Surface data assimilation at ECMWF

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Surface data assimilation at ECMWF
Sebastien.lafont_at_ecmwf.int
ECMWF turned 30 last week
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European Centre for Medium range Weather Forecast

• weather forecasting
• 10 days deterministic forecast (resolution 40 km,
  soon 25 km)
• 10 days Ensemble forecast
• Monthly forecast
• Seasonal forecast
• Reanalyse (ERA40)
• Annual Training course data assimilation (see
  website)
• Additional mission to ECMWF (2005)
• "To develop, and operate on a regular basis,
  global models and data assimilation systems for
  the dynamics, thermodynamics and composition of
  the Earth's fluid envelop and the interacting
  part of the earth-system".
• GEMS project (Global and regional Earth-system
  Monitoring using Satellite and in-situ data)
• Greenhouse gases, reactive gases, air quality,
  aerosols.
• Atmospheric CO2 concentration assimilation gt
  need for CO2 surface fluxes

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Operational Forecast System

• Data assimilation in 2 steps
• 1) Atmospheric variables
• 4D VAR assimilation (since 1999)
• 12 h windows
• 23 satellites sources
• adjoint and tangent linear models
• 2) Surface Variables
• Analysis of snow
• Analysis of sea-ice concentration and SST
• Land-surface analysis (soil moisture)

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OPERATIONAL SYSTEM 4D-VAR

• the goal of 4D-Var is to define the atmospheric
  state x(t0) such that the distance between
• the model trajectory and observations is
  minimum over a given time period t0, tn

• finding the model state (at the initial time t0)
  that minimizes the cost-function ?

H is the observation operator (model space ?
observation space)
xi is the model state at time step ti such as
M is the nonlinear forecast model integrated
between t0 and ti
From Philippe Lopez
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INCREMENTAL FORMULATION OF 4D-VAR
From Philippe Lopez
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ECMWF incremental 4D-Var implementation

• Use all data in a 12-hour window (0900-2100 UTC
  for 1800 UTC analysis)
• Group observations into ½ hour time slots
• Run the T511 (40km) forecast from the previous
  analysis and compute Jo observation-
  background departures
• Adjust the model fields at the start of
  assimilation window (0300 UTC) so the 12-hour
  forecast better fits the observations. This is
  an iterative process using a lower resolution
  linearized model T95 (200km) or T159 (125km) and
  its adjoint model
• Rerun the T511 high resolution model from the
  modified (improved) initial state and calculate
  new observation departures
• The 3-4 loop in repeated twice to produce a good
  high resolution estimate of the atmospheric state
  the result is the ECMWF analysis

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Multi-incremental quadratic 4D-Var at ECMWF
T511L60
T95L60 T159L60
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LAND SURFACEDATA ASSIMILATION

• SOIL MOISTURE ELDAS project
• VEGETATION GEOLAND project

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TESSEL scheme in a nutshell

• Tiled ECMWF Scheme for Surface Exchanges over Land

2 tiles (ocean sea-ice)
Limitations single soil type No seasonal
cycle of LAI
P. Viterbo
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SURFACE ASSIMILATION (1)

• Lower troposphere is sensitive to land
  surface/soil specification (i.e evaporation and
  transpiration respond to soil moisture)
• To initialise prognostic variables of land
  surface parameterisations in NWP
• Forecast drifts are possible due to
• Atmospheric forcing (radiation, rainfall)
  deficiencies, that may trigger positive feedback
  loops
• i.e Positive feedback lower soil moisture
  /decrease evaporation/ higher temperature, drier
  air, reduced precipitation
• Misrepresentation of land surface processes

From Janneke Ettema
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Optimal Interpolation at ECMWF

• No routine measurement of soil moisture. -gt
  indirect estimation
• The soil moisture is updated by a linear
  combination of the forecast errors of the
  parameters T2m and RH2m.
• Benefits
• It prevents drifts of land surface variables
• No use of climatology
• Drawbacks
• Increments smaller than (but of the order of)
  seasonal variability
• Run at synoptic time only
• No handling of biases
• Focus on a correct evaporative fraction, not
  necessarily on a correct land surface state
• A rigid framework difficult to add different
  observation types or to change the land surface
  model

From J, Ettema
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ELDAS Soil moisture analysis systems

