Hydro-climatic forecasting using sea surface temperatures

Statistical model;El Nino-southern oscillation;Teleconnection;Prediction;Water resources;Meteorology

Chen, Chia-Jeng ; Georgakakos, Aris Civil and Environmental Engineering Black, Robert Luo, Jian Sturm, Terry Yao, Huaming ; Georgakakos, Aris

University:Georgia Institute of Technology

Department:Civil and Environmental Engineering

关键词: Statistical model;    El Nino-southern oscillation;    Teleconnection;    Prediction;    Water resources;    Meteorology;   

Others  :  https://smartech.gatech.edu/bitstream/1853/48974/1/chen_chiajeng_201208_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

A key determinant of atmospheric circulation patterns and regional climatic conditions is sea surface temperature (SST). This has been the motivation for the development of various teleconnection methods aiming to forecast hydro-climatic variables. Among such methods are linear projections based on teleconnection gross indices (such as the ENSO, IOD, and NAO) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if the predefined indices or EOFs cannot account for climatic variability in the region of interest. This study introduces a new hydro-climatic forecasting method that identifies SST predictors in the form of dipole structures. An SST dipole that mimics major teleconnection patterns is defined as a function of average SST anomalies over two oceanic areas of appropriate sizes and geographic locations. The screening process of SST-dipole predictors is based on an optimization algorithm that sifts through all possible dipole configurations (with progressively refined data resolutions) and identifies dipoles with the strongest teleconnection to the external hydro-climatic series. The strength of the teleconnection is measured by the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate ensemble hydro-climatic forecasts. The dipole teleconnection method is applied to the forecasting of seasonal precipitation over the southeastern US and East Africa, and the forecasting of streamflow-related variables in the Yangtze and Congo Rivers. These studies show that the new method is indeed able to identify dipoles related to well-known patterns (e.g., ENSO and IOD) as well as to quantify more prominent predictor-predictand relationships at different lead times. Furthermore, the dipole method compares favorably with existing statistical forecasting schemes. An operational forecasting framework to support better water resources management through coupling with detailed hydrologic and water resources models is also demonstrated.