regionalization of precipitation models in eastafrica using meteosat data

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 1011±1027 (1997)

REGIONALIZATION OF PRECIPITATION MODELS IN EAST AFRICAUSING METEOSAT DATA

GUNTER MENZ

Department of Geography, University of Jena, LoÈbdergraben 32, D-07743 Jena, GERMANYemail: [email protected]

Received 12 February 1996Revised 28 October 1996

Accepted 1 November 1996

ABSTRACT

The following article outlines the possibilities, prospects and limitations of estimating precipitation on the basis of hightemporal resolution METEOSAT Real Time Window satellite data for the region of East Africa and the adjacent IndianOcean. We regionalized the study area for both the typical dry season (September 1993) and the rainy season (April 1994)using two distinct precipitation models. These are the statistical METEOSAT Precipitation Index (MPI) model and thephysical Convective Stratiform Technique (CST) model. The variation of input values for the respective models were ®rstexamined using the threshold temperature of cloud surfaces for the MPI model or the dividing straight line for theclassi®cation into either convective or stratiform clouds for the CST model. In addition, we discuss natural conditions oftopography, land-water ratio and the interrelation between highland and lowland areas within the study area as well asclimatic/geographic factors of air-mass transport and cloud systems and we evaluate the effects of these variables.

By using regression analysis, the relationship between estimated MPI model index values and monthly precipitation isanalysed for a maximum of 129 rainfall stations. Subsequent correlation coef®cients vary between r� 0�87 for September1993 and r� 0�49 for April 1994. For the physical CST model, the correlation coef®cients reach values of 0�83 for September1993 and 0�36 for April 1994.

In addition, the study area was segmented on the basis of precipitation/climatological considerations. By using thisprocedure, we developed four subregions:1) WEST, the area around Lake Victoria and the Kenyan highlands;2) CENTER, the lowlands of northern and eastern Kenya;3) EAST, the coastal area along the Indian Ocean; and4) SEA, from the Indian Ocean coastline eastward to longitude 56�E.

Although this division leads to a dramatic improvement of the precipitation-index-relation within the WEST region forApril 1994, it also results in a further reduction of the already weak values, as seen in the extremely dry core CENTER regionduring September 1993. # 1997 by the Royal Meteorological Society. Int. J. Climatol., 17: 1011±1027 (1997)

(No. of Figures: 15. No. of Tables: 2. No. of References: 28)

KEY WORDS: East Africa; precipitation models (statistical and physical); regionalization; METEOSAT; rainfall

1. INTRODUCTION

In addition to basic climatology research, the development of methodologies and models that quantify areal

precipitation above land surfaces is of special importance to several ®elds of research, including applied

hydrology, agricultural geography, vegetational geography and climatological meteorology. Typical analyses that

are performed within a framework of varying spatial and temporal context include: 1, the estimation of water

volumes available for supplying urban areas (i.e. the local scale); 2, the determination of precipitation during one

growing season to evaluate the agricultural potential in semi-arid areas (i.e. the regional scale); or 3, the detection

of current areas of precipitation with the aim of either initializing models that describe numerical weather

forecasting or with respect to assessing the quality of rainfall simulation in global climatic models (GCMs).

Because of spatial and temporal variability in tropical regions, precipitation cannot be determined accurately

by means of interpolating or various methods of weighting station measurements; error rates of up to 50% are

CCC 0899-8418/97/101011-17 $17.50

# 1997 by the Royal Meteorological Society

* Correspondence to: Gunter Menz, Department of Geography, University of Jena, LoÈbdergraben 32, D-07743 Jena, Germany.

typical (Chahine, 1992; Rudolf et al., 1992). These errors are caused by low density and heterogeneous

distribution of rainfall stations as well as by inaccuracies in the measurements themselves (Sharon 1981).

A possible solution is to use remote sensing of precipitation via satellites. In this indirect technology, the

amount of precipitation is derived from radiometric signals that have been detected and quanti®ed by satellite. In

principle, this approach can be applied across the entire electromagnetic spectrum, thus allowing the

methodological approaches to extend from passive visible and infrared wavelengths to passive and active

wavelengths in the microwave spectrum.

Currently, microwave radiation of 17±85 GHz rarely provides useful data in these ®elds of applied research

(Bauer and Schluessel, 1993a). This spectral domain is most often used to record relative precipitation intensities

above ocean surfaces and quantitative estimates of rainfall rates above land surfaces are not made using data

gathered in this region. This methodology is based on the proportionality of re¯ected energy to the size of

raindrops, which in turn is correlated to rainfall intensity. This relationship may be of high variability and

empirical analysis must be included in the conversion process (Bauer and Schluessel, 1993b).

Future improvements in rainfall quanti®cation can be expected from multifrequency and multipolarization

radars as well as double radars because the additional information obtained by sensing the re¯ected signal within

extra frequencies can be used in combination with multiple or variable polarization and doubleshifting (Simpson

and Theon, 1991). However, due to their experimental status, these methods cannot currently be applied to large-

scale land surface data capture.

