The statistical prediction of East African rainfalls using quasi-biennial oscillation phases information

doi:10.4236/ns.2010.212172

Paper Menu >>

Journal Menu >>

Vol.2, No.12, 1407-1416 (2010)

doi:10.4236/ns.2010.212172

Natural Science

The statistical prediction of East African rainfalls using

quasi-biennial oscillation phases information

Hashim K. Ng’ongolo, Sergei P. Smyshlyaev*

Department of Meteorology forecast, Russian State Hydrometeorological University (RSHU), Saint-Petersburg, Russia;

hngongolo@mail.ru, *Correspon ding Author: smyshl@rshu.ru

Received 22 September 2010; revised 25 October 2010; accepted 28 October 2010.

ABSTRACT

A simple correlation method and a quasi-biennial

oscillation (QBO)/rainfall composite analysis

were used to examine the teleconnections be-

tween the seasonal rainfall anomalies of March

through May (long-rains) over East Africa and

the different QBO phases in the stratospheric

zonal winds, and also explore the predictive

potential of the long rainy season using infor-

mation about the phases of the QBO for the pe-

riod 1979-2003. We study the spatial correlation

patterns statistically to understand the climatic

associations between the equatorial strato-

spheric zonal wind and regional rainfall at the

interannual time scale. The aim of this analysis

is to establish whether this global signal can be

employed as predictor variable in the long-range

forecasts. Principal component analysis (PCA)

is employed in the first instance to reduce the

large dimensionality of the predictant (monthly

rainfall data), to retain the time series of the

principal components ( P Cs ) and to delineate the

rain gauge network of East Africa into homo-

geneous zones. Spatial patterns of the factor

loading were used to delineate East Africa into

11 homogeneous zones.

Keywords: Principal Component Analysis (PCA);

Climatological Rainfall Zones;

QBO-Index; SOI-Index;

March to May Seasonal Rainfall in East Africa

1. INTRODUCTION

East Africa experiences two main rainy seasons,

namely the ‘long’ rains (March to May) and the ‘short’

rains (October to December). Significant evidence of the

relationships between short-rains over eastern Africa,

and SST and ENSO have been observed [1-3], and rela-

tively less attention has been directed at the predictive

potential of the long-rains season over th e region, which

is more critical than the short-rains in many parts of the

region for the agricultural industry and other social eco-

nomic activities. The long-rains season has been associ-

ated with complex interactions between many regional

and large-scale mechanisms which generally induce

large heterogeneities in the spatial rainfall distribution

[4,5] and virtually neglig ible co rrelations with ENSO [3].

Recent studies of interannual variability in the tropics

have largely focused on the ENSO, so much so that

other important long-term sources of climate variability

may have been overlooked. Therefore the objective of

this study is to investigate the relationships between the

different quasi-biennial oscillation (QBO) phases in the

stratospheric zonal wind and the long-rains season of

eastern Africa, and also explore the predictive potential

of the long rainy season using information about the

phases of the QBO. Several investigators have reported

the presence of the QBO in various atmospheric pa-

rameters and at different regions of the globe [5-7].

Studies by Holton and Lindzen, Plumb, Holton and Tan

[8-10] have indicated that the stratospheric equatorial

QBO is forced locally by alternating downward propa-

gating patterns of westerly and easterly mean zonal

winds which repeat with somewhat irregular period av-

eraging about 26 months. It has been pointed out that the

stratospheric QBO is excited primarily by vertically

propagating equatorial wave modes, and that these

modes excite a quasi-biennial mean zonal wind response

through the mechanism of radioactive damping which

causes the waves to decay in amplitude with height and

thus to transfer momentum to the mean zonal flow [8].

Lau and Sheu [11] have indicated that the fundamental

period of the Southern Oscillatio n (SO) is approx imately

double that of the QBO, which in turn twice that of the

annual cycle. QBO has been found to be strongly phase

locked with the annual cycle and it also tends to enhance

major negative swings in the SO associated with the El

Niño-Southern Oscillation (ENSO) events. Evidence

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

1408

suggests that the development of ENSO tends to be as-

sociated with the easterly phase of the lower strato-

spheric QBO [11]. Many attempts have been made to

examine the predictability potential of the QBO signals

because of its persistence and appearance in many at-

mospheric parameters [6,12,13]. Mukherjee et al. [6]

