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IGAD Climate Predictions and Applications Centre Technical Report
20/01/06/2009
ASSESSMENT OF THE SKILL/ACCURACY OF THE REGIONAL SPECTRAL MODEL (RSM) OVER THE GHA
REGION
Verification Analysis for Seasonal Temperature and Rainfall Prediction in the GHA Region Using RSM
Franklin J . Opijah, Joseph M. Mutemi
CONTENTS
1. Introduction ............................................................................................................ 1
a. The Main Precipitating Weather Systems over the GHA Region ..................... 1 b. Forcing of Organized Convection ...................................................................... 1 c. The Regional Spectral Model .......................... Error! Bookmark not defined.
2. Verification Methodology Applied ........................................................................ 2 a. Simple Correlation and Error Analysis .............................................................. 5 b. Contingency Tables and Skill Scores ................................................................. 5
3. RSM Performance over Kenya .............................................................................. 7 a. RSM Temperature Verification over Kenya for OND Season .......................... 7 b. RSM Rainfall Verification over Kenya for OND Season .................................. 9 c. RSM Temperature Verification over Kenya for MAM Season ....................... 11 d. RSM Rainfall Verification over Kenya for MAM Season .............................. 13 e. RSM Temperature Verification over Kenya for JJA Season ........................... 15 f. RSM Rainfall Verification over Kenya for the JJA Season ............................. 17
4. RSM Performance over Tanzania ........................................................................ 19 a. RSM Rainfall Verification over Tanzania for OND Season ............................ 19 b. RSM Rainfall Verification over Tanzania for MAM Season .......................... 22 c. RSM Rainfall Verification over Tanzania for JJA Season .............................. 24
5. RSM Performance over Burundi, Eritrea and Djibouti ........................................ 27 a. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for OND Season 27 b. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for MAM Season ...................................................................................................................... 29 c. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for JJA Season 31
6. RSM Performance over Ethiopia ......................................................................... 33 a. RSM Rainfall Verification over Ethiopia for OND Season ............................. 33 b. RSM Rainfall Verification over Ethiopia for MAM Season ........................... 35 c. RSM Rainfall Verification over Ethiopia for JJA Season ............................... 37
7. RSM Performance over Sudan ............................................................................. 39 a. RSM Rainfall Verification over Sudan for OND Season ................................ 39 b. RSM Rainfall Verification over Sudan for MAM Season ............................... 41 c. RSM Rainfall Verification over Sudan for JJA Season ................................... 44 d. RSM Rainfall Verification over Sudan for JJA Season ................................... 47
8. Dynamical Predictability over the Greater Horn of Africa .................................. 50 a. Degree of Predictability over Greater Horn of Africa ..................................... 50 b. The Challenge of Limitation to Predictability ................................................. 51
References .................................................................................................................... 52
ii
List of Figures
Figure 1: Forecasted seasonal rainfall (left) and temperature (right) over the GHA region using RSM and ECHAM for JJA, 2008 ......................................................................... 3
Figure 2: Difference fields (anomalies) for OND, 2008. .................................................... 3 Figure 3: Forecast Difference fields (anomalies) for MAM, 2009. .................................... 4 Figure 4: Inter-annual temperature variability over Wajir, Kenya during the OND season.
........................................................................................................................................ 8 Figure 5: Inter-annual temperature variability over Mombasa, Kenya during the OND
season. ........................................................................................................................... 9 Figure 6: Inter-annual rainfall variability over Nyeri, Kenya during the OND season. .. 10 Figure 7: Inter-annual rainfall variability over Malindi, Kenya during the OND season.
...................................................................................................................................... 11 Figure 8: Inter-annual temperature variability over Kitale, Kenya during the MAM
season. ......................................................................................................................... 12 Figure 9: Inter-annual temperature variability over Kisumu, Kenya during the MAM
season. ......................................................................................................................... 13 Figure 10: Inter-annual rainfall variability over Kisii, Kenya during the MAM season. 14 Figure 11: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall
season. ......................................................................................................................... 15 Figure 12: Inter-annual temperature variability over Kitale, Kenya during the JJA season.
...................................................................................................................................... 16 Figure 13: Inter-annual temperature variability over Kisumu, Kenya during the JJA
season. ......................................................................................................................... 17 Figure 14: Inter-annual temperature variability over Dagoretti, Kenya during the JJA
season. ......................................................................................................................... 17 Figure 15: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall
season. ......................................................................................................................... 18 Figure 16: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall
season. ......................................................................................................................... 19 Figure 17: Inter-annual rainfall variability over Dodoma, Tanzania during the OND
rainfall season. ............................................................................................................ 20 Figure 18: Inter-annual rainfall variability over Iringa, Tanzania during the OND rainfall
season. ......................................................................................................................... 21 Figure 19: Inter-annual rainfall variability over Dar es Salaam International Airport,
Tanzania during the OND rainfall season. .................................................................. 21 Figure 20: Inter-annual rainfall variability over Mtwara, Tanzania during the OND
rainfall season. ............................................................................................................ 22 Figure 21: Inter-annual rainfall variability over Dodoma, Tanzania during the MAM
rainfall season. ............................................................................................................ 23 Figure 22: Inter-annual rainfall variability over Mwanza, Tanzania during the MAM
rainfall season. ............................................................................................................ 24 Figure 23: Inter-annual rainfall variability over Mtwara, Tanzania during the JJA rainfall
season. ......................................................................................................................... 25 Figure 24: Inter-annual rainfall variability over Mbeya, Tanzania during the JJA rainfall
season. ......................................................................................................................... 26 Figure 25: Inter-annual rainfall variability over Kigoma, Tanzania during the JJA rainfall
season. ......................................................................................................................... 26 Figure 26: Inter-annual rainfall variability over Bukoba, Tanzania during the JJA rainfall
season. ......................................................................................................................... 27 Figure 27: Inter-annual rainfall variability over Bujumbura, Burundi during the OND
rainfall season. ............................................................................................................ 28
iii
Figure 28: Inter-annual rainfall variability over Djibouti, Djibouti during the OND rainfall season. ............................................................................................................ 28
Figure 29: Inter-annual rainfall variability over Asmara, Eritrea during the OND rainfall season. ......................................................................................................................... 29
Figure 30: Inter-annual rainfall variability over Bujumbura, Burundi during the MAM rainfall season. ............................................................................................................ 30
Figure 31: Inter-annual rainfall variability over Djibouti, Djibouti during the MAM rainfall season. ............................................................................................................ 30
Figure 32: Inter-annual rainfall variability over Asmara, Eritrea during the MAM rainfall season. ......................................................................................................................... 31
Figure 33: Inter-annual rainfall variability over Bujumbura, Burundi during the JJA rainfall season. ............................................................................................................ 32
Figure 34: Inter-annual rainfall variability over Djibouti, Djibouti during the JJA rainfall season. ......................................................................................................................... 32
Figure 35: Inter-annual rainfall variability over Asmara, Eritrea during the JJA rainfall season. ......................................................................................................................... 33
Figure 36: Inter-annual rainfall variability over Nekemte, Ethiopia during the OND rainfall season. ............................................................................................................ 34
Figure 37: Inter-annual rainfall variability over Combolcha, Ethiopia during the OND rainfall season. ............................................................................................................ 34
Figure 38: Inter-annual rainfall variability over Debrezeit, Ethiopia during the MAM rainfall season. ............................................................................................................ 36
Figure 39: Inter-annual rainfall variability over Gonder, Ethiopia during the MAM rainfall season. ............................................................................................................ 36
Figure 40: Inter-annual rainfall variability over Baherdar, Ethiopia during the JJA rainfall season. ............................................................................................................ 38
Figure 41: Inter-annual rainfall variability over Jima, Ethiopia during the JJA rainfall season. ......................................................................................................................... 38
Figure 42: Inter-annual rainfall variability over Port Sudan, Sudan during the OND rainfall season. ............................................................................................................ 40
Figure 43: Inter-annual rainfall variability over Kassala, Sudan during the OND rainfall season. ......................................................................................................................... 41
Figure 44: Inter-annual rainfall variability over Wau, Sudan during the OND rainfall season. ......................................................................................................................... 41
Figure 45: Inter-annual rainfall variability over Kadugli, Sudan during the OND rainfall season. ......................................................................................................................... 41
Figure 46: Inter-annual rainfall variability over Juba, Sudan during the MAM rainfall season. ......................................................................................................................... 43
Figure 47: Inter-annual rainfall variability over Kadugli, Sudan during the MAM rainfall season. ......................................................................................................................... 43
Figure 48: Inter-annual rainfall variability over Nyala, Sudan during the MAM rainfall season. ......................................................................................................................... 44
Figure 49: Inter-annual rainfall variability over Sennar, Sudan during the MAM rainfall season. ......................................................................................................................... 44
Figure 50: Inter-annual rainfall variability over Wad Halfa, Sudan during the JJA rainfall season. ......................................................................................................................... 46
Figure 51: Inter-annual rainfall variability over Malakal, Sudan during the JJA rainfall season. ......................................................................................................................... 46
Figure 52: Inter-annual rainfall variability over Wau, Sudan during the JJA rainfall season. ......................................................................................................................... 47
Figure 53: Inter-annual rainfall variability over Kassala, Sudan during the JJAS rainfall season. ......................................................................................................................... 48
iv
Figure 54: Inter-annual rainfall variability over El Geneina, Sudan during the JJAS rainfall season. ............................................................................................................ 49
Figure 55: Inter-annual rainfall variability over Wad Medani, Sudan during the JJAS rainfall season. ............................................................................................................ 49
Figure 56: Inter-annual rainfall variability over Wau, Sudan during the JJAS rainfall season. ......................................................................................................................... 50
Figure 57: Inter-annual rainfall variability over Abu Naama, Sudan during the JJAS rainfall season. ............................................................................................................ 50
1
1. Introduction
As a whole, Eastern Africa, including the Greater Horn of Africa (GHA) that
encompasses the Intergovernmental authority on Development (IGAD) countries
together with East Africa region, the Western Indian Ocean, and North-eastern parts
of the Southern Africa Development Corporation (SADC), has varied weather and
climate controls.