Other satellite based microwave methodologies reveal additional disadvantages. These include: 1, low spatial

resolution, typically 20±60 km; and 2, low temporal resolution ± a maximum of just two orbital passes per day ±

allowing only a limited representation of tropical rainfall events that are typically temporally and spatially highly

variable.

The indirect relationship between precipitation and the measured radiance value is especially relevant within

the re¯ected solar domain (approx. 0�3±3�0 mm; VIS-channel) and the terrestrically emitted radiation spectrum

(10�5±12�5 mm; IR-channel). Clouds with high re¯ectance in the solar spectrum and low thermal emission have a

high stratum thickness, which in turn determines the probability of precipitation (Arkin, 1979). This rather simple

correlation is used in many statistical models to relate the degree of cloud cover to the probability of precipitation

on the ground, whenever re¯ectance exceeds a certain luminosity value (i.e. albedo) and falls below a ®xed

radiation temperature (Papadakis and Schultz, 1990; Menz and Bachmann, 1992).

The advantages of using these data are the operational availability and global coverage of digital data scanned

by geo-stationary and polar-orbiting satellites. All methods that rely upon data sensed within these spectral ®elds

VIS & IR) reveal speci®c weaknesses, however.

� Results must be calibrated with ground measurements and/or with results of other surface-based methods.

� The interrelationship between cloud geometry and areas of precipitation is often unknown.

� The approach presumes a convective type of cloud. Thus, the application of the models to situations of

stratiform clouds or of frontal systems and clouds with little vertical extension is questionable.

� The precision of the algorithms remain unknown, when applied to study areas in which terrain and high altitude

features in¯uence precipitation systems.

Monthly world-wide and continental maps of areal precipitation are currently derived from satellite data as part

of the Global Precipitation Climatology Project (GPCP) conducted by the World Meteorological Organisation

(WMO) (Rudolph et al., 1992) and of the African Real Time Environment Monitoring System (ARTEMIS) by

the Food and Agriculture Organisation (FAO) (Snijders, 1991). In this study focused on East Africa we compared

the monthly totals of these maps for our test area, covering 36 3 image elements and 7�5�6 7�5� respectively.

This analysis revealed the differences in a global approach to precipitation mapping; results showed differences

between mean precipitation values of 60% and variations among monthly precipitation ®gures up to 200%.

These results provoke several questions.

� Is it possible to `downscale' these maps for use in regional studies?

� Are the assumptions concerning input parameters (e.g. threshold temperatures) for global climatic maps valid,

as well?

1012 G. MENZ

INT. J. CLIMATOL, VOL. 17: 1011±1027 (1997) # 1997 Royal Meteorological Society

� Why are global precipitation models unable to integrate expected wet/dry seasonal precipitation variability?

� What is the in¯uence of relief upon the spatial distribution of precipitation?

Following this introduction, section 2 provides a brief description of the METEOSAT, terrestrial and airborne

data used in this research. Section 3 describes the MPI and CST models implemented here to quantify

numerically regional precipitation. Section 3 also summarizes results produced for the study area as a whole as

well as sub-regions within the study area. Finally, section 4 includes a climatic/geographic interpretation of the

results of this analysis and brie¯y describes the monthly precipitation maps that were produced as output. Of

particular interest are the monthly precipitation maps, which were superimposed on a digital elevation model

(DEM) of Kenya.

2. The Database

2.1. METEOSAT-Real-Time-Window data

METEOSAT produces images unsurpassed for quantitative investigation of dynamic atmospheric processes

such as circulation or convection (Menz, 1993). METEOSAT data have a maximum temporal resolution of

30 min and, in the thermal infrared spectral region (10�5±12�5 mm), a mean spatial resolution in East Africa (at

38�E) of 5�1 km (x-direction) by 6�7 km (y-direction). The high temporal resolution and synoptic perspectives

offered by these geo-stationary satellites enable detailed analyses of the evolutionary stages of convective cloud

systems and their spatial movement in up to 48 single images (or `slots') per day.

For this research, we used three channels of METEOSAT 4 spectral data (VIS-channel: 0�5±0�9 mm, WV-

channel: 2�3±3�5 mm and IR-channel: 10�5±12�5 mm) covering the study area (shown in Figure 1 and for a

geographic orientation see also Figure 2). Because of the special realtime aquisition, this product is called `Real-

Time-Window'. For this study, we obtained METEOSAT data from the European Space Operation Centre

(ESOC) in Darmstadt (Germany) for all of September 1993 and April 1994 ± two typical months in the longterm

rainfall distribution (1961±1990).

