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Home > Research > Ifsdocs > ASSIMILATION >  
   

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

REFERENCES

 
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5.7 Surface observation operators




All surface data are processed in the routine SURFACEO. Preparations for the vertical interpolation is done as for all other data in PREINT (see Subsection 5.3.2), and for surface data there are a few additional tasks which are performed in a separate routine, PREINTS. In PREINTS surface roughness over sea, dry static energy (SURBOUND), Richardson number, drag coefficients and stability functions (EXCHCO), are computed, as detailed in the following.


5.7.1 Mathematical formulation




An analytical technique (Geleyn, 1988) is used to interpolate values between the lowest model level and the surface. When Monin-Obukhov theory is applied:

 
(5.19)

 
(5.20)

 
(5.21)


where are wind and energy variables, are friction values and is von Kármán's constant.


The temperature is linked to the dry static energy by:

 
(5.22)

 
(5.23)


Defining the neutral surface exchange coefficient at the height as:

 
(5.24)


The drag and heat coefficients as:

 
(5.25)

 
(5.26)


we can set the following quantities:

 
, ,
(5.27)


and considering the stability function in stable conditions as:

 
(5.28)


we obtain integrating Eqs. (5.19) and (5.20) from 0 to (the lowest model level):

 
(5.29)

 
(5.30)


In unstable conditions the stability function can be expressed as:

 
(5.31)


and the vertical profiles for wind and dry static energy are:

 
(5.32)

 
(5.33)


The temperature can then be obtained from as:

 
(5.34)


When is set to the observation height, Eqs. (5.29) and (5.30) and Eqs. (5.32)-(5.34) give the postprocessed wind and temperature. To solve the problem, we have to compute the dry static energy at the surface (Subsection 5.7.2), with , and values depending on the drag and heat exchange coefficients Eq. as detailed in Subsection 5.7.3.


5.7.2 Surface values of dry static energy




To determine the dry static energy at the surface we use Eqs. (5.22) and (5.23) where the humidity at the surface is defined by:

 
(5.35)


is given by (Blondin, 1991):

 
(5.36)


with

 
(5.37)


where is the soil moisture content and is the soil moisture at field capacitiy (2/7 in volumetric units). Eq. (5.36) assigns a value of 1 to the surface relative humidity over the snow covered and wet fraction of the grid box. The snow-cover fraction depends on the snow amount :


where m is a critical value. The wet skin fraction is derived from the skin-reservoir water content :
,


where


with m being the maximum amount of water that can be held on one layer of leaves, or as a film on bare soil, is the leaf-area index, and is the vegetation fraction.


5.7.3 Transfer coefficients




Comparing the Eqs. (5.19) - (5.20) integrated from to with Eqs. (5.24) to (5.26), and can be analytically defined:

 
(5.38)

 
(5.39)


Because of the complicated form of the stability functions, the former integrals have been approximated by analytical expressions, formally given by:

 
(5.40)


where is given by Eq. (5.24). The bulk Richardson number is defined as:

 
(5.41)


where is the virtual potential temperature. The functions and correspond to the model instability functions and have the correct behaviour near neutrality and in the cases of high stability (Louis, 1979; Louis et al. 1982)
(a)   unstable case

 
(5.42)

 
(5.43)
  C=5
(b)   Stable case

 
(5.44)

 
(5.45)
  d = 5


5.7.4 Two-metre relative humidity




In GPRH relative humidity is computed according to Eq. (5.13). The relative humidity depends on specific humidity, temperature and pressure ( and , respectively) at the lowest model level. It is constant in the surface model layer, see PPRH2M.





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