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Using Probalistic Quantitative Precipitation Forecasts PQPFs within a hydro-meteorological chain within a hydro-meteorological chain R. Marty, A. Djerboua,

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Presentation on theme: "Using Probalistic Quantitative Precipitation Forecasts PQPFs within a hydro-meteorological chain within a hydro-meteorological chain R. Marty, A. Djerboua,"— Presentation transcript:

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

2 I. General Organization of the chain  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) Plan : A Hydro-meteorological Chain IV. Conclusions & Perspectives Real Time Operation III.  Case study (Ardèche 2000)  Updating/refreshing of the forecasts

3 Chain: Modules

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

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

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

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

8 Generator: Cond. Past

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

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

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

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

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

14 Eg.: ingredient available : a meteo forecast, every 24h (resp. 12h ou 6h…) day DF1(x) i.e. the precipitation distribution for day D  F1(x) day D+1F2(x) + the precipitation distribution for day D+1  F2(x) 24h IF required lead-time is « at least 12 h ahead » and if the updating cycle is 24h, then rainfall scenarios are conditioned as follow :  by PQPF precipitation distribution day D F1(x) of day D i.e. F1(x)  and by the sum of the distributions day Dday D+1F1 + F2(x) for day D & day D+1 i.e. F1 + F2(x) For 13 ~ 24h : For time-steps 13 ~ 24h : For 1 ~ 12h : For time-steps 1 ~ 12h : Real Time Real Time: Updating

15 distributions of precipitation forecast D for Nov. 12 th Analog distribution Day D D+1 for Nov. 13 th Analog distribution Day D+1 D  D mm Observed rainfall over 24h Analog distribution D  D+1 Observed rainfall over 48h Analog distribution : sum Days D & D mm D  D+1 Observed rainfall over 48h Analog distribution Sunday Nov. 12 th at 18h UTC 31.5 mm68.1 mm future real rainfall observed rainfall observed discharge simulated discharge quantile Real Time Real Time: Updating

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

17 Conclusions and Perspectives assimilationofPQPF from the analog method assimilation of Probabilistic Quant. Precip. Forecasts PQPF from the analog method to produce PQDF to produce Probabilistic Quant. Discharge Forecasts PQDF with a more appropriate time-step via a rainfall generator take into account operational constraints which take into account operational 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 For each episode : we consider at first NA NA : Storms number Principle and architecture of the generator Then for each storm :  DA  DA : Storm duration ITEA  ITEA : Duration of the dry period between storms HPA  HPA : Rainfall total of the storm HPMX  HPMX : Maximum of hourly rainfall HEMA  HEMA : Position of the maximum of hourly rainfall

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

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

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

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

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


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