HIGHLIGHTS
- who: MAY and colleagues from the Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, Potsdam Institute for Climate Impact Research, Postdam, Germany have published the research: Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe, in the Journal: (JOURNAL)
- what: This study shows that complex statistical models when made explainable can complement research with NWP models by diagnosing drivers that need further understanding a correct numerical representation for better future forecasts. The authors demonstrate how explainability tools, which have become more and more mature in . . .
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