Challenges in Monitoring Climate Rainfall Variability and Trends

Currently available long-term global rainfall datasets such as CMAP and GPCP provide widely varying estimates of climate rainfall variability and trends. While these differences are the result of a number of issues related to the integration of multiple diverse satellite and in-situ datasets, they are due in large part to time-dependent regional biases in the satellite estimates. Investigating differences between rainfall estimates from coincident TRMM precipitation radar (PR) and microwave imager (TMI) observations has led to a better understanding of the magnitude and nature of these biases, which stem from regional and time-dependent variations in unobserved cloud properties assumed by the retrieval algorithms to be globally unchanging. While random errors in the instantaneous rainfall estimates quickly diminish when averaged over large time and space scales, the systematic component of the retrieval error can have significant consequences for many climate, regional, and data assimilation applications. For example, time-dependent regional biases are responsible for large differences between TMI and PR estimates of tropical mean rainfall variability associated with the 1997/98 El Niņo.

The upcoming Global Precipitation Mission, with the core satellite currently scheduled for launch in 2010, will utilize data from a constellation of passive microwave radiometers to provide frequent high quality near global rainfall observations. Data are already available from a number of radiometers including TRMM TMI, SSM/I, SSM/IS, AMSR-E, and WindSat. In anticipation of GPM, we have begun to investigate a number of issues related to integrating observations from diverse sensors/platforms into a high quality global rainfall record suitable for a wide range of climate applications. This includes intercalibrating brightness temperatures between sensors, developing a consistent retrieval methodology applicable to a variety of sensors, identifying and removing biases in the retrieval, and developing a strategy to validate and produce uncertainty estimates over a wide range of climate regimes including those with little or no in-situ observations.