TY  - JOUR
A1  - Steininger, Michael
A1  - Abel, Daniel
A1  - Ziegler, Katrin
A1  - Krause, Anna
A1  - Paeth, Heiko
A1  - Hotho, Andreas
T1  - ConvMOS: climate model output statistics with deep learning
JF  - Data Mining and Knowledge Discovery
N2  - Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.
KW  - Klima
KW  - Modell
KW  - Deep learning
KW  - Neuronales Netz
KW  - climate
KW  - neural networks
KW  - model output statistics
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324213
SN  - 1384-5810
VL  - 37
IS  - 1
ER  -