have identified a significant relationship between the

phases of the QBO in the zonal wind in the lower

stratosphere (30-mb) and the percentage departure of the

summer monsoon rainfall of India. They showed that the

strong easterly phase of the QBO is associated with

weak monsoons and weak easterly/westerly phases with

active monsoons. The weakening of the easterly winds is

generally a manifestation of westerly phases of the QBO

in the lower stratosphere, as the prevailing winds in the

stratosphere during summer monsoon are broadly east-

erly. Mason and Tyson [13] have analyzed the phases of

QBO and southern Africa rainfall and found a significant

correlation (+ 0.6) between QBO and regional rainfall

when the QBO is the west phase. Ogallo et al. [14] have

investigated the characteristics of QBO over tropical

eastern Africa using zonal wind composites from Nai-

robi, Kenya (1° 18´S, 36° 45´E) for the period 1 966-19 87.

Their results, based on spectral analysis indicated the

dominance of a 28 months period in the zonal wind

component. The results also indicated some significant

(at 5% level) association between rainfall and QBO sig-

nal based on the reversal in zonal winds.

2. AREA OF STUDY

The domain of study is the eastern Africa region

which lies within longitude lines 29-42°E and by latitud e

lines 5-12°S. The region has complex topographical fea-

tures: East African highlands that include high moun

tains; Kilimanjaro 5895 m above sea level, Kenya 5199

m and Elgon 4321 m. Some of these mountains like

Kilimanjaro and Kenya have permanent glaciers at their

top throughout the year, which makes them very special

as potential indicators of regional or large-scale

long-term climate fluctuations. The complex mountains

are also the source watersheds for some of the major

rivers of the region [15], they therefore form an integral

component of the regional hydrological cycle. The other

unique physical characteristics of the region include the

water masses of Lake Tanganyika, Turkana, Albert, Vic-

toria and Indian Ocean. The total rainfall amount for

meteorological stations in East Africa varies from year-

to-year as well as having large seasonal variations. The

mean annual rainfall totals range from below 500 mm in

the semi-arid areas, which include the northern and east-

ern parts of Kenya and eastern and central Tanzania.

However, the areas around the Lake Victoria have a rela-

tively high mean annual rainfall of 1200-1600 mm [16].

The coastal areas receive over 1000 mm, the highlands

of central Kenya and southern Tanzania, much of

Uganda and western Tanzania also receive rainfall of

more than 800 mm. The region experiences bimodal and

unimodal rainfall seasons Figure 1.

Thus Uganda, Kenya and most of the locations in the

northern half of Tanzania experience two rainfall seasons

(bimodal) which includes March-May (MAM) long

rains’ and ‘short rains’ October-December (OND). The

southern half of Tanzania is characterized by a unique

unimodal rainfall pattern characteristic to the southern

Africa rainfall that occurs between November and April

in the subsequent year. These rainfall patterns are con-

trolled by the seasonal migration of the Inter-Tropical

Convergence Zone (ITCZ).

(a) (b)

igure 1. Annual cycle of rainfall for 2 selected stations, bimodal (a) Dagoretti and unimodal (b) Songea.

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

140

1409

3. DATA DESCRIPTION AND

METHODOLOGY

The data used as predictor variables in this study are

Southern Oscillation Index (SOI) and globally averaged

equatorial stratospheric 30-mb zonal wind index (QBO).

This data set was obtained from climate analysis center

(CPC/NOAA) database. The data used as the predictant

variables are long-term monthly rainfall records for 71

meteorological stations scatted over the East African

region Figure 2, and are supplied by the Tanzania Mete-

orological Agency (TMA) and, the Ugandan and Kenyan

Meteorological Departments. To ensure consistency with

the SOI and QBO-indices data; we extracted rainfall

records from the stations for the period from 1979 to

2003. A simple correlation method and a QBO/rainfall

composite analysis were used to examine the telecon-

nections between the seasonal rainfall anomalies of

March through May (long-rains) over East Africa and

the different QBO phases in the stratospheric zonal

winds, and also explore the predictive potential of the

long rainy season using information about the phases of

the QBO for the period 1979-2003. We study the spatial

correlation patterns statistically to understand the cli-

matic associations between the equatorial stratospheric

zonal wind and regional rainfall at the interannual time

scale. The aim of this analysis is to establish whether

this global signal can be employed as predictor variable

in the long-range forecasts. Principal Component Analy-

sis (PCA) is employed in the first instance to reduce the

large dimensionality of the predictant, to retain the time

series of the principal components (PCs) and to delineate

the rain gauge network of East Africa into homogeneous

zones. Spatial p atterns of the facto r loading were used to

delineate East Africa into 11 homogeneous zones Figure

Figure 2. The location of stations used in the study.