The objective of this research report was to assess the performance of the
Regional Spectral Model (RSM) in downscaling Echam model outputs over the GHA
region, with respect to rainfall and temperature prediction.
a. The Main Precipitating Weather Systems over the GHA Region
Thunderstorms within the ITCZ, more commonly as ordinary isolated thunderstorms,
but occasionally as super cell storm-type, are the main precipitating systems over the
Greater Horn of Africa during the main rainfall seasons. Multi-cell storms occur
mainly during non-seasonal rainfall events. Although tropical cyclones mostly affect
countries in south-eastern Africa during the southern summer, with devastating
consequences, these systems also affect parts of the Greater Horn of Africa region by
modifying the pressure and wind patterns; they are associated with depressed
precipitation over certain areas, but heavy continuous rainfall over some other parts.
Squall-systems have also been reported in portions neighbouring Lake Victoria,
although these are not very well documented. The Indian Ocean Monsoons are not
associated with heavy rainfall activity over the Equatorial East Africa, but Northeast
monsoons may have a contribution to the storms and torrential rainfall events that
affect parts of SADC during December. The Congo Air Mass is a major player in the
observed rainfall over the region, especially during the Northern Hemisphere summer
season. Along the Congo air boundary, rainfall is experienced over the wind-ward
side and very little on the leeward side of the Ethiopian highlands.
b. Forcing of Organized Convection
Convection over the Greater Horn of Africa region peaks at about midday, resulting
into afternoon convective rainfall over most places. The interaction between synoptic
scale trade winds and mesoscale circulations in form of sea-breeze circulation and
topographically induced local circulations are an important player in determining the
nature, intensity and duration of the observed diurnal cycle of precipitation.
2
Organized convective cloud systems stem from:
o Scale-interaction involving mainly the north-east and south-east trade winds and
Congo air mass, with thermally-generated mesoscale circulations induced by
land-water contrasts, particularly the Lake Victoria trough, and orographically-
induced convergence over parts of the GHA.
o Weather systems associated with low-level jet streams, particularly the wind
channelling-induced Turkana jet stream and its impacts over the Marsabit/
Turkana region of Kenya, as well as the EALLJ associated with subsident and
diffluent flow and light rainfall over the Eastern Africa region. The TEJ flows
over Northern parts of the GHA and may exert some control on surface weather
over countries like Ethiopia and Sudan.
o Equator-ward displacement of extra-tropical weather systems in certain portions
of the GHA region during winter, particularly those associated with westerlies,
blocking highs and migratory systems of the mid-latitudes
o Squall-type cloud systems over the Lake Victoria region driven by sea-breeze
circulations.
2. Seasonal Rainfall and Temperature Prediction Using RSM
The RSM
Figure 1 shows the predicted rainfall and temperature distributions over the
GHA region during the June-August season of 2008, obtained using the Regional
Spectral Model, and the original output from the global model, Echam. By and large,
the model predicted little rainfall over the region. Besides, the ITCZ that is by this
time displaced northward, the rainfall distribution was influenced by the Congo Air
mass and mesoscale features. The model predicted higher amounts of rainfall in
portions neighbouring the Lake Victoria Basin, the Ethiopian highlands and the
coastal strip. The distribution of predicted temperature was governed by the
forecasted cloud cover over the region.
3
Figure 1: Forecasted seasonal rainfall (left) and temperature (right) over the GHA region using RSM and ECHAM for JJA, 2008
Figure 2: Difference fields (anomalies) of forecasted seasonal rainfall (left) and temperature (right) over the GHA region using ECHAM and RSM for OND, 2008. The red end of the spectrum shows values above the climatological average; the blue end shows values below the average.
Figure 2 shows the predicted amounts of rainfall and temperature over the
GHA averaged for the OND, 2008, season as difference fields, where the
climatological means are subtracted from the simulated seasonal values. The diagram
shows above-normal rainfall conditions over most of the region, and below normal
temperature conditions. Evidently, simulated temperature is inversely related to cloud
cover, and hence rainfall patterns.
Figure 3 shows the forecasted total rainfall and averaged temperature
difference fields over the GHA region, for MAM, 2009. Outputs from the global
model are also shown. Both models predicted normal, tending to below normal
rainfall conditions over most of the region.
But how well does the model simulate rainfall and temperature anomalies over
the region? This question is best addressed by comparing the simulated fields with the
observed values, which must be investigated using some established criteria.
4
Figure 3: Forecast Difference fields (anomalies) of forecasted seasonal rainfall (top panel, where dark green shading shows predicted heavier rainfall; dark brown shows more arid conditions), and temperature (bottom panel, where the red end of the spectrum shows temperature values above the climatological average; the blue end shows values below the average) over the GHA region using ECHAM (Right) and RSM (Left) for MAM, 2009.
3. Verification Methodology Applied
Model verification entails the use of phrases like “correctness”, “accuracy” and
“skill”. Whereas a correct forecast is one whose outcome matches that of the forecast,
the accuracy of a forecast relates to how close the outcome of a forecast is to the
forecast itself, while the skill tells us how the forecast of one prediction method
compares to that of another method. Yet we must understand that the quality of a set
of forecasts cannot be summarised in a single number. The choice of a verification
scheme should therefore be pegged on the questions the scheme is set to address.
The overall question verification procedures attempt to address then is, is the
forecast good? The goodness of a forecast depends on the interest it generates, so a
more appropriate question would be, is the forecast good enough? The answer to this
question then relates to the application of the forecast—what the forecast is to be used
for.
Verification analysis is done using statistical techniques including correlation
and error analysis, contingency tables and graphical analysis, as described below.
5
a. Simple Correlation and Error Analyses
The simple correlation coefficient between observed and forecast fields is
computed using the following formula, where O and F are observed and forecast
fields.
( ) ( ) ⎥⎦
⎤⎢⎣
⎡−−
−−=
∑∑
∑
=
=
N
i
2i
N
1i
2i
N
1iii
FFOO
FFOOr
))((
The statistical significance of the correlation coefficient is normally assessed using the
standard t-test.
The absolute mean error (MAE) is defined as follows, where Oi and Fi are
observations and forecasts respectively, and N denotes the total number of forecasts.
