National Weather Service 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

National Weather Service Water Predictions for Life Decisions 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

National Weather Service Water Predictions for Life Decisions Locations of NWS Hydrologic Probability Forecast Points 1/13/2009

National Weather Service Water Predictions for Life Decisions 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 Advanced Hydrologic Prediction Services (AHPS)

National Weather Service Water Predictions for Life Decisions 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 Advanced Hydrologic Prediction Services (AHPS)

National Weather Service Water Predictions for Life Decisions 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 ( ) RFC single-value forecasts used to create ensemble forcing for ESP Experimental Use of Short, Medium and Long Range Forecasts ( ) 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

National Weather Service Water Predictions for Life Decisions Development of a Hydrologic Ensemble Forecast System (HEFS)

National Weather Service Water Predictions for Life Decisions 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.) Some Elements of Hydrologic Forecasting 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.

National Weather Service Water Predictions for Life Decisions

National Weather Service Water Predictions for Life Decisions 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)

National Weather Service Water Predictions for Life Decisions Ensemble Precipitation Forecast

National Weather Service Water Predictions for Life Decisions Ensemble Temperature Forecast

National Weather Service Water Predictions for Life Decisions Ensemble Streamflow Forecast

National Weather Service Water Predictions for Life Decisions NCEP Global Ensemble Forecasts

National Weather Service Water Predictions for Life Decisions

National Weather Service Water Predictions for Life Decisions 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?

National Weather Service Water Predictions for Life Decisions RFC Field Testing 10/27/ Ensemble Pre-Processor  Hydrologic Model Output Statistics (HMOS) Ensemble Processor  Hydrologic Ensemble Hindcaster  Ensemble Verification

National Weather Service Water Predictions for Life Decisions CNRFC Ensemble Prototype Smith River Salmon RiverMad River Navarro River American River (11 basins) Van Duzen River

National Weather Service Water Predictions for Life Decisions 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

National Weather Service Water Predictions for Life Decisions Atmospheric Post-Processing Strategy: 2 – Step Process Raw Atmospheric Forecasts Estimate Probability Distributions Assign Values to Ensemble Members ESP Input Forcing 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 (Schaake Shuffle)

National Weather Service Water Predictions for Life Decisions Canonical Temperature Events Canonical Event Number Event Duration (Days) Canonical Event Number Number of CFS Members Used Canonical Event Number Start of Event (Lead Days) Canonical Event Number End of Event (Lead Days)

National Weather Service Water Predictions for Life Decisions Canonical Precipitation Events Canonical Event Number Event Duration (6hrs) Canonical Event Number Number of CFS Members Used Canonical Event Number Start of Event (Lead 6hrs) Canonical Event Number End of Event (Lead 6hrs)

National Weather Service Water Predictions for Life Decisions Estimate Probability Distributions for each Canonical Event Use historical single-value forecasts and observations (for a common period of time)

National Weather Service Water Predictions for Life Decisions 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

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

National Weather Service Water Predictions for Life Decisions July 15-17, 2008 National DOH Workshop, Silver Spring, MD Forecast Observed Joint distribution Model Space x fcst Ensemble Generation X Y Obtain conditional distribution given a single- value forecast x fcst xixi xnxn Conditional distribution given x fcst Ensemble forecast Probability 0 1 … Ensemble members x1x1 xnxn x1x1

National Weather Service Water Predictions for Life Decisions 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 Individual time steps Aggregate periods

National Weather Service Water Predictions for Life Decisions July 15-17, 2008 National DOH Workshop, Silver Spring, MD 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) 1 Probability 0 Historical ensemble distribution 1 Probability 0 Conditional distribution given x fcst (1996) x1x1 xixi xnxn Forecast Ensemble Historical Ensemble y1y1 yiyi ynyn