Figure 1 shows the study area on 15 April 1990, 10 : 14 GMT (SLOT 19). It consists of 1380 lines6 1180

columns; corresponding to 4623 km north±south and 3009 km west±east. Because of reduced radiant ¯ux

density, the WV- and IR-channels contain only one half the number of lines and columns. The `Real-Time-

Window' is situated symmetrically to the equator and extends from the Eritrean±Ethiopian border (10�N) to the

Tanzanian±Mozambiquan border in the south (10�S) and from central Zaire in the west (25�E) to MaheÂ, the main

island of the Republic of Seychelles, in the east (57�E).

A series of preprocessing steps were required to make METEOSAT infrared data, which consists of 1440

images per month, suitable for this study (Menz and Bachmann, 1992).

� The raw data were calibrated according to the MIEC method (METEOSAT Exploitation Project, 1987);

� the atmospheric disruptions were corrected (Schmetz, 1986);

� the geometric correction of recti®ed base data were evaluated;

� the spatial resolution (i.e. the size of pixels) was calculated;

� a standardized data structure was developed in order to allow ¯exible data use; and

� all point and areal data were compiled in a GIS (Geographic Information System) to allow georeferencing.

2�2. In situ Measurements

Daily precipitation totals for 129 weather stations in September 1993 and 101 stations in April 1994 were

employed as in situ data to calibrate the MPI model and verify the precipitation values calculated according to the

CST model. These data are published by the Kenyan Meteorological Department (KMD), the Drought

Monitoring Centre for Eastern Africa and the Meteorological Service of the Republic of Seychelles. The

distribution of the weather stations covering our study is shown in Figure 2.

This study area has a pattern of alternatingly humid and dry hydrological conditions. An extremely wide

spectrum of precipitation climatological subregions are also found within the region (Hastenrath, 1988). These

range from the sub-humid to humid (precipitation> 2200 mm/a) highlands of West Kenya to the semi-arid to

METEOSAT AND EAST AFRICAN RAINFALL 1013

# 1997 Royal Meteorological Society INT. J. CLIMATOL, VOL. 17: 1011±1027 (1997)

sub-arid (precipitation< 400 mm/a) lowland regions in northern and eastern Kenya and to the more humid

coastal area in the east with an annual precipitation between 500 and 1000 mm/a.

Four study area subregions were identi®ed as being distinctly different in terms of precipitation and

climatology and automatic climate recording stations were installed in each of these subregions (Ojany and

Ogendo, 1988). These stations were located at Maseno (Lake Victoria), Mount Kenya, Mombasa and Desroches,

Seychelles. In situ data were used in error analysis as well as to allow detailed analysis of irregular weather such

as the occurrence of single precipitation events. The sampling rate of the rainfall gauges at these locations was

increased automatically from 10 min intervals to 1 min intervals during a rainfall event (Figure 3). This

modi®cation in the recording program of the data logger allowed differentiated analysis of several precipitation

phases during a single rainfall event. It also allowed a direct comparison of selected events with the development

of convective cloud-cells as represented in concurrent METEOSAT images.

Figure 1. The East Africa study area shown by the VIS-channel of METEOSAT 4-04/15/1990, 10:14 GMT (SLOT 19). White and lighttones represent cloud surfaces, dark and black hues represent land and water surfaces

1014 G. MENZ


2�3. Radiosonde data

To apply the CST model on a regional basis, it was ®rst necessary to calibrate the one-dimensional physical

cloud model according to the method developed by Adler and Mack (1984). This calibration was accomplished

by incorporating radiosonde data collected during the period of investigation. For this purpose, the cloud model

was calculated with different sets of input variables (Table II) and compared with the midday radiosonde ascent

from MaheÂ, Seychelles. Figure 4a shows vertical temperature changes, recorded by the MaheÂ radiosonde on 7

September 1993; 1153 UTC (Universal Time Convention). In Figure 4b both cloud temperature (as calculated by

the cloud model) and measured air temperature have been plotted against each other.

As seen in Figure 4b, the maximum deviation between calculated cloud temperature and measured air

temperature is 4 K. Air temperature remains lower than the computed cloud temperature up to the tropopause

ceiling (at approx. 15±16 km altitude). Above this atmospheric ceiling the relationship becomes inverted,

resulting in a stable strati®cation.

Important information on lower atmosphere strati®cation (a key factor in precipitation) can be derived from the

daily radiosonde ascent (at about 0000 UTC). A shortage of precipitation in the study area accompanies an

extremely high humidity gradient in the mid-troposphere which suppresses convection (Datta, 1981).

Figure 2. Distribution of precipitation stations in the study area overlayed with the 4 subregions: West, Center, Coast and Sea



3. PRECIPITATION MODELS

Enormous progress has been made during the past two decades in development of precipitation models on the

basis of polar-orbiting and geo-stationary satellite data (e.g. NOAA, METEOSAT and GOES). In most of these

models the key quantitative values for precipitation were derived from satellite high temporal resolution data,

mostly acquired within the thermal infrared spectrum (Barret and Kidd, 1989).