∑=
−=N
1iii FO
N1MAE
The root mean square error (RMSE) systematically represents the error that is
similar to all points in the data. Accurate models have low systematic RMSE. The
advantage of this measure of skill is that it retains the unit of the forecast variable. It is
given by the following formula, where O and F are observed and forecast fields:
( )∑=
−=N
1i
2ii FO
N1RMSE
RMSE is not a perfect tool and weaknesses include favouring model forecasts that
underestimate variability (Brier and Allan 1951).
b. Contingency Tables and Skill Scores
Contingency tables are useful in assessing the goodness of forecasts that give us four
possibilities: a “hit” (forecast above normal matches with the observed above normal),
a “false alarm” (forecast above normal (YES), but observed below normal (NO)), a
“miss” (forecast below normal (NO), but observed above normal (YES)), and a
“correct rejection” (forecast below normal (NO) matches with the observed below
normal (NO)). This scenario may be represented in tabular form as shown below:
6
Forecast ‘Yes’ Forecast ‘No’
Observed ‘Yes’ a b
Observed ‘No’ c d
The Hit Rate (HR) is a ratio of the number of hits to the number of events. But
the hit rate tells us at least as much about how often events are forecast as it does
about how good the forecasts are. It is given by:
baaHR+
=
The Proportion Correct (PC) is the ratio of the number of correct forecasts to
the number of forecasts. But the proportion correct (or hit score) tells us at least as
much about how often events occur as it does about how good the forecasts are. It is
given by:
dcbadaPC+++
+=
The Frequency Bias Index (FBI) measures the event frequency with no regard
for the forecast accuracy. Its value is 100% for a perfect forecast, and it is larger
(smaller) than 100% if the system is over forecasting (under forecasting). It is given
by:
cabaFBI
+
+=
The Equitable Threat Score (ETS) is a modified version of the threat score
rendered equitable by taking away the random forecast. Therefore, a chance forecast
will score 0, as will a constant forecast. A perfect forecast will have an ETS equal to
100%. It is given by:
)()(aRcba
aRaETS−++
−= , where the random forecast is
dcbacabaaR
+++
++=
))(()(
The True Skill Statistic (TSS) or Hansen-Kuipers discriminant, like in the
ETS, give the random and constant forecasts a zero score, while a higher score is
obtained if a rare event is forecast correctly. The TSS can also be written as the
7
probability of detection [a/(a + c)] minus the probability of false detection [b/(b +d)].
It is given by:
))(( dbcabcadTSS++
−=
The Heidke skill score (HSS) is a popular verification statistic, often
associated with chance as the standard of comparison. It is the ratio of the difference
between the number of correct forecasts and the number expected to be correct based
on chance, persistence, climatology, etc.) to the difference between the total number of
forecasts and the number expected to be correct based on chance. The HSS is given
by:
)()()(
aR-dcbaaR-d)(a= HSS
++++ where
)())(())(()(
dcbadbdc+caba= aR
+++++++
The Two-Alternative Forced Choice Test (2AFC) test is a test to correctly
identify which of two options has a characteristic of interest. A score of 100%
indicates a perfect set of answers. A score of 0% indicates a perfectly bad set of
answers. A score of 50% would be expected by guessing the answer every time. A
score of more than 50% suggests we have some skill in answering the questions. The
2AFC is given by:
( )))(( dcbabdacadP 2
1
AFC2 ++
+−=
4. RSM Performance over Kenya
a. RSM Temperature Verification over Kenya for OND Season
Table 1-1a: Correlation Coefficients (r) , Variance (r2) and RMSE for RSM OND Rainfall Output over Kenya
Station r r2 RMSE MAE Dagoretti -0.1 0.02 3.5 3.4 Garissa -0.2 0.03 8.2 8.1 JKIA -0.2 0.02 1.9 2.5 Kisumu 0.0 0.00 1.2 1.0 Kitale -0.2 0.03 3.6 3.5 Makindu 0.0 0.00 2.6 2.4 Malindi -0.2 0.04 4.6 4.6 Mombasa 0.0 0.00 5.6 5.6 Nakuru -0.2 0.03 3.4 3.2
8
Station r r2 RMSE MAE Narok -0.2 0.06 3.0 2.8 Voi -0.2 0.04 5.6 5.5 Wajir 0.2 0.03 8.3 7.5 Average -0.11 0.03 4.29 4.18
Table 1-1b: Verification Skill Scores for RSM OND Temperature Output over Kenya
HR PC FBI ETS TSS P2AFC HSS
Dagoretti 36 40 100 -9 -21 35 -21 Garissa 33 48 150 -1 -3 45 -2 JKIA 41 43 121 -6 -13 42 -12 Kisumu 46 43 81 -6 -13 42 -12 Kitale 45 47 73 -3 -7 45 -7 Makindu 38 41 123 -7 -16 36 -16 Malindi 40 30 83 -17 -42 18 -40 Mombasa 73 67 88 20 34 99 33 Nakuru 50 43 82 -6 -13 42 -12 Narok 38 40 87 -9 -20 35 -20 Voi 44 40 100 -9 -21 35 -21 Wajir 57 66 117 18 31 88 31 Average 45.08 45.67 100.42 -2.92 -8.67 46.83 -8.25
OND Temperature Verification for Wajirr=0.2, r2=0.03, RMSE=8.3
-3
-2
-1
0
1
2
3
197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999
Year
Tem
pera
ture
Ano
mal
y
OND_RSMOND_OBS
Figure 4: Inter-annual temperature variability over Wajir, Kenya during the OND season. RSM-Simulated values are also shown.
9
OND Temperature Verification for Mombasar=-0.03, r2=0.001, RMSE=5.6
-3
-2
-1
0
1
2
3
197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999
Year
Tem
pera
ture
Ano
mal
y
OND_RSMOND_OBS
Figure 5: Inter-annual temperature variability over Mombasa, Kenya during the OND season. RSM-Simulated values are also shown.
b. RSM Rainfall Verification over Kenya for OND Season
Table 1-2a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Kenya
Station r r2 Dagoretti 0.4 0.17 JKIA 0.3 0.07 Kakamega 0.2 0.04 Kisii -0.1 0.01 Kisumu 0.2 0.04 Lamu 0.2 0.03 Lodwar -0.1 0.01 Makindu 0.4 0.16 Malindi 0.4 0.18 Mandera 0.4 0.17 Mombasa 0.1 0.01 Moyale 0.1 0.02 Nakuru 0.3 0.12 Narok 0.4 0.16 Nyeri 0.3 0.09 Voi 0.4 0.19 Wilson 0.5 0.22 Wajir 0.3 0.09 Average 0.26 0.1
Table 1-2b: Verification Skill Scores for RSM Rainfall Output over Kenya
10
Station HR PC FBI ETS TSS P2AFC HSS
Dagoretti 54 60 100 10 19 77 19 JKIA 46 53 100 3 5 60 5 Kakamega 33 57 133 3 6 52 6 Kisii 43 53 117 3 6 60 5 Kisumu 31 53 144 1 2 48 1 Lamu 50 60 83 8 14 65 14 Lodwar 0 63 175 -9 -27 0 -20 Makindu 38 60 144 8 17 65 15 Malindi 50 67 120 17 30 84 29 Mandera 63 67 67 15 25 75 26 Mombasa 60 67 83 17 28 84 29 Moyale 50 50 80 0 0 54 0 Nakuru 42 57 109 4 9 60 8 Narok 50 63 109 13 23 78 23 Nyeri 58 73 120 27 45 105 43 Voi 36 50 127 -1 -2 50 -2 Wajir 50 67 100 14 25 75 25 Wilson 50 57 92 6 11 66 11
Average 44.67 59.83 111.28 7.72 13.11 64.33 13.17
OND Rainfall Verification for Nyerir=0.3, r2=0.09
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970
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94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 6: Inter-annual rainfall variability over Nyeri, Kenya during the OND season. RSM-Simulated values are also shown.
