Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability

Abstract

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability by Luciano Rodriguez This dissertation evaluates response variables (classifiers) on various models applied to the detection of El Niño Southern Oscillation (ENSO) on California's seven climate divisions by using modeled and gauge (in-situ/ground) precipitation measurements and various climate indices. Three scientific studies were conducted as part of this research for evaluation of spatial and temporal ENSO events from modeled and gauge data using: 1) Wavelets 2) Autoregressive-moving-average (ARMA) model / Empirical Mode Decomposition (EMD) 3) Vector Generalized Linear Model (VGLM). This dissertation aims to propose and evaluate a methodology for developing a model to measure ENSO events accurately. The hypothesis is that precipitation data (either modeled or gauge) can be used to forecast ENSO events. One can create an assimilated index from various weighted indices as opposed to solely relying on popular climate indices as SOI, PDO, or Niño 3.4. Another objective is to identify how well modeled precipitation compares to gauge precipitation (ground truth) and if the composition of the indices are the same for both. A methodology for validating the generalization performance of the model is proposed and implemented (Controlled Parameter Cross-Validation). An analysis was performed using modeled data (regeneration of observable measurements and ground measurements) and gauge from seven climate divisions in California. VI