UC Irvine

Precipitation is one of the key elements of the Earth's water cycle. Long-term precipitation observations in global scale and at high spatial and temporal resolutions are imperative for understanding how global warming has affected our climate system, particularly in terms of changes in the characteristics of precipitation. This dissertation contributes to the advancement of our understanding of the water cycle by 1) development of a new long-term high-resolution satellite-based precipitation product to be used for different hydrological and climate studies at a higher spatial and temporal resolution than previously possible, and 2) presenting a probabilistic framework to study the observed variations, trends and changes in global precipitation, particularly extreme precipitation events, in the course of time.

In the first part of the dissertation, using the Geostationary Earth Orbit (GEO) satellites Infrared (IR) channel observations of the brightness temperature of the cloud, and the Global Precipitation Climatology Project (GPCP) monthly product, a retrospective high-resolution (daily, 25km) satellite-based precipitation climate data record is developed and introduced. The product, namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), provides more than 30 years of global rainfall estimates from 01/01/1983 to delayed present covering the latitude band 60°S-60°N and longitude band 0°-360°. The results from a number of different verification studies (Hurricane Katrina, 1986 Sydney flood, precipitation probability density function, extreme precipitation indices, rainfall-runoff modeling) over different regions (U.S., Australia, China) are presented and are encouraging.

In the second part, a non-stationary probabilistic framework based on Extreme Value Theory (EVT) is developed to assess potential footprints of climate change on characteristics (intensity and frequency) of extreme precipitation events. The main goal was to investigate if there have been statistically significant changes and trends in the Probability Distribution Function (PDF) of precipitation extremes over time. The proposed probabilistic scheme which is based on Generalized Extreme Value (GEV) and Generalized Pareto (GP) distributions proved to be effective in stochastically modeling the behavior of precipitation extremes over the past three decades. The statistically significant trends in the time-variant GEV distribution are tested over the U.S. The results show that Eastern and particularly the Northeastern parts of the U.S. are experiencing positive trends in the intensity and frequency of extreme precipitation events.