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Using P robalistic Q uantitative P recipitation F orecasts PQPFs

Using P robalistic Q uantitative P recipitation F orecasts PQPFs within a hydro-meteorological chain. R. Marty , A. Djerboua, Ch. Obled & I. Zin LTHE - INPG, Grenoble - France. renaud.marty@hmg.inpg.fr. Plan : A Hydro-meteorological Chain. General Organization of the chain. I.

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Using P robalistic Q uantitative P recipitation F orecasts PQPFs

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  1. Using Probalistic Quantitative Precipitation Forecasts PQPFs within a hydro-meteorological chain R. Marty, A. Djerboua, Ch. Obled & I. Zin LTHE - INPG, Grenoble - France. renaud.marty@hmg.inpg.fr

  2. Plan : A Hydro-meteorological Chain GeneralOrganization of the chain I. •  The different modules required • Meteorological forecasts and processing II. Generation / disagregations of rainfall scenarios •  Principle and architecture of the generator • Conditioning by the past (as observed) • Conditioning by the future (as forecast) III. Real Time Operation • Case study (Ardèche 2000) • Updating/refreshing of the forecasts Conclusions & Perspectives IV.

  3. Chain: Modules

  4. Chain: Meteo. Forecasts • Forecast Suppliers • Deterministic : 1 model / 1 trace • Ensemble / Probabilistic :1 model / multiple traces • Lead time • Nowcasting 0h 3h(Radar) • Short term forecasting6h -18h / 18h – 30 or 6h ----- 30h But…! Requires Adaptation (for basin rainfall, etc…)

  5. Chain: Processing Selecting a Forecast e.g. ECMWF or ARPEGE… + Adaptatione.g. ANALOG PQPFProbabilisticprecip.Forecast totalized on time-steps∆Mt Disagregation If : Meteo model time-step ∆Mt (24h)>>∆Ht (1h) Hydro model time-step Then Disagregation at ∆Ht e.g.:viaRainfall Generator Temporal /spatio-temporal ? FutureScenarios (hyeto.) conditioned by the PQPF ~ rainfall ‘‘Traces’’ at Hteventuallyspatio-temporal… Hydrological Models

  6. Generator: Principles • Rainfall generation / disagregation: • Purposes :to be able to: • generate « plausible » intense rainfall events • propose an extension for a current event • respect a rainfall forecast … • + Ifforecast probabilistic (PQPF): • respect a distributionof future rainfall…

  7. Generator: Principles Description and characterization of a rain event P(mm) t(h) Requires at least :~ 20 events  statistical laws of these parameters

  8. Generator: Cond. Past

  9. Generator: Cond. future Number of wanted scenarios e.g. 500~1000 X Taking into account a Probabilistic Quantitative Precipitation Forecast PQPF of 24h totals issued at 6h Number of scenarios to retain for each class Probability density

  10. Generator: Cond. future Calculation of the total on fixed 24h (06-06h UT) 42mm on 24h Scenario conditioned by the past

  11. Generator: Cond. future Number of scenarios to collect for each class Retain this generated scenario for the class [40-45]mm except if there are already 120 Selection or Rejection of the scenario 42mm en 24h

  12. Real Time:Ardèche 2000 Event of 12th Nov.2000 Ardèche at Vogüé 635 km²

  13. Real Time:Ardèche 2000 D for Nov. 12th 99.6mm future real rainfall observed rainfall observed discharge simulated discharge quantile distributions of precipitation forecast Observed rainfall over 24h Analog distribution Sunday Nov. 12that 6h UTC(adapted PQPF’s)

  14. Real Time: Updating Eg.: ingredient available : a meteoforecast , every 24h (resp. 12h ou 6h…) i.e. the precipitation distribution for day DF1(x) + the precipitation distribution for day D+1F2(x) IF required lead-time is « at least 12 h ahead »andif the updating cycle is 24h, then rainfall scenarios are conditioned as follow : Fortime-steps1 ~ 12h : • by PQPF precipitation distribution of day D i.e. F1(x) Fortime-steps13 ~ 24h : • and by the sum of the distributions for day D & day D+1 i.e. F1 + F2(x)

  15. Real Time: Updating D+1 D for Nov. 12th for Nov. 13th Analog distribution Day D Analog distribution Day D+1 68.1 mm 31.5 mm future real rainfall observed rainfall observed discharge simulated discharge 68.1 mm 68.1 mm D  D+1 D  D+1 D  D+1 quantile Observed rainfall over 48h Analog distribution : sum Days D & D+1 Observed rainfall over 24h Analog distribution Observed rainfall over 48h Analog distribution distributions of precipitation forecast Sunday Nov. 12that 18h UTC

  16. Real Time:Refreshing 58.3 mm future real rainfall observed rainfall observed discharge simulated discharge quantile Refreshing : Distribution of precipitation forecast for the Nov. 13th Observed rainfall over 24h Analog distribution Monday Nov. 13th at 6h TU New forecast (adapted PQPF’s)

  17. Conclusions and Perspectives assimilationof Probabilistic Quant. Precip. Forecasts PQPFfrom the analog method to produceProbabilistic Quant. Discharge ForecastsPQDF with a more appropriate time-step via a rainfall generator whichtake into accountoperational constraints hourly updating and daily refreshing • meteorological uncertainties and propagation • also with ensemble meteorological forecasts • hydrological model uncertainties • multi-model technique • rainfall generator regionalisation

  18. Thanks for your attention !

  19. 2.1 Principle and architecture of the generator • For each episode :we consider at first • NA : Storms number • Then for each storm : • DA: Storm duration • ITEA: Duration of the dry period between storms • HPA: Rainfall total of the storm • HPMX: Maximum of hourly rainfall • HEMA: Position of the maximum of hourly rainfall

  20. 2.1 Principle and architecture of the generator Draw of the storms number : NA t

  21. 2.1 Principle and architecture of the generator Draws of storms and Inter-storm durations : DA - ITEA t

  22. 2.1 Principle and architecture of the generator Draws of rainfall totals: HPA = f(DA) t

  23. Draws of the maximum hourly intensities : HPMXRPON = RPA/DAn RPA = HPMX/HPA = G(DA) Draws of the maximum positions: HEMA

  24. 2.1 Principle and architecture of the generator Repartition of the storm volume HPA around HPMX

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