Geoscience Reference
In-Depth Information
2.2 The MUSCAT analysis
The wind retrieval technique used by the French Weather Service is the Multiple-Doppler
Synthesis and Continuity Adjustment Technique (MUSCAT), which is a variational
algorithm allowing for a simultaneous and computationally efficient solution of the three
Cartesian wind components (
u, υ, w
). This method was originally proposed by Bousquet and
Chong (1998) to overcome the drawbacks of iterative analysis techniques used to process
data collected by airborne Doppler radars. It was later adapted to ground-based radars
(Chong and Bousquet 2001) in order to analyze observations collected during the field phase
of Mesoscale Alpine Programme (Bougeault et al. 2001) during which it was used in a semi-
operational mode to guide research aircrafts in the field (Chong et al., 2000). The MUSCAT
algorithm has been used successfully for more than 10 years in order to synthesize data
collected by ground-based and mobile research radars (e.g. Bousquet and Chong 2000,
Georgis al. 2003, Bousquet and Smull 2006), and has even been applied to wind lidars
(Drechsel et al. 2009). In order to use this algorithm in an operational framework a
modification of the initial MUSCAT formalism was proposed by Bousquet et al. (2008a) to
take into account extensive radar separation distances prevailing in operational radar
networks.
The current form of the MUSCAT algorithm is given hereafter. It consists in a global
minimization, in a least-squares sense, of the function F:
F uvw
,,
Auvw
,,
Buvw
,,
C uvw
,,
Duv
(,)
dxdy
(1)
S
such that,
F
F
F
0,
0
and
0.
(2)
u
v
w
The expression of term
A
is given by:
nnp
()
1
pq
2
(3)
Auvw
,,
u
v
(
wv V
)
ij
,
q
q
q
q
T
q
N
pq
11
where
u, v, w,
are the components of the wind field at grid point (
i,j
);
V
T
is the terminal
particle fallspeed at grid point
(i,j)
evaluated from empirical relationships with pre-
interpolated radar reflectivity
,
subscript
q
defines the
q
th
measurement of a total number
n
q
that is observed from the
p
th
radar and that falls inside an ellipsoid of influence centered on
the grid point (
i,j
);
N
is the total number of
n
q
's over the considered domain;
q
is the
Cressman weighting function depending on the distance between measurement
q
within the
ellipsoid and the considered grid point; and
n
p
is the total number of radars covering grid
point
(i,j)
(≥ 2).
This term represents the optimal least-squares fit of the observed radial Doppler velocities to
the derived wind component. The Cressman distance-dependent weighting function
accounts for non-collocated data and grid point values, and allows the interpolation of the
radar data onto the Cartesian grid of interest, in the data fit. In the current framework, the
interpolation is performed using a fixed horizontal influence radius of the Cressman
