, R., and Plant, C.: AWT - Clustering using an Aggregated Wavelet Tree: A novel automatic unsupervised clustering and outlier detection algorithm for time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12324, https://doi.org/10.5194/egusphere-egu21-12324, 2021.

Renewable energy sources such as wind energy play a crucial role in most climate change mitigation scenarios because of their ability to significantly reduce energy-related carbon emissions. In order to understand and design future energy systems, detailed modeling of renewable energy sources is important. In the light of making energy system modelling possible at all variability scales of local weather conditions, renewable energy source information with high resolution in both space and time are required.

Nowadays, renewable energy resources data that are widely used among the energy modeling community are reanalysis data such as ERA5, COSMO REA6, and MERRA2. Taking wind speed as an example, reanalysis data can provide long term spatially resolved wind information on any desired height in a physically consistent way. However, their spatial resolution is coarse. In order to obtain a fine spatial resolution data focusing on wind speed, this paper proposes a statistical downscaling method based on reanalysis data, observation data, and the local topography.

While most statistical wind downscaling studies have focused on obtaining site specific data or downscaling probability density functions, this paper focuses on downscaling one-year hourly wind speed time series for Europe to 0.00833 degree X 0.00833 degree (approximately 1km X 1km) resolution. It has been proven by various studies that the local topography influences wind speed. The topographic structure in this study is determined by two metrics: TPI, a topographic position index that compares the elevation of each cell to the mean elevation of the neighborhood areas and Sx, a slope-based, direction-dependent parameter that describes the topography in the upwind direction. The observation data used in this study are MeteoSwiss measurement values which provide the hourly wind speed time series at the station heights. For each weather station with observation data, biases described by the local terrain features are introduced to minimize the root mean square error (RMS) and Kolmogorov-Smirnov D (KSD) statistic between the corrected and the observed wind speed. These biases are then assigned to grid points with the same terrain types as the weather station, which enables downscaling of the wind speed for whole Europe.

The results show that this downscaling method can improve the RMS and KSD for both ERA5 and COSMO REA6, especially at mountain ridges, which indicates that it can not only decrease the bias, but also provide a better match to the observed wind speed distributions.

How to cite: Hu, W., Scholz, Y., Yeligeti, M., von Bremen, L., and Schroedter-Homscheidt, M.: Statistical downscaling of wind speed time series data based on topographic variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12734, https://doi.org/10.5194/egusphere-egu21-12734, 2021.

Natural hazards are inherently spatio-temporal processes. Spatio-temporal clustering methodologies applied to natural hazard data can help distinguish clustering patterns that would not only identify point-event dense regions and time periods, but also provide insight into the hazardous process. Here we review spatio-temporal clustering methodologies applicable to point event datasets representative of natural hazards and we evaluate their performance using both synthetic and real life data. We first present a systematic overview of major spatio-temporal clustering methodologies used in the literature, which include clustering procedures  that are (i) global (providing a single quantitative measure of the degree of clustering in the dataset) and (ii) local (i.e. assigning individual point events to a cluster). A total of seven methodologies from these two groups of clustering procedures are applied to real-world (lightning) and synthetic datasets. For (i) global procedures, we explore Knox, Mantel, Jacquez k-NN tests and spatio-temporal K-functions and for (ii) local procedures we consider spatio-temporal scan statistic, kernel density estimation and density-based clustering method OPTICS. The dataset of 7021 lightning strikes is from 1 and 2 July 2015 over the UK, when a severe three-storm system crossed the region with different convective modes producing each of the storms. The synthetic datasets are representative of various topologies of a point-event natural hazard data with a moving source. We introduce a two-source model with input parameters related to the physical properties of the source. Each source has a set number of points events, initiation point in space and time, movement speed, direction, inter-event time distribution and spatial spread distribution. In addition to a base model of two identical moving sources with a set temporal separation, we produce four different topologies of the data by incrementally varying the speed parameter of the source, spatial spread parameters, direction and initiation points, and angle of two sources. With these five synthetic datasets representative of various two-source models, we evaluate the performance of the methodologies. The performance is assessed based on the ability of each methodology to separate the point events produced by the two sources and the sensitivity of these results to changes in the model input parameters. We further discuss the benefits of combining global and local clustering procedures in the analyses as we gain an initial understanding of the spatial and temporal scales over which clustering is present in the data by using global clustering procedures. This information then helps to inform and limit the choice of input parameters for the local clustering procedures.

