university of nairobi, nairobi, kenya department of meteorology, august 15-19, 201111th...

August 15-19, 2011 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC

1

University of N

airobi, Nairobi, K

enya

Departm

ent of Meteorology, w

ww

.uonbi.ac.ke

Predictability of Weather on Extended NWP Timescales over Kenya Using the GFS

Model

Franklin J. OpijahUniversity of Nairobi, Kenya

www.uonbi.ac.ke


2

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Early-Warning Systems may reduce vulnerabilityto floods, disease, pestilence, strong winds, hazardous air

Dust Storms/Hazardous AirDust Storms/Hazardous AirMalaria EpidemicsMalaria Epidemics

Communication ImpairmentCommunication ImpairmentStrong WindsStrong Winds


3

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

EWS can reduce vulnerability to pests, drought and famine

Food InsecurityFood Insecurity

Livestock ManagementLivestock ManagementPest InvasionsPest Invasions

Food AvailabilityFood Availability

Water Resource ManagementWater Resource Management

Heat WavesHeat Waves

Hydropower GenerationHydropower Generation


4

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Strong Linkage between Weather Conditions and Disease

• Malaria is rife in humid, high temperature areas

• Meningitis is rife in dusty, low-humidity areas


5

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Traditional Forecasting Techniques in Kenya

(UNDP Report)

• Is it possible to forecast impending weather using indigenous knowledge (IK)?

• Modelling Challenge: Is NWP Superior to IK?

Indicator Coming rains

Dry spell

Croaking frogs

Pronounced Reduced

Migrant birds Appearance Disappearance

Indigenous trees

Leaving, flowering

Shedding

Cattle Stampedes

Bird nests (weaver birds )

More nests Fewer nests

Red ants Appearance

Human body Discomfort (hot)

Discomfort (cold)


6

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline of Presentation

• Introduction• The Global Forecast System• Prevailing Weather Conditions over Kenya (OND

2008)• Error and Skill Analysis (Formulation and

Techniques)• Is RFE Data useful over Kenya? • Predictability of Daily Rainfall and Temperature• Predictability of Seven-Day Weather Outlooks• Summary and Conclusions


7

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





8

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Global Forecast SystemHorizontal Resolution 35 km (T382)

Solution technique Spectral triangular; Nonlinear advection: Leapfrog Gravity waves: Semi-implicit

Vertical grid Hybrid p-sigma; 64 levels

PBL Bulk-Richardson approach + Monin-Obukhov similarity

Radiation scheme (LWR/3hr; SWR/1hr)

GHGs (O3, H2O, CO2, CH4, N2O, O2, CFCs ), atmospheric aerosols, Cloud-radiative properties

Convection Deep: Arakawa and Schubert (1974):

Shallow: bulk mass-flux parameterization

Gravity-wave drag: Nonlinear function of the surface wind speed and the local Froude number


9

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





10

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Weather/Climate Controls over Kenya

• Quasi-permanent systems– ITCZ– Anticyclones

• Unusual systems – El Niño/La Nina– IOD– QBO

• Migratory Systems– Tropical cyclones – Easterly waves– MJOs

• Mesoscale systems– Great lakes– High mountains– Urban areas


11

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Domain of Study Topography & Homogeneous Climate Zones

ARC

BAM

BAR

BAR COL

DAG

ELD

ELG

GAL

GARB

GAR

IS I

K ISU

KAJ

KAK

KAP

KAS

KAT

KER

K IBW

KINA

K IR I

K IS I

KAIS

LAIK

LAM

LOD

MAC

MAG

MAK I

MAL

MAN

MARL

MARS

MAT I

MBO

MOM

MON

MOY

MUT

NAIV

NAK

NAN NYK

NAR

NK IN

NYA

NYEOLEN

RUM

TAV

T IMAT IMB

ADU

VOI

WA

TOD

HOR

RHA

ELW

34.00 35.00 36.00 37.00 38.00 39.00 40.00 41.00

-4 .00

-3 .00

-2 .00

-1 .00

0.00

1.00

2.00

3.00

4.00

5.00

34.00 35.00 36.00 37.00 38.00 39.00 40.00 41.00

-4 .00

-3 .00

-2 .00

-1 .00

0.00

1.00

2.00

3.00

4.00

5.00

1

2

3

4 5

6

8

91 0

1 1

1 2

7

Rainfall climatic zones


12

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Observed Weather Patterns over Kenya in the 2008 OND SeasonObserved Rainfall and Maximum and

