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DATA ASSIMILATION
IFS documentation Front Page
Table of contents
CHAPTER 1 Incremental
formulation of 3D/4D variational assimilation-an overview
CHAPTER 2 3D variational assimilation
CHAPTER 3 4D variational assimilation
CHAPTER 4 Background term
CHAPTER 5 Conventional observational
constraints
CHAPTER 6 Satellite observational
constraints
CHAPTER 7 Background, analysis
and forecast errors
CHAPTER 8 Gravity-wave control
CHAPTER 9 Data partitioning (OBSORT)
CHAPTER 10 Observation screening
CHAPTER 11 Analysis of snow
CHAPTER 12 Land surface analysis
CHAPTER 13 SST and sea-ice analysis
CHAPTER 14 Reduced-rank Kalman filter
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5.1 Introduction
The observation operators provide the link between the analysis variables and the observations (Lorenc, 1986, Pailleux, 1990). The operator
in Eq. (1.4) signifies
the ensemble of operators transforming the control variable 
into the equivalent of each observed
quantity, 
, at observation locations. The 3D/4D-Var implementation allows
to be (weakly) non-linear, which is seen to be an advantage
for the use of TOVS radiance data, for example. In this chapter we define
the content of each of the observation operators and describe the observational
data used in 3D/4D-Var. The use of satellite data is described in Chapter 6 `Satellite observational
constraints' .Next Section
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20.03.2002 |
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