News Finding: "While the findings were hard to derive, local institutions can now apply them quite easily by using readily available data, which helps a lot," says co-author Jose A. Marengo of the National Institute for Space Research in Sao Paulo, Brazil.
Paper Finding: We emphasize that we did not train the proposed forecast rule in the sense of parameter optimization.
News Context: The new method makes it possible to correctly predict 90 percent of extreme rainfall events in the Central Andes occurring during conditions of the El Nino weather phenomenon when floods are generally more frequent, and 60 percent of those occurring under any other conditions. "While the findings were hard to derive, local institutions can now apply them quite easily by using readily available data, which helps a lot," says co-author Jose A. Marengo of the National Institute for Space Research in Sao Paulo, Brazil. It is now up to the affected countries to adapt their disaster preparation planning."
Paper Context: We note that, while the mechanism responsible for these extreme rainfall events in the ECA was uncovered using network divergence, the conditions used for the forecast rule can be determined directly and with little computational efforts by spatially averaging rainfall and geopotential height data. We emphasize that we did not train the proposed forecast rule in the sense of parameter optimization. Instead, the rule is derived directly from the results of the network divergence analysis and we show that its forecast skill does not change rapidly when changing the conditions used to define prediction times.