Using CPC long lead climate outlooks for ensemble streamflow forecasting
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Transcript of Using CPC long lead climate outlooks for ensemble streamflow forecasting
Using CPC long lead climate outlooks for ensemble streamflow forecasting
Andy Wood and Dennis P. Lettenmaier
University of WashingtonDept. of Civil and Environmental Engineering
Session A24A2006 Joint Meeting of the AGU
Baltimore, MDMay 23, 2006
Climate forecast importance: temporal variability
Western US Water Cycle
Climate Forecasts
Imp
ort
ance Monthly Timestep
In Western US:
Jan – April forecasts of summer streamflow are critical for decision-making related to:
• agriculture• environmental flows• hydropower• navigation• water supply
Most basins east of the Sierras and Cascade Mtnsare heavilyinfluenced by spring precipitation.
Water supply forecaststhere have unavoidably high uncertainty because spring precipitationis relatively unknown.
Wet spring
Dry spring15%
65%
Apr-JunOct-Jun
Courtesy of Tom Pagano, NRCS
PCP
Climate forecast importance: spatial variability
Wet spring
Dry spring15%
65%Precip
Apr-JunOct-Jun
Example: Climate forecasts relatively unimportant by late Winter
Forecast Skill
Low High
Summer flow forecast skillAreas with dry spring ….
Climate forecast importance: spatial variability
Courtesy of Tom Pagano, NRCS
Wet spring
Dry spring15%
65%Precip
Apr-JunOct-Jun
Example: Climate Forecasts very important through Spring
Forecast Skill
Low High
Summer flow forecast skillAreas with wet spring ….
Climate forecast importance: spatial variability
Courtesy of Tom Pagano, NRCS
BackgroundCurrent Practice for Western US Streamflow Forecasting
combine: (1) estimate of current hydrologic state(2) forecast of historical climate…usually*
produce: streamflow forecast with uncertainty information
UPPER HUMBOLDT RIVER BASIN
Streamflow Forecasts - May 1, 2003
<==== Drier === Future Conditions === Wetter ====>
Forecast Pt ============ Chance of Exceeding * ===========
Forecast 90% 70% 50% (Most Prob) 30% 10% 30 Yr Avg
Period (1000AF) (1000AF) (1000AF) (% AVG.) (1000AF) (1000AF) (1000AF)
MARY'S R nr Deeth, Nv
APR-JUL 12.3 18.7 23 59 27 34 39
MAY-JUL 4.5 11.3 16.0 55 21 28 29
Research ObjectiveCurrent Practice for Western US Streamflow Forecasting
combine: (1) estimate of current hydrologic state(2) forecast of historical climate CPC Outlook
produce: streamflow forecast with uncertainty information
ICsSpin-up Forecast
obs
recently observedmeteorological data
ensemble of met. datato generate forecast
ESP-type forecastmethod
hydrologicstate
We use a hydrologic model-based approach similar to the NWS River Forecast Center’s Ensemble Streamflow Prediction (ESP)
NWS Climate Prediction Center (CPC) Seasonal Outlooks
e.g., precipitation
CPC Seasonal Outlook UseChallenge: Seasonal (3-month) probabilities must be
converted to daily meteorological values at the scale of the hydrology model
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Mon1
Mon2
Mon3
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Mon5
Mon6
deg
C
CPC Seasonal Outlook Use spatial unit for raw forecasts is the Climate Division (102 for U.S.)
CDFs defined by 13 percentile values (0.025 - 0.975) for P and T, and μ and σ
Hydrologic Prediction using CPC Seasonal Outlooks
CPC climate outlooksvariables: mean temperature (Tavg) total precipitation (Ptot)scales: 102 climate division (CD) / US overlapping 3-month timestepinformation: forecast (μ, σ) at each timestep normal (μ, σ) at each timestep
disaggregate spatiallyclimate division unit
--- becomes ---1/8 degree (~12-13 km)
disaggregate to a daily timestep1/8 degree monthly Tavg and Ptot
--- becomes ---1/8 degree daily Ptot, Tmin and Tmax
Use CPC forecasts as inputs to a hydrologic model to produce
streamflow forecast ensembles
disaggregate temporallyoverlapping 3-month timestep
--- becomes ---non-overlapping 1-month timestep
link Tavg & Ptot ensemblesAssociate monthly variables
spatially & temporally
create Tavg & Ptot ensemble forecasts (μ, σ) at each timestep/CDgenerate seasonal ensemble data
CDscale
Several methods of doing this work well but not perfectly.