11
OND Rainfall Verification for Malindir=0.4, r2=0.18
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970
19
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98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 7: Inter-annual rainfall variability over Malindi, Kenya during the OND season. RSM-Simulated values are also shown.
c. RSM Temperature Verification over Kenya for MAM Season
Table 1-3a: Correlation Coefficients (r), Variance (r 2), RMSE and MAE for RSM Rainfall Output over Kenya
Station r r2 RMSE MAE Dagoretti 0.3 0.08 3.3 3.2 Garissa 0.0 0.00 8.7 8.7 JKIA 0.3 0.11 1.6 1.4 Kisumu 0.5 0.25 0.7 0.6 Kitale 0.6 0.32 3.5 3.4 Makindu -0.1 0.01 3.0 2.9 Malindi 0.1 0.02 4.5 4.5 Mombasa 0.2 0.05 5.5 5.4 Nakuru 0.5 0.26 2.7 2.6 Narok 0.4 0.19 4.3 4.2 Voi 0.0 0.00 5.5 5.5 Average 0.25 0.12 3.94 3.85
Table 1-3b: Verification Skill Scores for RSM Temperature Output over Kenya
Station HR PC FBI ETS TSS P2AFC HSS Dagoretti 33 44 150 -4 -10 12 -9 Garissa 25 31 114 -16 -38 6 -38
12
JKIA 38 50 160 2 4 12 4 Kisumu 56 56 113 7 13 20 13 Kitale 63 71 114 25 41 35 41 Makindu 63 67 114 20 34 25 34 Malindi 44 47 129 -3 -5 12 -5 Mombasa 44 53 129 4 7 20 7 Nakuru 56 56 113 7 13 20 13 Narok 50 53 114 4 7 16 7 Voi 50 59 114 9 17 24 17 Average 47.45 53.36 124 5 7.55 18.36 7.64
MAM Temperature Verification for Kitaler=-0.2, r2=0.03, RMSE=3.6
-3
-2
-1
0
1
2
3
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Tem
pera
ture
Ano
mal
y
MAM_RSMMAM_OBS
Figure 8: Inter-annual temperature variability over Kitale, Kenya during the MAM season. RSM-Simulated values are also shown.
13
MAM Temperature Verification for Kisumur=0.01, r2=0.000, RMSE=1.2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Tem
pera
ture
Ano
mal
y
MAM_RSMMAM_OBS
Figure 9: Inter-annual temperature variability over Kisumu, Kenya during the MAM season. RSM-Simulated values are also shown.
d. RSM Rainfall Verification over Kenya for MAM Season
Table 1-4a: Correlation Coefficients (r) and Variance (r 2) for RSM MAM and Observed Rainfall Output over Kenya
Station r r2 Dagoretti -0.3 0.11 JKIA -0.3 0.11 Kakamega 0.3 0.11 Kisii 0.6 0.37 Kisumu 0.4 0.14 Lamu -0.2 0.05 Lodwar 0.0 0.00 Makindu -0.1 0.01 Malindi 0.0 0.00 Mandera 0.0 0.00 Mombasa 0.2 0.02 Moyale 0.2 0.03 Nakuru 0.4 0.14 Narok 0.2 0.03 Nyeri 0.0 0.00 Voi 0.3 0.08 Wajir 0.1 0.01 Wilson -0.4 0.19 Average 0.08 0.08
Table 1-4b: Verification Skill Scores for RSM Rainfall Output over Kenya
Station HR PC FBI ETS TSS P2AFC HSS
14
Dagoretti 67 56 90 5 10 18 10 JKIA 50 38 80 -11 -27 8 -25 Kakamega 71 63 78 15 27 25 26 Kisii 67 63 75 14 25 24 25 Kisumu 67 56 67 8 16 20 15 Lamu 80 44 42 3 8 12 5 Lodwar 25 50 200 0 0 12 0 Makindu 56 38 82 -13 -35 5 -31 Malindi 67 50 82 -2 -5 12 -5 Mandera 67 56 67 8 16 20 15 Mombasa 67 56 90 5 10 18 10 Moyale 71 56 70 8 17 20 15 Nakuru 88 75 80 33 53 35 50 Narok 50 56 114 7 13 20 13 Nyeri 88 69 73 23 44 28 38 Voi 86 63 64 17 35 24 28 Wajir 75 44 62 -6 -21 6 -13 Wilson 50 38 80 -11 -27 8 -25 Average 66.22 53.94 83.11 5.72 8.83 17.5 8.39
MAM Rainfall Verification for Kisiir=0.6, r2=0.37
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 10: Inter-annual rainfall variability over Kisii, Kenya during the MAM season. RSM-Simulated values are also shown.
15
MAM Rainfall Verification for Nakurur=0.4, r2=0.14
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 11: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall season. RSM-Simulated values are also shown.
e. RSM Temperature Verification over Kenya for JJA Season
Table 1-5a: Correlation Coefficients (r), Variance (r 2), RMSE and MAE for RSM JJA Temperature Output over Kenya
Station r r2 RMSE MAE Dagoretti 0.5 0.23 4.9 4.8 Garissa 0.2 0.06 8.9 8.8 JKIA 0.4 0.17 3.2 5.0 Kisumu 0.7 0.52 1.1 0.9 Kitale 0.7 0.53 5.8 5.8 Makindu 0.5 0.24 2.5 2.4 Malindi 0.7 0.45 3.7 3.7 Mombasa 0.3 0.11 5.0 5.0 Nakuru 0.5 0.21 4.7 4.6 Narok 0.4 0.13 5.7 5.6 Voi 0.5 0.24 5.3 5.3 Average 0.49 0.26 4.62 4.72
Table 1-5b: Verification Skill Scores for RSM JJA Temperature Output over Kenya
Station HR PC FBI ETS TSS P2AFC HSS Dagoretti 83 83 100 50 67 25 67 Garissa 60 62 83 12 21 15 22 JKIA 67 70 120 25 40 12 40 Kisumu 100 73 57 33 57 16 49 Kitale 100 73 57 33 57 16 49
16
Makindu 83 83 100 50 67 25 67 Malindi 60 67 100 19 31 15 31 Mombasa 33 42 120 -8 -17 6 -17 Nakuru 80 64 71 17 32 12 29 Narok 83 73 86 28 46 15 44 Voi 83 83 100 50 67 25 67 Average 75.64 70.27 90.36 28.09 42.55 16.55 40.73
JJA Temperature Verification for Kitaler=0.7, r2=0.53, RMSE=5.8, MAE=5.8
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Tem
pera
ture
Ano
mal
y
JJA_RSMJJA_OBS
Figure 12: Inter-annual temperature variability over Kitale, Kenya during the JJA season. RSM-Simulated values are also shown.
JJA Temperature Verification for Kisumur=0.7, r2=0.52, RMSE=1.1, MAE=0.9
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Tem
pera
ture
Ano
mal
y
JJA_RSMJJA_OBS
17
Figure 13: Inter-annual temperature variability over Kisumu, Kenya during the JJA season. RSM-Simulated values are also shown.
JJA Temperature Verification for Dagorettir=0.5, r2=0.23, RMSE=4.9, MAE=4.8
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Tem
pera
ture
Ano
mal
y
JJA_RSMJJA_OBS
Figure 14: Inter-annual temperature variability over Dagoretti, Kenya during the JJA season. RSM-Simulated values are also shown.
f. RSM Rainfall Verification over Kenya for the JJA Season
Table 1-6a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed JJA Rainfall Output over Kenya
Station r r2 Dagoretti 0.1 0.01 JKIA 0.2 0.04 Kakamega 0.2 0.03 Kisii 0.0 0.00 Kisumu 0.1 0.02 Lamu -0.1 0.00 Lodwar -0.1 0.02 Makindu -0.1 0.02 Malindi -0.3 0.09 Mandera -0.3 0.10 Mombasa -0.3 0.10 Moyale -0.1 0.01 Nakuru -0.4 0.17 Narok 0.0 0.00 Nyeri 0.6 0.41 Voi 0.0 0.00 Wajir -0.2 0.04 Wilson 0.0 0.00 Average -0.04 0.06
18
Table 1-6b: Verification Skill Scores for RSM JJA Rainfall Output over Kenya
Station HR PC FBI ETS TSS P2AFC HSS
Dagoretti 80 42 50 -2 -10 4 -5 JKIA 100 58 50 14 50 10 25 Kakamega 60 58 83 9 17 12 17 Kisii 100 75 40 28 40 14 44 Kisumu 100 83 67 50 67 24 67 Lamu 100 50 40 10 40 8 18 Lodwar 67 50 43 4 9 8 8 Makindu 50 25 22 -6 -22 2 -13 Malindi 83 50 60 0 0 5 0 Mandera 50 50 67 0 0 8 0 Mombasa 83 50 60 0 0 5 0 Moyale 100 42 42 0 - 1 0 Nakuru 67 33 60 -14 -60 0 -33 Narok 75 33 40 -4 -20 3 -9 Nyeri 60 25 50 -15 -70 0 -35 Voi 75 58 57 12 23 12 21 Wajir 67 50 43 4 9 8 8 Wilson 83 50 60 0 0 5 0 Average 77.78 49 51.89 5 -19.67 7.17 6.28
JJA Rainfall Verification for Nyerir=0.6, r2=0.41
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 15: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall season. RSM-Simulated values are also shown.
