Probabilistic Hydrometeorological Forecasts Hydromet 00-3 Thursday, 11 May 2000 Bill Drzal NOAA/NWS Pittsburgh, PA

OVERVIEW Probabilistic Hydrometeorological System PQPF Methodology Interactive PQPF Software Probabilistic Reasoning PQPF Case Study Probabilistic River Stage Forecast River Forecast Interface

NWS End-to-End Probabilistic Risk Reduction Define AWIPS-compatible PQPF/PRSF methodologies, PQPF guidance, and public product formats. Approach is grid-based and benefits from HPC, TDL and OH input With funding, similar Risk Reductions in other Regions after UVA/PBZ/RLX/OHRFC/TDL/HPC/OH/ OM Users (County EMA & Barge Industry)

PROBABILISTIC HYDROMETEOROLOGICAL FORECASTING SYSTEM Probabilistic Quantitative Precipitation Precipitation Forecasting System Forecasting SystemPQPF WFO To improve the reliability and lead time of flood warnings. Probabilistic River Stage Forecasting System Forecasting SystemPRSF River Flood Warning System RFI USERS RFC WFO Probabilistic RSFs Flood Watches & Warnings

FORECASTMETHODOLOGY LOCALCLIMATICDATA FORECASTVERIFICATION THE PQPF SYSTEMWFO RFC GUIDANCE

PQPF METHODOLOGY

PQPF TOTAL AMOUNT Precipitation amount accumulated during a period: W Probability of Precipitation: PoP=P(W>0) Conditional Exceedance Fractiles of Amount: – P(W>X 25 |W>0)=0.25 – P(W>X 50 |W>0)=0.50

Conditional Probability X75X50 X25 calculated

ASSESSMENT OF CONDITIONAL EXCEEDANCE FRACTILES X 50 Judgments of equally likely events X 25 ACTUAL PRECIPITATION W HYPOTHESIS: X 50 <W ACTUAL PRECIPITATION W P ( W>X 25 |W >0)=.25 P(W>X 50 |W>0)=.50 HYPOTHESIS: 0<W

PQPF Temporal Disaggregation Precipitation amount during subperiod i: W i Expected subamounts: m i =E(W i |W>0); i=1,2,3,4;12,34 Expected fractions: z i =E(W i /W|W>0); i=1,2,3,4;12,34

A PQPF is Never Right or Wrong It Just Needs to be Well Calibrated!

INTERACTIVE SOFTWARE FOR PROBABILISTIC QUANTITATIVE PRECIPITATION FORECASTING

Purpose Aids field forecasters in preparing PQPFs. Provides crucial input to Probabilistic River Stage Forecast System. Prototype Testing –Weather Service Forecast Offices Pittsburgh, PA Charleston, WV

PROBABILISTIC REASONING

SCHEME FOR JUDGMENTAL PROCESSING OF INFORMATION INTO PQPF

NMC NUMERICAL MODELS TDL MODEL OUTPUT STATISTICS NMC MANUAL GUIDANCE LOCAL SUBJECTIVE ANALYSIS REVIEW -MODEL ASSESSMENT/COMPARISON -GUIDANCE REVIEW IS PRECIP PROBABLE? STOP IS SIGNIFICANT AMOUNT PROBABLE? FURTHUR ANALYSIS -MODEL OUTPUTS -LOCAL ANALYSIS WHAT IS PREDICTABILITY OF PATTERN? WHAT IS PREDICTABILITY OF PATTERN LIMITED FURTHER ANALYSIS -FOLLOW CLOSELY LOCAL ADJUSTMENTS TO GUIDANCE -LARGE UNCERTAINTY -FOLLOW CLOSELY GUIDANCE WITH MINOR LOCAL ADJUSTMENTS -SMALLER UNCERTAINTY -MIX GUIDANCE WITH LOCAL ADJUSTMENTS -LARGER UNCERTAINTY -FOLLOW GUIDANCE CLOSELY -SMALLER UNCERTAINTY LOCAL CLIMATOLOGICAL GUIDANCE INTEGRATION EXPERT KNOWLEDGE OF LOCAL HYDROMET INFLUENCES OBSERVATIONS NO YES NO LOW HIGH LOW HIGH WORKING QPF POSTERIOR QPF REVIEW DEVELOPMENT ADJUSTMENT INTEGRATION

MAKING A PQPF DEVELOPMENT REVIEW ADJUSTMENT INTEGRATION

THE REVIEW PHASE Examine Observations and Guidance Review Initial Conditions –Diagnose past/current conditions, trends and how well models initialized. –Compare Model Outputs If Agree…confidence is increased. If Not…uncertainty decreases confidence.

THE DEVELOPMENT PHASE Judge Likelihood/Predictability of Precipitation Ask three questions: –Is precipitation probable? –Is a significant amount probable? –What is predictability of pattern? No significant amount & predictability: –high…more confidence in guidance. –low…less confidence/further analysis Significant amount…further analysis.

