The ATMODAT Standard enhances FAIRness of Atmospheric Model data

Atmospheric Model data form the basis to understand and predict weather, climate and air quality phenomena. Access to this data is not only of interest to a wide scientific community but also to public services, companies, politicians and citizens. One way to make the data available is to publish them via a data repository. To ensure that datasets in a repository are indeed Findable, Accessible, Interoperable, and Reusable (i.e. FAIR1), it is essential that the data are stored together with detailed metadata and that the file structure and metadata follow an established standard. Furthermore, datasets are easier to find and reuse if&#160; the corresponding metadata is machine-readable and uses a standardised vocabulary. While data standardization is well established in large, internationally coordinated model intercomparison projects (e.g. for climate models in CMIP2), joint standards are still lacking in many atmospheric modelling sub-disciplines, such as e.g. urban climate or cloud-resolving modelling.&#160;The AtMoDat project (Atmospheric Model Data)3, led by a team of atmospheric scientists and infrastructure providers, aims to improve the overall FAIRness of atmospheric model data and thus promote their re-use. Within the project, the ATMODAT standard4 has been developed which includes precise recommendations to achieve enhanced FAIRness of atmospheric model data in repositories. A prerequisite of this standard is that the data are published with a DataCite DOI5. The ATMODAT standard specifies requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages holding discipline-specific metadata are a core element of this standard.&#160;The ATMODAT standard is easy to implement and provides checklists for data curators and data producers. In addition, to facilitate the compliance check with the ATMODAT standard, the atmodat data checker6 has been developed. A dataset that complies with this standard will follow the FAIR principles and its metadata will be of high quality. If this compliance has been verified by the respective repository, the dataset can be labelled with the Earth System Data Branding (EASYDAB)7. This branding makes it easy for users to verify that the data are properly curated and the metadata has been quality assured.1&#160; Juckes et al., 2020: https://doi.org/10.5194/gmd-13-201-2020&#160; 2 &#160;Eyring et al., 2016: https://doi.org/10.5194/gmd-9-1937-2016 3 &#160;www.atmodat.de 4 &#160;https://doi.org/10.35095/WDCC/atmodat_standard_en_v3_0 5 &#160;https://datacite.org 6 &#160;https://github.com/AtMoDat/atmodat_data_checker&#160; 7&#160; https://easydab.de

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EASYDAB (Earth System Data Branding) for FAIR and Open Data

10.5194/egusphere-egu21-2139 ◽

2021 ◽

Author(s):

Anette Ganske ◽

Amandine Kaiser ◽

Angelina Kraft ◽

Daniel Heydebreck ◽

Andrea Lammert ◽

...

Keyword(s):

Open Data ◽

Atmospheric Model ◽

Research Data ◽

Quality Data ◽

Data Repository ◽

Earth System ◽

Model Data ◽

Core Element ◽

The Earth ◽

High Maturity

As in many scientific disciplines, there are a variety of activities in Earth system sciences that address the important aspects of good research data management. What has not been sufficiently investigated and dealt with so far is the easy discoverability and re-use of quality-checked data. This aspect is taken up by the EASYDAB label.EASYDAB1 is a currently developed branding for FAIR and open data from the Earth System Sciences. The branding can be adopted by institutions running a data repository which stores data from the Earth System Sciences. EASYDAB is always connected to a research data publication with DataCite DOIs. Data published under EASYDAB are characterized by a high maturity, extensive metadata information and compliance with a comprehensive discipline-specific standard. For these datasets, the EASYDAB logo is added to the landing page of the data repository. Thereby, repositories can indicate their efforts to publish data with high maturity.The first standard made for EASYDAB is the ATMODAT standard2, which has been developed within the AtMoDat3 project (Atmospheric Model Data). It incorporates concrete recommendations and requirements related to the maturity, publication and enhanced FAIRness of atmospheric model data. The requirements are for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages are a core element of the ATMODAT standard and should hold and present discipline-specific metadata on simulation and variable level.&#160;The ATMODAT standard includes checklists for the data producer and the data curator so that the compliance with the standard can easily be obtained by both sides. To facilitate automatic checking of the netCDF files headers, a checker program will also be provided and published with DOI. Moreover, a checker for the compliance with the requirements for the DOI Metadata will be developed and made openly available.&#160;The integration of standards from other disciplines in the Earth System Sciences, such as oceanography, into EASYDAB is helpful and desirable to improve the re-use of reviewed, high-quality data.&#160;&#160;1www.easydab.de2https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_03www.atmodat.de

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openEHR is FAIR-Enabling by Design

10.1101/2021.02.18.21251988 ◽

2021 ◽

Author(s):

Francesca Frexia ◽

Cecilia Mascia ◽

Luca Lianas ◽

Giovanni Delussu ◽

Alessandro Sulis ◽

...