Minimum Temperature at Dagoretti, Kenya

0

10

20

30

40

50

60

70

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Date

Rai

nfa

ll (

mm

)

0

5

10

15

20

25

30

Tem

peratu

re (C)

Rainfall Tmax Tmin

Observed Rainfall and Maximum and Minimum Temperature at Eldoret, Kenya

05

1015202530354045

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Date

Rai

nfa

ll (

mm

)

0

5

10

15

20

25

30

Tem

peratu

re (C)

Rainfall Tmax Tmin

Observed Rainfall and Maximum and Minimum Temperature at Lodwar, Kenya

0

2

4

6

8

10

12

14

16

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Date

Rai

nfa

ll (

mm

)

0

5

10

15

20

25

30

35

40

Tem

peratu

re (C)

Rainfall Tmax Tmin

Observed Rainfall and Maximum and Minimum Temperature at Voi, Kenya

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Date

Rai

nfa

ll (

mm

)

0

5

10

15

20

25

30

35

40

Tem

peratu

re (C)

Rainfall Tmax Tmin


13

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





14

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Verification Techniques• Signal/direction test

– Space-time graphical analysis – Correlation analysis

• Accuracy test– Root mean square error analysis

• Skill analysis: – Hit rate (HR)– Proportion Correct (PC)– Equitable Threat Score (ETC)– True Skill Statistic (TSS)– Heidke skill score (HSS)– Two-Alternative Forced Choice Test (2AFC)


15

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

OMOMr ),cov(

221

iii OM

NRMSE Forecast

‘Yes’Forecast

‘No’

Observed ‘Yes’

a b

Observed ‘No’

c d

ba

aHR

dcba

daPC

)(

)(

aRcba

aRaETS

dcba

cabaaR

))((

)(

))(( dbca

bcadTSS

)()(

)(

aR-dcba

aR-d)(a= HSS

)(

))(())(()(

dcba

dbdc+caba= aR

))(( dcba

bdacadP 2

1

AFC2


16

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





17

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Station-Averaged Error Analysis: RFE and Observed Rainfall over Kenya

2004 2005 2006 2007 2008April Corr coef (%) -2.5 -12.6 -4.5 6.9 -7.4November Corr coef (%)

-6.5 -8.0 8.6 4.4 -6.3

April RMSE (mm) 12.8 8.9 15.4 9.6 9.6November RMSE (mm) 8.5 5.7 15.0 8.0 12.2


18

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Spatial Distribution of Correlation Coefficients and RMSE between Observed and RFE Rainfall over Kenya

April 2008 November 2008

Root mean square error

Correlation coefficients

34 35 36 37 38 39 40 41 42

Longitude

R M SE Analysis of R FE vs O bserved R ainfa ll over Kenya for April 2008

-5

-4

-3

-2

-1

0

1

2

3

4

5

Latit

ude

3 m m

6 m m

9 m m

12 m m

15 m m

5 m m

8 m m

10 m m

13 m m

15 m m

18 m m

20 m m

34 35 36 37 38 39 40 41 42

Longitude

R M SE Analysis o f R FE vs O bserved R ainfa ll over Kenya in N ovem ber, 2008

-5

-4

-3

-2

-1

0

1

2

3

4

5

Latit

ude

34 35 36 37 38 39 40 41 42

Longitude

C orre lation Analysis Betw een O bserved andR FE R ainfa ll over Kenya for April 2008

-5

-4

-3

-2

-1

0

1

2

3

4

5

Latit

ude

-0 .25-0.20-0.15-0.10-0.050.000.050.100.150.200.25

34 35 36 37 38 39 40 41 42

Longitude

C orre lation Analysis Betw een O bserved andR FE R ainfa ll over Kenya in N ovem ber, 2008

