national weather service application of cfs forecasts in nws hydrologic ensemble prediction john...

National Weather Service

Application of CFS Forecasts in NWS Application of CFS Forecasts in NWS Hydrologic Ensemble PredictionHydrologic Ensemble Prediction

John Schaake

Office of Hydrologic Development

NOAA National Weather Service

Presentation to

Climate Prediction Center

July 28, 2009


Water PredictionsforLife Decisions

ContentsContents

• Current long range hydrologic forecasts (AHPS)• History of U.S. Ensemble Streamflow Prediction

(ESP)• Development of a hydrological ensemble forecast

system (HEPS)• Implementation Status (XEFS/FEWS)• RFC CFS forecast and hindcast requirements



Locations of NWS Hydrologic Locations of NWS Hydrologic Probability Forecast Points 1/13/2009Probability Forecast Points 1/13/2009



•90-day probability of exceedance

• Broken into weekly outlooks

• Based on ESP runs at each RFC

• Based on climate adjustments (i.e. CPC outlooks at some RFCs)

• Updated monthly

Advanced Hydrologic Prediction Advanced Hydrologic Prediction Services (AHPS)Services (AHPS)



• 90-day probability of exceedance

• Blue line is an historical simulation based on averages

• Black line is the conditional simulation with CPC inputs

• Conditional simulation based on CPC inputs yield lower potential for flooding and high flows.

•Essentially the Twedt et al, 1977 plot

Advanced Hydrologic Prediction Advanced Hydrologic Prediction Services (AHPS)Services (AHPS)



Historical PerspectiveHistorical Perspective

•ESP with Climatological Forcing (1970’s – 2009)• Historical precipitation and temperature observations used to drive

ESP

• Used in current NWS Operational Products

•Experimental Use of Short Range Forecasts (2001-2009)• RFC single-value forecasts used to create ensemble forcing for ESP

•Experimental Use of Short, Medium and Long Range Forecasts (2007-2009)• RFC/HPC/MOS single value forecasts (1 – 7 days)

• GFS ensemble mean forecasts (1 – 14 days)

• CFS long range (1 day – 8 months) ensemble mean forecasts



Development of a Hydrologic Ensemble

Forecast System (HEFS)



ESP forecasts typically are made for many forecast points in a river basin using a lumped hydrologic model

River basins are partitioned into connected segments (which may include elevation zones, reservoirs, river routing segments, etc.)

Some Elements of Hydrologic Forecasting

Hydrologic models are calibrated (i.e. parameters are estimated) using historical data

Recent observations (precipitation, temperature, SWE, streamflow are used to estimate inititial conditions at the time a forecast is created.



ESP Input RequirementsESP Input Requirements

Forecast precipitation and temperature ensemble forcing:

• For every forecast segment

• Member time series at ~6hr step for entire forecast period

• Individual members must be “consistent” over all segments and for the entire forecast period.

Statistical Properties:

• Ensembles must be unbiased at all time and space scales and for all lead times

• Ensembles must account for space/time scale dependency in the variability of precipitation and temperature and in the forecast uncertainty at all space and time scales

• Each member must be equally likely to occur (i.e. a random sample)



Ensemble Precipitation Ensemble Precipitation ForecastForecast



Ensemble Temperature Ensemble Temperature ForecastForecast



Ensemble Streamflow ForecastEnsemble Streamflow Forecast



NCEP Global Ensemble ForecastsNCEP Global Ensemble Forecasts



Observed vs Global Ensemble Climatologies(July, Lat = 35.0, Lon = 82.5)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40

Daily Precipitation (mm)

Pro

babi

lity

Observations

Global Ensemble

Forecast vs Observed Precipitation - July

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Forecast (mm/day)

Ob

serv

ed (

mm

/day

)



GFS Precipitation Forecast Correlation vs Lead TimeNFDC1HUF - January 15 Forecast Creation Time

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 20 40 60

Forecast Valid Time (6-hr periods)

Co

rre

lati

on

Co

eff

icie

nt

moving 6hr window

average from t=0

average from t=4

average from t=8

average from t=16

Precipitation Forecasts arePrecipitation Forecasts are Temporally (and Spatially) Scale Temporally (and Spatially) Scale

DependentDependent

What must we do to extract ALL of the information in

atmospheric forecasts to produce skillful and reliable

ensemble forcing for hydrologic forecasting?



