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The 3rd Workshop of the International Precipitation Working Group, 23-27 October, 2006

Basin Scale Precipitation Data Merging Using Markov Chain Monte Carlo Method K. Hsu, F. Boushaki, S. Sorooshian, and X. Gao Center for Hydrometeorology and Remote Sensing University of California Irvine. The 3rd Workshop of the International Precipitation Working Group, 23-27 October, 2006.

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The 3rd Workshop of the International Precipitation Working Group, 23-27 October, 2006

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  1. Basin Scale Precipitation Data Merging Using Markov Chain Monte Carlo MethodK. Hsu, F. Boushaki, S. Sorooshian, and X. GaoCenter for Hydrometeorology and Remote SensingUniversity of California Irvine The 3rd Workshop of the International Precipitation Working Group, 23-27 October, 2006

  2. Outline • PERSIANN Rainfall • Precipitation Data Merging • Grid-Based Precipitation Data Merging • Basin Scale Precipitation Data Merging • Case Study • Summary

  3. Products PERSIANN System “Estimation” Hourly Global Precipitation Estimates Global IR ANN Satellite Data High Temporal-Spatial Res. Cloud Infrared Images Feedback MW-RR (TRMM, NOAA, DMSP Satellites) Sampling Hourly Rain Estimate Error Detection Quality Control MW-PR Hourly Rain Rates Merging GPCC & CPC Gauge Analysis • Merged Products • Hourly rainfall • 6 hourly rainfall • Daily rainfall • Monthly rainfall Ground Observations Gauges Coverage Center for Hydrometeorology and Remote Sensing, University of California, Irvine Precipitation Estimationfrom RemotelySensedInformation using ArtificialNeuralNetworks (PERSIANN)

  4. PERSIANN-CCS (Cloud Classification System)

  5. PERSIANN Precipitation Products US PERSIANN-CCS: http://hydis8.eng.uci/CCS Global PERSIANN: http://hydis8.eng.uci.edu/hydis-unesco/ 0.04ox0.04o Hourly 0.25ox0.25o Hourly

  6. PERSIANN (0.25° 0.25°) 07/25-27/2006 High resolution precipitation data are needed for hydrologic applications in SW. Severe storms propagate from mountains to low-elevated areas. PERSIANN CCS (0.04° 0.04°) 07/24-27/2006 Acknowledgement. This research is partially funded by NSF/SAHRA and NASA/GPM programs A SHORT MOVIE OF PERSIANN PRODUCTS (PERSIANN: Precipitation estimation from Remote Sensing Information using Artificial Neural Network)

  7. RESEARCH TO SUPPORT MODELING EFFORTS Flash Flood Monitoring (7/27-28/2006) Poor radar coverage over mountainous southwest can result in missing flood warning for the areas radar network does not cover (Maddox et al., 2003). The demo shows our on-going study to check how the missing portions of a severe storm can be retrieved by the concurrent PERSIANN storm images and also reduce false warning. Differences between PERSIANN and radar images exist. Red: PERSIANN Rain vs. Radar No Rain Blue: PERSIANN No Rain vs. Radar Rain Radar beams (3-km above ground level) are blocked by mountains in southwest United States. Strong convections start over mountains where radar coverage is poor. PERSIANN monitors the lifetimes of storm systems and provides information for early warning.

  8. 6-Hour Accumulated Rainfall: Hurricane Ivan hydis8.eng.uci.edu/CCS

  9. Precipitation Measurement is one of the KEY hydrologic Challenges

  10. Hydrologic Models Sacramento Model R F i q IA t t API Model Mike SHE Model, DHI VIC Model QR QB

  11. Streamflow Simulation vs. Precipitation Uncertainty:

  12. Streamflow Simulation vs. Precipitation Uncertainty:

  13. Streamflow Simulation vs. Precipitation Uncertainty:

  14. Multiple Sources for Rainfall Estimation Low Orbiting Satellites VIS, IR, MV, and Radar Geosynchronous Satellites VIS, IR, Sounding Radar Gauge Surface Temperature Soil Moisture Vegetation LABZ