Figure 3. Illustration of a single precipitation event on Desroches Island, Republic of Seychelles on 11/27/1993 shown at varying samplingrates: (a) month, (b) day, (c) single event

1016 G. MENZ


Since July 1987 Special Sensor Microwave/Imager (SSM/I) has been operated on a series of American

satellites. The availability of global microwave data from this sensor system has contributed to the development

of more directly applied precipitation models (Bauer and Schluessel, 1993b). These microwave radiometers

provide very precise results above ocean surfaces. To date, however, their application above land surfaces

principally has been problematic, due to variability in emissivity.

Two different model approaches have been further developed with the application of the MPI and the CST

models on a regional scale in East Africa.

3.1. METEOSAT Precipitation Index (MPI) model

The original METEOSAT Precipitation Index (MPI) model was developed by Arkin (1979). By using a series

of ESSA space photographs, this was the ®rst systematic examination of the physical relationship between

cloud optical density and the likelihood of precipitation. For the inner tropics (with their dominant convective

precipitation), this relationship may be applied instead to the correlation of cloud-top temperature and

precipitation. By using a threshold value for surface temperature, a binary decision can be made whether a cloud

will precipitate or not. By summing binary images over a given period (in our case one month) a precipitation

index image can be derived. The decision rules used for the calculation of a precipitation map are derived through

regression analysis utilising the precipitation index values (which have no dimension) and the corresponding

terrestrially measured precipitation values (expressed in millimetres) (Figure 5).

Within the Global Precipitation Climatology Project (GPCP), a temperature of 235 K is used as a globally

valid precipitation threshold value for all seasons (Arkin, 1989). However, for the speci®c month of April 1990,

Menz (1993) has established 232 K as the optimal value in East Africa.

Despite the progress in modelling precipitation with satellite data, the following questions remain.

1. Is there a seasonal variation in threshold precipitation temperatures and if so, what is the magnitude of this

variation?

Figure 4. Vertical pro®le of air temperature of the MaheÂ radiosonde 7 September 1993, 1153 UTC (a) and comparison between thecalculated cloud temperature and the measured air temperature (b)



2. How does the precipitation-index relationship change when subregions are included in a regionalized model?

Figure 6 illustrates the correlation coef®cients (r values) for the entire study area for threshold temperatures

between 200 K and 300 K (nsep93� 129; napr94� 101).

As an initial step in this analysis, measured precipitation (in millimetres) was correlated with the arithmetic

mean index in a 36 3 pixel area on METEOSAT imagery. An examination of September 1993 correlations for

threshold temperatures between 223 and 265 K results in r values greater than 0�8, with the highest value

(r� 0�87) at 252 K (ÿ21�C). Correlation is dramatically reduced for threshold temperatures below 220 K and

below 201 K; in fact, r values tend towards 0. Between 265 and 280 K, r values diminish steadily towards 0�5and remain on this level at temperatures above 280 K.

April 1994 was the month of highest precipitation and results show an overall weak correlation between

measured precipitation and calculated index values. The highest correlation coef®cient (r� 0�49) occurs at a

Figure 5. Flow chart of the METEOSAT Precipitation Index (MPI) model

Figure 6. Variation of r values for threshold temperatures between 200 and 300 K (at intervals of 10 K) for (a) September 1993 and (b) April1994

1018 G. MENZ


threshold temperature of 280 K (� 7�C). The r values decrease steadily from both sides of this value, falling

below 0�40 for threshold temperatures between 235 and 290 K.

These ®ndings lead us to conclude that the choice of a speci®c threshold temperature value is of secondary

importance when using the MPI model. Linear correlation coef®cients range within 0�10 of the highest value:

between 223 and 265 K for September 1993 (an interval of 43 K); and between 235 and 290 K in April 1994 (an

interval of 56 K). Interestingly, the precipitation-index relationship is clearly more intense during the dry season

September 1993 with its low cloud coverage, than during the rainy season in April. There are several possible

explanations for this result: 1, the presence of extremely cold cirrus caps with very little precipitation activity; 2,

lower altitude advective cloud systems origin (e.g. cumulus humilis and cumulus congestus), generated by trade-

winds, especially in coastal areas; and 3, the temporally and seasonally variable energy budget of the cloud

systems of the region.

As a second step in this study we determined the correspondence between in situ precipitation totals expressed

as the sum of recorded precipitation at all of the 129 recording stations and the predicted monthly precipitation

totals calculated according to the MPI model (Figure 7).

As seen in Figure 7, the model with the highest correlation coef®cient (r� 0�87 at 252 K) does not produce the

best estimate for the calculation of total precipitation. Measured precipitation at all 129 stations in September

1993 totals 5192 mm. The MPI model (Tsep� 252 K) results in an estimate of 6000 mm; a difference of 808 mm.

As shown on Figure 7, areal precipitation is calculated most accurately at the point where the straight line

representing measured precipitation crosses the graphed estimated values. For the months of September and

April, the MPI model has been optimized for the best possible ®t to the recorded precipitation in accordance with

the combined method described above. This process of converting index values (which represent the basis of the

respective model) into precipitation totals produces what we term the `index-equivalent rainfall rate'.

