1 PQPF: THEORY AND OPERATIONAL USE Theresa Rossi NOAA/NWS Pittsburgh, PA Presented at Hydromet 00-2 Monday, 28 February 2000

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

3 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)

4 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

5 FORECASTMETHODOLOGY LOCALCLIMATICDATA FORECASTVERIFICATION THE PQPF SYSTEMWFO RFC GUIDANCE

6 PQPF METHODOLOGY

7 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

8 Conditional Probability X75X50 X25 calculated

9 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

10 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

11 INTERACTIVE SOFTWARE FOR PROBABILISTIC QUANTITATIVE PRECIPITATION FORECASTING

12 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

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23 PROBABILISTIC REASONING

24 SCHEME FOR JUDGMENTAL PROCESSING OF INFORMATION INTO PQPF

25 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

26 MAKING A PQPF DEVELOPMENT REVIEW ADJUSTMENT INTEGRATION

27 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.

28 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.

29 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.

30 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

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

32 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

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46 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

47 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

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

49 24hour POP

50 X50

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63 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.

64 WFOMosaic Stage 3 Precip(actual)

65 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%

66 RIVER FORECAST INTERFACE

67 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

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