1. AMS’04, Seattle, WA. January 12, 2004Slide 1 HYDROS Radiometer and Radar Combined Soil Moisture Retrieval Using Kalman Filter Data Assimilation X. Zhan, UMBC-GEST P. Houser, NASA-GSFC, J. Walker, University of Melbourne, and HYDROS Science Team

2. AMS’04, Seattle, WA. January 12, 2004Slide 2 HYDROS: Hydrosphere States Mission Spinning 6m dish NASA Earth System Science Pathfinder mission; Surface soil moisture w/  4%vol. accuracy and Freeze/Thaw state transitions; Revisit time: Global 3 days, boreal area 2 days L-band Radiometer sensing 40km brightness temp. with H & V polarization; L-band Radar measuring 3km backscatters with hh, vv, hv polarization; Soil moisture products: 3km radar retrievals, 40km radiometer retrievals and 10km radar and radiometer combined retrievals.

3. AMS’04, Seattle, WA. January 12, 2004Slide 3 36 km – Radiometer footprint 9 km Soil moisture product 3 km Radar footprint 123 456 1 789 234 7 8 65 9 131415 16 1211 10 SM retrieval approaches: 1) Fine scale radar; 2) Coarse scale radiometer; 3) Median scale combined; Why combined method? 1) Account for missing data. 2) Use noisy high-res radar to downscale coarse radiometer. 3) Use information in overlapped observations. Assimilation approach: Assimilate radar backscatter and radiometer brightness observations into a combined soil moisture retrieval. HYDROS OSSE: Observing System Simulation Experiment To access the potential accuracy of HYDROS instruments in soil moisture retrievals using a set of 1km land surface states simulation data

4. AMS’04, Seattle, WA. January 12, 2004Slide 4  TOPLATS 1km hydrological model input and output from Crow [2001] (SM, vegetation, soil, Tsoil, Tskin, Precip(R f )) for the Red-Arkansas River Basin for 34 days from May 26 to June 28, 1994.  AVHRR NDVI composite from June 1995;  Vegetation and Soil parameters derived by HYDROS Science Team; Data DomainLand Cover OSSE Simulation Data Set

5. AMS’04, Seattle, WA. January 12, 2004Slide 5 Update State estimate with observation: Update the error Covariance: Forecast steps: Project the State ahead: Project the error Covariance ahead: Update steps: Compute the Kalman gain:  Data Assimilation merges observations & model predictions to provide a superior state estimate: X a = X b + K (O - Ô) Ô = h(X b,0)  Extended Kalman Filter (EKF) tracks the conditional mean of a statistically optimal estimate of a state vector X through a series of forecast and update steps Extended Kalman Filter Data Assimilation

6. AMS’04, Seattle, WA. January 12, 2004Slide 6 1 km SM, LC, ST, Tsoil, Tskin, NDVI, rf 3/36 km Sigmas 36 km Tbs 3/36 km Sigmas 36 km Tbs 1 km Sigmas1 km Tbs Radar forward model Radiometer forward model Gaussian Noise 3/9/36 km SM Retrievals aggregate 3/9/36 km SM “Truth” 3/9/36 km SM Retrieval Errors Resample or aggregate EKF DA Retrieval Data Flow Chart aggregate 3/36 km Precipitation 3/36 km SM Estimate LSM Aggregate forcing EKF Data Assimilation Algorithms

7. AMS’04, Seattle, WA. January 12, 2004Slide 7 EKF Data Assimilation Algorithm

8. AMS’04, Seattle, WA. January 12, 2004Slide 8 1.Do DA retrievals only at 3km scale and aggregate them up to 9km scale, use a former instrument error rate setup to compare the DA retrieval accuracy with mathematical inversion method: tb1: Use T bv & T bh only ts1:Combine T bv & T bh with  vv,  hh &  vh T bv & T bh :36km obs having 1.0K noise  vv,  hh &  vh :3km obs having 0.5dB noise 2.Retrieve SM by using 36km Tb inversed SM rather than a LSM as X b and assimilating sigmas into X b with reproduced OSSE data: Kp = 0.15 and 3x3 moving average smoothing; 3.Retrieve SM by using 36km Tb inversed SM rather than a LSM as X b and assimilating sigmas into X b with various sigma noise levels: Kp = 0.05, 0.10, or 0.15 EKF Data Assimilation Retrieval Experiments

9. AMS’04, Seattle, WA. January 12, 2004Slide 9 ___ EKF DA Retrieval, ___ Math Inversion Previous OSSE data set with sigma noise = 0.5dB tb1 ts1 RMSD of EKF DA SM Retrievals

10. AMS’04, Seattle, WA. January 12, 2004Slide 10 RMSE of Different SM Retrievals Reproduced OSSE data set with sigma noise Kp = 0.15 Sigma Inversion: Mathematically inverse sigmas EKF Assimilation: 2D EKF 144 elements of X and 434 element Z Tb Inversion: Mathematically inverse Tbh or Tbv

11. AMS’04, Seattle, WA. January 12, 2004Slide 11 Spatial Comparison of Different SM Retrievals Reproduced OSSE data set with sigma noise Kp = 0.15 Sigma Inversion EKF Assimilation Tb Inversion -50 -20 -10 -4 4 10 20 50 %VMS RMSE = 6.7% RMSE = 6.5% RMSE = 10.5%

12. AMS’04, Seattle, WA. January 12, 2004Slide 12 Impact of Sigma Noise on SM Retrievals Kp = 0.05 Kp = 0.10 Kp = 0.15 Dry area

13. AMS’04, Seattle, WA. January 12, 2004Slide 13 Impact of Sigma Noise on SM Retrievals Kp = 0.05 Kp = 0.10 Kp = 0.15 Wet area

14. AMS’04, Seattle, WA. January 12, 2004Slide 14 -50 -20 -10 -4 4 10 20 50 %VMS Impact of Sigma Noise on SM Retrievals Kp = 0.10 RMSE = 9.2% Kp = 0.15 RMSE = 10.3% Kp = 0.05 RMSE = 6.3%

15. AMS’04, Seattle, WA. January 12, 2004Slide 15  Using Kalman Filter data assimilation algorithm may combine HYDROS passive and active observations to produce useful median resolution soil moisture data;  KF DA can also be used for SM retrieval with a more physically detailed land surface model for the background estimate X b ;  With EKF DA retrieving SM, VWC and Ts simultaneously may be possible by using all radar and radiometer observations. Summary and Discussions