National Weather Service Water Predictions for Life Decisions Temperature Parameters Verification Results Parameter Estimator Temperature Verification Hindcast Ensemble MATs Precipitation Verification Ensemble Generator Hindcast Ensemble MAPs Ensemble Generator Operational Ensemble MATs 6hr RFC QPF Verifications Files and Operational MAPs 6hr MAP Calibration Files Raw Historical Data Files MAP Time Series Identifiers Precipitation Parameters Historical 6hr RFC Operational MAP QPF Data files Historical GFS Ensemble Mean Precipitation Forecasts 6hr MAP Unformatted Calibration Data Files Historical 6hr RFC Operational MAP Observed Data Files MAP Area Locations GFS Processor Historical Data Sets Index Files Parameter Estimator RFC-specific Utility MAP Conversion Utility Data Analysis Statistics Operational Ensemble MAPs Verification Results Precipitation Ensemble Pre-Processor 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 Pre-Processor Precipitation Algorithms CFS Ensemble Forecast (Application Under Construction) RFC-specific Utility RFC Historical Operational Observed TMX/TMN RFC TMX/TMN Forecast Verification Files and Operational Observed TMX/TMN RFC Historical Operational Forecast TMX/TMN MOS Utility MOS TMX/TMN Forecast Verification Files 6hr to TMX/TMN 6hr MAT Calibration Files Conversion Utility Unformatted TMX/TMN Calibration Data Files MAT Time Series Identifiers TMX/TMN Calibration Data MAT Analysis Temperature Normals Stations used for MAT Analysis MAT Area Locations Index Files GFS Processor Historical Data Sets Raw Historical Data Files Historical GFS Ensemble Mean Temperature Forecasts Temperature Ensemble Pre-Processor Pre-Processor Temperature Algorithms CFS Ensemble Forecast (Application Under Construction) MOS Historical Forecast TMX/TMN Average observed values of 6-hour precipitation corresponding to RFC and GFS forecasts (mm) Average forecast values of 6-hour precipitation for RFC and GFS (mm) Average observed values of daily minimum temperature corresponding to RFC and GFS forecasts Average forecast values of daily minimum temperature for RFC and GFS Continuous Rank Probability Skill Score (CRPSS) for daily minimum temperature forecasts RFC single-value forecasts Ensemble forecasts based on RFC single-value forecasts GFS single-value forecasts Ensemble forecasts based on GFS single-value forecasts Continuous Rank Probability Skill Score (CRPSS) for 6- hour precipitation forecasts RFC single-value forecasts Ensemble forecasts based on RFC single-value forecasts GFS single-value forecasts Ensemble forecasts based on GFS single-value forecasts TMX: maximum temperatureTMN: minimum temperature Operational GFS Files Operational RFC QPF Files Operational Forecast Files Operational GFS Files Operational RFC QTF Files Operational Forecast Files

National Weather Service Water Predictions for Life Decisions Forecast Skill (Correlation) of GFS and CFS Tmin Forecasts for North Fork American River Basin (Upper Zone) CFS Forecasts GFS Forecasts

National Weather Service Water Predictions for Life Decisions Forecast Skill (Correlation) of GFS and CFS Precipitation Forecasts for North Fork American River Basin (Upper Zone) CFS Forecasts GFS Forecasts

National Weather Service Water Predictions for Life Decisions Seasonal Tmin Forecasts for North Fork American River Basin (Upper Zone)

National Weather Service Water Predictions for Life Decisions Seasonal Precipitation Forecasts for North Fork American River Basin (Upper Zone)

National Weather Service Water Predictions for Life Decisions Forecast Skill of GFS and CFS Tmin Forecasts for North Fork American River (Upper Zone)

National Weather Service Water Predictions for Life Decisions Forecast Skill of GFS and CFS Precipitation Forecasts for North Fork American River Basin (Upper Zone)

National Weather Service Water Predictions for Life Decisions CREC1 (Smith River at Crescent City, CA) – Forecasts and Simulations

National Weather Service Water Predictions for Life Decisions 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).

National Weather Service Water Predictions for Life Decisions NOAA Hydrology Strategic Science Plan Thank you!