1 objective drought monitoring and prediction recent efforts at climate prediction ct. kingtse mo...
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Objective Drought Monitoring andObjective Drought Monitoring andPredictionPrediction
Recent efforts at Climate Prediction Ct.Recent efforts at Climate Prediction Ct.
Kingtse Mo &
Jinho YoonClimate Prediction Center
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ObjectivesObjectives
Develop objective drought monitoring and prediction based on drought indices
Support drought monitor and outlook operation
Develop regional applications with users and the River Forecast Centers
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Outline of presentationOutline of presentation
Current operation Drought briefing each month (8-11 of the
month, dial in is available)If you would like to participate, [email protected] of drought indicesPrediction of SPIFuture plan
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Define drought based on Drought IndicesDefine drought based on Drought Indices
Meteorological droughtMeteorological drought: : Precipitation deficit. Precipitation deficit.
Index: Standardized Precipitation IndexIndex: Standardized Precipitation Index Hydrological droughtHydrological drought: Runoff deficit: Runoff deficit Index: Standardized runoff indexIndex: Standardized runoff index Agricultural droughtAgricultural drought: S: Soil moisture deficit oil moisture deficit
Index: SM anomaly percentileIndex: SM anomaly percentile
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SPISPISPI3:
SPI3 shows dryness over the Great Lake area
Wetness over AZ, New Mexico and western Texas.
For longer terms
A very wet picture over the Southeast and eastern central United States
D3 D2 D1
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Multi model SM percentilesMulti model SM percentiles
Univ of Washington
NCEPU. Washington
Uncertainties in the NLDASUncertainties in the NLDAS
Feb 2010
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•Differences between two systems are larger than the spread among members of the same system
•The differences are not caused by one model. They are caused by forcing.
• In general, extreme values from the UW (Green) are larger than from the NCEP (red) NCEP(red),UW(green)NCEP(red),UW(green)
standardized SM anomalies for area 38-42N,110-115W
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Number of station P reports averaged over a year
Reports dropped for real time operation
Historical period Real time
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Southeast & CBRFC pilot projectsSoutheast & CBRFC pilot projectsEnsemble hydrologic Forecasts in Ensemble hydrologic Forecasts in
support of NIDISsupport of NIDIS
CPC: Kingtse Mo, Jinho YoonCPC: Kingtse Mo, Jinho Yoon
EMC: Michael Ek, Youlong XiaEMC: Michael Ek, Youlong Xia
Princeton University : Eric WoodPrinceton University : Eric Wood
SERFC: John Schmidt, John Feldt ,Jeff DoburSERFC: John Schmidt, John Feldt ,Jeff Dobur
OHD: John Schaake and D. J. Seo OHD: John Schaake and D. J. Seo
CBRFC: Kevin WernerCBRFC: Kevin Werner
Funded by TRACS programFunded by TRACS program
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A wet regionA wet region
droughtdrought
6 mo running mean black line6 mo running mean black line
3 mo running mean (black line)3 mo running mean (black line)
No smoothing No smoothing
Red line: monthly mean, no smoothing
75-85W,31-35N
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A dry region6 mo running mean
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Hydrologic predictionHydrologic prediction
Develop hydrologic forecasts out to 6 months for drought indices
P , Tmin and Tmax from the CFS P , Tmin and Tmax from the CFS forecasts=> downscaling (from 250km to forecasts=> downscaling (from 250km to 50km) and error correction=> Vic 50km) and error correction=> Vic model=> SM % and runoffmodel=> SM % and runoff
Based on the Princeton System developed Based on the Princeton System developed by Eric Wood’s groupby Eric Wood’s group
Corrected P => append P time series=> Corrected P => append P time series=> SPI indices SPI indices
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Linear Interpolation Linear Interpolation Linear Interpolation : correct meanLinear Interpolation : correct mean Correct the model climatology and bilinear Correct the model climatology and bilinear
interpolation to a high resolution gridinterpolation to a high resolution grid
For variable A ensemble fcst: assume normal For variable A ensemble fcst: assume normal distributiondistribution
anomaly A’ rwt to model climatologyanomaly A’ rwt to model climatology
AA’’= A-model climatology = A-model climatology Corrected A = A’+ observed Corrected A = A’+ observed
climatologyclimatology
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Bias correction & Downscaling Bias correction & Downscaling (BCSD) (BCSD)
Probability mapping based on distributionsProbability mapping based on distributions
