What is it about?
Integrate slowly varying oceanic signals and synoptic scale atmospheric responses in a data-driven network to improve extreme precipitation prediction skills
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Why is it important?
The novel application of the correlation network extraction algorithm to the identification of stable, correlation climate patterns achieved an unprecedented 30-day extreme precipitation predictability. Such 30-day timescale is considered the most difficult subseasonal-to-seasonal (S2S) scale that corresponds to the gap in predictability between the weather forecast and climate prediction. The unprecedented prediction skill within the S2S timescale offers the most valuable information for flood preparation at a lead time that is well beyond the lead time of meteorological forecasts. This study advances the integration of computer science, atmospheric science and hydroclimate study in the face of global climate and environmental change.
Read the Original
This page is a summary of: Exploring the Predictability of 30-Day Extreme Precipitation Occurrence Using a Global SST–SLP Correlation Network, Journal of Climate, February 2016, American Meteorological Society,
DOI: 10.1175/jcli-d-14-00452.1.
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