This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.

This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.

The demand for greater computer power in numerical weather prediction and meteorological research is as strong as ever. The world meteorological community has tried to meet this demand by exploiting parallelism. In this field, the European Centre for Medium-Range Weather Forecasts has established itself as the central venue for bringing together operational weather forecasters, climate researchers and parallel computer manufacturers to share their experiences through a series of workshops held every other year. This book reports on the latest workshop (2-6 December 1996) and is an excellent overview of the success which parallel systems have gained in meteorology worldwide, and how it was achieved. In addition, future trends in computer hardware and software development and its implications for meteorological computing are outlined.

Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).