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

1410

Figure 3. The homogeneous climatological zones over eastern Africa derived from PCA.

The stratification of the QBO indices was based on

four seasons: March to May (MAM), June to August

(JJA), September to November (SON), and December to

February (DJF). The standardized departures of the

rainfall for East Africa from 71 meteorological stations,

normals were calculated for the long-rains season of

MAM based on the relation:





, where Y is

MAM rainfall in a year, Y and



are mean and

standard deviation respectively, based on the 1979-2003

period.

3.1. Principal Component Analysis

The following is PCA procedures [17]. Consider the

matrix

XN , whose elements mn

are standardized

values, when m = 1, 2, 3, …M (stations) or grid points

and n = 1, 2, 3, …N (time i.e. months, years, seasons

etc).

1) Compute the covariance matrix



 (1)

2) Determine the eigenvectors





,...

ee e and

eigenvalues





,.... M



 

 from the characteristics

equation of Z, where I is a unit matrix









 (2)

3) Compute the principal component time series, C

such that its elements

C, are the projection of mn

e given by

CEF (3)

Each of the PCs will be orthogonal to the other, the

(PC1) will be the most dominant pattern and it explains

most of the variances, then (PC2) will be second fol-

lowed by PC3, etc. The Kaiser criterion was used to de-

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

141

1411

termine the number of significant factors retained in the

PCA varimax rotation [18]. The magnitude of the load-

ing of each variable (station) on each of the common

factors is the index, which is used to assign the variable

(station) to its group or type. Finally other verification

method which included the use of relief and physical

processes playing role in rainfall generation over East

Africa were used to determine the reality of the deline-

ated PCA rainfall pattern s.

3.2. Correlation Analysis

In view of the large variability of distribution of rain-

fall over the region, the analysis was based on the 11

homogeneous climatological zones as can be seen in

Figure 3, in order to examine the relation ship of rainfall

of those zones with QBO. Each of the 71 rainfall indices

and the regional rainfall index time series for 11 zones

were first correlated with the equatorial averaged strato-

spheric zone wind in order to identify the spatial extent

of the associations between QBO and rainfall. The zones

that significantly correlated with QBO were identified

and were cross-correlated with the SOI to test the con-

tribution of each of the two glob al climate indices on the

long-rains season. These zones time series were then

generalized into categories using contingency tables

based on the west and east phases of the QBO. Zero and

non-zero lag correlation analysis were used in this study

and the statistical significance of the correlation coeffi-

cient (R) is tested based on the Z-transformation of

Fisher and criterion student. The student’s t-statistic was

used to decide the significance of the correlation from

the matrices [19]. The space and time patterns of the

significant correlations were then used to investigate the

relationships between QBO and SOI, and regional rain-

fall. Simple correlation coefficient (R) can be expressed

as follows.



XXYY

XYY











(4)

where ,

Y are predictors and predictants, and ,

are the mean values respectively; is the sample size,

positive and negative values of (R) are in-

dicative of the positive or negative relationship respec-

tively. The statistical sign ificance of the correlation coef-

ficient () is tested based on the Z-transformation of

Fisher.

1R 1,

In R







(5)

and criterion student, zKP





 where



coin-

cide with 5, 1 or 0.1% level of significance. The stan-

dard deviation of parameter Z can be estimated using the

relationships:



 (6)

3.3. Composite Analysis

In the composite analysis we used the years with

above normal rainfall and coinciding with west phases of

the QBO and years with below normal rainfall and coin-

ciding with the east phase of the QBO. The years having

a standardized rainfall index of were classified

as high-rainfall and the years having a standardized

rainfall index

0.12

0.12



, classified as low-rainfall years.

The choice of this range of the standardized rainfall in-

dex is based on a student t-test applied on a sample size

of 25 years. The t-scores on the high- and low-rainfall

indices indicate that the two series are significantly dif-

ferent at 95% significance level.

