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

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Presentation on theme: "The SCM Experiments at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Progress Meeting 12./13.12.2002."— Presentation transcript:

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 First guess: T 2m,RH 2m,Tb Soil moisture analysis scheme OI or Extended Kalman Filter Soil moisture Background error Increments (daily) Observations: T 2m,RH 2m,Tb Observation of precipitation + radiation

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, m w-Tb -Model forecast operator accounts for water transfer between soil layers -Test adaptive EKF

5 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

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): Perturbed forecast layer i t t+24 time forecast jj  p,j

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: T 2m, RH 2m, synthetic m w-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: T 2m, RH 2m, m w-Tb

8 Correction of downward longwave radiation Procedure to correct downward longwave radiation: 1.Bias 2.Height difference between model and observation 3.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 Murex Experiment (1.6- 9.10.1997) Soil Moisture Latent Heat Flux Sensible Heat Flux

12 T 2m error RH 2m error

13 Soil moisture, Ts, Tg (5cm), m w-Tb at 6 LT

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

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

16 Soil moisture, Ts, Tg, m w-Tb at 12 LT

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

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

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