Nathalie Voisin1, Florian Pappenberger2, Dennis Lettenmaier1, Roberto Buizza2,
and John Schaake3
1 University of Washington2 ECMWF3 National Weather Service – NOAA
European Geophysical Union General Assembly , May 5 2010
2
Limpopo 2000Early Flood Alert System for Southern Africa (Artan et al. 2001)
South Asia 2000Mekong River Commission – basin wide approach for flood forecasting
Bangladesh 2004 (Hopson and Webster 2010)*
Horn of Africa 2004 (Thiemig et al. 2010, EU - AFAS)*
Zambezi 2001,2007,2008 (EU-AFAS, in process)*
Existing Flood Alert Systems in mostly-ungauged basins
* Ensemble flow forecasting
Develop a medium range probabilistic quantitative hydrologic forecast system applicable globally:
Using only (quasi-) globally available tools:▪ Global Circulation Model ensemble weather forecasts▪ High spatial resolution satellite-based remote sensing
Using a semi distributed hydrology model ▪ applicable for different basin sizes, not basin dependent▪ flow forecasts at several locations within large ungauged
basins
Daily time steps, up to 2 weeks lead time
Reliable and accurate for potential real time decision in areas with no flood warning system, sparse in situ observations (radars, gauge stations, etc) or no regional atmospheric model.
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Today
Voisin et al. (2010, in review)
Initial State
1.What is the forecast skill of the system?
2.What are the resulting hydrologic forecast errors related to errors in the calibrated and downscaled weather forecasts?
3.Is the forecast skill different for basins of different size?
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Analog method vs interpolation:- maintained resolution & discrimination- slightly lower predictability- BUT largely improved reliability- smaller mean error- more realistic precipitation patterns
15-member ensemble, 15-day daily forecast:Day 1-10: ECMWF EPS fcstDay 11-15: Zero precip.
ECMWF EPS fcstInterpolated to .25o
VIC2003-2007 period
Routing model2003-2007 period
ECMWF analysis fields:with TMPA precipitation
Daily, 2003-2007 period, 0.25 degree
Daily 2003-2007 simulated runoff,
soil moisture, SWESubstitute for observed
runoff
2003-2007 simulated daily flow
Substitute for observations
Reference(substitute for observations,
Climatology)
ECMWF EPS fcst Calibrated & downscaled( analog method)
15-member ensemble 15-day distributed
runoff forecast
15-member ensemble 15-day flow forecast
at 4 stations with different drainage areas
VIC15-day simulation
Routing model15-day simulation
Initial hydrologic
state
Initial flow
conditions
…
…
…
…
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2 3
Deterministic 15-day daily fcst
Day 1-15:- Zero precip.
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Forecast – Clim & null Precip
deterministic15-day distributed
runoff forecast
15-day deterministic flow forecast
at 4 stations with different drainage areas
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→ Use “simulated observed flow” as reference(ECMWF Analysis and TMPA precipitation)→Focus on weather forecasts errors
- No flow observation uncertainties
- No hydrology model and routing model ( structure, parameter estimation) uncertainties
Ohio River Basin2003-20071826 15-day forecasts (10 day fcst, +5 days 0-precip)848 0.25o grid cells
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Ensemble reliability at Metropolis and Elizabeth
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A preliminary probabilistic quantitative hydrologic forecast system for global application was developed and evaluated:
1. Skill for 10 days for spatially distributed runoff
2. Skill for 1-12+ day forecasts depending on concentration times at the flow forecast locations
For small basins : skills for 10 days, with good reliability for short lead times For larger basins: for 10 days + concentration time
3. Ensemble weather forecasts need to be calibrated: for better hydrologic probabilistic forecasts ( reliability ) For better forecast accuracy in sub basins locations
4. Will incorporate PUB and HEPEX results and ideas. ( PUB: Predictions in Ungauged Basins HEPEX: Hydrologic Ensemble Prediction Experiment)
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Which forecasts?- Spatially distributed ensemble runoff forecasts- Ensemble flow forecasts at 4 locations
Verification:Deterministic Forecast Skill Measures:- Bias ( accuracy, mean errors)- RMSE (accuracy)- Correlation (accuracy, predictability)
Probabilistic Forecasts Skill Measures:- Continuous Rank Probability Skill Score (accuracy, reliability, resolution,
predictability) - Rank Histograms ( ensemble spread i.e. probabilistic forecast reliability)
For forecast categories: What can I expect when a forecast falls in a certain forecast category? ( oriented for real-time decision )
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-Differences between TMPA and observed precipitation
-Daily flow fluctuations due to navigation, flood control, hydropower generation
-Uncertainties in VIC and routing models physical processes, structure and parameters
→ Use “simulated observed flow” as reference
→Focus on weather forecasts errors
Relative Operating characteristic (ROC)
Plot Hit Rate vs. False Alarm Rate for a set of increasing probability threshold to make the yes/no decision.
Diagonal = no skillSkill if above the 1:1 line
Measure resolutionA bias forecast may still have good
discrimination. 16
Ensemble reliability: Reliability plot: PROBABILISTIC fcsts
Choose an event = event specific Each time the event was forecasted with a specific
probability ( 20%, 40%, etc), how many times did it happen ( observation >= chosen event). It requires a sharpness diagram to give the confidence in each point. It should be on a 1:1 line.
Talagrand diagram (rank):PROBABILISTIC QUANTITAVE fcsts Give a rank to the observation with respect to the
ensemble forecast ( 0 if obs below all ensemble members, Nmember + 1 if obs larger )
Is uniform if ensemble spread is reliable, (inverse) U-shaped if ensemble is too small (large), asymetric is systematic bias.
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Probabilistic quantitative forecast verification
measures the difference between the predicted and observed cumulative distribution functions: resolution, reliability, predictability
For one forecast(gridcell, lead time, t): 18
d1
d2
d3
dNmember
magnitudeP
rob
Fcs
t∆P1
2
∆PN2
0
1
1 1 1