Introduction to Freva – A Free Evaluation System Framework for Earth
                        System Modeling

Kadow, Christopher; Illing, Sebastian; Lucio-Eceiza, Etor E.; Bergemann, Martin; Ramadoss, Mahesh; Sommer, Philipp S.; Kunst, Oliver; Schartner, Thomas; Pankatz, Klaus; Grieger, Jens; Schuster, Mareike; Richling, Andy; Thiemann, Hannes; Kirchner, Ingo; Rust, Henning W.; Ludwig, Thomas; Cubasch, Ulrich; Ulbrich, Uwe

doi:10.5334/jors.253

Cited by 15 publications

(11 citation statements)

References 19 publications

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“…We are currently facing up to the challenges: The Free Evaluation System Framework for Earth System Modelling [Freva,18] is being further developed at DKRZ. Freva can be used to orchestrate all steps of scientific workflows necessary to perform research in data intensive climate science.…”

Section: Discussionmentioning

confidence: 99%

“…Various statistical methods are used for the scientific evaluation, but also for the processing and use of climate model data [e.g., 14,15,16]. Further, the ever increasing data amount and complexity of analysis workflows has spawned the development of novel data access and analysis infrastructures [e.g., 17,18,19]. Upcoming major funding initiatives facilitating a step-change in climate science already cast their shadows [e.g., 20] and will allow for the major investments in infrastructure needed to transform the way simulation-based climate sciences is performed [21].…”

Section: Introductionmentioning

confidence: 99%

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Canonical Workflows in Simulation-based Climate Sciences

2022

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In this paper we present the derivation of Canonical Workflow Modules from current workflows in simulation-based climate science in support of the elaboration of a corresponding framework for simulation-based research. We first identified the different users and user groups in simulation-based climate science based on their reasons for using the resources provided at the German Climate Computing Center (DKRZ). What is special about this is that the DKRZ provides the climate science community with resources like high performance computing (HPC), data storage and specialised services, and hosts the World Data Center for Climate (WDCC). Therefore, users can perform their entire research workflows up to the publication of the data on the same infrastructure. Our analysis shows, that the resources are used by two primary user types: those who require the HPC-system to perform resource intensive simulations to subsequently analyse them and those who reuse, build-on and analyse existing data. We then further subdivided these top-level user categories based on their specific goals and analysed their typical, idealised workflows applied to achieve the respective project goals. We find that due to the subdivision and further granulation of the user groups, the workflows show apparent differences. Nevertheless, similar “Canonical Workflow Modules” can be clearly made out. These modules are “Data and Software (Re)use”, “Compute”, “Data and Software Storing”, “Data and Software Publication”, “Generating Knowledge” and in their entirety form the basis for a Canonical Workflow Framework for Research (CWFR). It is desirable that parts of the workflows in a CWFR act as FDOs, but we view this aspect critically. Also, we reflect on the question whether the derivation of Canonical Workflow modules from the analysis of current user behaviour still holds for future systems and work processes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Canonical Workflows in Simulation-based Climate Sciences

2022

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“…Since the recalibration might destroy the standardization of the SPI (Paxian et al, 2019) it is executed before the SPI is calculated in this study [see Section Standardized Precipitation Index (SPI)]. This study applies the recalibration software tool (Pasternack et al, 2021) of the "Free Evaluation System Framework for Earth System Modeling" (FREVA, Kadow et al, 2021). Information on code availability can be found in these cited articles.…”

Section: Recalibrationmentioning

confidence: 99%

“…The correlation coefficients and (Fair)RPSS of this study were assessed based on the FREVA (Kadow et al, 2021) software tools MurCSS (Murphy-Epstein decomposition and Continuous Ranked Probability Skill Score, Illing et al, 2014) and ProblEMS (PROBabiListic Ensemble verification for MiKlip using SpecsVerification, Richling et al, 2017, based on routines from Siegert, 2014), respectively. Code is available as described in these cited articles or on request.…”

Section: Skill Assessmentmentioning

confidence: 99%

“…An inherent bias correction is included when the probabilistic model prediction is shown in conjunction with the tercile thresholds from observations in the reference period. The probabilistic predictions were estimated based on FREVA (Kadow et al, 2021) software tools. Original code can be accessed as pointed out in this article, and code adapted for this case study can be provided on request.…”

Section: Calculation and Display Of Probabilistic Predictionsmentioning

confidence: 99%

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High-Resolution Decadal Drought Predictions for German Water Boards: A Case Study for the Wupper Catchment

et al. 2022

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Water boards in Germany require decadal predictions to develop optimized management and adaptation strategies, especially within the claims of flood protection and water distribution management. Specifically, the Wupper catchment water board in western Germany is interested in decadal predictions of drought indices, which are correlated to dam water levels. For the management of small catchments, they need multi-year means and multi-year seasonal means of the hydrological seasons for forecast years 1–3 at high spatial resolution. Thus, the MPI-ESM-LR global decadal prediction system with 16 ensemble members at 200 km resolution was statistically downscaled with EPISODES to ~11 km in Germany. Simulated precipitation was recalibrated, correcting model errors and adjusting the ensemble spread. We tested different recalibration settings to optimize the skill. The 3-year mean and 3-year seasonal mean SPI (Standardized Precipitation Index), indicating excess or deficit of precipitation, was calculated. We evaluated the prediction skill with HYRAS observations, applying skill scores and correlation coefficients, and tested the significance of the skill at a 95% levelvia1,000 bootstraps. We found that the high-resolution statistical downscaling is able to preserve the skill of the global decadal predictions and that the recalibration can clearly improve the precipitation skill in Germany. Multi-year annual and August–October mean SPI predictions are promising for several regions in Germany. Additionally, there is potential for skill improvement with increasing ensemble size for all temporal aggregations, except for November–January. A user-oriented product sheet was developed and published on the Copernicus Climate Change Service website (https://climate.copernicus.eu/decadal-predictions-infrastructure). It provides 3-year mean probabilistic SPI predictions for the Wupper catchment and north-western Germany. For 2021–2023, a high probability of negative SPI (dry conditions) is predicted in most of the area. The decadal prediction skill is higher than using the observed climatology as reference prediction in several parts of the area. This case study was developed in cooperation with the Wupper catchment water board and discussed with further German water managers: The skill of high-resolution decadal drought predictions is considered to be promising to fulfill their needs. The product sheet is understandable, well-structured and can be applied to their working routines.

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Non-stationary large-scale statistics of precipitation extremes in central Europe

Fauer¹,

Rust²

2023

Stoch Environ Res Risk Assess

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Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model enables insights into the effect of large-scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.

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Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling

Cited by 15 publications

References 19 publications

Canonical Workflows in Simulation-based Climate Sciences

Canonical Workflows in Simulation-based Climate Sciences

High-Resolution Decadal Drought Predictions for German Water Boards: A Case Study for the Wupper Catchment

Non-stationary large-scale statistics of precipitation extremes in central Europe

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