RICHARD 1.0 – Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data

Autor: Scharun, Christian, Ruhnke, Roland, Braesicke, Peter

Here, we introduce version 1.0 of the RICHARD algorithm, a Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data, e.g. in satellite measurement datasets (level-2 and above) or atmospheric chemistry model output. The overall goal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=copernicuspu::3168da2db40c87d6b2c25f988387069c
https://gmd.copernicus.org/preprints/gmd-2023-91/

Data Science and Earth System Science

Autor: zu Castell, Wolfgang, Ruhnke, Roland, Bouwer, Laurens M., Brix, Holger, Dietrich, Peter, Dransch, Doris, Frickenhaus, Stephan, Greinert, Jens, Petzold, Andreas

Publikováno v: Integrating Data Science and Earth Science ISBN: 9783030995454
zu Castell, W.; Ruhnke, R.; Bouwer, L.; Brix, H.; Dietrich, P.; Dransch, D.; Frickenhaus, S.; Greinert, J.; Petzold, A.: Data Science and Earth System Sciences. In: Bouwer, L.M.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; Greinert, J. (ed.): Integrating Data Science and Earth Science : Challenges and Solutions. Cham: Springer, 2022. 1-6. (DOI: /10.1007/978-3-030-99546-1_1) (ISBN: 978-3-030-99545-4)

Data-driven science has turned into a fourth fundamental paradigm of performing research. Earth System Science, following a holistic approach in unraveling the complex network of processes and interactions shaping system Earth, particularly profits f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7c6a0ae42fc5e94fd21ba48607de368
https://doi.org/10.1007/978-3-030-99546-1_1

Evaluating the Success of the Digital Earth Project

Autor: Bouwer, Laurens M., Rechid, Diana, Fritzsch, Bernadette, Henkel, Daniela, Kalbacher, Thomas, Köckerlitz, Werner, Ruhnke, Roland

Publikováno v: Bouwer, L.; Rechid, D.; Fritzsch, B.; Henkel, D.; Kalbacher, T.; Köckeritz, W.; Ruhnke, R.: Evaluating the Success of the Digital Earth Project. In: Bouwer, L.M.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; Greinert, J. (ed.): Integrating Data Science and Earth Science : Challenges and Solutions. Cham: Springer, 2022. 131-143. (DOI: /10.1007/978-3-030-99546-1_8) (ISBN: 978-3-030-99545-4)
Integrating Data Science and Earth Science ISBN: 9783030995454

The Digital Earth project aims at a strong interrelation between Data and Earth Science and a step-change in implementing data science methods within Earth science research. During the project, the progress of interdisciplinary collaboration and adop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cea3cfc6d9192d4a8419f7337ce33181
https://publications.hereon.de/id/50802

Data Analysis and Exploration with Computational Approaches

Autor: Wichert, Viktoria, Bouwer, Laurens M., Abraham, Nicola, Brix, Holger, Callies, Ulrich, González Ávalos, Everardo, Marien, Lennart Christopher, Matthias, Volker, Michaelis, Patrick, Rabe, Daniela, Rechid, Diana, Ruhnke, Roland, Scharun, Christian, Valizadeh, Mahyar, Vlasenko, Andrey, zu Castell, Wolfgang

Publikováno v: Wichert, V.; Bouwer, L.; Abraham, N.; Brix, H.; Callies, U.; González Ávalos, E.; Marien, L.; Matthias, V.; Michaelis, P.; Rabe, D.; Rechid, D.; Ruhnke, R.; Scharun, C.; Valizadeh, M.; Vlasenko, A.; zu Castell, W.: Data Analysis and Exploration with Computational Approaches. In: Bouwer, L.M.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; Greinert, J. (ed.): Integrating Data Science and Earth Science : Challenges and Solutions. Cham: Springer, 2022. 29-53. (DOI: /10.1007/978-3-030-99546-1_4) (ISBN: 978-3-030-99545-4)
Integrating Data Science and Earth Science ISBN: 9783030995454

Artificial intelligence and machine learning (ML) methods are increasingly applied in Earth system research, for improving data analysis, and model performance, and eventually system understanding. In the Digital Earth project, several ML approaches

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d19a89739ee95a0cc12927cb07fe66f6

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