As a result of this analysis, a new threshold temperature of 260 K (rather than 252 K) for the MPI model was

determined for the month of September 1993. No change of the threshold temperature value for April 1994 has

been implemented because the modeled and measured values have been identical.

The regionalization of the study area into the WEST, CENTER, EAST and SEA subregions (Figure 2) is based

on the presumption that the energy budget of cloud systems (and their resultant spatial precipitation/climatology

patterns) will be affected by the following factors: 1, topography (lowland, highland and high mountain areas);

2, type of surface (land or water); 3, locational relationships between physiographic elements (land/sea and

mountains/lowland); and, 4, tropical circulation patterns (trade-wind currents, easterlies and west-winds).

These factors are of varying importance within each of the four subregions. Menz (1993) conducted detailed

studies of convective cloud systems above the west Kenyan highlands (about 2000 m NN) as well as in the north

Figure 7. Comparison between measured precipitation and monthly precipitation predicted for September 1993 (MPI model) at a range ofthreshold temperature levels between 200 and 300 K (with 10 K intervals)



Kenyan lowlands. Cumulonimbus clouds induce precipitation at roughly similar surface temperatures in the west

Kenyan highlands whereas in the lowlands of north Kenya they do not. This is due to the long vertical falling

distance of rain above hot lowland areas, which results in precipitation only very rarely reaching the surface, and

also because of the low relative humidity in the layers of atmosphere below the precipitating clouds. In the

coastal areas, it was determined that precipitation originates principally in relatively low altitude cloud

systems ± mostly cumulus congestus. The movement of these clouds is interrupted when moving from smooth

water to relatively rough land surfaces and they are subsequently diverted or divided and precipitate thereafter.

A statistical analysis of the four subregions produces the following results (see Table I).

1. For September 1993, the MPI model precipitation index relationship is either reduced (as shown in the West

subregion) or no correlation exists at all (Center, East and Sea subregions). For the sub-humid, west Kenyan

highlands, however, a correlation of r� 0�619 has been calculated (n� 58). When the data acquired from the

relatively humid Nandi Forest were omitted from the test, the correlation increased to r� 0�73 (n� 56).

For the Center and East subregions, the results indicate a systematic underestimation of precipitation values.

The MPI model cannot accurately account for measured precipitation in dry areas or for a condition of very

low total monthly precipitation that result from a small number of singular precipitation events. The lack of

correlation within the Sea subregion is due to the spatial distribution of the 11 raingauge stations spread over 6

different islands and show how poorly island gauge measurements represent the true rainfall over the open sea.

2. During April 1994 (the month of highest precipitation), a dramatic improvement is seen in the statistical

relationships between three subregions, supporting our hypothesis. Within the West subregion (an area of high

relief) the exclusion of the 4 monitoring stations located in the relatively dry Kavirondo Gulf leads to a

correlation coef®cient of r� 0�838 (n� 42). Similarly, a close relationship can be found for the Center

subregion (r� 0�82; n� 34) and East subregion (r� 0�814; n� 10). The Sea subregion has r� 0 for both

seasons.

3.2. Convective Stratiform Technique (CST) model

Precipitation that originates from tropical cloud systems may be classi®ed into two types: 1, precipitation

directly associated with convective clouds; and 2, precipitation from stratus clouds mostly of upper tropospheric

levels. This perspective is of signi®cant value when analysing precipitation in tropical-convective cloud systems

(Houze and Betts, 1981). Stratiform cloud systems are responsible for up to 40±50% of total precipitation in the

tropics (Houze and Rappaport, 1984). In their analysis of the CST model, Adler and Negri (1988) have

determined it is in agreement with these basic considerations (Figure 8). Adler and Negri (1988) ®rst identi®ed all

local minimum temperatures below 253 K (ÿ20�C) as so-called convective kernels. Second, active convective

cells were differentiated from thin, dry cirrus clouds via the `cirrus-test'. The cirrus-test is an empirically derived

linear equation that takes into account both the minimum temperature and the temperature gradient (the `SLOPE'

parameter) of cloud surfaces. Thus, cirrus clouds are characterized by low minimum temperatures and low

horizontal thermal gradients; convective kernels are de®ned by low surface temperatures and a high temperature

gradient.

Table I. Correlation coef®cients for the West, Center, East and Sea subregions calculatedaccording to the MPI model for September 1993 and April 1994

September 1993 April 1994(sub-)region (threshold temperature� 260 K) (threshold temperature� 280 K)

West r� 0�62 (n� 58) r� 0�67 (n� 46)r� 0�73 (without Nandi-Forest) r� 0�84 (without Kavirondo Gulf)

Center r� 0 (n� 43) r� 0�82 (n� 34)East r� 0 (n� 17) r� 0�81 (n� 10)Sea r� 0 (n� 11) r� 0 (n� 11)East Africa r� 0�87 (n� 129) r� 0�49 (n� 101)

1020 G. MENZ


In this study of East Africa, we also attempted to modify and optimally implement the one-dimensional

physical cloud model developed by Adler and Mack, (1984). This model allows the calculation of a medium

precipitation rate (Rmean) and precipitation area (Ar) for cirrus clouds and convective kernels. Both Rmean and Ar

values can be derived from the temperature minimum (Tmin) with the help of an empirically based linear or

exponential function. Assuming a constant pixel area of 35 km2, the calculated precipitation rate is spatially

distributed in a spiral around the local minimum until the area of precipitation (Ar) is fully covered.

