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The SCM Experiments at ECMWF

The SCM Experiments at ECMWF. Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts. ELDAS Progress Meeting 12./13.12.2002. The Goals at ECMWF in ELDAS.

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The SCM Experiments at ECMWF

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  1. The SCM Experiments at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Progress Meeting 12./13.12.2002

  2. The Goals at ECMWF in ELDAS • Build a system that complements the use of 2T/2RH information to get an optimal estimate of soil water assimilating: - thermal IR heating rates - MW brightness temperature - precipitation and radiation • Test, validate amd intercompare that system (Single-Column Experiments, comparison with measurements) • Annual soil moisture data base for Europe (1.10.1999 – 31.12.2000) • ECMWF expects to have a system that can go into pre-production by the end of ELDAS (2004)

  3. Experiment Design Atm. initial conditions + dynamics forcing from ECMWF reanalysis (ERA40) Single-column model of the ECMWF NWP model + microwave emissivity model Observation of precipitation + radiation Increments (daily) First guess: T2m,RH2m,Tb Soil moisture analysis scheme OI or Extended Kalman Filter Observations: T2m,RH2m,Tb Soil moisture Background error

  4. 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 - corrections when T and RH error are negatively correlated • Extended Kalman Filter: • Used in the operational DWD- • forecast since 2000 (Hess, 2001) * • Updated forecast errors • Criteria for the applicability of the method • - no ‘direct’ atmospheric exceptions • - soil exceptions still to be tested • * Changes: • - Assimilation of 2m- T and RH, mw-Tb • Model forecast operator accounts for water transfer between soil layers • Test adaptive EKF

  5. 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

  6. Changes to the original algorithm • Model forecast operator M accounts for water transfer between soil layers: • Q-Problem: 1) Q constant: - defined by innovation error and size of soil moisture increments: 2) Adpative Kalman Filter (Mayer and Tapley’s estimator, 1976): forecast j t t+24 time p,j Perturbed forecast layer i

  7. Observations Murex: • 1.6 – 9.10.1997 (1995- 1998) • Forcing: SW , (unbiased) LW , precipitation • Validation: Soil Moisture, Rnet, H, G, LE=Rnet-H-G, Ts • Assimilation/Validation: T2m, RH2m, synthetic mw-Tb SGP 97: • 15.6 – 19.7.1997 • Little Washita site (2) (Central Facility site(3)) • Forcing: SW , LW , precipitation • Validation: Soil Moisture, Rnet, H, G, LE, Ts • Assimilation/Validation: T2m, RH2m, mw-Tb

  8. Correction of downward longwave radiation • Procedure to correct downward longwave radiation: • Bias • Height difference between model and observation • Model error using measurements at Carpentras

  9. Comparison of OI-Weights and EKF-Gain matrix Temperature: blue - OI weights green/black – EKF gain matrix Relative Humidity: blue - OI weights green/black – EKF gain matrix • OI weights and KF gain matrix • adapt similarly to atmospheric • conditions • OI puts more weight on • RH-observations

  10. Soil moisture increments

  11. Sensible Heat Flux Murex Experiment (1.6- 9.10.1997) Soil Moisture Latent Heat Flux

  12. T2m error RH2m error

  13. Soil moisture, Ts, Tg (5cm), mw-Tb at 6 LT

  14. Soil moisture, Ts, Tg, mw-Tb at 6 LT (Tb every 3rd day)

  15. SGP97 (15.6 – 20.7. 1997) Soil moisture Latent Heat Flux

  16. Soil moisture, Ts, Tg, mw-Tb at 12 LT

  17. Gisela Seuffert: Conclusions • EKF and OI give nearly similar results • Assimilation of mw-Tb improves the soil moisture simulation • Assimilation of screen level T, RH and mw-Tb gives best results - especially when mw-Tb data are not available every day • Assimilation of T, RH and mw-Tb improves either soil moisture or latent heat flux

  18. Plans Assimilation aspects: • Minimize the combined errors in prediction of soil moisture, latent heat flux and screen level observations • Further mw-Tb assimilation experiments (viewing angle, times) • Assimilation of heating rates Technical aspects: • Paper(s) focusing on the - new features of assimilation method - assimilation of mw-Tb - assimilation of heating rates • Summer 2003: Build production system for the annual data base • End of 2003: Start production

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