1 / 18

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.

eunice
Télécharger la présentation

The SCM Experiments at ECMWF

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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 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

More Related