Ensemble-Based Statistical

Prediction of Ethiopian Monthly-to-Seasonal Kiremt Rainfall

1Cooperative Institute for Mesoscale Meteorological Studies/2School of Meteorology

The University of Oklahoma, Norman, OK (zewdu@ou.edu)

Zewdu T. Segele1, Peter J. Lamb1, 2, and Lance M. Leslie2

Kiremt is mostly June-September except in Southwest and West where it starts earlier

Physical & Human Characteristics Population: 85 million (projected to be 174 million by the year 2050 according to the 2010 World Population Data Sheet, Population Reference Bureau , Washington DC)

Area: 1.2 m square km ( ~ twice the size of Texas) Lowest point: -125 m/410 ft below MSL (Danakil depression) Highest point: 4620 m/15157 ft above MSL (Ras Deshen) Blue Nile: contributes more than 85% of the Nile water

Long.

Blue Nile

Eritrea

Djibouti

Somalia

Somalia Kenya

Rainfall Climatology Two main rainy seasons for the northern two-thirds of the country: Feb.-May and Jun.-Sept. (also called Kiremt)

Southwest and West: mono-modal rainfall pattern

Central and Northeast: bi-modal rainfall pattern, one major

East and South: bi-modal rainfall pattern, comparable seasons

Jan Mar May Jul Sep Nov

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Jan Mar May Jul Sep Nov

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Jan Mar May Jul Sep Nov

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Southwest & West (Gore)

East & South (Diredawa)

Central & North (Combolcha)

J a n M a r M a y J u l S e p N o v

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1 2

Rainfall Pattern for Dry-Summer Areas

Near equatorial southern lowlands are dry during summer Two short seasons; one in spring and another in fall in association with the Inter-tropical Convergence Zone (ITCZ)

These pastoral regions (including Somalia and northern Kenya) are currently experiencing severe drought and famine

Southern lowlands (Negele)

Seasonal prediction is important!

Rainfall Data Distribution

100 rain gauge stations for 1970-99 for all-Ethiopian seasonal prediction

52 stations for 2000-2002

• 11 stations for regional prediction Combolcha for local prediction

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct-1.0

-0.5

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Month

Cor

relat

ion

Monsoon

Y(-1) Y(0) Y(+1)

West Eq. Pacific East Eq. Pacific

99%95%

99%95%

Correlation between Ethiopian Kiremt Rainfall (1970-99) and Zonal Wind (unfiltered) and SST (filtered at the

ENSO time scale) Concurrent correlations between Kiremt rainfall and zonal wind (> 0.5) and ENSO time-scale filtered sea surface temperature (> 0.9) are very high

Although useful for diagnostics, such concurrent relationships cannot be used for predictions (note below the low SST correlations prior to the onset of Kiremt)

East Pacific

West Pacific

U (1000 hPa)

ENSO (filtered) TIME-LAGGED SST CORRELATION

CORRELATION MAPS

Use both Atmospheric and Oceanic Predictors

SST variations over the Pacific Ocean are widely used for seasonal predictions. However, they explain only a fraction of Kiremt rainfall variability (concurrent unfiltered R2 ~ 35%). Moreover, their effects must be realized through regional circulations => Identify regional circulation predictors also.

Regional: wind, vertical velocity, GPH, temperature, moisture, pressure

Global: sea surface temperature

Time: March, April (pre-Kiremt)

Space: 2.5º lat. x 2.5º lon., 12 vertical levels

Challenge: Too many (> 4000) predictors

Problem of multicollinearity (linearly interrelated predictors)

Predictors To reduce the number of predictors and handle multicollinearity issues,

apply Principal Component Analysis (PCA)

PCA redistributes the variability of a large set of variables into a few components

It produces mutually independent time series

PCA was applied at each level from surface to 100 hPa; up to 470 PCs prescreened as potential predictors

Prediction Design Summary y1 y2 y3 . yi .

yn

Rainfall

Predictor PCs

x11 x12 x13 .

x1i .

x1n

x21 x22 x23 .

x2i .

x2n

xp1 xp2 xp3 .

xpi .

xpn

… … … … … … …

Time

Exclude year i of time series & develop a multiple linear regression equation from the remaining time series by initializing the model with PC1 => Model M1i, Prediction Y1i

Repeat the model fitting procedure by initializing the model with PC2 => Model M2i, Prediction Y2i

Continue until each PC initializes the model, yielding a set of p models and p prediction values for year i => M1i, M2i, …,Mpi, and Y1i, Y2i,…, Ypi

The final prediction for year i is then the average of predicted values of the best models, which are selected based on several criteria that largely involve intra/inter-model correlation strength and significance levels

The above model-fitting procedure is repeated for all years to be predicted

Leave-One-Out Strategy Ensures no information of the future state is leaked into the prediction

Ensemble Procedure

PC1 PC2 PCp

The maximum model ensemble size is the total number of prescreened PCs because each PC initializes a model (the number of unique models, however, is less than the number of PCs as some models may contain identical predictors).

For a leave-one-out prediction strategy, any PC with at least 0.2 correlation magnitude with rainfall (excluding data for predicted year) is used as a potential predictor provided that the correlation is stable (i.e., remain fairly the same) for any 9/10 data-pair segments of the training data

ENSEMBLE SIZE

All-Ethiopian Standardized Kiremt Rainfall Prediction 1990-2002

(leave-one-out, based on predictors observed in April) For the Prediction, only fitted model statistics were used to select ensemble members

For the Analysis, concurrent observations were used to select the best model combination of the predicted Ypi’s

For all (most) years, the ensemble prediction correctly identified the sign ( magnitude) of the observed standardized Kiremt rainfall anomalies.

High correlation predictions (r = +0.82) for Combolcha (northeastern Ethiopia) based on the state of the ocean-atmosphere system observed in April

Deviations from the observations are large for excessively wet (e.g., 1975) or excessively dry (e.g., 1984) years

The interannual variability of monthly rainfall is well captured

Local Prediction of August Total Rainfall Amount 1970-99, Combolcha

(leave-one-out, April predictors)

Key Conclusions • Both regional circulations and global SSTs

are required for skillful Kiremt rainfall predictions

• Ensemble-based statistical prediction technique provides high-quality local and regional forecasts for Ethiopian Kiremt one to two months in advance of onset month

• Leave-one-out (and also retroactive) prediction technique identified observed all-Ethiopian seasonal anomalies

PUBLICATIONS Segele, Z. T., P.J. Lamb, 2005: Characterization and variability of Kiremt rainy

season over Ethiopia. Meteorol. Atmos. Phys., 89, 153-180. Segele, Z. T., L. M. Leslie, and P. J. Lamb, 2009: Evaluation and adaptation of a

regional climate model for the Horn of Africa: Rainfall climatology and interannual variability. Int. J. Climatol., 29, 47-65.

Segele, Z. T., P. J. Lamb, and L. M. Leslie, 2009: Large-scale atmospheric

circulation and global sea surface temperature associations with Horn of Africa June-September rainfall. Int. J. Climatol., 29, 1075-1100.

Segele, Z. T., P. J. Lamb, and L. M. Leslie, 2009: Seasonal-to-interannual variability

of Ethiopia/Horn of Africa monsoon. Part I: Associations of wavelet-filtered large-scale atmospheric circulation and global sea surface temperature. J. Climate, 22, 3396–3421.

Segele, Z. T., P. J. Lamb, and L. M. Leslie, 2011: Seasonal-to-interannual variability

of Ethiopia/Horn of Africa monsoon. Part II: Ensemble-based statistical predictions of Ethiopian monthly-to-seasonal rainfall. J. Climate (In preparation).