RFC Field TestingRFC Field Testing

04/21/23 17

Ensemble Pre-Processor Hydrologic Model Output

Statistics (HMOS) Ensemble Processor

Hydrologic Ensemble Hindcaster

Ensemble Verification

Ensemble Pre-Processor Hydrologic Model Output Statistics (HMOS) Ensemble Processor Hydrologic Ensemble Hindcaster Ensemble Verification



CNRFC Ensemble PrototypeCNRFC Ensemble Prototype

Smith River

Salmon RiverMad River

Navarro River American River

(11 basins)

Van Duzen River



CFS Forecast Application in XEFSCFS Forecast Application in XEFS

• RFC ensemble forecast requirements

• Canonical forecast event strategy

• Construction of ensembles of CFS forecasts for each Canonical event

• XEFS Ensemble PreProcessor to generate ensemble members for ESP



Atmospheric Post-ProcessingAtmospheric Post-ProcessingStrategy: 2 – Step ProcessStrategy: 2 – Step Process

RawAtmospheric

Forecasts

EstimateProbability

Distributions

AssignValues to

Ensemble Members

ESPInput

Forcing

This step includes downscaling, and correction of bias and spread problems and uses all available forecast informationThis step assures that

members are both constrained by forecast probabilities and are “consistent” over all basins for the entire forecast period (Schaake Shuffle)



Canonical Temperature EventsCanonical Temperature Events

Canonical Event Number

Eve

nt

Du

ratio

n (

Da

ys)


Nu

mb

er

of

CF

S

Me

mb

ers

Use

d


Sta

rt o

f E

ven

t (L

ea

d

Da

ys)


En

d o

f E

ven

t (L

ea

d D

ays

)



Canonical Precipitation EventsCanonical Precipitation Events


Eve

nt

Du

ratio

n (

6h

rs)


Nu

mb

er

of

CF

S

Me

mb

ers

Use

d


Sta

rt o

f E

ven

t (L

ea

d 6

hrs

)


En

d o

f E

ven

t (L

ea

d 6

hrs

)



Estimate Probability Distributions Estimate Probability Distributions for each Canonical Eventfor each Canonical Event

Use historical single-value forecasts and observations (for a common period of time)

NFDC1HL - January Day 1 RFC Forecast vs Observed

0

20

40

60

80

100

0 20 40 60 80 100

RFC Forecast (mm)

Ob

serv

ed (

mm

)

GFS Forecast vs NDFC1HL ObservationsJanuary Day 1

0

20

40

60

80

100

0 5 10 15 20 25

GFS Ensemble Mean Forecast (mm)

NF

DC

1HL

Ob

serv

atio

n

(mm

)



Estimate Probability DistributionsEstimate Probability Distributions(Cont’d)(Cont’d)

Estimate climatological distributions of forecasts and observations

Use climatologies to transform forecasts and observations to Standard Normal Deviates

Estimate correlation parameter of Joint Distribution

July 15-17, 2008 National DOH Workshop, Silver Spring, MD

X

Y

Forecast

Ob

serv

ed

0

Forecast

Ob

serv

ed

Joint distribution

Model Space

Joint distributionSample Space

PDF of Observed PDF of STD Normal

PDF of Forecast

NQT

PDF of STD Normal

CalibrationCalibration

X

Y

NQT

Correlation(X,Y)


Water PredictionsforLife Decisions July 15-17, 2008 National DOH Workshop, Silver Spring, MD