How to cite: Zandovskis, U., Malamud, B. D., and Pigoli, D.: Spatio-temporal clustering methodologies for point-event natural hazards, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13210, https://doi.org/10.5194/egusphere-egu21-13210, 2021.

Wildfires constitute a complex environmental disaster triggered by several interacting natural and human factors that can affect the biodiversity, species composition and ecosystems, but also human lives, regional economies and environmental health. Therefore, wildfires have become the focus on forestry and ecological research and are receiving considerable attention in forest management. Current advances in automated learning and simulation methods, like machine learning (ML) algorithms, recently aroused great interest in wildfires risk assessment and mapping. This quantitative evaluation is carried out by taking into account two factors: the location and spatial extension of past wildfires events and the geo-environmental and anthropogenic predisposing factors that favored their ignition and spreading. When dealing with risk assessment and predictive mapping for natural phenomena, it is crucial to ascertain the reliability and validity of collected data, as well as the prediction capability of the obtained results. In a previous study (Tonini et al. 2020) authors applied Random Forest (RF) to elaborate wildfire susceptibility mapping for Liguria region (Italy). In the present study, we address to the following outstanding issues, which are still unsolved: (1) the vegetation map included a class labeled “burned area” that masked to true burned vegetation; (2) the implemented model based on RF gave good results, but it needs to be compared with other ML based approaches; (3) to test the predictive capabilities of the model, the last three years of observations were taken, but these are not fully representative of different wildfires regimes, characterizing non-consecutives years. Thus, by improving the analyses, the following results were finally achieved. 1) the class “burned areas” has been reclassified based on expert knowledge, and the type of vegetation correctly assigned. This allowed correctly estimating the relative importance of each vegetation class belonging to this variable. (2) Two additional ML based approach, namely Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM), were tested besides RF and the performance of each model was assessed, as well as the resulting variable ranking and the predicting outputs. This allowed comparing the three ML based approaches and evaluating the pros and cons of each one. (3) The training and testing dataset were selected by extracting the yearly-observations based on a clustering procedure, allowing accounting for the temporal variability of the burning seasons. As result, our models can perform on average better prediction in different situations, by taking into considering years experiencing more or less wildfires than usual. The three ML-based models (RF, SVM and MLP) were finally validated by means of two metrics: i) the Area Under the ROC Curve, selecting the validation dataset by using a 5-folds cross validation procedure; ii) the RMS errors, computed by evaluating the difference between the predicted probability outputs and the presence/absence of an observed event in the testing dataset.

Bibliography:

Tonini, M.; D’Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences 2020, 10, 105. https://doi.org/10.3390/geosciences10030105

How to cite: Trucchia, A., Isnardi, S., D'Andrea, M., Biondi, G., Fiorucci, P., and Tonini, M.: Wildfire susceptibility assessment: evaluation of the performance of different machine learning algorithms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7162, https://doi.org/10.5194/egusphere-egu21-7162, 2021.

The Free Evaluation System Framework (Freva - freva.met.fu-berlin.de , xces.dkrz.de , www-regiklim.dkrz.de - https://github.com/FREVA-CLINT/Freva) is a software infrastructure for standardized data and tool solutions in Earth system science. Freva runs on high performance computers (HPC) to handle customizable evaluation systems of research projects, institutes or universities. It combines different software technologies into one common hybrid infrastructure, where all its features are accessible via shell and web environment. Freva indexes different data projects into one common search environment by storing the metadata information of the self-describing model, reanalysis and observational data sets in a database. The database interface satisfies the international standards provided by the Earth System Grid Federation (ESGF). This implemented metadata system with its advanced but easy-to-handle search tool supports users, developers and their plugins to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Facilitation of the provision and usage of tools and climate data automatically increases the number of scientists working with the data sets and identifying discrepancies. Plugins are also able to integrate their e.g. post-processed results into the database of the user. This allows e.g. post-processing plugins to feed statistical analysis plugins, which fosters an active exchange between plugin developers of a research project. Additionally, the history and configuration sub-system stores every analysis performed with the evaluation system in a database. Configurations and results of the tools can be shared among scientists via shell or web system. Therefore, plugged-in tools benefit from transparency and reproducibility. Furthermore, the system suggests existing results already produced by other users – saving CPU hours, I/O, disk space and time. An integrated web shell (shellinabox) adds a degree of freedom in the choice of the working environment and can be used as a gate to the research projects on a HPC. Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science. New Features and aspects of further development and collaboration are discussed.

