%0 Journal Article %1 rehfeld2021potential %A Rehfeld, Kira %A Wider, Jonathan %A Theisen, Nadine %A Werner, Martin %A Köthe, Ullrich %A Weitzel, Nils %D 2021 %J EGU General Assembly Conference Abstracts %K imported topic_earthenvironment %P EGU21-2979 %T The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation %X The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation - NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation Rehfeld, Kira ; Wider, Jonathan ; Theisen, Nadine ; Werner, Martin ; Köthe, Ullrich ; Weitzel, Nils Abstract Tracing the spatio-temporal distribution of water isotopologues (eg, H216O, H218O,HD16O, D216O), in the atmosphere allows insights in to the hydrological cycle and surface-atmosphere interactions. Strong relationships between atmospheric circulation and isotopologue variability exist, mitigated by fractionation during phase transitions of water. Isotopic gradients correlate with precipitation amount, temperature, with distance to source areas of evaporation and often follow topographic …

@article{rehfeld2021potential, abstract = {The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation - NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation Rehfeld, Kira ; Wider, Jonathan ; Theisen, Nadine ; Werner, Martin ; Köthe, Ullrich ; Weitzel, Nils Abstract Tracing the spatio-temporal distribution of water isotopologues (eg, H216O, H218O,HD16O, D216O), in the atmosphere allows insights in to the hydrological cycle and surface-atmosphere interactions. Strong relationships between atmospheric circulation and isotopologue variability exist, mitigated by fractionation during phase transitions of water. Isotopic gradients correlate with precipitation amount, temperature, with distance to source areas of evaporation and often follow topographic …}, added-at = {2024-11-29T11:54:40.000+0100}, author = {Rehfeld, Kira and Wider, Jonathan and Theisen, Nadine and Werner, Martin and Köthe, Ullrich and Weitzel, Nils}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2f712607ae5791c6345cddeabfe39f1f4/joum576e}, citation = {EGU General Assembly Conference Abstracts, EGU21-2979, 2021}, interhash = {3614488e0d745039eeed77b92d3fd8c9}, intrahash = {f712607ae5791c6345cddeabfe39f1f4}, journal = {EGU General Assembly Conference Abstracts}, keywords = {imported topic_earthenvironment}, pages = {EGU21-2979}, timestamp = {2024-11-29T11:54:40.000+0100}, title = {The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation}, year = 2021 }