Forecast

Ob

serv

edJoint distribution

Model Space

xfcst

Ensemble GenerationEnsemble Generation

X

Y

Obtain conditional distribution given a single-

value forecast xfcst

xi xn

Conditional distribution given xfcst

Ensemble forecast

Pro

bab

ility

0

1

… Ensemble members

x1

xn

x1



Use EPP Forecast Probability Use EPP Forecast Probability Distributions to Assign Values to Distributions to Assign Values to

Individual Members)Individual Members)

•Estimate forecast probability distribution for each Canonical Period

•Use “Schaake Shuffle” to assign sample values from each probability distribution to individual members

•Distribute values for Canonical Events to individual time series

Example CascadeOf Canonical Events

Individual time steps

Aggregate periods


Water PredictionsforLife Decisions July 15-17, 2008 National DOH Workshop, Silver Spring, MD

Schaake ShuffleSchaake ShuffleFor each segment, at each time step, associate forecast ensemble members (left panel) with historical ensemble members (right panel) by rank (and hence year)

1

Pro

bab

ility

0

Historical ensemble distribution

1

Pro

bab

ility

0

Conditional distribution given xfcst

(1996)(1996)x1 xi xn

Forecast Ensemble Historical Ensemble

y1 yi yn



Temperature Parameters

VerificationResults

Parameter Estimator

Temperature Verification

HindcastEnsemble MATs

Precipitation Verification

Ensemble Generator

HindcastEnsemble MAPs

Ensemble Generator

OperationalEnsemble

MATs

6hr RFC QPF Verifications Files

and Operational MAPs

6hr MAPCalibration Files

Raw Historical Data Files

MAP Time SeriesIdentifiers

PrecipitationParameters

Historical 6hr RFCOperational MAPQPF Data files

Historical GFSEnsemble Mean

Precipitation Forecasts

6hr MAPUnformatted Calibration

Data Files

Historical 6hr RFCOperational MAP

Observed Data Files

MAP AreaLocations

GFS Processor Historical Data Sets

Index Files

Parameter Estimator

RFC-specific Utility

MAP Conversion Utility

Data Analysis

Statistics

OperationalEnsemble

MAPs

VerificationResults

Precipitation Ensemble Pre-Processor

National Weather Service Hydrologic Ensemble Pre-Processor (EPP) GFS Subsystem

J. Schaake, R. Hartman, J. Demargne, L. Wu, M. Mullusky, E. Welles, H. Herr, D. J. Seo, and P. Restrepo

Pre-ProcessorPrecipitationAlgorithms

CFS Ensemble Forecast(Application Under Construction)

RFC-specific Utility

RFC HistoricalOperational

Observed TMX/TMNRFC TMX/TMN Forecast

Verification Filesand Operational

Observed TMX/TMN

RFC HistoricalOperational

Forecast TMX/TMN

MOS UtilityMOS TMX/TMN

ForecastVerification Files

6hr to TMX/TMN6hr MAT

Calibration Files

Conversion UtilityUnformattedTMX/TMNCalibration Data Files

MAT Time SeriesIdentifiers

TMX/TMNCalibration Data

MAT Analysis

TemperatureNormals

Stations used forMAT Analysis

MAT AreaLocations

Index Files

GFS Processor Historical Data Sets

Raw Historical Data Files

Historical GFSEnsemble Mean

Temperature Forecasts

Temperature Ensemble Pre-Processor

Pre-ProcessorTemperatureAlgorithms

CFS Ensemble Forecast(Application Under Construction)

MOS HistoricalForecast TMX/TMN

Average observed values of 6-hour precipitation

corresponding to RFC and GFS forecasts

(mm)

(mm)

Average forecast values of 6-hour precipitation for

RFC and GFS(mm)

(mm)