19
JJA Rainfall Verification for Kisumur=0.1, r2=0.02
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 16: Inter-annual rainfall variability over Nakuru, Kenya during the MAM rainfall season. RSM-Simulated values are also shown.
5. RSM Performance over Tanzania
a. RSM Rainfall Verification over Tanzania for OND Season
Table 2-1a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Tanzania
Station r r2 Bukoba 0.0 0.00 DIA 0.4 0.16 Dodoma 0.3 0.07 Iringa 0.5 0.28 Kigoma -0.1 0.02 Mbeya 0.0 0.00 Mtwara 0.2 0.03 Mwanza 0.0 0.00 Songea 0.2 0.04 Tabora 0.2 0.04 Average 0.17 0.06
Table 2-1b: Verification Skill Scores for RSM OND Rainfall Output over Tanzania
HR PC FBI ETS TSS P2AFC HSS
20
Bukoba 25 47 120 -7 -15 33 -14 DIA 47 53 115 3 7 63 7 Dodoma 50 73 125 23 40 85 37 Iringa 64 69 108 23 38 99 38 Kigoma 8 55 650 1 6 15 2 Mbeya 33 67 200 14 33 64 24 Mtwara 46 60 118 9 18 72 17 Mwanza 41 50 142 1 3 56 3 Songea 31 53 144 1 2 48 1 Tabora 24 47 243 0 1 40 0 Average 36.9 57.4 196.5 6.8 13.3 57.5 11.5
OND Rainfall Verification for Dodomar=0.3, r2=0.07
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 17: Inter-annual rainfall variability over Dodoma, Tanzania during the OND rainfall season. RSM-Simulated values are also shown.
21
OND Rainfall Verification for Iringar=0.5, r2=0.24
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 18: Inter-annual rainfall variability over Iringa, Tanzania during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for DIA
r=0.4, r2=0.16
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 19: Inter-annual rainfall variability over Dar es Salaam International Airport, Tanzania during the OND rainfall season. RSM-Simulated values are also shown.
22
OND Rainfall Verification for Mtwarar=0.2, r2=0.03
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 20: Inter-annual rainfall variability over Mtwara, Tanzania during the OND rainfall season. RSM-Simulated values are also shown.
b. RSM Rainfall Verification over Tanzania for MAM Season
Table 2-2a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed MAM Rainfall Output over Tanzania
Station r r2 Bukoba 0.0 0.00 DIA 0.2 0.04 Dodoma 0.4 0.19 Iringa 0.3 0.09 Kigoma 0.2 0.05 Mbeya 0.4 0.17 Mtwara -0.2 0.06 Mwanza 0.3 0.10 Songea -0.1 0.01 Tabora 0.1 0.02
Average 0.16 0.07
Table 2-2b: Verification Skill Scores for RSM MAM Rainfall Output over Tanzania
Station HR PC FBI ETS TSS P2AFC HSS Bukoba 67 56 90 5 10 18 10 DIA 86 50 54 4 13 12 7 Dodoma 83 75 75 33 50 35 50
23
Station HR PC FBI ETS TSS P2AFC HSS Iringa 57 50 78 1 2 16 2 Kigoma 40 47 71 -4 -9 10 -9 Mbeya 67 50 60 3 7 16 6 Mtwara 89 63 69 10 28 16 19 Mwanza 50 56 143 8 16 20 15 Songea 44 38 100 -12 -27 8 -27 Tabora 71 56 70 8 17 20 15 Average 65.4 54.1 81 5.6 10.7 17.1 8.8
MAM Rainfall Verification for Dodomar=0.4, r2=0.19
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 21: Inter-annual rainfall variability over Dodoma, Tanzania during the MAM rainfall season. RSM-Simulated values are also shown.
24
MAM Rainfall Verification for Mwanzar=0.3, r2=0.10
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 22: Inter-annual rainfall variability over Mwanza, Tanzania during the MAM rainfall season. RSM-Simulated values are also shown.
c. RSM Rainfall Verification over Tanzania for JJA Season
Table 2-3a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed JJA Rainfall Output over Tanzania
Station r r2 Bukoba 0.6 0.36 DIA -0.2 0.04 Dodoma 0.3 0.08 Iringa -0.1 0.01 Kigoma 0.5 0.23 Mbeya 0.7 0.53 Mtwara 0.8 0.58 Mwanza -0.2 0.05 Songea 0.5 0.28 Tabora 0.2 0.05 Average 0.31 0.22
25
Table 2-3b: Verification Skill Scores for RSM JJA Rainfall Output over Tanzania
HR PC FBI ETS TSS P2AFC HSS Bukoba 40 50 100 -1 -3 8 -3 DIA 100 58 55 9 55 6 17 Dodoma 100 25 10 2 10 2 4 Iringa 100 17 17 0 - 1 0 Kigoma 50 64 100 12 21 10 21 Mbeya 100 58 38 17 38 12 29 Mtwara 100 58 55 9 55 6 17 Mwanza 43 25 88 -23 -63 0 -59 Songea 100 50 40 10 40 8 18 Tabora 100 58 29 14 29 10 25 Average 83.3 46.3 53.2 4.9 20 6.3 6.9
JJA Rainfall Verification for Mtwarar=0.8, r2=0.58
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 23: Inter-annual rainfall variability over Mtwara, Tanzania during the JJA rainfall season. RSM-Simulated values are also shown.
26
JJA Rainfall Verification for Mbeyar=0.7, r2=0.53
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 24: Inter-annual rainfall variability over Mbeya, Tanzania during the JJA rainfall season. RSM-Simulated values are also shown.
JJA Rainfall Verification for Kigomar=0.5, r2=0.23
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 25: Inter-annual rainfall variability over Kigoma, Tanzania during the JJA rainfall season. RSM-Simulated values are also shown.
27
JJA Rainfall Verification for Bukobar=0.6, r2=0.36
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 26: Inter-annual rainfall variability over Bukoba, Tanzania during the JJA rainfall season. RSM-Simulated values are also shown.
6. RSM Performance over Burundi, Eritrea and Djibouti
a. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for OND Season
Table 3-1a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed OND Rainfall Output over Burundi, Eritrea and Djibouti
Station r r2 Bujumbura 0.0 0.00 Djibouti 0.6 0.31 Asmara 0.1 0.02
Table 3-1b: Verification Skill Scores for RSM Rainfall Output over Burundi, Eritrea and Djibouti
HR PC FBI ETS TSS P2AFC HSS Bujumbura 33 53 120 0 0 48 0 Djibouti 44 70 113 15 27 68 26 Asmara 45 63 110 11 20 70 20
28
OND Rainfall Verification for Bujumbura r=0.0, r2=0.00
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 27: Inter-annual rainfall variability over Bujumbura, Burundi during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for Djiboutir=0.6, r2=0.31
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 28: Inter-annual rainfall variability over Djibouti, Djibouti during the OND rainfall season. RSM-Simulated values are also shown.
29
OND Rainfall Verification for Asmarar=0.1, r2=0.02
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 29: Inter-annual rainfall variability over Asmara, Eritrea during the OND rainfall season. RSM-Simulated values are also shown.
b. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for MAM Season
Table 3-2a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed MAM Rainfall Output over Burundi, Eritrea and Djibouti
Station r r2 Bujumbura -0.2 0.05 Djibouti 0.1 0.01 Asmara -0.1 0.02
Table 3-2b: Verification Skill Scores for MAM RSM Rainfall Output over Burundi, Eritrea and Djibouti
HR PC FBI ETS TSS P2AFC HSS Bujumbura 63 44 73 -6 -15 10 -13 Djibouti 71 50 64 2 5 15 4 Asmara 83 50 50 6 17 15 11
30
MAM Rainfall Verification for Bujumbura r=-0.2, r2=0.05
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 30: Inter-annual rainfall variability over Bujumbura, Burundi during the MAM rainfall season. RSM-Simulated values are also shown.
