000905607 001__ 905607
000905607 005__ 20230127125339.0
000905607 0247_ $$2doi$$a10.5194/gmd-2022-2
000905607 0247_ $$2Handle$$a2128/30446
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000905607 037__ $$aFZJ-2022-00839
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000905607 1001_ $$0P:(DE-Juel1)171435$$aBetancourt, Clara$$b0$$eFirst author
000905607 245__ $$aGlobal, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
000905607 260__ $$aKatlenburg-Lindau$$bCopernicus$$c2022
000905607 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1642779648_11563
000905607 3367_ $$2ORCID$$aWORKING_PAPER
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000905607 3367_ $$2BibTeX$$aARTICLE
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000905607 520__ $$aAbstract. Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here we present a data-driven ozone mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1° × 0.1°. The machine learning model is trained on AQ-Bench, a precompiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data. Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the feature space, we ensure that the model is only applied in regions where it is reliable. We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.
000905607 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000905607 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x1
000905607 536__ $$0G:(DE-Juel1)kiste_20200501$$aAI Strategy for Earth system data (kiste_20200501)$$ckiste_20200501$$fAI Strategy for Earth system data$$x2
000905607 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x3
000905607 588__ $$aDataset connected to CrossRef
000905607 7001_ $$0P:(DE-HGF)0$$aStomberg, Timo T.$$b1
000905607 7001_ $$0P:(DE-HGF)0$$aEdrich, Ann-Kathrin$$b2
000905607 7001_ $$0P:(DE-Juel1)186635$$aPatnala, Ankit$$b3$$ufzj
000905607 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin G.$$b4
000905607 7001_ $$0P:(DE-HGF)0$$aRoscher, Ribana$$b5
000905607 7001_ $$0P:(DE-HGF)0$$aKowalski, Julia$$b6
000905607 7001_ $$0P:(DE-Juel1)180752$$aStadtler, Scarlet$$b7$$eCorresponding author$$ufzj
000905607 773__ $$0PERI:(DE-600)2456729-2$$a10.5194/gmd-2022-2$$tGeoscientific model development discussions$$x1991-9611$$y2022
000905607 8564_ $$uhttps://juser.fz-juelich.de/record/905607/files/gmd-2022-2.pdf$$yOpenAccess
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000905607 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171435$$aForschungszentrum Jülich$$b0$$kFZJ
000905607 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Universität Bonn$$b1
000905607 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b2$$kRWTH
000905607 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186635$$aForschungszentrum Jülich$$b3$$kFZJ
000905607 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)6952$$aForschungszentrum Jülich$$b4$$kFZJ
000905607 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aUniversität Bonn$$b5
000905607 9101_ $$0I:(DE-588b)36241-4$$6P:(DE-HGF)0$$aTechnische Universität München$$b5$$kTUM
000905607 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b6$$kRWTH
000905607 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180752$$aForschungszentrum Jülich$$b7$$kFZJ
000905607 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000905607 9141_ $$y2022
000905607 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
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000905607 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2022-11-10
000905607 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2022-11-10
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000905607 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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