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Application of CFS Forecasts in NWS Hydrologic Ensemble Prediction

Application of CFS Forecasts in NWS Hydrologic Ensemble Prediction. John Schaake Office of Hydrologic Development NOAA National Weather Service. Presentation to Climate Prediction Center July 28, 2009. Contents. Current long range hydrologic forecasts (AHPS)

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Application of CFS Forecasts in NWS Hydrologic Ensemble Prediction

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  1. Application of CFS Forecasts in NWS Hydrologic Ensemble Prediction John Schaake Office of Hydrologic Development NOAA National Weather Service Presentation to Climate Prediction Center July 28, 2009

  2. Contents • Current long range hydrologic forecasts (AHPS) • History of U.S. Ensemble Streamflow Prediction (ESP) • Development of a hydrological ensemble forecast system (HEPS) • Implementation Status (XEFS/FEWS) • RFC CFS forecast and hindcast requirements

  3. Locations of NWS Hydrologic Probability Forecast Points 1/13/2009

  4. Advanced Hydrologic Prediction Services (AHPS) • 90-day probability of exceedance • Broken into weekly outlooks • Based on ESP runs at each RFC • Based on climate adjustments (i.e. CPC outlooks at some RFCs) • Updated monthly

  5. Advanced Hydrologic Prediction Services (AHPS) • 90-day probability of exceedance • Blue line is an historical simulation based on averages • Black line is the conditional simulation with CPC inputs • Conditional simulation based on CPC inputs yield lower potential for flooding and high flows. • Essentially the Twedt et al, 1977 plot

  6. Historical Perspective • ESP with Climatological Forcing (1970’s – 2009) • Historical precipitation and temperature observations used to drive ESP • Used in current NWS Operational Products • Experimental Use of Short Range Forecasts (2001-2009) • RFC single-value forecasts used to create ensemble forcing for ESP • Experimental Use of Short, Medium and Long Range Forecasts (2007-2009) • RFC/HPC/MOS single value forecasts (1 – 7 days) • GFS ensemble mean forecasts (1 – 14 days) • CFS long range (1 day – 8 months) ensemble mean forecasts

  7. Development of a Hydrologic EnsembleForecast System (HEFS)

  8. Some Elements of Hydrologic Forecasting ESP forecasts typically are made for many forecast points in a river basin using a lumped hydrologic model River basins are partitioned into connected segments (which may include elevation zones, reservoirs, river routing segments, etc.) Hydrologic models are calibrated (i.e. parameters are estimated) using historical data Recent observations (precipitation, temperature, SWE, streamflow are used to estimate inititial conditions at the time a forecast is created.

  9. ESP Input Requirements • Forecast precipitation and temperature ensemble forcing: • For every forecast segment • Member time series at ~6hr step for entire forecast period • Individual members must be “consistent” over all segments and for the entire forecast period. • Statistical Properties: • Ensembles must be unbiased at all time and space scales and for all lead times • Ensembles must account for space/time scale dependency in the variability of precipitation and temperature and in the forecast uncertainty at all space and time scales • Each member must be equally likely to occur (i.e. a random sample)

  10. Ensemble Precipitation Forecast

  11. Ensemble Temperature Forecast

  12. Ensemble Streamflow Forecast

  13. NCEP Global Ensemble Forecasts

  14. Precipitation Forecasts are Temporally (and Spatially) Scale Dependent What must we do to extract ALL of the information in atmospheric forecasts to produce skillful and reliable ensemble forcing for hydrologic forecasting?

  15. RFC Field Testing Ensemble Pre-Processor • Hydrologic Model Output Statistics (HMOS) Ensemble Processor • Hydrologic Ensemble Hindcaster • Ensemble Verification 9/9/2014 17

  16. CNRFC Ensemble Prototype Smith River Mad River Salmon River Van Duzen River American River (11 basins) Navarro River

  17. CFS Forecast Application in XEFS • RFC ensemble forecast requirements • Canonical forecast event strategy • Construction of ensembles of CFS forecasts for each Canonical event • XEFS Ensemble PreProcessor to generate ensemble members for ESP

  18. Atmospheric Post-ProcessingStrategy: 2 – Step Process Raw Atmospheric Forecasts Estimate Probability Distributions This step includes downscaling, and correction of bias and spread problems and uses all available forecast information This step assures that members are both constrained by forecast probabilities and are “consistent” over all basins for the entire forecast period Assign Values to Ensemble Members ESP Input Forcing (Schaake Shuffle)

  19. Event Duration (Days) End of Event (Lead Days) Canonical Event Number Canonical Event Number Number of CFS Members Used Start of Event (Lead Days) Canonical Event Number Canonical Event Number Canonical Temperature Events

  20. Event Duration (6hrs) End of Event (Lead 6hrs) Canonical Event Number Canonical Event Number Number of CFS Members Used Start of Event (Lead 6hrs) Canonical Event Number Canonical Event Number Canonical Precipitation Events

  21. Estimate Probability Distributions for each Canonical Event • Use historical single-value forecasts and observations (for a common period of time)

  22. Estimate Probability Distributions(Cont’d) • Estimate climatological distributions of forecasts and observations • Use climatologies to transform forecasts and observations to Standard Normal Deviates • Estimate correlation parameter of Joint Distribution

  23. Calibration PDF of STD Normal PDF of Observed Joint distribution Sample Space Y NQT Observed Correlation(X,Y) Joint distribution X Y 0 Model Space Forecast PDF of Forecast PDF of STD Normal Observed NQT X Forecast National DOH Workshop, Silver Spring, MD July 15-17, 2008