MAM Rainfall Verification for Djiboutir=0.1, r2=0.01
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 31: Inter-annual rainfall variability over Djibouti, Djibouti during the MAM rainfall season. RSM-Simulated values are also shown.
31
MAM Rainfall Verification for Asmarar=-0.1, r2=0.02
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 32: Inter-annual rainfall variability over Asmara, Eritrea during the MAM rainfall season. RSM-Simulated values are also shown.
c. RSM Rainfall Verification over Burundi, Eritrea and Djibouti for JJA Season
Table 3-3a: Correlation Coefficients (r) and Variance (r 2) for RSM and Observed JJA Rainfall Output over Burundi, Eritrea and Djibouti
Station r r2 Bujumbura 0.3 0.10 Djibouti -0.2 0.04 Asmara 0.0 0.00
Table 3-3b: Verification Skill Scores for JJA RSM Rainfall Output over Burundi, Eritrea and Djibouti
HR PC FBI ETS TSS P2AFC HSS Bujumbura 33 42 50 -8 -17 4 -17 Djibouti 20 50 167 -4 -11 5 -9 Asmara 20 58 250 3 10 6 6
32
JJA Rainfall Verification for Bujumbura r=0.3, r2=0.1
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 33: Inter-annual rainfall variability over Bujumbura, Burundi during the JJA rainfall season. RSM-Simulated values are also shown.
JJA Rainfall Verification for Djiboutir=-0.2, r2=0.04
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 34: Inter-annual rainfall variability over Djibouti, Djibouti during the JJA rainfall season. RSM-Simulated values are also shown.
33
JJA Rainfall Verification for Asmarar=0.0, r2=0.00
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 35: Inter-annual rainfall variability over Asmara, Eritrea during the JJA rainfall season. RSM-Simulated values are also shown.
7. RSM Performance over Ethiopia
a. RSM Rainfall Verification over Ethiopia for OND Season
Table 4-1a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Ethiopia
Station r r2 Addis 0.1 0.02 Baherdar 0.2 0.05 Combolcha 0.3 0.06 Debremarkos 0.0 0.00 Debrezeit 0.1 0.02 Gonder 0.3 0.07 Jima 0.2 0.03 Nekemte 0.4 0.17 Mekele 0.1 0.02 Average 0.19 0.05
Table 4-1b: Verification Skill Scores for RSM Rainfall Output over Ethiopia
Station HR PC FBI ETS TSS P2AFC HSS Addis 33 60 150 6 14 56 12 Baherdar 36 57 175 8 18 55 14 Combolcha 42 60 120 8 15 65 14 Debremarkos 29 52 175 1 2 44 2 Debrezeit 71 71 70 23 36 60 37
34
Station HR PC FBI ETS TSS P2AFC HSS Gonder 33 55 133 2 4 48 4 Jima 36 43 108 -7 -14 40 -14 Nekemte 46 68 163 20 40 78 34 Mekele 11 56 225 -4 -13 13 -9 Average 37.44 58 146.56 6.33 11.33 51 10.44
OND Rainfall Verification for Nekemter=0.4, r2=0.17
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 36: Inter-annual rainfall variability over Nekemte, Ethiopia during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for Combolchar=0.3, r2=0.06
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 37: Inter-annual rainfall variability over Combolcha, Ethiopia during the OND rainfall season. RSM-Simulated values are also shown.
35
b. RSM Rainfall Verification over Ethiopia for MAM Season
Table 4-2a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Ethiopia
Station r r2 Addis -0.3 0.07 Baherdar -0.2 0.06 Combolcha -0.2 0.02 Debremarkos -0.3 0.08 Debrezeit 0.2 0.03 Gonder -0.1 0.02 Jima -0.2 0.04 Nekemte -0.2 0.03 Mekele 0.1 0.00 Average -0.13 0.04
Table 4-2b: Verification Skill Scores for RSM Rainfall Output over Ethiopia
HR PC FBI ETS TSS P2AFC HSS Addis 11 19 150 -22 -63 2 -58 Baherdar 60 50 63 2 4 12 4 Combolcha 0 40 200 -15 -50 0 -36 Debremarkos 38 27 89 -20 -50 3 -49 Debrezeit 20 50 167 -4 -11 5 -9 Gonder 50 47 100 -3 -7 12 -7 Jima 25 20 40 -16 -50 2 -38 Nekemte 38 36 114 -13 -29 6 -29 Mekele 50 54 67 3 5 10 5 Average 32.44 38.11 110 -9.78 -27.89 5.78 -24.11
36
MAM Rainfall Verification for Debrezeitr=0.2, r2=0.03
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 38: Inter-annual rainfall variability over Debrezeit, Ethiopia during the MAM rainfall season. RSM-Simulated values are also shown.
MAM Rainfall Verification for Gonderr=-0.1, r2=0.02
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 39: Inter-annual rainfall variability over Gonder, Ethiopia during the MAM rainfall season. RSM-Simulated values are also shown.
37
c. RSM Rainfall Verification over Ethiopia for JJA Season
Table 4-3a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Ethiopia
Station r r2 Addis -0.3 0.10 Baherdar 0.6 0.39 Combolcha -0.3 0.08 Debremarkos 0.0 0.00 Debrezeit -0.2 0.03 Gonder -0.1 0.00 Jima 0.2 0.03 Nekemte 0.0 0.00 Mekele -0.5 0.25 Average -0.07 0.1
Table 4-3b: Verification Skill Scores for RSM Rainfall Output over Ethiopia
Station HR PC FBI ETS TSS P2AFC HSS Addis 67 42 67 -8 -22 4 -17 Baherdar 71 75 117 33 50 20 50 Combolcha 33 25 86 -20 -51 2 -50 Debremarkos 40 42 83 -8 -17 6 -17 Debrezeit 80 70 83 25 42 12 40 Gonder 50 55 80 4 7 8 7 Jima 83 58 67 9 22 10 17 Nekemte 57 55 117 4 7 8 7 Mekele 0 27 167 -21 -63 0 -52 Average 53.44 49.89 96.33 2 -2.78 7.78 -1.67
38
JJA Rainfall Verification for Baherdarr=0.6, r2=0.39
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 40: Inter-annual rainfall variability over Baherdar, Ethiopia during the JJA rainfall season. RSM-Simulated values are also shown.
JJA Rainfall Verification for Jimar=0.2, r2=0.03
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 41: Inter-annual rainfall variability over Jima, Ethiopia during the JJA rainfall season. RSM-Simulated values are also shown.
39
8. RSM Performance over Sudan
a. RSM Rainfall Verification over Sudan for OND Season
Table 5-1a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Sudan
Station r r2 Abu Hamad -0.1 0.01 Abu Naama 0.0 0.00 Atbara 0.2 0.05 Ed Dueim 0.0 0.00 El Fasher 0.2 0.04 El Gedref 0.1 0.02 El Geneina 0.2 0.03 El Obeid 0.0 0.00 En Nahud 0.0 0.00 Juba 0.2 0.04 Kadugli 0.3 0.10 Karima -0.1 0.01 Kassala 0.2 0.05 Khartoum 0.4 0.15 Malakal 0.2 0.04 Ndogola -0.1 0.01 Nyala -0.1 0.01 Port Sudan 0.2 0.03 Rashad 0.1 0.01 Sennar -0.2 0.04 Wadhalfa 0.0 0.00 Wad Medani 0.1 0.02 Wau 0.4 0.16 Average 0.1 0.04
Table 5-1b: Verification Skill Scores for RSM Rainfall Output over Sudan
Station HR PC FBI ETS TSS P2AFC HSS Abu Hamad 0 77 133 -6 -15 0 -13 Abu Naama 31 57 163 4 9 52 8 Atbara 25 70 160 7 16 38 13 Ed Dueim 40 67 125 12 23 64 21 El Fasher 11 55 150 -7 -18 15 -15 El Gedref 30 60 125 3 6 45 5 El Geneina 27 60 157 3 8 45 7 El Obeid 40 67 125 12 23 64 21 En Nahud 18 50 138 -7 -16 26 -14 Juba 25 60 200 5 13 45 9 Kadugli 38 60 144 8 17 65 15 Karima 0 83 67 -4 -7 0 -9 Kassala 50 77 143 28 50 90 43 Khartoum 38 73 133 15 29 57 26 Malakal 31 57 229 9 24 60 16
40
Station HR PC FBI ETS TSS P2AFC HSS Ndogola 0 70 80 -8 -16 0 -17 Nyala 9 53 220 -6 -20 15 -14 Port Sudan 54 67 118 18 32 91 31 Rashad 43 60 140 10 20 72 18 Sennar 33 63 113 5 10 48 10 Wadhalfa 0 83 25 -3 -4 0 -6 Wad Medani 33 63 113 5 10 48 10 Wau 46 67 144 17 33 84 30 Average 27.04 65.17 136.74 5.22 9.87 44.52 8.48
OND Rainfall Verification for Port Sudan
r=0.2, r2=0.03
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 42: Inter-annual rainfall variability over Port Sudan, Sudan during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for Kassalar=0.2, r2=0.05
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
41
Figure 43: Inter-annual rainfall variability over Kassala, Sudan during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for Waur=0.4, r2=0.16
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 44: Inter-annual rainfall variability over Wau, Sudan during the OND rainfall season. RSM-Simulated values are also shown.
