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1 Soil Moisture Assimilation in NCEP Global Forecast System Weizhong Zheng 1, Jerry Zhan 2, Jiarui Dong 1, Michael Ek 1 1 Environmental Modeling Center,

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Presentation on theme: "1 Soil Moisture Assimilation in NCEP Global Forecast System Weizhong Zheng 1, Jerry Zhan 2, Jiarui Dong 1, Michael Ek 1 1 Environmental Modeling Center,"— Presentation transcript:

1 1 Soil Moisture Assimilation in NCEP Global Forecast System Weizhong Zheng 1, Jerry Zhan 2, Jiarui Dong 1, Michael Ek 1 1 Environmental Modeling Center, National Centers for Environmental Prediction (NCEP/EMC), National Weather Service, NOAA 2 National Environmental Satellite, Data and Information Service/Satellite Applications and Research (NESDIS/STAR), NOAA 3rd COSMOS Workshop, 10-12 December 2012 University of Arizona, Tucson, Arizona, USA

2 NOAA Center for Weather and Climate Prediction (NCWCP), College Park, Maryland 2

3  The simplified ensemble Kalman Filter (EnKF) was embedded in the NCEP GFS to assimilate satellite soil moisture observation.  Future plan: Test assimilation of COSMOS soil moisture measurements. Data assimilation via the NASA Land Information System (LIS)  Other in situ soil moisture data sets/networks, e.g. Soil Climate Analysis Network (SCAN; www.wcc.nrcs.usda.gov/scan), and others identified by the International Soil Moisture Network (www.ipf.tuwien.ac.at/insitu).  KEY REQUIREMENT FOR NWP OPERATIONS: RELIABLE, NEAR-REALTIME Soil Moisture Data assimilation in NCEP GFS

4  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 Testing with SMOS Soil Moisture

5 GFS_CTL EnKF-CTL GFS_EnKF SMOS Comparison of soil moisture 18Z, 1-17 June 2012

6 GFS Top Layer SM Validation With USDA-SCAN Measurements 1-17 of June, 2012 East CONU S (28 sites) West CONU S (25 sites) Whole CONU S RMSEBiasCorr-CoefRMSEBiasCorr-CoefRMSEBiasCorr-Coef CTL 0.1490.0150.4580.1220.0490.4880.1360.0310.472 EnKF 0.1390.0010.5960.1170.0460.5590.1290.0230.579

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

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

9 EnKF: 60-84h EnKF: 84-108h CTL: 60-84hObs Precipitation forecast 24h Accum (mm) Ending at 12Z 4 June 2012 CTL: 84-108h Improved !

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

11 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 positve impact on precipitation forecasts. Future: assimilate SMAP (remote sensing), COSMOS & other in situ measurements Results Summary

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