Average observed values of daily minimum temperature corresponding to

RFC and GFS forecasts

Average forecast values of daily minimum temperature for

RFC and GFS

Continuous Rank Probability Skill Score (CRPSS) for daily

minimum temperature forecasts

RFC single-value forecasts

Ensemble forecasts based on RFC single-value forecasts

GFS single-value forecasts

Ensemble forecasts based on GFS single-value forecasts

Continuous Rank Probability Skill Score (CRPSS) for 6-hour

precipitation forecasts

RFC single-value forecasts

Ensemble forecasts based on RFC single-value forecasts

GFS single-value forecasts

Ensemble forecasts based on GFS single-value forecasts

TMX: maximum temperature TMN: minimum temperature

OperationalGFS Files

Operational RFCQPF Files

Operational Forecast Files

OperationalGFS Files

Operational RFCQTF Files

Operational Forecast Files



Forecast Skill (Correlation) of GFS and CFS Forecast Skill (Correlation) of GFS and CFS Tmin Forecasts for North Fork American River Tmin Forecasts for North Fork American River

Basin (Upper Zone) Basin (Upper Zone)

•CFS Forecasts

•GFS Forecasts



Forecast Skill (Correlation) of GFS and CFS Forecast Skill (Correlation) of GFS and CFS Precipitation Forecasts for North Fork American Precipitation Forecasts for North Fork American

River Basin (Upper Zone) River Basin (Upper Zone)

•CFS Forecasts

•GFS Forecasts



Seasonal Tmin Forecasts for North Fork Seasonal Tmin Forecasts for North Fork American River Basin (Upper Zone)American River Basin (Upper Zone)

North Fork American River Basin (Upper Zone)Event 27 - Tmin - Average

1-month Lead Seasonal Forecast

-6-4-202468

10

0 30 60 90 120 150 180 210 240 270 300 330 360

Forecast Day of Year

Te

mp

era

ture

(D

eg

C)

Avg Fcst

Avg Obs

North Fork American River Basin (Upper Zone)Event 27 - Tmin - Rho


0

0.2

0.4

0.6

0.8

1

0 30 60 90 120 150 180 210 240 270 300 330 360


Co

rre

lati

on

Co

eff

icie

nt

Rho

North Fork American River Basin (Upper Zone)Event 35 - Tmin - Average


-6-4-202468

10

0 30 60 90 120 150 180 210 240 270 300 330 360


Tem

pe

ratu

re (

De

gC

)

Avg Fcst

Avg Obs

North Fork American River Basin (Upper Zone)Event 35 - Tmin - Rho


0

0.2

0.4

0.6

0.8

1

0 30 60 90 120 150 180 210 240 270 300 330 360


Co

rre

lati

on

Co

eff

icie

nt

Rho



Seasonal Precipitation Forecasts for North Seasonal Precipitation Forecasts for North Fork American River Basin (Upper Zone)Fork American River Basin (Upper Zone)

North Fork American River Basin (Upper Zone)Event 61 - 6hr Precipitation - Forecast Skill


0

0.2

0.4

0.6

0.8

1

0 30 60 90 120 150 180 210 240 270 300 330 360


6h

r P

rec

ipit

ati

on

(m

m)

RHO

North Fork American River Basin (Upper Zone)Event 61 - 6hr Precipitation - Average


0

0.5

1

1.5

2

2.5

0 30 60 90 120 150 180 210 240 270 300 330 360


6h

r P

rec

ipit

ati

on

(m

m)

AVG OBS

AVG FCST

North Fork American River Basin (Upper Zone)Event 69 - 6hr Precipitation - Forecast Skill


0

0.2

0.4

0.6

0.8

1

0 30 60 90 120 150 180 210 240 270 300 330 360


6h

r P

rec

ipit

ati

on

(m

m)

RHO

North Fork American River Basin (Upper Zone)Event 69 - 6hr Precipitation - Average