Four variants of the CST model were implemented for the months of September 1993 and April 1994 (Table

II).

The results of CST model (Version 4) will be introduced as maps and analysed in section 4. In a seasonal

comparison, Figure 9 shows linear correlation coef®cients from the monthly totals derived using the CST model

and corresponding weather station values. As in the case using the MPI model, the correlation values for

September 1993 are distinctly higher than the values for April 1994 (r� 0.84 versus 0�36)� Thus, we conclude

that the variation of model parameters has little or no in¯uence on results (Table II).

When implementing the CST model within the four subregions, the following results are seen for September

1993 (see Figure 10):

1, the correlation coef®cient for West subregion is insigni®cantly reduced (from 0�84 to 0�71);

2, the interrelation of the northern and eastern Kenyan lowlands within the Center subregion disappears

completely (r� 0);

3, a signi®cant correlation (r� 0�92) is shown for the East subregion, which includes the coastal zone along the

Indian Ocean.

These results demonstrate that lower trade-wind clouds could be quanti®ed much more precisely using Version

4 of the CST model, along with a threshold temperature value of 285 K and with a slightly modi®ed boundary for

convective and stratiform precipitation areas. This approach resulted, however, in decreased r values in the West

Table II. Parameters employed for implementing the one-dimensional cloud model on a regional basis in East Africa.

Version 1: original Florida parameters from Adler et al. (1988):

Size of pixels 35 km2

Threshold temperature 253 KSurrounding value 11 pixelWindow whole regionCalculation of precipitation rate: Rmean� 74.89 ± 0.266*Tcor

Calculation of the dividing straight line: SLOPE� 0.568* (Tminÿ217.0)Calculation of precipitation area: Ar� exp (15.27ÿ0.0465 Tcor)with: Tcor� atmospheric corrected temperature of the black body (TBB)

Tmin� local temperature minima

Version 2: identical to Version 1 but with revised SLOPE value

Calculation of the dividing straight line: SLOPE� 0.275 (Tminÿ210.0)

Version 3: revised Rmean (all other parameters as Version 1)

Calculation of precipitation rate Rmean� 57.34 ± 0.194 Tcor

(as no suitable radiosonde data was available for April 1994, this equation hasonly been used for September 1993)

Version 4: modi®ed Tcon and Tstr (see also ®gure 8: all other parameters as Version 1):

Tcon� 285 K ± threshold value for convective precipitationTstr� 230 K ± threshold value for stratiform precipitation



and Center subregions where very cold cumulonimbus clusters dominate the cloud patterns, sometimes extending

vertically to tropopause level.

The CST model produces signi®cantly improved `r' values for the West and Center subregions in April 1994,

while r values are reduced for the East subregion (Figure 11). When compared with the result of the CST model

for the entire study area, increased correlation for the West subregion (r� 0�40 versus r� 0�74) proves that

modifying the cloud parameters within the models is applicable on a regional scale.

Figure 9. Seasonal comparison of the CST model results for East Africa dry and rainy seasons

Figure 8. Flow-chart of the Convective Stratiform Techniques (CST) model

1022 G. MENZ


The statistical analysis of the CST model for the study area as a whole, as well as for its subregions,

demonstrates the following.

1. Seasonal comparisons of r values, precipitation differences and magnitude between those calculated using the

CST model and surface measurements, are similar to those which result from the MPI model.

2. There are marked improvements when implementing the CST model on a regional basis (e.g. for the East

subregion in September 1993) as well as instances of reduced r values (as seen in the East subregion during

April 1994).

3. Differences in topography and/or the dominance of different cloud types (with their speci®c heat balance), are

the most relevant factors which in¯uence the correlation on a small regional scale, notwithstanding the

relatively coarse parameterization of cloud surfaces and their surface temperatures. A distinct improvement is

seen in the estimation of precipitation when SSM/I or ERS 1 & 2 microwave data are integrated into the

analysis to provide information on the integral vapour column.

Figure 11 Implementation of the CST model in the West, Center and East subregions for April 1994.

Figure 10. Subregional implementation of the CST model for September, 1993



4. RESULTS ± MONTHLY MAPS OF KENYA

In addition to empirical and statistical methods, compiling information on hydrology, agriculture, vegetation and

climatology should allow a more accurate and useful mapping of the spatial±temporal distribution of

precipitation. In addition, integrating 1 km digital elevation model (DEM) of Kenya allows an effective three-

dimensional interpretation of the spatial precipitation pattern.