  15. Grid-Based Data Merging Bias Correction and Downscaling of Daily Rainfall to Hourly Rainfall CPC Daily Analysis PERSIANN Rainfall (non-adjusted) PERSIANN Rainfall (bias adjusted) Downscaled to Hourly Rainfall CPC Daily Gauge Analysis PERSIANN Rainfall Grid size: 0.04ox0.04o Daily Rainfall: Summer 2005 Grid size: 0.25ox0.25o Time Step: Day

  16. Basin Scale Precipitation Data Merging

  17. PERSIANN Rainfall Estimates in Hydrologic Simulation OBSERVED vs. SIMULATED DISCHARGE (TRMM-MULTI SATELLITE RAINFALL ESTIMATES) OBSERVED vs. SIMULATED DISCHARGE (RADAR/GAGE MERGED RAINFALL ESTIMATES) PERSIANN 6-hour Rainfall Radar/Gauge 6-hour Rainfall Observed Radar/Gage Merged Observed TRMM/Multi Satellite Radar/Gage Merged Gages used by NWS Leaf River Near Collins Mississippi USGS # 02472000 Basin Area : 753 mi2 Hydrologic Model Sacramento Soil Moisture Accounting Model (NWS) (RFC parameters) Input time step : 6 hours Output time step : 24 hours

  18. Basin Scale Precipitation Data Merging Pg (g ,  g)  Pi Ps (s ,  s) I : Weighting parameters P : Input  : Errors i : Hydro. Model parameters Q : Output Hydrologic Model (SAC-SMA Model) Hydrologic Model(i) Qtcomp Optimization output Qtobs (I, Model)  I : Bias parameters

  19. Parameter Calibration = observations = simulated flows * Flow Hours Probability distribution to be maximized

  20. Uncertainty of Parameters Uncertainty associated with parameters Total Uncertainty including structural errors Hours Probability distribution to be maximized 95%

  21. Bayesian Model Analysis • Learn model parameters from data: • p(ө): Priori distribution of parameters • p(D|ө): Likelihood function • p(ө|D): Posterior distribution of parameters

  22. Markov Chain Monte Carlo (MCMC) Sampling Probability distribution to be maximized w.r.t Current guess

  23. > 1 Always accept New guess Markov Chain Monte Carlo (MCMC) Sampling 100% acceptance of new points having higher probability than the old point

  24. MCMC – Acceptance of New Points Having Lower Probability than the Old Point is Probabilistic < 1 Accept ifa > R~ Uniform (0,1) Markov Chain Monte Carlo (MCMC) Sampling α% acceptance of new points having lower probability than the old point If thearatio is small, then the probability of acceptance is small

  25. Rainfall Runoff Time Series Gauge Precipitation (mm/day) PERSIANN Gages used by NWS Streamflow (CMSD) Time: Day Leaf River Near Collins Mississippi USGS # 02472000 Basin Area : 753 mi2

  26. Runoff Forecasting from Gauge, PERSIANN, and Merged Rainfall Gauge Rainfall Rainfall (mm/day) Satellite: PERSIANN Rainfall Merged Rainfall 1000 Gauge PERSIANN Merged RMSE 51.82 80.78 34.91 CMSD Corr. 0.876 0.706 0.901 Bias 15.34 -17.68 -3.52 CMSD 750 Streamflow (m3/day) 500 250 0 100 100 100 50 50 50 300 200 0 100 Time (Day)

  27. Parameter Distribution Distribution of Merging Parameters(5000 samples) Weighting factor (αg ) Weighting factor (αs ) Bias parameter (βs ) Bias parameter (βg )

  28. Interaction Between Parameters Parameter: βg Parameter: αs Parameter: αg Parameter: αg Parameter: βs Parameter: βg Parameter: αs Parameter: βg

  29. Confidence Interval of Merged Rainfall (95%) 95% confidence interval

  30. 120 95% Uncertainty Bound 80 Rainfall (mm/day) 40 Precipitation 0 99% Uncertainty Bound 95% Uncertainty Bound 800 Observed Streamflow 600 Streamflow (m3/day) 400 200 0 100 300 200 0

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