-5

-4

-3

-2

-1

0

1

2

3

4

5

Latit

ude

-0 .2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2


19

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





20

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Comparison of Observed and GFS Rainfall : November 2008

GFS Model 7-Day Rainfall Forecast over Nyahururu, Kenya

0

5

10

15

20

25

30

05-Nov-08

06-Nov-08

07-Nov-08

08-Nov-08

09-Nov-08

10-Nov-08

11-Nov-08

Target Date

Rai

nfa

ll (

mm

)

Observed Forecast

GFS Model 7-Day Rainfall Forecast over Kisumu, Kenya

0

10

20

30

40

50

60

70

05-Nov-08

06-Nov-08

07-Nov-08

08-Nov-08

09-Nov-08

10-Nov-08

11-Nov-08

Target Date

Rai

nfa

ll (

mm

)

Observed ForecastGFS Model 7-Day Rainfall Forecast over Lodwar, Kenya

02468

1012141618

05-Nov-09

06-Nov-09

07-Nov-09

08-Nov-09

09-Nov-09

10-Nov-09

11-Nov-09

Target Date

Rai

nfa

ll (

mm

)

Observed Forecast

GFS Model 7-Day Rainfall Forecast over Mombasa, Kenya

05

1015202530354045

05-Nov-08

06-Nov-08

07-Nov-08

08-Nov-08

09-Nov-08

10-Nov-08

11-Nov-08

Target Date

Rai

nfa

ll (

mm

)

Observed Forecast


21

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Rainfall Spatial Distribution in Kenya: 1 November 2008 and 3 November 2008

GFS Observed Reanalysis RFE


22

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Observed and GFS Rainfall, Maximum and Minimum Temperature (7 November 2008)Observed 1-Day Lead Time 4-Day Lead Time 7-Day Lead Time


23

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

RMSE and Correlation Analysis: Rainfall, Maximum and Minimum Temperature

Averaged RMSE and Correlation Coefficients between GFS 7-Day Forecasts and Observed Rainfall over

Kenya at Various Lead Times

9.5

10.0

10.5

11.0

11.5

12.0

12.5

1 2 3 4 5 6 7

Lead Time (Days)

RM

SE

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Co

rrelation

C

oefficien

t

RMSE Corr Coef

Averaged RMSE and Correlation Coefficients between GFS 7-Day Forecasted and Observed Maximum Temperature over Kenya at Various Lead times

4.404.454.504.554.604.654.704.754.804.854.90

1 2 3 4 5 6 7

Lead Time (Days)R

MS

E

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

Co

rrelation

C

oefficien

t

RMSE Corr Coef

Averaged RMSE and Correlation Coefficients between GFS 7-Day Forecasted and Observed Minimum Temperature over Kenya at Various Lead Times

2.65

2.70

2.75

2.80

2.85

1 2 3 4 5 6 7

Lead Time (Days)

RM

SE

0.07

0.12

0.17

0.22 Co

rrelation

C

oefficien

t

RMSE Corr Coef

RMSE and Correlation Analyses between Averaged GFS 7-Day Forecasts and Observed Rainfall over

Kenya for Various Lead Times

1.5

2.5

3.5

4.5

5.5

6.5

7.5

1 2 3 4 5 6 7

Lead Time (Days)

RM

SE

-0.7

-0.5

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

Co

rrelation

C

oefficien

t

RMSE Corr Coef

RMSE and Correlation Coefficients Between Averaged GFS 7-Day Forecasts and Observed Maximum

Temperature over Kenya at Various Lead Times

4.1

4.2

4.3

4.4

4.5

4.6

4.7

1 2 3 4 5 6 7

Lead Time

RM

SE

0.400.450.500.550.600.650.700.750.800.850.90

Co

rrelation

C

oefficien

t

RMSE Corr Coef

RMSE and Correlation Coefficients Between Averaged GFS 7-Day Forecasts and Observed Minimum

Temperature over Kenya at Various Lead Times

1.8

1.9

2.0

2.1

2.2

2.3

2.4

1 2 3 4 5 6 7

Lead Time (Days)

RM

SE

0.29

0.31

0.33

0.35

0.37

0.39

0.41

0.43

0.45

Co

rrelation

C

oefficien

t

RMSE Corr Coef


24

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Averaged Hit Rate and Proportion Correct for Rainfall and Temperature

