Northeast River Forecast Center Taunton, MA National Oceanic and Atmospheric Administration’s...

Northeast River Forecast CenterTaunton, MA

National Oceanic and Atmospheric Administration’s

National Weather Service

Hydrologic Ensemble Forecast Service(HEFS)

Revisited

Erick BoehmlerNortheast River Forecast Center

Northeast River Forecast CenterTaunton, MA


National Weather Service


National Weather Service 2 Northeast River Forecast CenterTaunton, MA

HEFS Revisited

• HEFS Objective• Meteorological Ensemble Forecast Processor (MEFP)

>Capabilities>Methodology

– Parameter estimation– Schaake Shuffle (Clark et al., 2004)

• Hydrologic Ensemble Processor• Ensemble Postprocessor• Ensemble Verification Service

– Validation results • Short – Long-range Products



HEFS Objective

Improve NWS hydrologic services

Feature ESP HEFS Forecast time horizon

Weeks to seasons Hours to years, depending on the input forecasts

Input forecasts (“forcing”)

Historical climate data with some variations between RFCs

Short-, medium- and long-range weather forecasts

Uncertainty modeling

Climate-based. No accounting for hydrologic uncertainty or bias. Suitable for long-range forecasting only

Captures total uncertainty and corrects for biases in forcing and flow at all forecast lead times

Products Limited number of graphical products (focused on long-range) and verification

A wide array of data and user-tailored products are planned, including standard verification


National Weather ServiceOffice of Hydrologic Development

Silver Spring, MD

HEFS Purpose

Quantify forecast uncertainty for

• Short-range (hours to days)– Flood watch and warning program.

– Local emergency management .

– Flood control system management.

– Reservoir management.

• Medium-range (days to weeks)– Local emergency management preparedness.


– Snowmelt runoff management.

• Long-range (weeks to months)– Water supply planning.




HEFS Components



Silver Spring, MD

Meteorological Ensemble Forecast Processor Capabilities• Forecast variables: precipitation and temperature.

• Forecast temporal horizon: up to about a year.

• Forecast spatial scale: basin.

• Forecast sources:

– WPC/RFC single-valued forecasts

>QPF for days 1-5 and QTF 1-7

– GEFS (days 1 -15)

– CFSv2 (days1- 270).

– Climatology (1 day – 1 year).

• Ensemble quality: bias-corrected.

• Multiple temporal scales: 6 hours to 3 months to capture forecast skill at various temporal scales.



Silver Spring, MD

MEFP Capabilities (Continued)

• Seasonality: accounted for by using moving window of user-specified size to pool data points in calibration.

• Temperature diurnal cycle: accounted for through equations relating 6-hr values and daily max and min values.

• Space-time coherence: preserved among upstream and downstream basins in forecast ensembles using Schaake Shuffle.

• Ensemble blending: Correlation-based. Ensemble forecasts are generated iteratively for all time scales and forecast sources from low correlation to high correlation.

• Operation modes: forecasting and hindcasting.

• Diagnostic tools: MEFPPE, GraphGen.



MEFP Component Function

MEFP

• Correct forcing bias• Merge in time• Downscale (basin)

WPC (2-day

Planned)

GEFS (Day 1 -15)

CFSv2(Days 16 – 270)

Climatology (Days 271-365)

NWS and external user applications

• Parameter Estimation

MEFPPE

ForecastEnsembles



Silver Spring, MD

MEFP General Methodology

Objective:

Produce reliable ensemble forcing variables that capture the skill and quantify the uncertainty in the source forecasts.

Key Idea:

Condition the joint distribution of single-valued forecasts and the corresponding observations using the forecast.

• Select source forecasts from multiple models to cover short- to long-range.

• Define durations useful for MEFP application.

• Use a common modeling framework (the meta-Gaussian model) for both precipitation and temperature.



Silver Spring, MD

MEFP General Methodology

• For a given forecast source, forecast start time, and duration, model the joint probability distribution between the single-valued forecast and the corresponding observation from historical records.

• Sample the conditional probability distribution of the joint distribution given the single-valued forecast.

• Rank short, medium, and long range ensembles according to the magnitude of the correlation coefficients between forecast and observation for the duration and associated forecast sources.

