Surface data assimilation at ECMWF ECMWF turned 30 last week

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 => need for CO2 surface fluxes

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)

OPERATIONAL SYSTEM : 4D-VAR the goal of 4D-Var is to define the atmospheric state x (t 0 ) such that the “distance” between the model trajectory and observations is minimum over a given time period [t 0, t n ] finding the model state (at the initial time t 0 ) that minimizes the cost-function : x i is the model state at time step t i such as: M is the nonlinear forecast model integrated between t 0 and t i H is the observation operator (model space  observation space) From Philippe Lopez

INCREMENTAL FORMULATION OF 4D-VAR 4D-Var can be then approximated to the first order by minimizing: where is the innovation vector In incremental 4D-Var, the cost function is minimized in terms of increments: with the model state defined at any time t i as: 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 Adjoint operators Tangent-linear operators From Philippe Lopez

ECMWF incremental 4D-Var implementation Use all data in a 12-hour window ( UTC for 1800 UTC analysis) 1.Group observations into ½ hour time slots 2.Run the T511 (40km) forecast from the previous analysis and compute J o “observation”- “background” departures 1.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 2.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

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

LAND SURFACE DATA ASSIMILATION SOIL MOISTURE ELDAS project VEGETATION GEOLAND project

TESSEL scheme in a nutshell High and low vegetation treated separately Variable root depth No root extraction or deep percolation in frozen soils snow under high vegetation + 2 tiles (ocean & sea-ice) Tiled ECMWF Scheme for Surface Exchanges over Land Limitations : single soil type No seasonal cycle of LAI P. Viterbo

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

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

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

Extended Kalman Filter Time t+24h t0t0 t+9h t+12h t+15h Minimization 3 perturbed forecasts for each state variable Forecast (first guess) Analysed forecast for new soil moisture at t+24h Comparison with observations T 2m,RH 2m,Tb Simulated T 2m,RH 2m,Tb Opt. Soil moisture Linearity of observation operator allows a simple minimisation

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

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

Observatory Natural Carbon Fluxes Jean-Christophe Calvet Météo-France Models Modelling of the carbon cycle in the geoland project geoland ISBA-A-g s / C-TESSEL Met. forcing LAI LE, H, Rn, W, Ts… Active Biomass CO 2 Flux [CO 2 ] atm ISBA / TESSEL Met. forcing LAI LE, H, Rn, W, Ts… ISBA-A-g s / C-TESSEL are CO 2 -responsive land surface models, new versions of operational schemes used in atmospheric models Prescribed INTERACTIF

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 ?

Future of land surface data assimilation system 1 st 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 2 nd 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

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)

Thank YOU

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

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.