OND Rainfall Verification for Kaduglir=0.3, r2=0.1
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970
19
71 19
72 19
73 19
74 19
75 19
76 19
77 19
78 19
79 19
80 19
81 19
82 19
83 19
84 19
85 19
86 19
87 19
88 19
89 19
90 19
91 19
92 19
93 19
94 19
95 19
96 19
97 19
98 19
99
Year
Rai
nfal
l Ano
mal
y
Obs_ONDRSM_OND
Figure 45: Inter-annual rainfall variability over Kadugli, Sudan during the OND rainfall season. RSM-Simulated values are also shown.
b. RSM Rainfall Verification over Sudan for MAM Season
Table 5-2a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Sudan
Station r r2
42
Station r r2 Abu Hamad -0.1 0.02 Abu Naama -0.1 0.00 Atbara -0.2 0.04 Ed Dueim -0.1 0.01 El Fasher -0.2 0.03 El Gedref -0.1 0.00 El Geneina -0.2 0.06 El Obeid -0.1 0.01 En Nahud 0.0 0.00 Juba 0.3 0.10 Kadugli 0.3 0.08 Karima -0.1 0.01 Kassala -0.4 0.14 Khartoum -0.1 0.00 Malakal 0.0 0.00 Ndogola 0.1 0.01 Nyala -0.1 0.02 Port Sudan -0.3 0.07 Rashad -0.2 0.03 Sennar -0.1 0.01 Wadhalfa - - Wad Medani -0.3 0.09 Wau -0.5 0.22 Average -0.4 0.16
Table 5-2b: Verification Skill Scores for RSM Rainfall Output over Sudan
HR PC FBI ETS TSS P2AFC HSS Abu Hamad 0 75 100 -7 -14 0 -14 Abu Naama 29 44 117 -7 -17 10 -16 Atbara 17 50 150 -7 -17 7 -14 Ed Dueim 25 63 100 0 0 9 0 El Fasher 0 53 133 -13 -33 0 -30 El Gedref 33 50 100 -3 -7 12 -7 El Geneina 25 50 200 0 0 12 0 El Obeid 17 56 200 -2 -5 8 -4 En Nahud 25 50 200 0 0 12 0 Juba 100 69 55 27 55 30 43 Kadugli 50 50 100 0 0 16 0 Karima 0 88 100 -3 -7 0 -7 Kassala 14 38 140 -13 -35 5 -31 Khartoum 20 63 167 1 3 9 2 Malakal 38 25 80 -20 -53 3 -50 Ndogola 50 88 100 27 43 13 43 Nyala 50 75 150 27 50 27 43 Port Sudan 20 50 100 -8 -16 7 -16 Rashad 50 50 50 0 0 12 0 Sennar 40 63 100 7 13 16 13 Wadhalfa - - - - - - -
43
HR PC FBI ETS TSS P2AFC HSS Wad Medani 0 44 125 -16 -42 0 -38 Wau 50 50 75 0 0 15 0 Average 25 47 100 -8 -21 7.5 -19
MAM Rainfall Verification for Jubar=0.3, r2=0.10
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 46: Inter-annual rainfall variability over Juba, Sudan during the MAM rainfall season. RSM-Simulated values are also shown.
MAM Rainfall Verification for Kaduglir=0.3, r2=0.08
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 47: Inter-annual rainfall variability over Kadugli, Sudan during the MAM rainfall season. RSM-Simulated values are also shown.
44
MAM Rainfall Verification for Nyalar=-0.1, r2=0.02
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 48: Inter-annual rainfall variability over Nyala, Sudan during the MAM rainfall season. RSM-Simulated values are also shown.
MAM Rainfall Verification for Sennarr=-0.1, r2=0.01
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970 1971 1972 1973 1974 1982 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_MAMRSM_MAM
Figure 49: Inter-annual rainfall variability over Sennar, Sudan during the MAM rainfall season. RSM-Simulated values are also shown.
c. RSM Rainfall Verification over Sudan for JJA Season
Table 5-3a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Sudan
Station r r2 Abu Hamad -0.1 0.00 Abu Naama -0.2 0.03
45
Atbara -0.1 0.02 Ed Dueim -0.6 0.36 El Fasher -0.3 0.12 El Gedref -0.7 0.51 El Geneina -0.1 0.02 El Obeid 0.0 0.00 En Nahud -0.6 0.33 Juba -0.1 0.01 Kadugli -0.2 0.02 Karima 0.0 0.00 Kassala -0.3 0.11 Khartoum 0.1 0.00 Malakal 0.6 0.35 Ndogola -0.2 0.03 Nyala -0.2 0.03 Port Sudan - - Rashad -0.4 0.13 Sennar - - Wadhalfa 1.0 1.00 Wad Medani -0.1 0.02 Wau 0.6 0.32 Average 0.5 0.45
Table 5-3b: Verification Skill Scores for RSM Rainfall Output over Sudan
HR PC FBI ETS TSS P2AFC HSS Abu Hamad 0 50 100 -14 -33 0 -33 Abu Naama 17 42 200 -8 -22 4 -17 Atbara 25 67 200 8 20 7 14 Ed Dueim 0 33 100 -20 -50 0 -50 El Fasher 14 36 350 -4 -17 3 -8 El Gedref 0 25 200 -20 -67 0 -50 El Geneina 0 25 350 -15 -70 0 -35 El Obeid 20 50 167 -4 -11 5 -9 En Nahud 33 42 120 -8 -17 6 -17 Juba 33 17 75 -25 -75 0 -67 Kadugli 33 42 120 -8 -17 6 -17 Karima 67 75 75 25 38 14 40 Kassala 20 50 167 -4 -11 5 -9 Khartoum 29 58 350 14 50 10 25 Malakal 80 75 83 33 50 20 50 Ndogola 25 58 133 0 0 6 0 Nyala 33 50 150 0 0 8 0 Port Sudan - - - - - - - Rashad 0 33 100 -20 -50 0 -50 Sennar 0 43 - 0 - 1 0 Wadhalfa 100 100 100 100 100 11 100 Wad Medani 20 50 167 -4 -11 5 -9 Wau 57 58 117 9 17 12 17 Average 35.4 56.8 128 17 35.33 5.8 11.6
46
JJA Rainfall Verification for Wadhalfar=1.0, r2=1.00
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 50: Inter-annual rainfall variability over Wad Halfa, Sudan during the JJA rainfall season. RSM-Simulated values are also shown.
JJA Rainfall Verification for Malakalr=0.6, r2=0.35
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 51: Inter-annual rainfall variability over Malakal, Sudan during the JJA rainfall season. RSM-Simulated values are also shown.
