Challenges in data assimilation with coupled models

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Challenges in data assimilation with coupled
models

• Pierre GauthierPresentation at the WOAP
  meeting30 september 2008, NCAR

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Outline

• Difficulties associated with coupled data
  assimilation systems
• Differences in temporal and spatial scales
• Land-surface and sea-ice data assimilation
• Examples
• Assimilation with a coupled ocean-atmosphere
  model
• Assimilation with a coupled atmospheric
  chemistry/global stratospheric model
• Bias correction in the stratosphere
• Truly coupled data assimilation systems would
  permit observations from one component to
  influence the other

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Difficulties associated with using coupled models
in data assimilation

• Brunet et al. (2008) paper on seamless
  prediction, section on data assimilation
• Composite system, applying different assimilation
  steps to different scales and components of the
  total Earth system model
• Attempt coupled land-atmosphere assimilation
• compensating errors can give soil moistures which
  reduce atmospheric forecast errors but do not
  correspond to actual soil moistures
• Need good characterization of the errors of the
  coupled model
• Requires close interaction between modelers and
  data assimilation experts to address the presence
  of biases

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Land Surface Models, Analyses, and Assimilation
in CMCs Operational System
ANALYSES
TS For snow anal Gaussian 1080x540
TM Gaussian 1080x540
TS, ES, TP Gaussian 1080x540
TS, ES 18 UTC Reg-576x641
TP
TS,ES
ASSIMILATION
SEQ. ASSIMILATION Global 800x600
SEQ. ASSIMILATION Regional 576x641
SNOW Gaussian 1080x540
SD
SD
SD
SD
ISBA fields
ISBA fields
ISBA fields
SD
6-h forecasts
18-h forecasts
PR
MODELS
ENSEMBLES GEM and SEF (ISBA, FR, glaciers, water)
GLOBAL GEM 800x600 uniform (ISBA, glaciers, water)
REGIONAL GEM 576x641 variable (ISBA, glaciers,
water)
LOCAL GEM-LAM East and West (ISBA, glaciers,
water)
ISBA and snow fields
GENESIS
DATABASES
Soil texture, orography, vegetation, lakes, and
glaciers
ISBA fields Tsurf(1,2), Wsoil(1,2), wice, snow
albedo, snow density, wsliq, wlveg
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Impact of Surface Processes on NWP
Medium-Range Global Model (2006)
Near surface soil moisture
m3m-3
120-h, Europe
(valid at 0000 UTC 15 December 2001)
Precipitation Threat Score (Day 4)- SHEF
ISBA soil moisture
Control
Has been implemented in the global forecasting
system (31 October 2006).
(Bélair et al.)
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Development of a coupled atmosphere-ocean data
assimilation system

• Assimilation of over different assimilation time
  windows
• Atmospheric 4D-Var uses a 6-h window
• Oceanic analysis over a 7-day period (typically)
• Coupled model will run with a 6-h assimilation
  window
• Oceanic assimilation will benefit of having a
  shorter assimilation because
• Analysis will be closer to the observation time
• Smaller analysis increments, which usually tend
  to reduce spin-up problems
• The background state will be produced with the
  fully coupled model
• Coupling will come in through the model
  integration over the length of the assimilation
  cycle (months to years)

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Schematic of a coupledatmosphere-ocean data
assimilation scheme
4D-Var atmospheric assimilation mode
Obs. insertion
Ocean assimilation in 3D-FGAT mode
Assimilation window
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Outer and inner loops
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Bias correction in the stratosphere

• Single type of data in the stratosphere
• Innovations include the model bias that cannot be
  removed in the analysis by using reliable data
  sources
• Consequence observation bias correction may
  compensate for model bias
• Reference analysis relying on unbiased
  observations
• Experiments of this study used the analyses from
  an experiment with MIPAS temperatures only in the
  stratosphere
• Observations departures between the background
  fields with respect to AMSU-a stratopheric
  channels were used to calibrate the bias
  correction scheme over a two-week training period

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Mean analysis temperature increments at 10 hPaNo
bias correction of AMSU-a channels
11-14(September 2003)
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Mean analysis temperature increments at 10
hPaWith bias correction of AMSU-a channels
11-14(September 2003)
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Impact in 4D-Var on winds from individual tracers
and all three combined 10 hPa
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Combined Use of ADJ and OSEs (Gelaro et al., 2008)
ADJ applied to various OSE members to examine
how the mix of observations influences their
impacts
Removal of AMSUA results in large increase in
AIRS impact in tropics
Removal of wind observations results in
significant decrease in AIRS impact in tropics
(in fact, AIRS degrades forecast without
satwinds!)
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Conclusions and remarks

• Assessment of the value of a given dataset must
  made in the context of all available observations
  used in the assimilation
• Reference observations are needed to correct and
  calibrate remotely sensed observations
• Temporal sampling is also important as current
  assimilation methods are now able to use them
• Use of climate models in assimilation mode
• bridge climate modeling and observation efforts
  to validate and monitor observations with a
  climatological perspective
• SPARC data assimilation working group is
  promoting such an effort