• Generate blended ensembles (with Schaake Shuffle applied) iteratively for all durations from low correlation to high correlation



HEFS Components



Ensemble Verification Service

• Supports verification of HEFS including for precipitation, temperature and streamflow

• Verification of all forecasts or subsets based on prescribed conditions (e.g. seasons, thresholds, aggregations)

• Provides a wide range of verification metrics, including measures of bias and skill

• Requires a long archive of forecasts or hindcasts

• GUI or command-line operation



Forecast quality: validation results

MEFP forcing

• Skill of the MEFP with GEFS forcing inputs

• Positive values mean fractional gain vs. climatology (e.g. 50% better on day 1 at FTSC1)

• MEFP temperature generally skillful, even after 14 days

• MEFP precipitation skillful during first week, but skill varies between basins

Forecast lead time (days)

Ski

ll (f

ract

iona

l gai

n ov

er c

limat

olog

y)

“50% better than climatology”



WALN6 (MARFC)

Forecast quality: validation results

CFSv2

GE

FS

Long-range forecasts

• Example of MEFP precipitation forecasts from Walton, NY

• Beyond one week of GEFS, there is little skill vs. climatology

• In other words, the CFSv2 adds little skill for the long-range (but forcing skill may last >2 weeks in flow)

• If climate models improve in future, HEFS can be updated Forecast lead time (days)

Ski

ll (f

ract

iona

l gai

n ov

er c

limat

olog

y) MEFP precipitation forecast

Walton, NY

CLIM

No skill after ~one week



Silver Spring, MD

HEFS Product ExamplesAHPS short-range probabilistic product



HEFS Product ExamplesAHPS medium-range probabilistic products



Silver Spring, MD

Managing NYC water supply

• Croton; Catskill; and Delaware

• Includes 19 reservoirs, 3 lakes; 2000 square miles

• Serves 9 million people (50% of NY State population)

• Delivers 1.1 billion gallons/day

• Operational Support Tool (OST) to optimize infrastructure, and avoid unnecessary ($10B+) water filtration costs

• HEFS forecasts are central to OST. The OST program has cost NYC under $10M

An early application of long-range HEFS forecasts



Summary and conclusions

Ensemble forecasts are the future• Forecasts incomplete unless uncertainty captured • Ensemble forecasts are becoming standard practice• HEFS implementation, products, and validation is

ongoing and expanding • Initial validation results are promising

HEFS will evolve and improve• Science and software will improve through feedback• Guidance will improve through experience• We are looking forward to supporting end users!



References

• Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: a method for reconstructing space–time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology 5 (1), 243–262.

• Demargne, J., Wu, L., Regonda, S.K., Brown, J.D., Lee, H., He, M., Seo, D.-J., Hartman, R., Herr, H.D., Fresch, M., Schaake, J. and Zhu, Y. (2014) The Science of NOAA's Operational Hydrologic Ensemble Forecast Service. Bulletin of the American Meteorological Society, 95, 79–98.

• Brown, J.D. (2014) Verification of temperature, precipitation and streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service: an evaluation of the medium-range forecasts with forcing inputs from NCEP's Global Ensemble Forecast System (GEFS) and a comparison to the frozen version of NCEP's Global Forecast System (GFS). Technical Report prepared by Hydrologic Solutions Limited for the U.S. National Weather Service, Office of Hydrologic Development, 139pp.

• Brown, J.D. (2013) Verification of long-range temperature, precipitation and streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service. Technical Report prepared by Hydrologic Solutions Limited for the U.S. National Weather Service, Office of Hydrologic Development,

128pp.



Silver Spring, MD

Model the Forecast / Observed Joint Distribution

X

Y

Forecast

Obs

erve

d

0

Forecast

Obs

erve

d

Joint distributionModel Space

Joint distributionSample Space

PDF of Observed PDF of STD Normal

PDF of Forecast

NQT

PDF of STD Normal

X

Y

NQT

Correlation(X,Y)



Silver Spring, MD

General Methodology



• Rank short, medium, and long range ensembles according to the magnitude of the correlation coefficients between forecast and observation for the selected duration and associated forecast sources.




Silver Spring, MD

Sample Conditional Joint Distribution

3.23

Forecast

Obs

erve

dJoint distribution

Model Space

xfcst X

Y

Obtain conditional distribution given a single-value forecast

xfcst

xi xn

Conditional distribution given xfcst

Ensemble forecast

Pro

babi

lity0

1

…x1

xn

x1

Ensemble members



Silver Spring, MD

General Methodology



• Rank short, medium, and long range ensembles according to the magnitude of the correlation coefficients between forecast and observation for the selected duration and associated forecast sources.




Silver Spring, MD

Blend Ensembles with Schaake Shuffle

3.25

Ensemble members

Blended ensemble members

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