47
JJA Rainfall Verification for Waur=0.6, r2=0.32
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJARSM_JJA
Figure 52: Inter-annual rainfall variability over Wau, Sudan during the JJA rainfall season. RSM-Simulated values are also shown.
d. RSM Rainfall Verification over Sudan for JJA Season
Table 5-3a: Correlation Coefficients (r) and Variance (r2) for RSM and Observed OND Rainfall Output over Sudan
r r2 Abu Hamad 0.0 0.00 Abu Naama 0.3 0.07 Atbara -0.4 0.15 Ed Dueim -0.3 0.10 El Fasher 0.1 0.02 El Gedref -0.6 0.35 El Geneina 0.2 0.06 El Obeid 0.0 0.00 En Nahud -0.6 0.31 Juba -0.1 0.01 Kadugli 0.2 0.05 Karima 0.1 0.01 Kassala 0.6 0.32 Khartoum -0.2 0.04 Malakal 0.3 0.09 Ndogola 0.4 0.15 Nyala 0.1 0.01 Port Sudan 0.3 0.07 Rashad 0.3 0.07 Sennar 0.0 0.00 Wadhalfa -0.1 0.00 Wad Medani 0.6 0.31 Wau 0.5 0.24 Average 0.07 0.11
48
Table 5-3b: Verification Skill Scores for RSM Rainfall Output over Sudan
HR PC FBI ETS TSS P2AFC HSS Abu Hamad 67 75 75 25 38 14 40 Abu Naama 67 58 86 9 17 12 17 Atbara 0 33 100 -20 -50 0 -50 Ed Dueim 60 50 71 1 3 9 3 El Fasher 50 45 100 -5 -10 6 -10 El Gedref 33 33 100 -14 -33 4 -33 El Geneina 100 83 78 47 78 21 64 El Obeid 40 42 83 -8 -17 6 -17 En Nahud 67 33 60 -14 -60 0 -33 Juba 80 42 50 -2 -10 4 -5 Kadugli 100 58 55 9 55 6 17 Karima 100 73 40 27 40 12 42 Kassala 80 83 100 49 66 24 66 Khartoum 25 42 80 -11 -23 4 -24 Malakal 100 67 56 24 56 15 38 Ndogola 50 67 100 14 25 12 25 Nyala 80 67 71 21 37 16 35 Port Sudan 0 58 - 0 - 1 0 Rashad 100 58 44 17 44 12 29 Sennar 40 43 167 -4 -8 2 -8 Wadhalfa 50 75 67 14 22 8 25 Wad Medani 60 67 100 19 31 15 31 Wau 100 67 56 24 56 15 38 Average 63 57.35 86.8 9.65 29 9.48 12.61
JJA Rainfall Verification for Kassalar=0.6, r2=0.32
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJASRSM_JJAS
Figure 53: Inter-annual rainfall variability over Kassala, Sudan during the JJAS rainfall season. RSM-Simulated values are also shown.
49
JJAS Rainfall Verification for El Geneinar=0.2, r2=0.06
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJASRSM_JJAS
Figure 54: Inter-annual rainfall variability over El Geneina, Sudan during the JJAS rainfall season. RSM-Simulated values are also shown.
JJAS Rainfall Verification for Wad Medanir=0.6, r2=0.31
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJASRSM_JJAS
Figure 55: Inter-annual rainfall variability over Wad Medani, Sudan during the JJAS rainfall season. RSM-Simulated values are also shown.
50
JJAS Rainfall Verification for Waur=0.5, r2=0.24
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJASRSM_JJAS
Figure 56: Inter-annual rainfall variability over Wau, Sudan during the JJAS rainfall season. RSM-Simulated values are also shown.
JJAS Rainfall Verification for Abu Naamar=0.3, r2=0.07
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1970 1971 1972 1973 1974 1982 1994 1995 1996 1997 1998 1999
Year
Rai
nfal
l Ano
mal
y
Obs_JJASRSM_JJAS
Figure 57: Inter-annual rainfall variability over Abu Naama, Sudan during the JJAS rainfall season. RSM-Simulated values are also shown.
9. Dynamical Predictability over the Greater Horn of Africa Using RSM
a. Degree of Predictability over Greater Horn of Africa
The goal of seasonal climate forecasting is to provide advance knowledge about the
expected conditions of the atmosphere to interested users. Predictability over the
51
GHA region is classified as short range (1-3 days), medium range (4-7 days, but may
extend to 8-10 days), and long range (beyond two weeks). The skill of forecasts
exceeding two weeks is enhanced by combining statistical and dynamical techniques,
including assumption of persistence of synoptic systems.
Does the RSM dynamical model capture the seasonality of weather well? By
and large, the pressure, and hence wind fields are fairly well simulated by dynamical
models.
The accuracy of simulating the seasonal/annual cycle of convection and
precipitation over the region is still poor. The skill of predictability is better for the
short-term range, and is best within the first 24 to 48 hours, and decreases
drammatically thereafter.
RSM simulates fairly well the general pattern of the seasonal migration of the
ITCZ over the GHA. However, the accuracy of dynamically downscaled prediction
products still poses a challenge over the area. The seasonal predictability over
Equatorial Eastern Africa is dramatically better during the short rainfall season
(October-December), when the weather systems and oceanic controls are steadier and
better organized than the long rainfall season (March-May). Unfortunately, the
predictability using RSM over the northern parts of the GHA in JJA season, when the
region experiences most rainfall, is still wanting.
The actual convective processes that govern “tropical cyclogenesis” and
rainfall generation in the region have not been addressed in dynamical modelling over
the region. This can be determined by the sensitivity analysis of cloud simulation
modules in the dynamical models applied.
b. The Challenge of Limitation to Predictability
The interaction between mesoscale forcing with large scale circulation is one of the
most important considerations in the widespread local convection over the Greater
Horn of Africa region.
Errors arise in the prediction of dynamical and physical processes, including
tropical convection, however, due to a variety of reasons including inadequate
horizontal resolution, improper cascading of energy from one scale to another,
unsuitable parameterization schemes, et cetera. The cumulative impact of these
modelling uncertainties on the predicted quantities may be significant. This is
aggravated by a lack of understanding of the roles of complex nonlinear interactions
and feedbacks between systems of different scales on the temporal evolution of
52
convection. Most cumulus convection modules in dynamical models, being ad hoc
and presuming linearity, fail to capture the diurnal and seasonal cycles of convection
accurately.
The subject of limitation to predictability in the Greater Horn of Africa region
needs to be addressed so as to meet one of the main goals of dynamical forecasting,
which is to improve the predictability of monthly and seasonal climate events on
regional to global scales to meet the sundry needs of users.
References
Anyah, R.O., 2005: Modeling the Variability of the Climate System over Lake Victoria Basin. PhD Thesis, North Carolina State University, USA.
Asnani G.C. and J.H Kinuthia, 1979: Diurnal variation of precipitation in East Africa. Kenya Meteorological Department. Research Report, No. 8/79, 1-58.
Asnani, G.C., 1993: Tropical Meteorology, Volume I and II, Sindh Colony, Aundh, Pune, India.
Camberlin, P. and N. Philippon, 2002: The East African March-May rainy season: Associated atmospheric dynamics and predictability over the 1968-97 period. J. Climate, 15, 1002-1019.
Indeje, M., F.H.M. Semazzi, L. Xie, L.J. Ogallo, 2001: Mechanistic Model Simulations of the East African Climate Using NCAR Regional Climate Model: Influence of Large-Scale orography on the Turkana Low-Level Jet. J. Climate, 14, 2710-2724.
Ininda, J. M, 1998: Simulation of the impact of sea surface temperature anomalies on the short rains over East Africa. J. African Meteorology Society, 3, 1, 127-140.
Mukabana, J.R., 1992: Numerical simulation of the influence of large scale monsoon flow on the diurnal weather patterns over Kenya. PhD Thesis, University of Nairobi.
Mutai, C.C. and M.N. Ward, 2000: East African rainfall and the tropical circulation/convection on intraseasonal to interannual timescales. J. Climate, 13, 3915-3939.
Mutai, C.C. M.N. Ward, and A.W. Coleman, 1988: Towards the prediction of the East Africa short rains based on sea-surface temperature-atmosphere coupling. Int. J. Climatol., 18, 975-997.
Mutemi, J. N, 2003: Climate anomalies over eastern Africa associated with various ENSO evolution phases. Ph.D. Thesis, University of Nairobi, Kenya.
Okeyo, A.E. 1987: Towards the development of a forecasting numerical model for Kenya. Ph.D. Thesis. University of Nairobi, Kenya.
Opijah, F.J., 2000: Numerical Simulation of the Impact of Urbanization on the Microclimate over Nairobi Area. PhD Thesis. University of Nairobi, Kenya.
Sakwa, V.N, 2004: Assessment of the Skill of the High Resolution Regional Model in the Simulation of Airflow and Rainfall over East Africa. MSc Dissertation. University of Nairobi, Kenya.
