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Assimilation of Satellite Soil Moisture Data Products in NCEP GFS

Assimilation of Satellite Soil Moisture Data Products in NCEP GFS . W. Zheng 1,2 , X. Zhan 3 , J. Liu 2,3 , J. Meng 1,2 , J. Dong 1,2 , H. Wei 1,2 , & M. Ek 1 1 NOAA/NCEP/EMC, 5830 University Research Ct, College Park, MD 2 IMSG, Kensington, MD

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Assimilation of Satellite Soil Moisture Data Products in NCEP GFS

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  1. Assimilation of Satellite Soil Moisture Data Products in NCEP GFS W. Zheng1,2, X. Zhan3, J. Liu2,3, J. Meng1,2, J. Dong1,2, H. Wei1,2, & M. Ek1 1NOAA/NCEP/EMC, 5830 University Research Ct, College Park, MD 2IMSG, Kensington, MD 3NOAA/NESDIS/STAR, 5830 University Research Ct, College Park, MD

  2. OUTLINE • Objective • GFS and LIS-EnKF Coupling • Embed EnKF in GFS • 1st Test for AMSR-E SM • Testing with SMOS SM • Next Step

  3. OBJECTIVES • “Online” soil moisture data assimilation for GFS • Examine how SM data impact GFS forecasts

  4. Forecast NCEP Global Forecast System Oceans RTOFS/HYCOM WaveWatch III Climate CFS Coupled Hurricane GFDL HWRF MOM3 1.7B Obs/Day Satellites 99.9% Dispersion ARL/HYSPLIT Regional NAM WRF NMM Global Forecast System Global Data Assimilation Severe Weather WRF NMM/ARW Workstation WRF Short-Range Ensemble Forecast North American Ensemble Forecast System WRF: ARW, NMM ETA, RSM Air Quality GFS, Canadian Global Model NAM/CMAQ Rapid Update for Aviation Noah Land Surface Model From Louis Uccellini (2009)

  5. NASA Land Information System Inputs Physics Outputs Applications Topography, Soils Land Surface Models Soil Moisture & Temperature Weather/ Climate Water Resources Homeland Security Military Ops Natural Hazards Land Cover, Vegetation Properties Evaporation Sensible Heat Flux Meteorological Forecasts, Analyses, and/or Observations Runoff Snow Soil Moisture Temperature Data Assimilation Modules Snowpack Properties From Christa Peters-Lidard (2007)

  6. yk Propagation tk-1to tk: xki+ = f(xk-1i-) + wki w = model error Ensemble Kalman Filter (EnKF) From Rolf Reichle (2008) Nonlinearlypropagates ensemble of model trajectories. Can account for wide range of model errors (incl. non-additive). Approx.: Ensemble size. Linearized update. xki state vector (eg soil moisture) Pk state error covariance Rk observation error covariance Update at tk: xki+ = xki- + Kk(yki - xki- ) for each ensemble member i=1…N Kk = Pk(Pk + Rk)-1 with Pk computed from ensemble spread

  7. Propagation tk-1to tk: xki+ = f(xk-1i-) + wki w = model error EnKF for Noah LSM in GFS Nonlinearlypropagates ensemble of model trajectories. Can account for wide range of model errors (incl. non-additive). Approx.: Ensemble size. Linearized update. xki state vector (eg soil moisture) Pk state error covariance Rk observation error covariance For Noah LSM 4 layer SM: xji+ = xji- + ( i - xji- )* Pj1 / (P11 + R) No matrix inversion. Scalars only

  8. GFS and LIS-EnKF Coupling GFS & LIS Coupling GFS Noah Pros: Flexibility for more LSMs, 2D, 3D EnKF, Multivariable EnKF, etc. Cons: Coding of the coupling system may require more time Coupler Noah LIS EnKF

  9. Embed Simplified EnKF in GFS EnKF Embedded in GFS Noah GFS EnKF Pros: GFS can demonstrate SM impact on forecasts GFS may take advantage of satellite SM obs ASAP Cons: Hardwiring limits more flexibility for assimilating other observational data

  10. Preliminary Test with AMSR-E SM Data: NESDIS AMSR-E daily soil moisture SM observation rate set to be 3% vol/vol Date: 2007 July 1-7 EnKF: Simplified for Noah LSM. Perturb SM state only GFS_CTL: GFS run without any EnKF SM data assimilation GFS_EnKF: GFS run with the simplified EnKF

  11. CONUS 24hr Total Rainfall day 5 forecast

  12. Testing with SMOS SM • Method: • A Simple Ensemble Kalman Filter (EnKF) embedded in latest version of GFS latest version • Assimilation time period: • 00Z May 1 – June 18, 2012. (GFS/GSI) • Experiments: • CTL: Control run • EnKF: Sensitivity run • Perturbations: • Precipitation, 4 layer soil moisture states

  13. Comparison of soil moisture 18Z, 1-17 June 2010 SMOS GFS_CTL EnKF-CTL GFS_EnKF

  14. Comparison of soil moisture 18Z, 1-17 June 2010 SMOS GFS_CTL GFS_EnKF EnKF-CTL

  15. GFS Top Layer SM Validation With USDA-SCAN Measurements 1-17 of June, 2012

  16. GFS Top Layer SM Validation With USDA-SCAN Measurements 1-17 of June, 2012

  17. GFS Top Layer SM Validation With USDA-SCAN Measurements 1-17 of June, 2012

  18. Comparison of Tsfc, T2m 18Z, 1-17 June 2010 2 m temperature Surface skin Temperature SMOS soil moisture assimilation generally decreased GFS surface temperature forecasts

  19. Comparison of SHF and LHF 18Z, 1-17 June 2010 Sensible Heat Flux Latent Heat Flux SMOS soil moisture assimilation increased GFS latent heat flux and decreased sensible heat flux estimates

  20. Precipitation forecast 24h Accum (mm) Ending at 12Z 4 June 2012 CTL: 12-36h Obs EnKF: 12-36h Obs EnKF: 36-60h CTL: 36-60h SMOS soil moisture assimilation have observable impact on rainfall forecasts of GFS

  21. Precipitation forecast 24h Accum (mm) Ending at 12Z 4 June 2012 CTL: 60-84h Obs EnKF: 60-84h Obs EnKF: 84-108h CTL: 84-108h SMOS soil moisture assimilation have observable impact on rainfall forecasts of GFS

  22. Results Summary • Assimilating SMOS in NCEP GFS • Improved GFS deeper layer soil moisture estimates comparing with in situ measurements • reduced GFS temperature forecast biases positively; • increased latent heat and decreased sensible heat fluxes for most CONUS regions; • had significant impact on precipitation forecasts.

  23. NEXT STEP • Implement semi-coupling of GFS and LIS; • Optimize model perturbation; • More testing with AMSR-E, SMOS, ASCAT and AMSR2 soil moisture data; • More validation with weather observations.

  24. GFS and LIS “Semi-Coupling” GFS Noah GFS Noah Forcing Forcing States States Noah Noah LIS LIS EnKF EnKF

  25. NEXT STEP • Implement semi-coupling of GFS and LIS; • Optimize model perturbation; • More testing with AMSR-E, SMOS, ASCAT and AMSR2 soil moisture data; • More validation with weather observations.

  26. Thanks …

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