Atmospheric Modeling, Data Assimilation and Predictability

Introduction

In previous chapters we saw that NWP is an initial/boundary value problem: given an estimate of the present state of the atmosphere (initial conditions), and appropriate surface and lateral boundary conditions, the model simulates (forecasts) the atmospheric evolution. Obviously, the more accurate the estimate of the initial conditions, the better the quality of the forecasts. Currently, operational NWP centers produce initial conditions through a statistical combination of observations and short-range forecasts. This approach has become known as “data assimilation”, whose purpose is defined by Talagrand (1997) as “using all the available information, to determine as accurately as possible the state of the atmospheric (or oceanic) flow.”

There are several excellent reviews of this subject, which has become an important science in itself. The book Atmospheric data analysis by Daley (1991) is a comprehensive description of methods for atmospheric data analysis and assimilation. Ghil and Malanotte-Rizzoli (1991) have written a rigorous discussion of present data assimilation methods with special emphasis on sequential methods. Talagrand (1997) gives an elegant introductory overview of current methods of data assimilation, and Zupanski and Kalnay (1999) also provide a short introduction to the subject. The book Data assimilation in meteorology and oceanography: Theory and practice (Ghil et al., editors, 1997) contains a wealth of important papers on current methods for data assimilation. An earlier but still useful book is Dynamic meteorology: Data assimilation methods (Bengtsson et al., editors, 1981). Thiebaux and Pedder (1987) provided a description of spatial interpolation methods applied to meteorology.