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disaggregate temporallyoverlapping 3-month timestep
--- becomes ---non-overlapping 1-month timestep
• Schneider et al., Weather & Forecasting (2005) – applied monthly/seasonal mean correction factors – approach being adopted by CPC
• We are trying multiple linear regression: monthly values = f(seasonal values)
Sample Results
ML regression approach appears to yield better variance, but is not markedly superior
CPC approach
std dev-10
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observed PCP anomaly
predicted PCP anomaly
disaggregate temporallyoverlapping 3-month timestep
--- becomes ---non-overlapping 1-month timestep
ML regression approach
Schneider et al. (2005)
disaggregate temporallyoverlapping 3-month timestep
--- becomes ---non-overlapping 1-month timestep
Sample Results
ML Regression based disaggregation
y = 0.6335x + 0.1099-15
-10
-5
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-15 -10 -5 0 5 10 15
observed pcp anomalies
pre
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cip
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R = 0.80
CPC approachML regression approach
Schneider et al. (2005)
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pre
cip
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m)
Challenge:Given monthly distributions for a climate variable, how do you associate the values in time to yield a single sequence of one variable? Of two variables?
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1015202530354045
0 1 2 3 4 5month
Tem
p (
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link Tavg & Ptot ensemblesAssociate monthly variables
spatially & temporally
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pre
cip
(m
m)
Challenge:Given monthly distributions in adjacent cells, how might sequences in one climate division be associated with those in another?
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1015202530354045
0 1 2 3 4 5month
Tem
p (
C)
link Tavg & Ptot ensemblesAssociate monthly variables
spatially & temporally
Schaake ShuffleClark et al., J. of Hydromet (2004)
link Tavg & Ptot ensemblesAssociate monthly variables
spatially & temporally
Spatial and Temporal Downscaling
disaggregate spatiallyclimate division unit
--- becomes ---1/8 degree (~12-13 km)
disaggregate to a daily timestep1/8 degree monthly Tavg and Ptot
--- becomes ---1/8 degree daily Ptot, Tmin and Tmax
• Spatial sampling of anomalies within climate divisions
• Re-sampling of daily patterns
• Scaling/shifting to reproduce CPC forecast anomalies
Average Flow, Columbia R. at The Dalles, OR
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jan feb mar apr may jun jul aug sep oct nov dec
cfs
coop avg
coop stdev
raw cpc avg
raw cpc stdev‘OBS’
downscaled
University of Washington Forecast System Website
project led by Dennis Lettenmaier
funded byNOAA, NASA
Streamflow Forecast Results: Westwide at a Glance
Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.
Streamflow Forecast Details
Clicking the stream flow forecast map also accesses current basin-averaged conditions
Streamflow Forecast Results: SpatialSWE Soil MoistureRunoffPrecip Temp
Apr-06
May-06
Jun-06
½ degree VIC implementation
Free running since last June
Uses data feed from NOAA ACIS server
“Browsable” Archive, 1915-present
UW Real-time Daily NowcastSM, SWE
(RO)
We are currently migrating the CPC forecast approach to a national US implementation
For more information:
http://www.hydro.washington.edu / forecast / westwide /
Conclusions
Our current approach for downscaling CPC seasonal outlooks is adequate from hydrologic perspective.
Simple temporal disaggregation approaches are sufficent, although it’s possible that slightly higher performance can be achieved via more elaborate disaggregation methods
Ensemble formation step bears further analysis at the monthly to seasonal time scale.
Translation of CPC outlooks to ensembles for hydrologic forecasting should not be an obstacle for their use.
Thank You