4. RESULTS AND DIS CUSSION

4.1. Relationships between Seasonal

Rainfall and the QBO-Index

The basis for using the lower equatorial stratospheric

zonal wind index in seasonal prediction is based on its

tendency to persist for several months after the phase

change from easterly to westerly and vice versa. Figure

4 shows plots of simultaneous and lag correlations be-

tween the long-rains over homogeneous rainfall zones in

eastern Africa and 30-mb QBO-index for the period

1979-2003. Based on the criterion student on a sample

size of 25 years, correlation coefficients [r] 0.51 are

above 5% confidence level. Table 1 gives a summary of

the seasonal and monthly correlation indices between the

two variables.



Figure 4. The mean correlation patterns of three seasons lag,

two seasons lag, one season lag and zero lag between QBO-

index and MAM seasonal rainfall over East Africa. Correlation

alues above 5% significant level area also indicated. v

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

1412

Ta bl e 1. Simultaneous and lag correlations coefficient between the QBO-index and MAM seasonal rainfall over 11 homogeneous

zones over East Africa based on a sample size of 25 years.

Period Zone A Zone B Zone C Zone D Zone E Zone F Zone G Zone H Zone I Zone J Zone K

JJA 0.58** 0.54** –0.42* 0.55** 0.11 0.60** 0.32 0.42* 0.41* 0.45* 0.20

SON 0.52** 0.53** –0.34 0.17 -0.19 0.53** 0.35 0.30 0.26 0.12 0.09

DJF 0.09 0.11 –0.15 0.40* 0.45* 0.32 0.33 0.41* 0.28 0.44* 0.25

MAM 0.46* 0.26 –0.21 0.43* 0.41* 0.46* 0.20 0.21 0.35 0.42* 0.23

JUNE –0.04 0.47* –0.34 0.40* 0.16 0.56** 0.23 0.41* 0.40* 0.43* 0.20

JULY –0.50* 0.54** –0.43* 0.46* 0.13 0.59** 0.33 0.40* 0.41* 0.40* 0.19

AUG –0.49* 0.57** –0.41* 0.37 0.03 0.57** 0.38 0.42* 0.36 0.29 0.18

SEPT –0.39 0.55** –0.38 0.26 –0.08 0.55** 0.38 0.42* 0.30 0.19 0.13

OKT –0.51** 0.52** –0.35 0.17 –0.19 0.52** 0.35 0.43* 0.26 0.13 0.10

NOV –0.55** 0.50* –0.29 0.07 –0.29 0.46* 0.31 0.40* 0.20 0.05 0.03

DEC –0.51** 0.44* –0.25 0.02 –0.35 0.32 0.20 0.41* 0.12 –0.02 –0.04

JAN 0.39 –0.20 0.04 0.27 0.52** 0.23 0.17 0.03 0.09 0.40* 0.17

FEB 0.36 –0.09 0.02 0.31 0.53** 0.29 0.15 0.12 0.18 0.31 0.29

MAR 0.18 0.08 –0.08 0.40* 0.48* 0.38 0.16 0.16 0.28 0.47* 0.26

APR –0.04 0.20 –0.23 0.43* 0.44* 0.46* 0.19 0.21 0.35 0.44* 0.24

MAY –0.27 0.35 –0.29 0.40* 0.30 0.51** 0.22 0.42* 0.38 0.32 0.18

**Significant at 99 % level. *Significant at 95% level.

Results indicate significant simultaneous and lag cor-

relations between the QBO-index and rainfall over zone

F, which covers the Lake Victoria region (+0.6), the

southern region of Tanzania (zone A) of about +0.6, the

western region of Tanzania (zone D) of about +0.6, the

central region of Tanzania (zone B), the eastern, central

and western of Uganda (zone J) and, central rift valley

and Nairobi area (zone H). The QBO/rainfall correla-

tions in these zones are significantly high for at least five

months prior to the MAM rainfall season. The highest

significant correlation between seasonal rainfall and

QBO-index is +0.6 observed between the MAM rainfall

index and the JJA QBO-index of the previous year and

decreases towards the target rainfall season (MAM).

These lagged relationships between the two variables

indicate high prospects for using them in the develop-

ment of prediction methodology. However, the correla-

tions suddenly collapse between 3 and 2 seasons lag for

zones D, H and J. The sudden collapse in correlations

suggests that long-term prediction (of two seasons or in

advance) may not be feasible in these three zones. The

observed areas of significant lag correlations suggest

that seasonal prediction may be feasible in those areas.

