TY  - JOUR
AU  - Betancourt, Clara
AU  - Stomberg, Timo T.
AU  - Edrich, Ann-Kathrin
AU  - Patnala, Ankit
AU  - Schultz, Martin G.
AU  - Roscher, Ribana
AU  - Kowalski, Julia
AU  - Stadtler, Scarlet
TI  - Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
JO  - Geoscientific model development
VL  - 15
IS  - 11
SN  - 1991-959X
CY  - Katlenburg-Lindau
PB  - Copernicus
M1  - FZJ-2022-02315
SP  - 4331 - 4354
PY  - 2022
AB  - 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 (“air quality benchmark dataset”), a pre-compiled 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 input features, 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000805424800001
DO  - DOI:10.5194/gmd-15-4331-2022
UR  - https://juser.fz-juelich.de/record/908001
ER  -