Keyword(s):

Knowledge Discovery ◽

Implementation Strategy ◽

Contextual Information ◽

Structured Data ◽

Data Repository ◽

Domain Specific ◽

Research Outcomes ◽

Fair Principles ◽

Clinical Data Repository ◽

Machine Readable

AbstractThe FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of structured data, coupled with contextual information about their creation, expressed in domain-specific standards and machine readable formats. This paper analyses the potential support to FAIRness of the openEHR e-health standard, by theoretically assessing the compliance with each of the 15 FAIR principles of a hypothetical Clinical Data Repository (CDR) developed according to the openEHR specifications. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant CDRs.

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A Standard for the FAIR publication of Atmospheric Model Data developed by the AtMoDat Project

10.5194/egusphere-egu21-8144 ◽

2021 ◽

Author(s):

Andrea Lammert ◽

Anette Ganske ◽

Amandine Kaiser ◽

Angelina Kraft

Keyword(s):

Climate Model ◽

Urban Climate ◽

Open Data ◽

Atmospheric Model ◽

Scientific Data ◽

Earth System ◽

Model Data ◽

Climate Research ◽

Future Data ◽

System Data

Due to the increasing amount of data produced in science, concepts for data reusability are of immense importance. One aspect is the publication of data in a way that ensures that it is findable, reusable, traceable and comparable (FAIR1 principles). However, putting these principles into practice often causes significant difficulties for researchers. Therefore some repositories accept datasets described only with the minimum metadata required for DOI allocation. Unfortunately, this contains not&#160; enough information to conform to the FAIR principles - many research data cannot be reused despite having a DOI. In contrast, other repositories aid the researchers by providing advice and strictly controlling the data and their metadata. To simplify the process of defining the needed amount of metadata and of controlling the data and metadata, the AtMoDat2 (Atmospheric Model Data) project developed a detailed standard for the FAIR publication of atmospheric model data.For this purpose we have developed a concept for the &#8220;ideal&#8221; description of atmospheric model data. A prerequisite for this is the data publication with a DataCite DOI. The ATMODAT standard3 was developed to implement this concept. The standard defines the data format as NetCDF, mandatory metadata (for DOI, landing page and data header), and naming conventions used in climate research - the Climate and Forecast conventions (CF-conventions4). However, many variable names used in urban climate research, for example, are not part of the CF-conventions. For this, standard names have to be defined together with the community and the inclusion in the list of CF-conventions has to be requested. Furthermore we developed and published Python routines which allow data producers as well as repositories to check model output data against the standard.&#160;The ATMODAT standard will first be applied by the project partners of the two participating universities (University of Hamburg and Leipzig). Here, climate model data are processed with a post-processor in preparation for publication. Subsequently, the files including the specified metadata for the DataCite metadata schema will be published by the World Data Center for Climate5 (WDCC). Data fulfilling the AtMoDat standard will be marked at the landing page by a special EASYDAB6 (Earth System Data Branding) logo. EASYDAB is a currently developed branding for FAIR and open data from the Earth System Sciences. This indicates to future data users that the dataset is a verified dataset that can be easily reused. The standardization of the data and the further steps are easily transferable to data from other disciplines.1 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18&#160;2 https://www.atmodat.de/3 https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_04 https://cfconventions.org/5 https://cera-www.dkrz.de/WDCC/ui/cerasearch/6 https://www.easydab.de/&#160;

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Urban climate is central to the next-generation weather and climate models, field studies, and societal needs

Urban Climate ◽

10.1016/j.uclim.2021.100873 ◽

2021 ◽

Vol 38 ◽

pp. 100873

Author(s):

J.E. Gonzalez ◽

D. Niyogi

Keyword(s):

Climate Models ◽

Urban Climate ◽

Field Studies ◽

Next Generation ◽

Societal Needs ◽

Weather And Climate

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Future Australian Severe Thunderstorm Environments. Part II: The Influence of a Strongly Warming Climate on Convective Environments

Journal of Climate ◽

10.1175/jcli-d-13-00426.1 ◽

2014 ◽

Vol 27 (10) ◽

pp. 3848-3868 ◽

Cited By ~ 30

Author(s):

John T. Allen ◽

David J. Karoly ◽

Kevin J. Walsh

Keyword(s):

Climate Models ◽

Global Climate ◽

Convective Available Potential Energy ◽

Vertical Wind Shear ◽

Atmospheric Model ◽

Global Climate Models ◽

Historical Period ◽

Severe Thunderstorm ◽

Warming Climate ◽

Eastern Australia

Abstract The influence of a warming climate on the occurrence of severe thunderstorm environments in Australia was explored using two global climate models: Commonwealth Scientific and Industrial Research Organisation Mark, version 3.6 (CSIRO Mk3.6), and the Cubic-Conformal Atmospheric Model (CCAM). These models have previously been evaluated and found to be capable of reproducing a useful climatology for the twentieth-century period (1980–2000). Analyzing the changes between the historical period and high warming climate scenarios for the period 2079–99 has allowed estimation of the potential convective future for the continent. Based on these simulations, significant increases to the frequency of severe thunderstorm environments will likely occur for northern and eastern Australia in a warmed climate. This change is a response to increasing convective available potential energy from higher continental moisture, particularly in proximity to warm sea surface temperatures. Despite decreases to the frequency of environments with high vertical wind shear, it appears unlikely that this will offset increases to thermodynamic energy. The change is most pronounced during the peak of the convective season, increasing its length and the frequency of severe thunderstorm environments therein, particularly over the eastern parts of the continent. The implications of this potential increase are significant, with the overall frequency of potential severe thunderstorm days per year likely to rise over the major population centers of the east coast by 14% for Brisbane, 22% for Melbourne, and 30% for Sydney. The limitations of this approach are then discussed in the context of ways to increase the confidence of predictions of future severe convection.

