Increasing lead time in short-range streamflow forecasting via the Hydrologic Ensemble Forecast...

AGU Fall Meeting Dec 9, 2013 1

INCREASING LEAD TIME IN SHORT-

RANGE STREAMFLOW FORECASTING

VIA THE HYDROLOGIC ENSEMBLE

FORECAST SERVICE (HEFS)

Dong-Jun Seo1, Manabendra Saharia1,*, Bob Corby2, Frank Bell2 and

James Brown3

1Dept of Civil Eng, The University of Texas at Arlington, Arlington, TX 2NWS West Gulf River Forecast Center, Fort Worth, TX 3Hydrologic Solutions Limited, Bournemouth, United Kingdom *Now at University of Oklahoma

Short-Range Ensemble streamflow forecasting

AGU Fall Meeting Dec 9, 2013 2 AGU Fall Meeting Dec 9, 2013

In this presentation

• Motivation

• Questions

• Approach

• Tools used

• Community Hydrologic Prediction System (CHPS)

• Hydrologic Ensemble Forecast Service (HEFS)

• Meteorological Ensemble Forecast Processor (MEFP)

• Ensemble Streamflow Prediction (ESP)

• Ensemble Post-Processor (EnsPost)

• Ensemble Verification System (EVS)

• See Demargne et al. in Jan 2014 issue of Bulletin of Amer.

Meteorol. Soc.

• Results

• Conclusions and recommendations



Why ensemble forecasting?

• Quantify forecast uncertainty

• Improve forecast accuracy

• Extend forecast lead time

• Improve cost-effectiveness of investment


In 2006, National Research Council

recommended that NWS produce uncertainty-

quantified products, expand verification and make

information easily available to all users in near

real time.


Motivation

• The current practice at the NWS/WGRFC

• Quantitative Precipitation Forecast (QPF) is produced out to

Day 3, but only Day-1 (or less) QPF is input to hydrologic

models (zero precipitation assumed beyond)

• In the single-valued forecasting paradigm, such a practice is

inevitable to avoid highly erroneous river forecasts

• In the ensemble forecasting paradigm, one may input longer-

lead QPF to potentially increase the lead time of river forecast

• Utilizes all available skill in short-range QPF



Questions

• What is the bang from the Hydrologic Ensemble

Forecast Service (HEFS) for short-range river

forecasting under the existing QPF process?

• Compared to using Day 1 QPF only, what does using

Day 1-3 QPF via HEFS bring to short-range

streamflow forecasting?

• What is the quality of:

•The ensemble QPF (EQPF) generated by MEFP

from RFC-produced single-valued QPF

•The resulting short-range ESP forecast

•The ESP forecast post-processed via EnsPost



Study area

• 5 headwater basins in the

Upper Trinity River Basin in

North Texas

• SAC-SMA, UHG operations


JAKT2

BRPT2 SGET2

GLLT2

DCJT2

6


Approach

• Ensemble hindcasting and verification using CHPS and

HEFS

• Generate EQPF from the RFC-produced single-valued

QPF using MEFP (Schaake et al. 2007, Wu et al. 2011)

• Input uncertainty processor

• Feed EQPF to ESP

• Post-process ESP ensembles using EnsPost (Seo et al.

2007)

• Hydrologic uncertainty processor

• Verify EQPF and short-range ESP ensembles using

EVS (Brown et al. 2010)



Ensemble hindcasting experiments

• Ensemble precipitation hindcasts are generated using MEFP for a

period of 7 years (2004-2010)

• 46 members, max lead time of 14 days

• Experiment 1

• Day 1 EQPF from Day 1 single-valued QPF

• Climatological ensembles for Days 2-14

• Experiment 2

• Day 1-3 EQPF from Day 1-3 single-valued QPF

• Climatological ensembles for Days 4-14

• The EQPFs are ingested into ESP to produce raw streamflow

hindcasts

• The ESP hindcasts are processed by EnsPost to produce post-

processed ensemble streamflow hindcasts



Results

• Reliability diagram

• Measures reliability (or unbiasedness in probability)

• Requisite for ensemble forecasts

• Relative Operating Characteristic (ROC)

• Measures discrimination

• Not sensitive to reliability

• Closely related to economic value (Zhu et al. 2002)

• Allows interpretation in the single-valued sense through

probability of detection (POD) and false alarm rate (FAR)

• ROC area is related to Pearson’s correlation of ensemble

mean forecast with verifying obs. via joint distribution

among ensemble member, ensemble mean and verifying

obs.




