Terascale data organization for discovering multivariate climatic trends

Current visualization tools lack the ability to perform full-range spatial and temporal analysis on terascale scientific datasets. Two key reasons exist for this shortcoming: I/O and postprocessing on these datasets are being performed in suboptimal manners, and the subsequent data extraction and analysis routines have not been studied in depth at large scales. We resolved these issues through advanced I/O techniques and improvements to current query-driven visualization methods. We show the efficiency of our approach by analyzing over a terabyte of multivariate satellite data and addressing two key issues in climate science: time-lag analysis and drought assessment. Our methods allowed us to reduce the end-to-end execution times on these problems to one minute on a Cray XT4 machine.