Surface data assimilation at ECMWF

1. Surface data assimilation at ECMWF Sebastien.lafont@ecmwf.int ECMWF turned 30 last week

2. 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 thedynamics, 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 => need for CO2 surface fluxes

3. 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)

4. OPERATIONAL SYSTEM : 4D-VAR • the goal of 4D-Var is to define the atmospheric statex(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

5. In incremental 4D-Var, the cost function is minimized in terms of increments: • Tangent-linear operators with the model state defined at any timetias: • 4D-Var can be then approximated to the first order by minimizing: • where • is theinnovation vector • Adjoint operators • Gradient of the cost function: •  computed with the nonlinear model at high resolution using full physics  M •  computed with the tangent-linear model at low resolution using simplified • physics  M’ •  computed with a low resolution adjoint model using simplified physics  M’T INCREMENTAL FORMULATION OF 4D-VAR From Philippe Lopez

6. LAND SURFACEDATA ASSIMILATION SOIL MOISTURE ELDAS project VEGETATION GEOLAND project

7. snow under high vegetation High and low vegetation treated separately Variable root depth No root extraction or deep percolation in frozen soils 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

8. 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

9. Optimal Interpolation at ECMWF • No routine measurement of soil moisture. -> 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

10. 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

11. Extended Kalman Filter Forecast (first guess) Analysed forecast for new soil moisture at t+24h Comparison with observations T2m,RH2m,Tb Opt. Soil moisture t+9h t+15h t+12h t0 t+24h Time Simulated T2m,RH2m,Tb Minimization 3 perturbed forecasts for each state variable Linearity of observation operator allows a simple minimisation

12. Evapor. fraction OI vs EKF: soil moisture and EF (SGP97) Soil moisture

13. Modelling of the carbon cycle in the geoland project Overview geoland Observatory Natural Carbon Fluxes Jean-Christophe Calvet Météo-France 05.09.2005 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

14. Modelling of the carbon cycle in the geoland project Models Met. forcing geoland LAI ISBA / TESSEL LE, H, Rn, W, Ts… LAI Met. forcing Active Biomass ISBA-A-gs / C-TESSEL LE, H, Rn, W, Ts… CO2 Flux [CO2]atm Observatory Natural Carbon Fluxes Jean-Christophe Calvet Météo-France 05.09.2005 Prescribed INTERACTIF ISBA-A-gs / C-TESSEL are CO2-responsive land surface models, new versions of operational schemes used in atmospheric models

15. 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 ?

16. 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

17. 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)

18. Thank YOU

19. forecast forecast (2 x) Comparison with observations Opt. Soil moisture t+24h t t+6h t+18h t+12h time 3 additional forecasts (1, 2, 3) Extended Kalman Filter for soil moisture

20. 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.