Reliability diagram for Day-1 EQPF, BRPT2

MEFP

ensembles

are, in general,

reasonably

reliable



Reliability diagram for Day-1 ESP forecast, BRPT2

Raw ESP

ensembles are

generally not

very reliable



Reliability diagram for Day-1 ESP forecast w/ EnsPost, BRPT2

Post-processed

ESP ensembles

are reasonably

reliable (a hint of

underforecasting

seen here)



Reliability diagram for Day-1 EQPF, GLLT2

MEFP

ensembles

are, in general,

reasonably

reliable



Reliability diagram for Day-1 ESP forecast, GLLT2

ESP

ensemble is

relatively of

high quality



Reliability diagram for Day-1 ESP forecast w/ EnsPost, GLLT2

EnsPost renders

ESP ensembles

reliable



N Improvement due to

Day 2-3 QPF

BRPT2, threshold = 95th percentile flow

Increase in ~ 1 day in lead time



Improvement due to Day

2-3 QPF & EnsPost

BRPT2, threshold = 95th percentile flow

EnsPost needs improvement



Improvement due

to Day 2-3 QPF

GLLT2, threshold = 95th percentile flow



Improvement due to Day

2-3 QPF & EnsPost

GLLT2, threshold = 95th percentile flow


Translating increase in ROC score

• To relate increase in ROC score to improvement

in forecast quality in a single-valued sense, we

compare ROC’s in terms of increase in probability

of detection (POD) at user-specified false alarm

rate (FAR)

• The more conservative the user is, the lower

the acceptable FAR is.




Acceptable FAR is 5%

ROC curves for Day-1 ESP forecast forced by Day-1 QPF ROC curves for Day-1 ESP forecast forced by Day1 QPF



Acceptable FAR is 5%

ROC curves for Day-1 post-processed ESP forecast forced by Day 1-3 QPF



Increase in POD in ESP forecast due to adding Day 2-3 QPF FAR = 5 %

About 10% increase in POD for Day 3-4



Increase in POD due to adding Day 2-3 QPF & EnsPost (FAR=5%)

EnsPost has the largest positive impact for Day 1.



Increase in POD due to adding Day 2-3 QPF (FAR=5%)

As the threshold flow decreases, the positive impact of Day 2-3 QPF decreases.



Increase in POD due to adding Day 2-3 QPF & EnsPost (FAR=5%)

As the threshold flow decreases, the positive impact of EnsPost increases.


Conclusions and recommendations

• Compared to using Day1-only QPF, using RFC-produced

Day 1-3 single-valued QPF via HEFS significantly

increases skill in short-range ESP forecast

• Increases POD by about 10% for Day 3-4 forecasts

• Extends lead time by about a day

• The margin of improvement is impacted by the quality of

streamflow simulation

• Demonstrated large-sample ensemble hindcasting using

CHPS and HEFS outside of NWS



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100

1987

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Pro

bab

ilit

y o

f D

ete

cti

on

in

%

0500100015002000250030003500400045005000

Nu

mb

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of

Even

ts

Probability of Detection Number of Events

Source

NWS/OCWWS

Perspective


Flash flood warning Probability Of Detection (POD) trend


Conclusions and recommendations

(cont.) • Apply to a large number of basins for larger-sample

verification, particularly for large events

• Carry out similar hindcasting experiments using the

GEFS reforecast data set

• Enhance EnsPost to deal with ephemeral basins

• Implement and evaluate ensemble data assimilation

within HEFS

• Include parametric uncertainty modeling

• Reduce reliance on stochastic modeling




Elements of a Hydrologic Ensemble Prediction System

Ensemble Pre-Processor

Parametric

Uncertainty

Processor

Data

Assimilator

Ensemble Post-

Processor

Hydrology & Water Resources

Ensemble Product Generator

Hydrology & Water

Resources Models

QPF, QTF QPE, QTE,

Soil Moisture

Streamflow

Ensem

ble

Verific

atio

n S

yste

m

Input

Uncertainty

Processor

Hydrologic

Uncertainty

Processor


THANK YOU

Q/A, Discussion

For more info, please contact [email protected].


mailto:[email protected]

Increasing lead time in short-range streamflow forecasting via the Hydrologic Ensemble Forecast...

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