Cross-correlation between the QBO-index, rainfall and

SOI was computed and the resulting indices are summa-

rized in Table 2. These results show some good associa-

tions between QBO-index and the region rainfall with

significant simultaneous and lag correlations of +0.5

(explained about 25% of variance) and +0.6 (explained

about 36% of the variance) respectively. The fact that the

two global climate indices (QBO and SOI) are statisti-

cally unrelated at both simultaneous and lagged time,

gives more confidence of using them as predictors of the

seasonal rainfall with low risk of introducing artificial

skill.

Table 2. Simultaneous and lag cross-correlation matrix for

Rainfall, QBO and SOI index for the period (1979-2003).

Rainfall

index zone F QBO-index

(MAM) SOI-index

(MAM) QBO-index

(July)

QBO-index (MAM)0.46*

SOI-index (MAM)–0.15 0.15

QBO-index (July) 0.59** 0.78** 0.10

SOI-index (July) 0.07 0.21 0.44* –0.04

** Significant at 99% level. *Significant at 95% level.

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

141

1413

4.2. Predictability Potential of Seasonal

Rainfall Using the QBO-Index

Figure 5 shows the time evolution of the QBO-index

and SOI for the period 1979-2003. The figures show

years with westerly and easterly phases of the QBO-

index and SOI. iii. The west phases of the QBO-index

were observed during 1980, 1983, 1985, 1986, 1988,

1990, 1993, 1995, 1997, 1999 and 2002 while east

phases of QBO-index occurred in 1979, 1982, 1984,

1987, 1989, 1992, 1994, 1996, 1998, 2000, 2001 and

2003. On the other hand, frequent negative phases have

been dominant by the SOI for the analysis period. Dur-

ing the years: 1982, 1984, 1987, 1992, 1994, 1998 and

2003, the east phases of the QBO-index coincided with

the low (negative) phases of the SOI. All these years

with exceptio n of 1984 and 1994 have been classified as

ENSO years [20]. This observation is consisted with the

notion that ENSO tends to be associated with east phases

of the QBO.

Ta b le 3 shows three homogeneous zones of East Af-

rica that we have identified to have significant correla-

tion with the QBO-index. Generalizations of the sea-

sonal rainfall in these three zones into categories using

contingency tables and the west and east phases of the

QBO are also summarized in Table 3. Based on these

three zones, stratospheric westerly wind phases corre-

sponding to above normal rainfall, were observed 7 out

of 11 cases for zone A, 7 out of 11 for zone B, and, 8 out

of 11 for zone F, giving conditional probabilities of

about 0.6, 0.6 and 0.7 for the associations of above nor-

mal rainfall during the long-rains over eastern African

region and west phase of the QBO. Below normal rain-

fall coinciding with the westerly phase of the QBO were

observed during 1983 for the period of analysis. This

(a)

(b)

Figure 5. Annual mean time series for QBO-index (a) and SOI index (b).

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

1414

Table 3. Contingency table for zonal wind phases at 30-mb

level and the March to May seasonal rainfall anomalies over

three homogeneous zones of East Africa.

Rainfall anomaly

QBO-index phases AN NN BN TOTAL

West phases 7 2 2 11

East phase 3 1 8 12 ZONE A

TOTAL 10 3 10 23

West phases 7 1 3 11

East phase 3 2 7 12

ZONE B

TOTAL 10 3 10 23

West phases 8 1 2 11

East phase 2 2 8 12 ZONE F

TOTAL 10 3 10 23

AN-above Normal rainfall (



0.12

imean



 ;



-The standard deviation;

BN-below Normal rainfall (



0.12

imean



 ;i

-Denotes an observa-

tion; NN-near Normal rainfall (



0.12

mean

X0.12

mean



 .

year has been classified as prolonged ENSO year [20].

