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Forecast Meteorological Drought based Forecast Meteorological Drought based on the Standardized Precipitation Indexon the Standardized Precipitation Index

Kingtse MoClimate Prediction Center

& Jinho Yoon

Pacific Northwest National Lab, Richland, wa

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ObjectivesObjectives

Develop objective drought Prediction based on drought indices

Meteorological droughtMeteorological drought: : based on the Precipitation deficit.

Index: Standardized Precipitation Index (SPI)

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SPISPISPI3-SPi12:

For drought monitoring: SPIs are updated daily using the CPC unified Precip analysis

Advantages:

Only need precipitation

Can be applied to station data

SPI3: shorter range P conditions

SPI6 comparable time scales with soil moisture

SPi12-24: long term droughtD3 D2 D1

Predicting SPI from CFS over the United States

Downscaling from the CFS forecasts (T62) to 50 KM. Four different downscaling methods are tested

Append the corrected P forecasts to the observed P data set

Compute SPI from 3 months to 12 months

Evaluate the forecasts

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Bilinear interpolationBilinear interpolation

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Correct the model climatology and Correct the model climatology and bilinear interpolation to a high bilinear interpolation to a high resolution gridresolution grid

Example: 1988 Nov

1. Too smooth over the mountain region,

2. amplitudes are too low because of ensemble mean , low resolution,and BI

Ensemble CFS T62 fcsts

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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 T62 Get probability distribution PDFs for A (coarse T62 fcsts ) and A(fine, obs)fcsts ) and A(fine, obs)

• 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)

1988 Nov

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Improvements over mountain regionsstronger amplitudes

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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 performancebased on the historical performance

For P, transfer to normal space first For P, transfer to normal space first

)(

)(),(*)(')('

coarse

fineobshindcastcoarseAfineA

Ref: Wood and Schaake (2008)

Schaake et al. (2007)

Can be negative=>make fcsts worse

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Bayesian merging & bias correction Bayesian merging & bias correction

Bayesian correction – calibrate Bayesian correction – calibrate Probability distribution based on Probability distribution based on forecast informationforecast information

Calibration based on the linear Calibration based on the linear regression regression

fcst=a*obs+b fcst=a*obs+b

and spreadand spread

Ref: Luo et al. (2007); Luo and Wood (2008)

Bayesian methodBayesian method For T62 CFS forecasts, the spread is

large because of low skill and ICs cover large interval (about 25 days apart)

Amplitudes of forecasts after Bayesian downscaling are too weak.

Here, we use no spread (Coelho et al. 2004), but screen to select only fcsts close to the ensemble mean

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1111

1988 NovA good fcst

Spread is too large, so it dumps fcst.Magnitudes are too weak

Screen fcstsNo spread

P CorrelationP Correlationfor lead=1for lead=1

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BCSD

Schaake

Bayesian

Ensemble

MayNov

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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. An example: Fcst Nov 1981 SPIs P : observed : Jan1950-oct1981 append fcsts with ICs in Oct-Nov lead 1 f1 lead 2 f2

to f9 etc P time series: Jan1950-oct 1981 (obs) Nov 1981 (fcst)For SPI6 lead 1, there are 5 months of observed P

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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. From lead 1 to 4 mo, the 2. From lead 1 to 4 mo, the fcsts are skillful and fcsts are skillful and differences between differences between downscaling methods are downscaling methods are small.small.

3. Large differences occur 3. Large differences occur after lag 4 when skill is lowafter lag 4 when skill is low

BCSD

Schaake

Bayesian

Ensemble mean

NOV

FEB

MAY

Aug

rmes skill for different methods (may fcsts spi6)

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BCSD

Schaake

Bayesian

lead1 Lead 2 Lead 3

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SPI6-Bayesian

Nov

Feb

May

Aug

RMSE Lead 1 Lead 2

SPI6 obs(colored) &fcsts(lead 2) (32-36N)

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SPI6 fcst & obs lead=3

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Dynamic downscaling based on Dynamic downscaling based on the 50-km RSMthe 50-km RSM

1.RSM (regional spectral model) nested in the CFS forecasts to downscale(April 28-May 3) ICs (50 km resolution) (Thanks, Henry Juang)

2.How does this compare with the statistical BCSD downscaling method

2020

Correlation

RSM-50km

P

T62 BCSD15mem

T26 BCSD5mem

SPI6

RSM better than 5 mem, but worse than 15mem T62

ConclusionsConclusions

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1. Attempt has been made to predict the meteorological drought based on SPI from downscaled P from T62 CFS seasonal forecasts

2. 2. For SPI3, the ACC is above 0.5 for 2 months and for SPI6, the ACC is above 0.5 for 4 months.

3. For SPI prediction, the skill is not sensitive to the downscaling methods.

4. Dynamic downscaling based on the 50-RSM does not have advantage in comparison with the simple BCSD downscaling.