  24. Ensemble Generation Conditional distribution given xfcst Joint distribution Model Space Y 1 Observed x1 Ensemble members … Probability xn xfcst X Forecast 0 x1 xi xn Ensemble forecast Obtain conditional distribution given a single-value forecast xfcst National DOH Workshop, Silver Spring, MD July 15-17, 2008

  25. Use EPP Forecast Probability Distributions to Assign Values to Individual Members) • Estimate forecast probability distribution for each Canonical Period • Use “Schaake Shuffle” to assign sample values from each probability distribution to individual members • Distribute values for Canonical Events to individual time series Example Cascade Of Canonical Events Aggregate periods Individual time steps

  26. Schaake Shuffle For each segment, at each time step, associate forecast ensemble members (left panel) with historical ensemble members (right panel) by rank (and hence year) Conditional distribution given xfcst Historical ensemble distribution 1 1 Probability Probability 0 0 y1 yi yn x1 xi xn (1996) (1996) Historical Ensemble Forecast Ensemble National DOH Workshop, Silver Spring, MD July 15-17, 2008

  27. Operational RFC QPF Files Operational RFC QTF Files Operational GFS Files Operational GFS Files Operational Forecast Files Operational Forecast Files MAT Time Series Identifiers MAT Area Locations Stations used for MAT Analysis Temperature Normals MAP Area Locations MAP Time Series Identifiers National Weather Service Hydrologic Ensemble Pre-Processor (EPP) GFS Subsystem J. Schaake, R. Hartman, J. Demargne, L. Wu, M. Mullusky, E. Welles, H. Herr, D. J. Seo, and P. Restrepo Average observed values of 6-hour precipitation corresponding to RFC and GFS forecasts Continuous Rank Probability Skill Score (CRPSS) for 6-hour precipitation forecasts Pre-ProcessorPrecipitation Algorithms Historical 6hr RFC Operational MAP Observed Data Files RFC single-value forecasts (mm) 6hr RFC QPF Verifications Files and Operational MAPs RFC-specific Utility Historical 6hr RFC Operational MAP QPF Data files Operational Ensemble MAPs Historical GFS Ensemble Mean Precipitation Forecasts (mm) Ensemble forecasts based on RFC single-value forecasts Precipitation Parameters Ensemble Generator Parameter Estimator 6hr MAP Calibration Files 6hr MAP Unformatted Calibration Data Files Hindcast Ensemble MAPs MAP Conversion Utility Raw Historical Data Files GFS Processor Historical Data Sets Average forecast values of 6-hour precipitation for RFC and GFS GFS single-value forecasts (mm) Precipitation Verification Index Files Data Analysis Ensemble forecasts based on GFS single-value forecasts CFS Ensemble Forecast (Application Under Construction) (mm) Verification Results Statistics Precipitation Ensemble Pre-Processor Average observed values of daily minimum temperature corresponding to RFC and GFS forecasts Continuous Rank Probability Skill Score (CRPSS) for daily minimum temperature forecasts RFC Historical Operational Observed TMX/TMN Pre-Processor Temperature Algorithms RFC single-value forecasts RFC TMX/TMN Forecast Verification Files and Operational Observed TMX/TMN RFC-specific Utility RFC Historical Operational Forecast TMX/TMN MAT Analysis Operational Ensemble MATs MOS TMX/TMN Forecast Verification Files MOS Historical Forecast TMX/TMN MOS Utility Ensemble forecasts based on RFC single-value forecasts Temperature Parameters Parameter Estimator Ensemble Generator Hindcast Ensemble MATs Average forecast values of daily minimum temperature for RFC and GFS 6hr MAT Calibration Files 6hr to TMX/TMN GFS single-value forecasts Historical GFS Ensemble Mean Temperature Forecasts Raw Historical Data Files Temperature Verification TMX/TMN Calibration Data Index Files Ensemble forecasts based on GFS single-value forecasts Unformatted TMX/TMN Calibration Data Files Conversion Utility Verification Results GFS Processor Historical Data Sets CFS Ensemble Forecast (Application Under Construction) Temperature Ensemble Pre-Processor TMX: maximum temperature TMN: minimum temperature

  28. Forecast Skill (Correlation) of GFS and CFS Tmin Forecasts for North Fork American River Basin (Upper Zone) GFS Forecasts CFS Forecasts

  29. Forecast Skill (Correlation) of GFS and CFS Precipitation Forecasts for North Fork American River Basin (Upper Zone) GFS Forecasts CFS Forecasts

  30. Seasonal Tmin Forecasts for North Fork American River Basin (Upper Zone)

  31. Seasonal Precipitation Forecasts for North Fork American River Basin (Upper Zone)

  32. Forecast Skill of GFS and CFS Tmin Forecasts for North Fork American River (Upper Zone)

  33. Forecast Skill of GFS and CFS Precipitation Forecasts for North Fork American River Basin (Upper Zone)

  34. CREC1 (Smith River at Crescent City, CA) – Forecasts and Simulations

  35. RFC CFS Forecast and Hindcast Requirements • A “seamless” approach to weather and climate prediction is needed to meet user requirements for hydrologic ensemble forecasts • Additional CFS members (as well as higher model resolution?) are needed to bring CFS forecast skill closer to GFS forecast skill before and after the end of week 2 • Additional CFS hindcasts are needed for the first several (<= 6?) weeks of the forecast period • RFC hindcasts must meet user needs. • This requires daily CFS sub-seasonal hindcasts (<= 6 wks) for all members used operationally to compute the ensemble mean. • This requires “frequent” CFS seasonal hindcasts (~weekly).

  36. NOAA Hydrology Strategic Science Plan Thank you! http://www.weather.gov/oh/src/docs/Strategic_Sience_Plan_2007-Final.pdf

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