TY  - JOUR
A1  - Bürger, Gerd
A1  - Heistermann, Maik
T1  - Shallow and deep learning of extreme rainfall events from convective atmospheres
T2  - Natural hazards and earth system sciences : NHESS
N2  - Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify daily ERA5 fields of convective indices according to CatRaRE, using an array of 13 statistical methods, consisting of 4 conventional (“shallow”) and 9 more recent deep machine learning (DL) algorithms; the classifiers are then applied to corresponding fields of
simulated present and future atmospheres from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The inherent uncertainty of the DL results from the stochastic nature of their optimization is addressed by employing an ensemble approach using 20 runs for each network. The shallow random forest method performs best with an equitable threat score (ETS) around 0.52, followed by the DL networks ALL-CNN and ResNet with an ETS near 0.48. Their success can be understood as a result of conceptual simplicity and parametric parsimony, which obviously best fits the relatively simple classification task. It is found that, on summer days, CatRaRE convective atmospheres over Germany occur with a probability of about 0.5. This probability is projected to increase, regardless of method, both in ERA5-reanalyzed and CORDEX-simulated atmospheres: for the historical period we find a centennial increase of about 0.2 and for the future period one of slightly below 0.1.
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64927
SN  - 1684-9981
VL  - 23
IS  - 9
SP  - 3065
EP  - 3077
PB  - European Geophysical Society
CY  - Katlenburg-Lindau
ER  -