The months of September 1993 (dry season) and April 1994 (rainy season) are quite different hydrologically in

East Africa. Plates 1±4 show the results of the MPI and CST models, respectively, for these periods.

Plate 1 shows the distribution of precipitation in Kenya for September 1993 as computed by the MPI model.

September is in the dry-season and represents the typical precipitation spectrum with a maximum of 82 mm in

West Kenya and completely dry areas (0 to 5 mm of rainfall) in the north and east of the country. Most of Kenya

is virtually without rainfall during this period. Distinctive features, which are shown on Plate 1, are three linear

`precipitation corridors' within Kenya, each approximately 50±150 km in width. Two of these corridors run

parallel in an east/west direction in northern Kenya and have a maximum precipitation of up to 20 mm. The third

corridor runs diagonally from south-west to north-east. The northern precipitation corridors are supplied with

moisture from the Indian Ocean. This is shown by the steady reduction in precipitation quantity and spatial extent

towards the west. It can be assumed that the third corridor is supplied by the constantly humid areas of central-

Africa.

Daily rainfall totals recorded by the Kenya Meteorological Department (KMD) and the analysis of monthly

METEOSAT ®lmloops reveal that the extremely low precipitation in northern and eastern Kenya results from 2±

5 singular precipitation events. Furthermore, these data show that sporadic, interregional cloud clusters pass

through the investigated area within 1 or 2 days, resulting in subsequent precipitation patterns if suf®cient relative

humidity exists.

Sub-humid western Kenya shows the highest monthly precipitation totals. The highlands between Mount

Elgon in the north and the Tanzanian border in the south comprise the agricultural `High Potential Area' of

Kenya. Precipitation during the study period reaches up to 82 mm in this region. The lower regions of the

Kavirondo rift achieve precipitation amounts equal to the ¯anking rims and the ensuing highland areas (see

Plate 1), although this contradicts both personal experience in the area and the rainfall measurements. This

variant is a artifact of the MPI model and is produced by the stringent methodology concerning cloud surface

temperatures. This leads to a general overestimation of monthly precipitation totals. In his study in the Cauca

rift in Latin America, Weischet (1965) documented the reduction in total precipitation due to evaporation of

raindrops. This phenomenon is known for other tropical mountain regions and also applies directly to the Rift

Valley in Kenya.

In addition, the three-dimensional analysis reveals an asymmetrical distribution of precipitation in a

windward±leeward effect at Mount Kenya (5199 m NN). A relative maximum appears on the west/north-west

side and a relative minimum on the east/south-east side of the mountain. It is not possible to draw conclusions

about the vertical distribution of precipitation, however. The continental airmasses originate in the constantly

humid central-African regions and advance to supply the highlands with precipitation during the so-called

`Continental Rains' (Berger, 1989).

The monthly map, which was computed for September 1993 using the CST model, is shown in Plate 2.

Generally, a spatial distribution pattern similar to the MPI model map is seen. This is emphasized by the

signi®cant areal correlation (r� 0.97) between the MPI and the CST maps. However, the variation of monthly

precipitation totals (217 mm) is signi®cantly higher. While northern and eastern Kenya show equally low results

on a large regional scale, single locations in the west Kenyan highlands receive rainfalls of up to 217 mm. The

CST model allows a greater differentiation of cloud systems compared to the MPI model; resulting in a more

heterogeneous cloud pattern. For example, the CST model renders the three precipitation corridors discussed

previously as several, distinctly differentiated precipitation areas. The windward±leeward effects at Mt Kenya

and Mt Elgon are easily discernible in Plate 2.

The CST model greatly improves precipitation modelling for the rift system with its high relief energy. The

Rift Valley has humid rims and a dry, central rift base. The distribution of precipitation in the valley re¯ects

actual circumstances on a much greater scale. It is evident that the ability of the CST model to separate

1024 G. MENZ


precipitation into convective and stratiform types, together with a relatively high cloud coverage, yields a better

approximation of the existing systems.

Plate 3 shows the results of the MPI model for the month of April 1994. A relatively wide semicircular, dry

zone with precipitation totals between 6 and 30 mm can be seen extending from North and East Kenya into

Tanzania in the south. In the east, towards Somalia, as well as near the coast, total rainfall rises to a maximum of

71±90 mm. Within the extensively homogenous precipitation areas, a few isolated areas appear to receive either

lower or higher monthly totals. The highlands of Marsabit are an example of this. Wide highland areas and all of

the extended coastal rim of Lake Victoria are characterized by values between 80 and 133 mm. As expected,

orographic features such as Mount Kenya reveal on their windward (south-east) side areas of higher precipitation.

This zone stretches from the foot of the mountain to the Nyandarua Range in the west. The region north-west of

Mount Kenya between the high Laikipia plateau (mean altitude of 1900 m) and the Samburu district (about

700 m) to the north is an area with a high precipitation gradient between the more humid highlands and the drier

lowlands (Plate 3).

