national weather service application of cfs forecasts in nws hydrologic ensemble prediction john...
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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
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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
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Locations of NWS Hydrologic Locations of NWS Hydrologic Probability Forecast Points 1/13/2009Probability Forecast Points 1/13/2009
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•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)
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• 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)
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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
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Development of a Hydrologic Ensemble
Forecast System (HEFS)
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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.
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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)
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Ensemble Precipitation Ensemble Precipitation ForecastForecast
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Ensemble Temperature Ensemble Temperature ForecastForecast
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Ensemble Streamflow ForecastEnsemble Streamflow Forecast
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NCEP Global Ensemble ForecastsNCEP Global Ensemble Forecasts
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Observed vs Global Ensemble Climatologies(July, Lat = 35.0, Lon = 82.5)
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Daily Precipitation (mm)
Pro
babi
lity
Observations
Global Ensemble
Forecast vs Observed Precipitation - July
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5
10
15
20
25
30
35
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Forecast (mm/day)
Ob
serv
ed (
mm
/day
)
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GFS Precipitation Forecast Correlation vs Lead TimeNFDC1HUF - January 15 Forecast Creation Time
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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?
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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
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CNRFC Ensemble PrototypeCNRFC Ensemble Prototype
Smith River
Salmon RiverMad River
Navarro River American River
(11 basins)
Van Duzen River
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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
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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)
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Canonical Temperature EventsCanonical Temperature Events
Canonical Event Number
Eve
nt
Du
ratio
n (
Da
ys)
Canonical Event Number
Nu
mb
er
of
CF
S
Me
mb
ers
Use
d
Canonical Event Number
Sta
rt o
f E
ven
t (L
ea
d
Da
ys)
Canonical Event Number
En
d o
f E
ven
t (L
ea
d D
ays
)
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Canonical Precipitation EventsCanonical Precipitation Events
Canonical Event Number
Eve
nt
Du
ratio
n (
6h
rs)
Canonical Event Number
Nu
mb
er
of
CF
S
Me
mb
ers
Use
d
Canonical Event Number
Sta
rt o
f E
ven
t (L
ea
d 6
hrs
)
Canonical Event Number
En
d o
f E
ven
t (L
ea
d 6
hrs
)
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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
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RFC Forecast (mm)
Ob
serv
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mm
)
GFS Forecast vs NDFC1HL ObservationsJanuary Day 1
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20
40
60
80
100
0 5 10 15 20 25
GFS Ensemble Mean Forecast (mm)
NF
DC
1HL
Ob
serv
atio
n
(mm
)
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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)
National Weather Service
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
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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
National Weather Service
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
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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
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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
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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
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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
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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
1-month Lead Seasonal Forecast
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1
0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
Co
rre
lati
on
Co
eff
icie
nt
Rho
North Fork American River Basin (Upper Zone)Event 35 - Tmin - Average
5-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
Tem
pe
ratu
re (
De
gC
)
Avg Fcst
Avg Obs
North Fork American River Basin (Upper Zone)Event 35 - Tmin - Rho
5-month Lead Seasonal Forecast
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1
0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
Co
rre
lati
on
Co
eff
icie
nt
Rho
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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
1-month Lead Seasonal Forecast
0
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0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
6h
r P
rec
ipit
ati
on
(m
m)
RHO
North Fork American River Basin (Upper Zone)Event 61 - 6hr Precipitation - Average
1-month Lead Seasonal Forecast
0
0.5
1
1.5
2
2.5
0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
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
5-month Lead Seasonal Forecast
0
0.2
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0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
6h
r P
rec
ipit
ati
on
(m
m)
RHO
North Fork American River Basin (Upper Zone)Event 69 - 6hr Precipitation - Average
5-month Lead Seasonal Forecast
0
0.5
1
1.5
2
2.5
0 30 60 90 120 150 180 210 240 270 300 330 360
Forecast Day of Year
6h
r P
rec
ipit
atio
n (
mm
)
AVG OBS
AVG FCST
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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 5 10 15 20 25 30 35
Canonical Event
Co
rrel
atio
n C
oef
fici
ent
CFS
GFS
Tmin Forecast Correlation SkillNorth Fork American River (Upper Zone)
Forecast Date = December 1
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0.9
1
0 5 10 15 20 25 30 35
Canonical Event
Co
rrel
atio
n C
oef
fici
ent
CFS
GFS
Tmin Forecast Correlation SkillNorth Fork American River (Upper Zone)
Forecast Date = January 1
0
0.1
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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)
Forecast Date = November 1
-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
Tmin Average Observed ValueNorth Fork American River (Upper Zone)
Forecast Date = December 1
-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
Tmin Average Observed ValueNorth Fork American River (Upper Zone)
Forecast Date = January 1
-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
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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)
Forecast Date = January 1
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
Precipitation Average Observed ValueNorth Fork American River Basin (Upper Zone)
Forecast Date = November 1
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 Average Observed ValueNorth Fork American River Basin (Upper Zone)
Forecast Date = December 1
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)
Forecast Date = January 1
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
Precipitation Forecast Correlation SkillNorth Fork American River Basin (Upper Zone)
Forecast Date = November 1
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
Precipitation Forecast Correlation SkillNorth Fork American River Basin (Upper Zone)
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 10 20 30 40 50 60 70
Canonical Event
6hr
Pre
cip
itat
ion
(m
m)
cfs
gfs
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CREC1 (Smith River at CREC1 (Smith River at Crescent City, CA) – Crescent City, CA) –
Forecasts and SimulationsForecasts and Simulations
CREC1: December Forecasts and Simulations
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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
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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
National Weather Service
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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).
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NOAA Hydrology Strategic NOAA Hydrology Strategic Science PlanScience Plan
http://www.weather.gov/oh/src/docs/Strategic_Sience_Plan_2007-Final.pdf
Thank you!