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A new ensemble-based statistical methodology to verify changes in weather and climate models

10.5194/egusphere-egu21-4255 ◽

2021 ◽

Author(s):

Christian Zeman ◽

Christoph Schär

Keyword(s):

Climate Models ◽

Climate Model ◽

Numerical Models ◽

Hypothesis Test ◽

Model Development ◽

Atmospheric Model ◽

Statistical Hypothesis ◽

Model Parameters ◽

Rounding Errors ◽

Weather And Climate

Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.

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Bayesian optimization for tuning chaotic systems

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-1283-2014 ◽

2014 ◽

Vol 1 (2) ◽

pp. 1283-1312

Author(s):

M. Abbas ◽

A. Ilin ◽

A. Solonen ◽

J. Hakkarainen ◽

E. Oja ◽

...

Keyword(s):

Chaotic Systems ◽

Climate Models ◽

Atmospheric Model ◽

Bayesian Optimization ◽

Tuning Parameters ◽

Performance Metric ◽

Weather And Climate ◽

Computationally Expensive ◽

Low Dimensional ◽

Complex Chaotic Systems

Abstract. In this work, we consider the Bayesian optimization (BO) approach for tuning parameters of complex chaotic systems. Such problems arise, for instance, in tuning the sub-grid scale parameterizations in weather and climate models. For such problems, the tuning procedure is generally based on a performance metric which measures how well the tuned model fits the data. This tuning is often a computationally expensive task. We show that BO, as a tool for finding the extrema of computationally expensive objective functions, is suitable for such tuning tasks. In the experiments, we consider tuning parameters of two systems: a simplified atmospheric model and a low-dimensional chaotic system. We show that BO is able to tune parameters of both the systems with a low number of objective function evaluations and without the need of any gradient information.

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Precipitation at Dumont d'Urville, Adélie Land, East Antarctica: the APRES3 field campaigns dataset

Earth System Science Data ◽

10.5194/essd-10-1605-2018 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1605-1612 ◽

Cited By ~ 9

Author(s):

Christophe Genthon ◽

Alexis Berne ◽

Jacopo Grazioli ◽

Claudio Durán Alarcón ◽

Christophe Praz ◽

...

Keyword(s):

Climate Models ◽

Data Repository ◽

X Band ◽

Intensive Observation ◽

Radar Measurements ◽

Scientific Results ◽

Antarctic Precipitation ◽

Severe Shortage ◽

Observation Period

Abstract. Compared to the other continents and lands, Antarctica suffers from a severe shortage of in situ observations of precipitation. APRES3 (Antarctic Precipitation, Remote Sensing from Surface and Space) is a program dedicated to improving the observation of Antarctic precipitation, both from the surface and from space, to assess climatologies and evaluate and ameliorate meteorological and climate models. A field measurement campaign was deployed at Dumont d'Urville station at the coast of Adélie Land in Antarctica, with an intensive observation period from November 2015 to February 2016 using X-band and K-band radars, a snow gauge, snowflake cameras and a disdrometer, followed by continuous radar monitoring through 2016 and beyond. Among other results, the observations show that a significant fraction of precipitation sublimates in a dry surface katabatic layer before it reaches and accumulates at the surface, a result derived from profiling radar measurements. While the bulk of the data analyses and scientific results are published in specialized journals, this paper provides a compact description of the dataset now archived in the PANGAEA data repository (https://www.pangaea.de, https://doi.org/10.1594/PANGAEA.883562) and made open to the scientific community to further its exploitation for Antarctic meteorology and climate research purposes.

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Data Papers as a New Form of Knowledge Organization in the Field of Research Data

KNOWLEDGE ORGANIZATION ◽

10.5771/0943-7444-2019-8-622 ◽

2019 ◽

Vol 46 (8) ◽

pp. 622-638

Author(s):

Joachim Schöpfel ◽

Dominic Farace ◽

Hélène Prost ◽

Antonella Zane

Keyword(s):

Business Models ◽

Open Science ◽

Research Data ◽

Data Driven ◽

Academic Publishing ◽

Science Data ◽

Research Data Management ◽

Fair Principles ◽

New Form ◽

Machine Readable

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

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A short guide to increase FAIRness of atmospheric model data

Meteorologische Zeitschrift ◽

10.1127/metz/2020/1042 ◽

2020 ◽

Vol 29 (6) ◽

pp. 483-491

Author(s):

Anette Ganske ◽

Daniel Heydebreck ◽

Heinke Höck ◽

Angelina Kraft ◽

Johannes Quaas ◽

...

Keyword(s):

Atmospheric Model ◽

Model Data

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