• Optimal Interpolation
• Used in the operational ECMWF-forecast since 1999
  (Douville et al., 2000)
• Fixed statistically derived forecast errors
• Criteria for the applicability of the method
• - atmospheric and soil exceptions
• - By design, corrections when T and RH error are
  negatively correlated

• Extended Kalman Filter
• (single column model)
• Used in the operational DWD-
• forecast since 2000 (Hess, 2001)
• Updated forecast errors
• Criteria for the applicability of the method
• - Reduced set of exceptions
• Changes
• Assimilation of 2m- T and RH, µw-Tb, TIR Tb
• Model forecast operator accounts for water
  transfer between soil layers

From Janneke Ettema
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Extended Kalman Filter
Forecast (first guess)
Analysed forecast for new soil moisture at t24h
Comparison with observations T2m,RH2m,Tb
Opt. Soil moisture
t9h
t12h
t15h
t24h
t0
Time
Simulated T2m,RH2m,Tb
Minimization 3 perturbed forecasts for each
state variable
Linearity of observation operator allows a simple
minimisation
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OI vs EKF soil moisture and EF (SGP97)
Soil moisture
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• The Observatory of Natural Carbon Fluxes of
  geoland
• Partners
• Research partners KNMI, LSCE, ALTERRA
• Service providers ECMWF, Météo-France
• Associated user LSCE
• Objectives
• Kyoto protocol
• Transpose the tools used for weather forecast to
  the monitoring of vegetation and of natural
  carbon fluxes
• Near real-time monitoring at the global scale
  (ECMWF) based on
• modelling,
• in situ data,
• assimilation of satellite data.
• Scientific validation of the system

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Prescribed
INTERACTIF
ISBA-A-gs / C-TESSEL are CO2-responsive land
surface models, new versions of operational
schemes used in atmospheric models
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Motivation for assimilation

• Again Forecast drifts are possible due to
• Atmospheric forcing (radiation, rainfall)
  deficiencies, that may trigger positive feedback
  loops
• Misrepresentation of vegetation process
  (phenology, photosynthesis).
• Control variable LAI
• Use of remote sensing observation to constrained
  the LAI values.
• 10 days window, (En?)KF, land-surface only
• (Land surface model are cheap to run )
• Obs LAI,
• Dataset mean LAI (N, STD) PER TILE
• resolution 0.5/0.5
• from spot4/VEGETATION
• Processed by POSTEL, Toulouse
• Operational dataset after 2007 ? MODIS ? VIIRS ?
• fAPAR ?
• Cloudy area, Missing data ?

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Future of land surface data assimilation system

• 1st tier Soil wetness/water fluxes
• 24-hour window assimilation system
• Post-ELDAS KF analysis, coupled
  surface-atmosphere
• Obs Ta, RHa, heating rates, MW data (?)
• Forcing Precipitation, radiation fluxes
• 2nd tier Carbon/water fluxes and green biomass
• 10 days window, (En?)KF,
• land-surface only
• Obs NDVI, LAI, (fPAR ?), tiled
• Forcing Precipitation, radiation fluxes,
  temperature

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Conclusions

• Soil moisture assimilation tested with EKF.
• EKF and IO gives similar result (Seuffert et al.)
  but EKf is more flexible (new observations types)
• Studies (Seuffert et al.) have shown the synergy
  of new observation types (TIR Tb, microW Tb)
• Production system need to be developed
• Model hydrology need to be improved
• Surface scheme TESSEL is being upgraded to
  C-TESSEL
• Description of the carbon cycle
• On going 1D test
• Global runs soon
• Assimilation scheme planned for next year
• 2D-Var Assimilation currently on-going at
  Météo-France on a similar model (ISBA-A-gs)
  (Jarlan and Calvet)

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• Thank YOU

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Extended Kalman Filter for soil moisture
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• From the SSM/I instrument ECMWF currently
  assimilates rain-free radiances and Total Column
  Water Vapour Retrievals. Rain affected radiances
  are monitored passively.
• The AMSU-A is a 15-channel microwave
  temperature/humidity sounder that measures
  atmospheric temperature profiles and provides
  information on atmospheric water in all of its
  forms (with the exception of small ice
  particles). The first AMSU was launched in May
  1998 on board the National Oceanic and
  Atmospheric Administration's (NOAA's) NOAA 15
  satellite.
• HIRS is a twenty channel atmospheric sounding
  instrument for measuring temperature profiles,
  moisture content, cloud height and surface albedo.