3DVAR Retrieval of 3D Moisture Field from Slantpath Water Vapor Observations of a Highresolution Hyp

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3DVAR Retrieval of 3D Moisture Field from
Slant-path Water Vapor Observations of a
High-resolution Hypothetical GPS Network

• Haixia Liu and Ming Xue
• Center for Analysis and Prediction of Stormsand
  School of Meteorology
• University of Oklahoma
• 16th Conf. Num. Wea. Pred
• January 2004

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Background and Motivation
Suominet (Ware, 2000)

• Accurate analysis of 3D water vapor field is
  important for NWP.
• GPS networks can potentially provide slant-path
  water vapor measurements at high spatial and
  temporal resolutions. Also, ground-based GPS
  receivers are relatively inexpensive.

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Background and Motivation continued

• The past works using 4DVAR and 3DVAR methods
  include
• Kuo et al. (1996), Guo et al. (2000), and Ha et
  al. (2003)
• MacDonald and Xie (2002)

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This Work

• Retrieve 3D moisture distribution using
  slant-path water vapor data from a hypothetical
  GPS observation network.
• Using 3DVAR data assimilation (Lorenc 1981 Daley
  1991) techniques, including treatment of
  background errors.

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Outline

• The 3DVAR method
• Observing System Simulation Experiments (OSSE)
• Numerical experiments
• Summary

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3DVAR Assimilation System
The following cost-function is minimized
variationally
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3DVAR Assimilation System - continued

• Define a new vector v (Huang, 2000),

The cost function can be written as
which does not involve the inverse of B.
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3DVAR Assimilation System - continued
B, the background error covariance matrix, is
modeled using isotropic Gaussian filter
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OSSE

• ARPS model (Xue et al, 2000) simulates one
  dryline case (nature run) during IHOP_2002
  field experiment period
• initialized at 12UTC June 18, 2002
• integrated for 3hours
• grid spacing 9km
• in a terrain-following coordinate.
• domain size 1620x1440km2 over Southern Great
  Plain
• This nature run result is considered as true
  atmosphere

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qv at 30m above ground
Specific humidity field (g kg-1) at the second
model level (30m) above ground, valid at 1500
UTC June 18, 2002, from the nature run.
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Hypothetical GPS network
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Hypothetical GPS network

• 9 GPS satellites
• simultaneously in view
• irregularly distributed
• 132 Ground-based GPS receivers
• evenly distributed in this domain
• station distance is 144 km.

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Slant-path Water Vapor (SWV) Observations

• With this hypothetical GPS observing network and
  moisture field of nature run is sampled to
  produce the SWV observations

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Control Experiment

• Observations
• SWV
• regular qv obs at the surface stations
• Background field
• 9-point smoothing 50 times to the natural run.

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qv increment 30m above ground
The increment fields of qv (g kg-1) at 30m above
ground
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qv increment 1.5km above ground
The increment fields of qv (g kg-1) at 1.5km
above ground
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qv increment 3km above ground
The increment fields of qv (g kg-1) at 3km above
ground
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West-East Vertical Cross-section of qv _at_ 34N
The red solid line is from the control experiment
and the purple dotted line from the nature run.
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Sensitivity experiments

• The impact of removing surface moisture
  observations.

qv increment 30m above ground
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• The impact of removing vertical filtering

The RMS error in g kg-1 with height. Solid line
is for the control run and dashed line for the
run without vertical filtering
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Summary

• Our 3DVAR system incorporating background error
  through an isotropic Gaussian filter properly
  recovers 3D meso-scale moisture structure in a
  dryline case.
• Surface observations are important for accurate
  analysis of qv field at low levels because of the
  absence of overlapping paths
• The vertical filter is beneficial, especially in
  data-sparse regions such as the low levels.

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Future work

• Riishojgaard (1998) points out that the
  background errors at nearby points that have
  similar values of the analysis field tend to be
  similar. Flow-dependent background error
  covariance based on such an assumption should
  improve the analysis, especially when data is
  sparse. So flow-dependent B is being tested.
• The analysis will be used to initialize a
  mesoscale model and the impact of assimilating
  GPS data will be further examined.
• Analysis cycles will be performed that hopefully
  will increase the impact of GPS SWV data
  distributed over time.

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Acknowledgement

• This work was supported by NSF grants
  ATM0129892 and ATM9909007.