Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fieldsmay determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered.
Bibliographical noteFunding Information:
It has been supported by the National Research Foundation of Korea by a grant funded by the Korean government (NRF-2010-220-D00083); by NOAA Grants NA10OAR4310159, NA10OAR4320137, and NA10OAR4310137 (Global Decadal Hydroclimate Predictability, Variability and Change); and by NSF Grant IIS-1029711. U. Lall was also supported by an IPA from the U.S. Army Corps of Engineers.
© 2016 American Meteorological Society.
- Atm/Ocean Structure/Phenomena
- Atmospheric circulation
- Extreme events
- Mathematical and statistical techniques
- Principal components analysis
- Regression analysis
- Short-range prediction
- Statistical techniques