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Demonstrating satellite soil moisture impact on GFS ... - jcsda - NOAA

VIEWS: 4 PAGES: 26

									        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




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012             1/26
                                                           OUTLINE



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




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   2/26
                                                   OBJECTIVES



                                         “Online” soil moisture data
                                          assimilation for GFS

                                         Examine how SM data impact
                                          GFS forecasts




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   3/26
                        NCEP Global Forecast System
                                     Climate                                                        Oceans
                                      CFS                       Hurricane                           RTOFS/HYCOM
                                      MOM3                          GFDL
                                                                    HWRF
                                                                                                    WaveWatch III
        1.7B Obs/Day
          Satellites
           99.9%



                                  Global                       Regional NAM                          Dispersion
                                                                   WRF NMM
   Global Data                   Forecast                                                            ARL/HYSPLIT
   Assimilation                  System
                                                                                                    Severe Weather
                                                                                                    WRF NMM/ARW
                                                                Short-Range                         Workstation WRF
               North American Ensemble                        Ensemble Forecast
                   Forecast System                            WRF: ARW, NMM                         Air Quality
              GFS, Canadian Global Model                        ETA, RSM
                                                                                                     NAM/CMAQ         For
                                                                                                                      eca
                                                                                                                       st


                                                                                                    Rapid Update
                                                                                                    for Aviation
                                   Noah Land Surface Model                        From Louis Uccellini (2009)
10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                                                4/26
                    NASA Land Information System
               Inputs                                             Physics                      Outputs         Applications

           Topography,                                                                           Soil
              Soils                                    Land Surface Models                    Moisture &
                                                                                             Temperature
                                                                                                                   Weather/
                                                                                                                   Climate
           Land Cover,                                                                       Evaporation
            Vegetation                                                                                      Water
                                                                                             Sensible Heat
            Properties                                                                                     Resources
                                                                                                 Flux
                                                                                                                   Homeland
       Meteorological                                                                                               Security
         Forecasts,
       Analyses, and/or                                                                          Runoff
                                                                                                                   Military
        Observations
                                                                                                                    Ops

              Snow                                                                                                 Natural
          Soil Moisture                             Data Assimilation Modules                 Snowpack             Hazards
          Temperature                                                                         Properties

10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   From Christa Peters-Lidard (2007)              5/26
                    Ensemble Kalman Filter (EnKF)
                                                                                              From Rolf Reichle (2008)

                                                                               Nonlinearly propagates
                                                                               ensemble of model trajectories.
                                         yk                                    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


   Propagation tk-1 to tk:                         Update at tk:
                                                   xki+ = xki- + Kk(yki - xki- )
   xki+ = f(xk-1i-) + wki
                                                           for each ensemble member i=1…N
   w = model error                                 Kk = Pk (Pk + Rk)-1
                                                           with Pk computed from ensemble spread
10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                                             6/26
                             EnKF for Noah LSM in GFS
                                                                                 Nonlinearly propagates
                                                                                 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


   Propagation tk-1 to tk:
                                                           For Noah LSM 4 layer SM:
   xki+ = f(xk-1i-) + wki                                   xji+ = xji- + ( i - xji- )* Pj1 / (P11 + R)
                                                           No matrix inversion. Scalars only
   w = model error

10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                                           7/26
                        GFS and LIS-EnKF Coupling

               GFS & LIS Coupling

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



10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                                     8/26
                    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



10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012      9/26
               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




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                     10/26
         CONUS 24hr Total
           Rainfall day 5
             forecast

10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   11/26
                                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

10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   12/26
            Comparison of soil moisture                                        18Z, 1-17 June 2010

                            SMOS                                                    GFS_CTL




                            GFS_EnKF                                                EnKF-CTL




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                         13/26
            Comparison of soil moisture                                        18Z, 1-17 June 2010

                            SMOS                                                    GFS_CTL




                             GFS_EnKF                                                EnKF-CTL




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                         14/26
                      GFS Top Layer SM Validation
                                          With USDA-SCAN Measurements
                                                 1-17 of June, 2012




                           East CONUS (28 sites)                    West CONUS (25 sites)         Whole CONUS

                                                      Corr-                            Corr-
                          RMSE          Bias                       RMSE        Bias            RMSE   Bias   Corr-Coef
                                                      Coef                             Coef

             CTL          0.149        0.015         0.458         0.122       0.049   0.488   0.136 0.031    0.472

            EnKF          0.139        0.001         0.596         0.117       0.046   0.559   0.129 0.023    0.579




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                                             15/26
                      GFS Top Layer SM Validation
                                          With USDA-SCAN Measurements
                                                 1-17 of June, 2012




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   16/26
                      GFS Top Layer SM Validation
                                          With USDA-SCAN Measurements
                                                 1-17 of June, 2012




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   17/26
            Comparison of Tsfc, T2m                                            18Z, 1-17 June 2010




                 Surface skin Temperature                                             2 m temperature




                 SMOS soil moisture assimilation generally decreased
                        GFS surface temperature forecasts


10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                            18/26
            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


10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                           19/26
         Precipitation forecast                           24h Accum (mm) Ending at 12Z 4 June 2012




                 CTL: 12-36h                                              Obs        EnKF: 12-36h




                 CTL: 36-60h                                              Obs        EnKF: 36-60h
                     SMOS soil moisture assimilation have observable
                          impact on rainfall forecasts of GFS
10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                         20/26
         Precipitation forecast                           24h Accum (mm) Ending at 12Z 4 June 2012




                 CTL: 60-84h                                              Obs        EnKF: 60-84h




                 CTL: 84-108h                                             Obs        EnKF: 84-108h
                     SMOS soil moisture assimilation have observable
                          impact on rainfall forecasts of GFS
10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                         21/26
                                            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.
10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   22/26
                                                      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.



10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   23/26
                        GFS and LIS “Semi-Coupling”



                GFS                Noah                                 GFS      Noah


                                         Forcing                                    Forcing




                                                                        States                   States

                                                               Noah                           Noah
                                              LIS                                       LIS
                                                               EnKF                           EnKF



10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012                              24/26
                                                      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.



10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   25/26
                                                         Thanks …




10th Joint Center for Satellite Data Assimilation Workshop, Oct. 10-12, 2012   26/26

								
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