Abstract: AbstractMultiple changes in Earth?s climate system have been observed over the past decades. Determining how likely each of these changes is to have been caused by human influence is important for decision making with ...

Abstract: ne of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial?temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are ...

Abstract: The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of ...

Abstract: nsembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization ...

Abstract: AbstractTo satisfy a wide range of end users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical postprocessing methods based on quantile regression ...

Abstract: AbstractBoth climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the ...