0

0.5

1

1.5

2

2.5

0 30 60 90 120 150 180 210 240 270 300 330 360


6h

r P

rec

ipit

atio

n (

mm

)

AVG OBS

AVG FCST



Forecast Skill of GFS and CFS Tmin Forecasts Forecast Skill of GFS and CFS Tmin Forecasts for North Fork American River (Upper Zone) for North Fork American River (Upper Zone)

Tmin Forecast Correlation SkillNorth Fork American River (Upper Zone)

Forecast Date = November 1

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0.1

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0.5

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0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

Canonical Event

Co

rrel

atio

n C

oef

fici

ent

CFS

GFS


Forecast Date = December 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

Canonical Event

Co

rrel

atio

n C

oef

fici

ent

CFS

GFS


Forecast Date = January 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

Canonical Event

Co

rrel

atio

n C

oef

fici

ent

CFS

GFS

Tmin Average Observed ValueNorth Fork American River (Upper Zone)


-6

-4

-2

0

2

4

6

0 5 10 15 20 25 30 35

Canonical Event

Ave

rag

e O

bse

rved

Tm

in

CFS

GFS



-6

-4

-2

0

2

4

6

8

10

0 5 10 15 20 25 30 35

Canonical Event

Ave

rag

e O

bse

rved

Tm

in

CFS

GFS



-6

-4

-2

0

2

4

6

8

10

0 5 10 15 20 25 30 35

Canonical Event

Ave

rag

e O

bse

rved

Tm

in

CFS

GFS



Forecast Skill of GFS and CFS Precipitation Forecast Skill of GFS and CFS Precipitation Forecasts for North Fork American River Basin Forecasts for North Fork American River Basin

(Upper Zone)(Upper Zone)

Precipitation Average Observed ValueNorth Fork American River Basin (Upper Zone)


0

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cipi

tati

on

(m

m)

cfs

gfs



0

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cip

itat

ion

(m

m)

cfs

gfs



0

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cip

itat

ion

(m

m)

cfs

gfs

Precipitation Forecast Correlation SkillNorth Fork American River Basin (Upper Zone)


0

0.1

0.20.3

0.4

0.5

0.6

0.70.8

0.9

1

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cip

itat

ion

(m

m)

cfs

gfs



0

0.1

0.2

0.3

0.4

0.50.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cip

itat

ion

(m

m)

cfs

gfs



0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70

Canonical Event

6hr

Pre

cip

itat

ion

(m

m)

cfs

gfs



CREC1 (Smith River at CREC1 (Smith River at Crescent City, CA) – Crescent City, CA) –

Forecasts and SimulationsForecasts and Simulations

CREC1: December Forecasts and Simulations

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

Forecast Period (Days)

Cor

rela

tion

with

O

bser

vatio

ns calibration

r0g14c30

r0g0c30

CREC1: March Forecasts and Simulation

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

Forecast Period (days)C

orre

latio

n w

ith

Obs

erva

tions calibration

r0g14c30

r0g0c30



RFC CFS Forecast and RFC CFS Forecast and Hindcast RequirementsHindcast Requirements

• A “seamless” approach to weather and climate prediction is needed to meet user requirements for hydrologic ensemble forecasts•Additional CFS members (as well as higher model resolution?) are needed to bring CFS forecast skill closer to GFS forecast skill before and after the end of week 2• Additional CFS hindcasts are needed for the first several (<= 6?) weeks of the forecast period• RFC hindcasts must meet user needs. • This requires daily CFS sub-seasonal hindcasts (<= 6

wks) for all members used operationally to compute the ensemble mean.

• This requires “frequent” CFS seasonal hindcasts (~weekly).



NOAA Hydrology Strategic NOAA Hydrology Strategic Science PlanScience Plan

http://www.weather.gov/oh/src/docs/Strategic_Sience_Plan_2007-Final.pdf

Thank you!

national weather service application of cfs forecasts in nws hydrologic ensemble prediction john...

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