Reanalysis: Data Assimilation for Scientific Investigation of Climate Richard B. Rood1 and Michael G. Bosilovich2 1University of Michigan, Ann Arbor, MI, USA,
[email protected] 2NASA Goddard Space Flight Center, MD, USA,
[email protected] 1 Introduction Reanalysis is the assimilation of long time series of observations with an unvarying assimilation system to produce datasets for a variety of applications; for example, climate variability, chemistry-transport, and process studies. Reanalyses were originally proposed for atmospheric observations as a method to generate “climate” datasets from “weather” observations. As the satellite records of chemical, land and oceanic parameters build with time, “reanalyses” are being developed for other types of observations. Coupled reanalyses, for example atmospheric-ocean reanalyses, are possible. In addition, very long reanalyses that use no satellite observations are being planned (e.g. Compo et al. 2006). Reanalysis datasets have become one of the most important datasets for scientific and application communities. As of July 2009, the Kalnay et al. (1996) paper, which describes one of the first reanalysis datasets, has more than 6600 recorded citations. In this chapter discussion will be drawn from the experience of atmospheric reanalysis, and the issues raised are relevant to all types of reanalysis. The provision of reanalyses was advocated by Bengtsson and Shukla (1988) and Trenberth and Olson (1988) in order to provide homogeneous datasets for climate applications and to encourage research in the use of satellite observations without the operational constraints of Numerical Weather Prediction. Trenberth and Olson (1988) calculated derived products, such as the Hadley circulation, from assimilation analyses used in operational weather forecasting.