Generally, the CST model produces very similar results for the month of April 1994 (Plate 4) compared to the

MPI model. The spatial distribution patterns in the highlands and lowlands largely correspond with those

produced by the MPI model. The semicircular zone of low precipitation (0±60 mm) roughly coincides in both

models. The same occurs for the climatic structures in the highland regions. However, the high mountain areas of

Mt Kenya and Nyandarua are quite different. Plate 4 shows a relative minimum of precipitation at the side of the

mountains which face the southeast trade winds. In addition, precipitation in April 1994 appears to be much less

pronounced in the dry Rift Valley system as compared with September 1993.

For the month of April (the precipitation peak) the CST model produces large errors in estimated precipitation;

both underestimations as well as overestimations. During this period, the study area has a relatively high cloud

coverage. Because of the dominance of extensively formed cloud surfaces with little thermal differences (i.e.

stratiform precipitation areas), the CST model underestimates rainfall in high mountain areas and overestimates

rainfall in the deeply subsided rift systems. We believe that this is due to the failure of the CST model to account

for the effect of evaporation of raindrops with the relatively long vertical falling distance within the rift valley

areas.

5. SUMMARY AND CONCLUSIONS

The survey of East Africa produces the following results.

1. While it may serve as an indicator of precipitation, the threshold temperature of cloud surfaces shows a

relatively large seasonal variation of up to 50 K. With regard to the MPI model, the linear correlation

coef®cients for the temperature interval of 223 to 285 K vary only slightly with r values of greater than 0�8(rMAX(SEPT.)� 0�87 at 252 K) for September 1993. April 1994 was the month of highest precipitation and the

results show an overall weak correlation (rMAX(APRIL)� 0�49 at 280 K). Expected subregional differences

between the four regions are clearly shown.

2. The CST model allows for (i) a separation of convective and stratiform rainfall clouds; and (ii) a classi®cation

of their speci®c precipitation rates and areas. It also includes a one-dimensional, physical cloud-model and

produces direct outputs of precipitation totals per spatial and temporal unit. As in the case using the MPI

model, the correlation values for September 1993 are distinctly higher than the values for April 1994

(rSEPT� 0�84 versus rAPRIL� 0�36). The CST model was empirically adapted to our study area with four

different sets of parameters. Vertical atmospheric pro®les acquired by radiosondes from MaheÂ, Republic of

Seychelles and Nairobi, Kenya, were used to test the precision between model results and real conditions. The

CST model yielded the following results: (i) an underestimation of areal precipitation during both periods of

measurement; (ii) similar correlation coef®cients as compared to the MPI model; and thus (iii) a signi®cant

areal correlation with the precipitation data produced by the MPI model.

3. When compared to interpolation methods that were surface point measurements (as exempli®ed by the

Spheremap model (Legates, 1987) of the Global Precipitation Centre (GPC)), or by numerous Kriging

processes (Barthel, 1994), model calculations, derived using high resolution satellite data, produce a more



accurate prediction of areal precipitation rates. This approach does not assume a direct relationship between

single image elements and, thus an assumption of spatial discontinuity. This characteristic can be of great

advantage considering the enormous regional variability of tropical precipitation patterns.

4. Precipitation maps were coregistered with digital elevation model (DEM) data and compiled using a

geographic information system (GIS). This facilitates the direct spatial correlation of precipitation patterns to

topographical features and, for the ®rst time, allows the identi®cation and analysis the rainfall patterns in three

dimensions based directly on terrain characteristics. This feature is shown at the south-east side of Mt Kenya

during the month of April, when south-eastern trade-winds interact with topography to produce a signi®cant

rise in precipitation. In the future it will be possible to perform detailed analysis and model trials using

regional scale climatological and hydrological parameters. Examples of this type of analysis include the

calculation of rates of net primary production in savanna areas or an estimate of erosive and denudative

processes in different sized water catchment areas.

ACKNOWLEDGEMENTS

This research project was conducted jointly with Prof. Dr. M. Winiger and Alexander Zock of the Geography

Department of the University of Bonn and ®nancially supported by the German Research Agency DFG. I thank

Prof. Dr. H.-D. Schilling (Meteorology Department of Bonn University) for the critical discussion of both model

approaches; my colleagues Dr. J. Bendix and U. Ritzmann for their openness with regard to this project and their

help in optimizing respective computer programmes; J. v. Arnim and Th. Barthel for their participation in

organizing and maintaining the climate station network in East Africa and Joe Scepan (University of California at

Santa Barbara) for revising and translating the current paper. Last, but not least, we would like to thank our

Kenyan colleague, Mr. Joseph Ndungu of the Laikipia Research Programme (LRP) in Nanyuki, for the

maintenance of the climate stations as well as the Meteorological Department of the Republic of Kenya and the

Republic of the Seychelles for making available comprehensive climatic data.

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