Stratospheric east wind phases and below normal

rainfall shows 8 out of 12 for zone A, 7 out 12 for zone

B, and, 8 out of 12 for zone F, giving conditional prob-

abilities of ab out 0.7, 0.6 and 0.7 fo r below normal rain-

fall in the three regions and the east phase of the strato-

spheric zonal wind. The results obtained in this study

support the notion that above/below normal rainfall is

associated with the stratospheric westerly/easterly zonal

wind phases. These good associations between phases of

QBO and seasonal rainfall indicate encouraging poten-

tial for rainfall predictability using th e information abou t

the QBO phases. Significant correlations between rain

fall in Zones A, B and F and, QBO-index persists for

two seasons prior to the long-rains season, but collapses

in Zones D, H and J. In the rest of the analyses, we use

the rainfall index for zone F as an example for testing

the prediction poten tial of rainfall using the QBO-index.

In Figure 6, we present the Ju ly QBO-index prior to the

onset of the long-rains which indicated a high significant

correlation with the MAM seasonal rainfall over the

Lake Victoria region of eastern Africa. It is evident from

this figure that about 70% of the large positive/negative

anomalies in the rainfall were observed during the period

of large positive/negative QBO-index. Some of the ex-

treme rainfall anomalies were however, not related to the

QBO-index. The correlation coefficient between the

QBO-index and sub-region rainfall is found to be 0.59,

which is at 99% significant level based on criterion stu-

dent. This highly significant correlation indicates some

robust associations between the seasonal rainfall and the

QBO. The shown robust relationship between the

long-rains and the QBO-index shows high predictive

potential.

For the purpose of predicting the regional rainfall, the

most useful index appears to be the trend for QBO-index

before the rainfall season. The positive October to De-

cember (OND) minus JJA QBO trend could be a good

indicator for the non-occurrence of drought over eastern

Africa. Similarly, a negative trend could be a good indi-

cator for the non-occurrence of high rainfall over the

region. Figure 7 shows a scatter diagram between March

to May seasonal rainfall anomaly around Lake Victoria

region and the July 30-mb equatorial zonal wind (a) and,

the October to December minus June to August trend in

the QBO-index (b). The correlation coefficient between

the East African rainfall anomaly from zone F and the

difference between OND and JJA QBO-index is 0.63

Figure 6. Time series of rainfall over Lake Victoria region (zone F) and previous year July

QBO-index.

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

141

1415

(a)

(b)

Figure 7. Scatter diagram between MAM seasonal rainfall anoanomaly over Lake Victoria re-

gion and the July 30-mb equatorial zonal wind (a), and the October to December minus June

to August trend in the QBO-index (b).

(explaining about 40% of the rainfall variance) which is

higher than that for JJA seaso n (0.6). As shown on Figure

7, most of the severe drought years are in the lower left

quadrant, and most of the very heavy rainfall years are in

the upper right quadrant of the scatter diagram. The near

absence of points in the lower right corner of this scatter

diagram suggests that a positive QBO-index trend should

be a very useful predictor for non-occurrence of droughts

over equatorial eastern Africa.

5. CONCLUSIONS

After analyzing the results obtained from the rela-

tionships between the different QBO phases in the

stratospheric zonal wind and long-rains season of eastern

Africa we are able to reach the following conclusions:

1) The relationship between the MAM seasonal rain-

fall and El Niño applies to limited number of years, the

ones when El Niño occurs, whereas the relationship be-

tween the QBO-index and seasonal rainfall is applicable

in all years. Therefore monitoring of both the parameters

can provide very useful guidance for the long-range

forecasting of seasonal rainfall in the region.

2) It is shown that, above/below normal rainfall in

eastern Africa is associated with the stratospheric west-

erly/easterly zonal wind phases.

3) It is shown that the phase of the QBO prior to the

MAM seasonal rainfall is a useful predictor index for the

seasonal rainfall. This is particularly the case for the

long-rains for which ENSO prov ides only limited skill in

the predictability of th e rains. This observatio n sh ould be

explored further in the search for more effective seasonal

climate predictors over eastern Africa and the other re-

gions of Africa.

4) In order to improve prediction of the MAM sea-

sonal rainfall in East African countries, which has a

foremost impact on all sectors of the economy in cluding

agriculture, water supply and hydropower generation

across much of the region we recommended using QBO

H. K. Ng’ongolo et al. / Natural Science 2 (2010) 1407-1416

1416

signals as predictors.

6. ACKNOWLEDGEMENTS

The authors wish to thank the Tanzania Meteorological Agency

(TMA), the Ugandan and Kenyan Meteorological Departments for

providing the rainfall data. The work was supported by the Russian

Oriented Program ‘‘Scientific Teaching Personnel in Innovative Rus-

sian’’ (Kadryi).

