@misc{Trifunov:AGU2018,
    type = {misc},
    key = {Trifunov:AGU2018},
    title = {Domain knowledge integration for causality analysis of carbon-cycle variables},
    author = {Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler},
    howpublished = {American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation},
    year = {2018},
    abstract = {Climate data has been vastly accumulated over the past several years, making climate science one of the most data-rich domains. Despite the abundance of data to process, data science has not had a lot of impact on climate research so far, due to the fact that ample expert knowledge is rarely exploited. Furthermore, the complex nature and the continuously changing climate system both contribute to the slow data science advances in the field. This issue was shown to be amend- able through the development of data-driven methodologies that are guided by theory to constrain search, discover more meaningful patterns, and produce more accurate models [1]. Causality analysis represents one of the most important tasks in climate research, its principal difficulties being the often found non-linearities in the data, in addition to hidden causes of the observed phenomena. We propose to ameliorate the problem of determining causal-effect dependencies to a certain extent by using deep learning methods together with domain knowledge integration. The suggested method is to be based on the causal effect variational auto-encoders (CEVAE) [2] and applied to half-hourly meteorological observations and land flux eddy covariance data. This will allow for exploration of the causal-effect relationships between air temperature (Tair), global radiation (Rg) and the CO2 fluxes gross primary productivity (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco). The aim of this study is to show whether prior domain knowledge could aid discovery of new causal relationships between certain carbon-cycle variables. In addition, the proposed method is presumed to find its application to similar problems, such as those related to CO2 concentration estimation and facilitate efforts towards better understanding of the Earth system.},
    url = {https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/354059},
}