Titlebook: Data Assimilation; Making Sense of Obse William Lahoz,Boris Khattatov,Richard Menard Book 2010 Springer-Verlag Berlin Heidelberg 2010 Atmos

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Research Satellitest which theories and models must be confronted and evaluated. Predictions of the variability of the Earth System require an understanding of the variability in the observations representing the “truth”. This requires observations that are: (i) high quality, i.e., have small errors and biases; (ii) c

Deference

The Role of the Model in the Data Assimilation Systemive model in an assimilation system. There are numerous books on atmospheric modelling, their history, their construction, and their applications (e.g. Trenberth 1992 Randall 2000 Jacobson 2005). This chapter will focus on specific aspects of the model and modelling in data assimilation.

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Numerical Weather Predictione over a period of several hours up to 1 or 2 weeks ahead. This approach is central to modern operational weather forecasting: it is the improvements in NWP systems that have led to continual improvements in the skill of weather forecasts over recent decades.

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Book 2010 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 observat

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Research Satellitespending on application, there may be further observational requirements. For example, for monitoring climate change, global coverage would generally be required; for studying high impact weather, high spatial and temporal resolution would generally be required.

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Variational Assimilationmporal sequence of model states and the observations that are available over a given assimilation window. As such, and contrary to the standard Kalman filter and, more generally, to sequential algorithms for assimilation, it propagates the information contained in the data both forward and backward in time.