REFERENCES

[1] Mutai, C.C., Ward, M.N. and Coleman, A.W. (1998)

Towards the prediction of the East Africa short rains

based on sea-surface temperature-atmosphere coupling.

International Journal of Climatology, 18, 975-997.

[2] Nicholson, S.E. and Nyenzi, B.S. (1990) Temporal and

spatial variability of SSTs in the tropical Atlantic and In-

dian Oceans. Meteorology and Atmospheric Physics, 42,

1-17.

[3] Ogallo, L.J., Janowiak, J.E. and Halpert, M.S. (1988)

Teleconnection between seasonal rainfall over East Af-

rica and Global seas surface temperature anomalies.

Journal of the Meteorological Society of Japan, 66, 807-

822.

[4] Beltrando, G. (1990) Space-time variability in April and

October-November over East Africa during the period

1932-1983. International Journal of Climatology, 10,

691-702.

[5] Ogallo, L.J. (1982) Quasi-periodic patterns in the East

Africa rainfall records. Kenya Journal of Science and

Technology, A3, 43-54.

[6] Mukherjee, B.K., Indira, K., Reddy, R.S. and Ramana

Murty, BH.V. (1985) Quasi-biennial oscillation in strato-

spheric zonal winds and Indian summer monsson.

Monthly Weather Review, 113, 1421-1424.

[7] Nicholson, S.E. and Entekhabi, D. (1986) The

quasi-periodic behavior of rainfall variability in Africa

and its relationship to the Southern Oscillation. Archives

for Meteorology, Geophysics, and Bioclimatology-Series

A, 34, 311.

[8] Holton, J.R. and Lindzen, R.S. (1972) An updated theory

for the quasi-biennial cycle of the tropical stratosphere.

Journal of the Atmospheric Sciences, 29, 1076-1080.

[9] Holton, J.R. and Tan, H.C. (1980) The influence of equa-

torial quasi-biennial oscillation on the global circulation

at 50-mb. Journal of the Atmospheric Sciences, 37,

2200-2208.

[10] Plumb, R.A. (1977) The interaction of two internal

waves with the mean flow: Implications for the theory of

the quasi-biennial oscillation. Journal of the Atmospheric

Sciences, 34, 1847- 1858.

[11] Lau, K. and Shoo, P.J. (1988) Annual cycle, Quasi-Biennial

Oscillation and Southern Oscillation in global precipita-

tion. Journal of Geophysical Research, 93, 10975-10988.

[12] July, M.R., Mc Queen, C. and Levey, K. (1994) SOI and

QBO signals in the African region. Theoretical and Ap-

plied Climatology, 50, 103-115.

[13] Mason, S.J. and Tyson, P.D. (1992) The modulation of

sea surface temperature and rainfall associations over

southern Africa with solar activity and Quasi-Biennial

Oscillation. Journal of Geophysical Research, 97, 5847-

5856.

[14] Ogallo, L.J. Okoola, R.E. and Wanjohi, D.N. (1994)

Characteristics of Quasi-Biennial Oscillation over Kenya

and their predictability potential for seasonal rainfall.

Journal of the African Meteorological society, 45, 57-62.

[15] Krishnamurti, T.N. and Ogallo, L.J. (1989) Recent Afri-

can climate (1979-1984). Dept. of Meteorology, Florida

State University, Tallahassee.

[16] Nicholson, S.E. (1998) Historical fluctuations of Lake

Victoria and other Lakes in the Northern Rift valley of

East Africa, In: Lehman, J.T. Ed., Environmental Change

and Response in East African Lakes, Kluwer Academic

Publishers, Dordrecht, 7-35.

[17] Wilks, D.S. (2006) Statistical methods in the atmospheric

sciences, International Geophysics Series, 2nd Edition,

Academic Press, New York.

[18] Kaiser, H.F. (1959) Computer program for varimax rota-

tion in factor analysis. Educational and Psychological

Measurement, 19, 413-420.

[19] Yevjevich, V., (1972) Probability statistics in hydrology.

Water Resources Publication, Fort Collins.

[20] Trenberth, K.E. (1997) The definition of El Niño. Bulle-

tin of the American Meteorological Society, 78, 2771-

2777.