• Get probability distribution PDFs for A (coarse) and Get probability distribution PDFs for A (coarse) and A(fine)A(fine)
• From A’ (coarse) get percentile based on PDF (coarse)From A’ (coarse) get percentile based on PDF (coarse)• => assume the same percentile for the fine grid and => assume the same percentile for the fine grid and
work backward based on the PDF fine get A’ fine work backward based on the PDF fine get A’ fine (anomaly)(anomaly)
• If normally distributed , time ratio of stdIf normally distributed , time ratio of std
)(
)(*)(')('
coarse
finecoarseAfineA
Ref Wood et al (U. Washington 2002,2006)
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Schaake’s linear regressionSchaake’s linear regression
Schaake’s linear regression – Schaake’s linear regression – calibrate P ensemble forecasts calibrate P ensemble forecasts based on the historical performance based on the historical performance
)(
)(),(*)(')('
coarse
fineobshindcastcoarseAfineA
Ref: Wood and Schaake (2008)
Schaake et al. (2007)
Do no harm
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Bayesian merging & bias correction Bayesian merging & bias correction
Bayesian correction – calibrate P Bayesian correction – calibrate P forecast based on the historical forecast based on the historical performance and spread of members performance and spread of members in the forecast ensemble in the forecast ensemble
use all members in the ensembleuse all members in the ensemble
Ref: Luo et al. (2007); Luo and Wood (2008)
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Standardized Precipitation Index Standardized Precipitation Index Forecasts Forecasts
Append the bias corrected and downscaled P to the observed P time series
Calculate SPI from extended time series The advantages are (1) no need of
hydrologic model and (2) can use any base period.
P :time series : Jan1950-oct1981 append fcsts with ICs in Oct lead 1 f1 lead
2 f2 etcJan1950-oct 1981 (obs) Nov 1981 (fcst)
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Seasonal dependence of skillSeasonal dependence of skill
1.For the first 3 months, 1.For the first 3 months, AC>0.6 and RMS < 0.8 AC>0.6 and RMS < 0.8
2.Overall, Bayesian wins2.Overall, Bayesian wins
3. Skill is higher for Nov ICs 3. Skill is higher for Nov ICs and low for May ICsand low for May ICs
LI
BCSD
Schaake
Bayesian
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NovNov
JanJan
DecDec
FebFeb
LI Bayesian SPI6 T62 RMSE
2020
LI Bayesian SPI6 T62 rmse
May
June
July
Aug
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Over the Southeast,
SPI6 out to 3 months
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SPI6 fcst (contours) /ana (colored)SPI6 fcst (contours) /ana (colored)
32-40N Hovmoller
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3 month later3 month later
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High resolution run (T382)High resolution run (T382)and dynamic downscaling (RSM)and dynamic downscaling (RSM)
1.High resolution T382 run from April 19-23 ICs run through Nov(5 members) (Thanks Jae Schemm)
2.RSM (regional spectral model) downscaling from the CFS forecasts (April 28-May 3) ICs (50 km resolution) (Thanks, Henry Juang)
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T62 LI 5 members vs T382T62 15ensm LI
T62 vs RSM 5 mem LI, T62 15 member LI
5 member T382 & T62 Bayesian 15 members
5 member RSM & T62 Bayesian 15 members
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JJA P anom over the Northern Plains JJA P anom over the Northern Plains
(34-42N, 100-85W(34-42N, 100-85W))
BCSD, RSM or T382, Obs
mm
/day
mm
/day
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Future plan &our needsFuture plan &our needs Develop real time prediction of SPI’s based on the
CFSRR hindcasts The bias corrected P and T will drive VIC model to
produce hydrologic forecasts of soil moisture and runoff
Develop regional applications with the RFCs What do we need? Better station reporting of real time P Better P analyses Better global model prediction of P Better model physics
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Discussion questionsDiscussion questions
What current activities (monitoring and What current activities (monitoring and forecasts) can we build on?forecasts) can we build on?
Regional vs entire United StatesRegional vs entire United States How can we network and coordinate How can we network and coordinate
drought related information such as drought related information such as drought impact, planning and information drought impact, planning and information exchange?exchange?
What gaps do we need to fill?What gaps do we need to fill? What issues are important to you, but What issues are important to you, but
have not been discussed?have not been discussed?
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Streamflow Streamflow fcstsfcsts
the binary event for observed monthly mean
Row 2-6 represents the exceedence probability for forecasts initialized from Nov 2006
Luo and Wood