How to cite: Lucio-Eceiza, E. E., Kadow, C., Bergemann, M., Ramadoss, M., Illing, S., Kunst, O., Schartner, T., Grieger, J., Schuster, M., Richling, A., Kirchner, I., Rust, H., Sommer, P., Cubasch, U., Ulbrich, U., Thiemann, H., and Ludwig, T.: Free Evaluation System Framework (Freva) - New Features and Development, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4468, https://doi.org/10.5194/egusphere-egu21-4468, 2021.

The Earth System Model Evaluation Tool (ESMValTool) is a free and open-source community diagnostic and performance metrics tool for the evaluation of Earth system models such as those participating in the Coupled Model Intercomparison Project (CMIP). Version 2 of the tool (Righi et al. 2020, www.esmvaltool.org) features a brand new design composed of a core that finds and processes data according to a ‘recipe’ and an extensive collection of ready-to-use recipes and associated diagnostic codes for reproducing results from published papers. Development and discussion of the tool (mostly) takes place in public on https://github.com/esmvalgroup and anyone with an interest in climate model evaluation is welcome to join there.

Since the initial release of version 2 in the summer of 2020, many improvements have been made to the tool. It is now more user friendly with extensive documentation available on docs.esmvaltool.org and a step by step online tutorial. Regular releases, currently planned three times a year, ensure that recent contributions become available quickly while still ensuring a high level of quality control. The tool can be installed from conda, but portable docker and singularity containers are also available.

Recent new features include a more user-friendly command-line interface, citation information per figure including CMIP6 data citation using ES-DOC, more and faster preprocessor functions that require less memory, automatic corrections for a larger number of CMIP6 datasets, support for more observational and reanalysis datasets, and more recipes and diagnostics.

The tool is now also more reliable, with improved automated testing through more unit tests for the core, as well as a recipe testing service running at DKRZ for testing the scientific recipes and diagnostics that are bundled into the tool. The community maintaining and developing the tool is growing, making the project less dependent on individual contributors. There are now technical and scientific review teams that review new contributions for technical quality and scientific correctness and relevance respectively, two new principal investigators for generating a larger support base in the community, and a newly created user engagement team that is taking care of improving the overall user experience.

How to cite: Andela, B., Alidoost, F., Brunner, L., Camphuijsen, J., Crezee, B., Drost, N., Gier, B., Hassler, B., Kalverla, P., Lauer, A., Loosveldt-Tomas, S., Lorenz, R., Predoi, V., Righi, M., Schlund, M., Smeets, S., Vegas-Regidor, J., Von Hardenberg, J., Weigel, K., and Zimmermann, K.: Recent developments on the Earth System Model Evaluation Tool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3476, https://doi.org/10.5194/egusphere-egu21-3476, 2021.

Ease of use can easily become a limiting factor to scientific quality and progress. In order to verify and build upon previous results, the ability to effortlessly access and process increasing data volumes is crucial.

To level the playing field for all researchers, a shared infrastructure had to be developed. In Europe, this effort is coordinated mainly through the IS-ENES projects. The current infrastructure provides access to the data as well as compute resources. This leaves the tools to easily work with the data as the main obstacle for a smooth scientific process. Interestingly, not the scarcity of tools, but rather their abundance can lead to diverging workflows that hamper reproducibility.

