Show simple item record

dc.contributor.advisorGeorgakakos, Aris
dc.contributor.authorChen, Chia-Jeng
dc.date.accessioned2013-09-18T15:41:45Z
dc.date.available2013-09-18T15:41:45Z
dc.date.issued2012-06-20
dc.identifier.urihttp://hdl.handle.net/1853/48974
dc.description.abstractA 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.
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectStatistical model
dc.subjectEl Nino-southern oscillation
dc.subjectTeleconnection
dc.subjectPrediction
dc.subjectWater resources
dc.subjectMeteorology
dc.subject.lcshOcean temperature
dc.subject.lcshOcean-atmosphere interaction
dc.subject.lcshThermoclines (Oceanography)
dc.subject.lcshAtmospheric circulation
dc.subject.lcshMathematical optimization
dc.subject.lcshPrecipitation forecasting
dc.subject.lcshRainfall probabilities
dc.titleHydro-climatic forecasting using sea surface temperatures
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentCivil and Environmental Engineering
dc.contributor.committeeMemberBlack, Robert
dc.contributor.committeeMemberLuo, Jian
dc.contributor.committeeMemberSturm, Terry
dc.contributor.committeeMemberYao, Huaming


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record