THE ADJUSTMENT PHASE Adjust Guidance/Ascertain Uncertainty Nonsignificant Event –Predictability high…follow guidance/uncertainty smaller. –Predictability low…may adjust guidance/ uncertainty larger. Significant Event –Predictability high…local analysis should corroborate guidance/uncertainty smaller. –Predictability low…extensive use of analysis, may significantly adjust guidance/uncertainty larger. “Working PQPF”…includes amounts & uncertainties.

THE INTEGRATION PHASE “Working PQPF” Integrated with LCG Integrate Information From: –“Working PQPF” –Knowledge of local influences –Local Climatic Guidance (LCG) Uncertainty small…tend toward “Working PQPF” Uncertainty large…tend toward LCG

PQPF CASE STUDY Well Organized Frontal System May18-19,1999

THE REVIEW PHASE Case Study May 18-19, 1999 Examine Observations and Guidance –00Z 5/18/99 ETA Model Models initialized well & in agreement –confidence increased

THE DEVELOPMENT PHASE Case Study May 18-19, 1999 Judge Likelihood/Predictability of Precipitation –A significant amount of precipitation probable –Predictability of pattern is high Models in agreement on speed & movement of system Precipitation of convective nature & spatially variable with localized higher amounts possible

THE ADJUSTMENT PHASE Case Study May 18-19, 1999 Adjust guidance/Ascertain Uncertainty Significant Event –Predictability high…local analysis corroborated guidance/uncertainty smaller “Working PQPF”…includes amounts & uncertainties

THE INTEGRATION PHASE Case Study May 18-19, 1999 Integrate “Working PQPF”, local influences & LCG Uncertainty small…tend toward “Working PQPF”

24hour POP

X50

X50

X25

X25

X75

X75

T50

Z1

Z1

Z2

Z3

Z4

Summary of Case Study May 18-19, 1999 Well Organized Frontal System Precipitation probable & significant. Predictability of pattern high…models in agreement. Analysis corroborate guidance. Convective nature, spatially variable, localized higher amounts possible. Uncertainty reflected in wide credible interval.

WFOMosaic Stage 3 Precip(actual)

Summary of Case Study May 18-19, 1999 Monongahela River Basin 24-h period ending 1200 UTC 5/19/99 Exceedance Fractiles Expected Fractions (inches) (%) X 75 X 50 X 25 Z1Z2Z3Z4Z1Z2Z3Z4 PQPF LCG* *LCG estimates are conditioned on a minimum of 0.25 inches. ACTUAL PoP = 100%

Probabilistic River Stage Forecast (PRSF)

PRSF Methodology Interfaces with NWSRFS –Ensemble Streamflow Prediction (ESP) - OH –Bayesian Forecast System (BFS) - UVA Output – Exceedance Function –Quantifies total uncertainty about river stage for a certain day

Integration of PRSF System with NWSRFS Gridded PQPF RFCWFO Preprocessing to get Deterministic QPF Precipitation Forecast Processor PFP River Forecast Viewer Operational Forecast System NWSRFS - OFS Ensemble Streamflow Prediction NWSRFS - ESP Bayesian Forecasting System BFS River Forecast Interface RFI River Forecast Viewer RFV WFO Web Site End-User

Bayesian Forecast System From NWSRFS: Input for forecast point Precipitation Uncertainty Processor (PUP) Hydrologic Uncertainty Processor (HUP) Integrator (INT) Interactive Review and Adjustment (IRA) Parameter estimates From off-line simulation Guidance PRSF Model PRSF

RIVER FORECAST INTERFACE

GRAPHICAL RIVER FORECAST INTERFACE Input - Probabilistic River Stage Forecasts (PRSF) Purpose –Display PRSF –Aid forecaster in deciding flood alarm (watch/warning) –Communicate flood alarms to users –Aid users in making decisions based on PRSF

SUMMARY Provided overview of Probabilistic Hydrometeorological Forecasting System Focused on PQPF –Methodology –Interactive Software –Probabilistic Reasoning Demonstrated concepts with May18-19, 1999 Case Study

PQPF Lab Exercise Objective: Prepare a probabilistic QPF of spatially averaged precipitation over four river basis to determine how well calibrated the forecasts aft for the class as a whole. Instructions: Review the Hurricane Floyd case using any available data through 12z on 16 Sep Prepare probabilistic QPFs of spatially averaged precipitation for the 24 hour period ending on 17 Sep 1999 for each of the four basins on your lab sheet. For X75, X50, and X25 enter the value to the nearest tenth of an inch. For Z(1,2,3,4) enter the percentage of the total rainfall that occurred in each time period.

X50 Verification RiverX50Z1Z2Z3 Z4 PASSAIC RARITAN SCHUYLKILL SE PA/DELAWARE