The Earth System Model eValuation Tool (ESMValTool) was originally developed as a command line tool for routine evaluation of important analytics workflows. This tool encourages some degree of standardization by factoring out common operations, while allowing for custom analytics of the pre-processed data. All scripts are bundled with the tool. Over time this has grown into a library of so-called ‘recipes’.

In the EUCP project, we are now developing a Python API for the ESMValTool. This allows for interactive exploration, modification, and execution of existing recipes, as well as creation of new analytics. Concomitantly, partners in IS-ENES3 are making their infrastructure accessible through JupyterLab. Through the combination of these technologies, researchers can easily access the data and compute, but also the workflows or methods used by their colleagues - all through the web browser. During the vEGU, we will show how this extended infrastructure can be used to easily reproduce, and build upon, previous results.

How to cite: Kalverla, P. C., Smeets, S., Drost, N., Andela, B., Alidoost, F., and Camphuijsen, J.: Bringing ESMValTool to the Jupyter Lab, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4805, https://doi.org/10.5194/egusphere-egu21-4805, 2021.

The Earth System Model Evaluation Tool (ESMValTool) has been developed with the aim of taking model evaluation to the next level by facilitating analysis of many different ESM components, providing well-documented source code and scientific background of implemented diagnostics and metrics and allowing for traceability and reproducibility of results (provenance). This has been made possible by a lively and growing development community continuously improving the tool supported by multiple national and European projects. The latest major release (v2.0) of the ESMValTool has been officially introduced in August 2020 as a large community effort, and since then several additional smaller releases have followed.

The diagnostic part of the ESMValTool includes a large collection of standard “recipes” for reproducing peer-reviewed analyses of many variables across ESM compartments including atmosphere, ocean, and land domains, with diagnostics and performance metrics focusing on the mean-state, trends, variability and important processes, phenomena, as well as emergent constraints. While most of the diagnostics use observational data sets (in particular satellite and ground-based observations) or reanalysis products for model evaluation some are also based on model-to-model comparisons. This presentation gives an overview on the latest scientific diagnostics and metrics added during the last year including examples of applications of these diagnostics to CMIP6 model data.

How to cite: Bock, L., Hassler, B., and Lauer, A. and the ESMValTool Develpoment Team: New scientific diagnostics in the ESMValTool – an overview, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7724, https://doi.org/10.5194/egusphere-egu21-7724, 2021.

Developing an Earth system model evaluation tool for a broad user community is a real challenge, as the potential users do not necessarily have the same needs or expectations. While many evaluation tasks across user communities include common steps, significant differences are also apparent, not least the investment by institutions and individuals in bespoke tools. A key question is whether there is sufficient common ground to pursue a community tool with broad appeal and application.

We present the main results of a survey carried out by Assimila for the H2020 IS-ENES3 project to review the model evaluation needs of European Earth System Modelling communities. Interviewing approximately 30 participants among several European institutions, the survey targeted a broad range of users, including model developers, model users, evaluation data providers, and infrastructure providers. The output of the study provides an analysis of  requirements focusing on key technical, standards, and governance aspects.

The study used ESMValTool as a  current benchmark in terms of European evaluation tools. It is a community diagnostics and performance metrics tool for the evaluation of Earth System Models that allows for comparison of single or multiple models, either against predecessor versions or against observations. The tool is being developed in such a way that additional analyses can be added. As a community effort open to both users and developers, it encourages open exchange of diagnostic source code and evaluation results. It is currently used in Coupled Model Intercomparison Projects as well as for the development and testing of “new” models.

A key result of the survey is the widespread support for ESMValTool amongst users, developers, and even those who have taken or promote other approaches. The results of the survey identify priorities and opportunities in the further development of the ESMValTool to ensure long-term adoption of the tool by a broad community.

How to cite: Servonnat, J., Guilyardi, E., Stott, Z., Serradell, K., Lauer, A., Zimmerman, K., Adloff, F., Greening, M.-C., Kazeroni, R., and Vegas, J.: Model evaluation expectations of European ESM communities: results from a survey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15681, https://doi.org/10.5194/egusphere-egu21-15681, 2021.