• Rainfall

• Temperature 0

10

20

30

40

50

60

70

80

90

100

1 Day 2 Days 3 Days 4 Days 5 Days 6 Days 7 Days

Lead Time

Sc

ore

(%

)

HR PC

0

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7

Lead Time

Sc

ore

(%

)

Tmax_HR Tmax_PC Tmin_HR Tmin_PC


25

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

GFS Skill Score Indices (%): Rainfall, Maximum and Minimum Temperature

Rainfall Maximum Temperature

Minimum Temperature

2AFCETSHSSTSS

Two-alternative forced choice test scoreEquitable threat scoreHeidke skill score True skill statistic

Averaged Skill Score Indices for the GFS Model Rainfall for Various Lead Times over Kenya

-30

-20

-10

0

10

20

30

40

50

1 2 3 4 5 6 7

Lead Time (Days)

Skill

Scor

e (%)

2AFC ETS HSS TSS

Averaged Skill Score Indices for the GFS Model Maximum Temperature for Various Lead Times over

Kenya

20

25

30

35

40

45

50

55

60

1 2 3 4 5 6 7

Lead Time (Days)

Skill

Score

(%)

2AFC_mean ETS_mean HSS_mean TSS_mean

Averaged Skill Score Indices for the GFS Model Minimum Temperature for Various Lead Times over

Kenya

3

5

7

9

11

13

15

1 2 3 4 5 6 7

Lead Time (Days)

Skill

Score

(%)

2AFC_mean ETS_mean HSS_mean TSS_mean


26

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Outline





27

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Spatial Distribution of 7-day cumulative Rainfall :

1-7 November 2008

GFS Model Observed Reanalysis RFE


28

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Spatial Distribution of 7-day Maximum and Minimum Temperature:

1-7 November 2008


29

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Rainfall Bias: GFS minus Observed Rainfall


30

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

Temperature Difference: GFS minus Observed Maximum and Minimum Temperature


31

University of N

airobi, Nairobi, K

enya

Departm


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.uonbi.ac.ke

7-day GFS and Observed Total Rainfall and Average Temperature

Rainfall Tmax Tmin

Eldoret

Mombasa

Narok

Temporal Variability of GFS and Observed 7-Day Total Rainfall at Eldoret, Kenya

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.51-

7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Maximum Temperature at Eldoret, Kenya

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Minimum Temperature at Eldoret, Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Total Rainfall at Mombasa, Kenya

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Maximum Temperature at Mombasa, Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Minimum Temperature at Mombasa, Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Total Rainfall at Narok, Kenya

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Maximum Temperature at Narok, Kenya

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Minimum Temperature at Narok, Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model


32

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airobi, Nairobi, K

enya

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Station-averaged Temporal Variability Rainfall, Maximum and Minimum Temperature

Temporal Variability of GFS and Observed 7-Day Total Rainfall Averaged over Kenya

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed ModelTemporal Variability of GFS and Observed 7-Day Mean Maximum Temperature Averaged over Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model

Temporal Variability of GFS and Observed 7-Day Mean Minimum Temperature Averaged over Kenya

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1-7

3-9

5-11

7-13

9-15

11-1

7

13-1

9

15-2

1

17-2

3

19-2

5

21-2

7

23-2

9

Dates

Sta

ndar

dize

d A

nom

aly

Observed Model


33

University of N

airobi, Nairobi, K

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.uonbi.ac.keError Analysis of 7-Day Total Rainfall (mm) and 7-Day Average Maximum

and Minimum Temperature (C)

Correlation Coefficient (%) Root Mean Square Error

Rain-fall

Max Temp

Min Temp

Rain-fall

Max Temp

Min Temp

12-Station Mean

57 76 21 26.6 4.6 2.5

Area Average 82 -12 38 14.8 7.1 1.3


34

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Skill Score Indices (%) : Rainfall, Maximum and Minimum Temperature


35

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Outline





36

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Summary of Results

• RFE rainfall estimates may not be representative indicators of the rainfall distribution over Kenya & should only be used with caution

• GFS displaces the location of the observed rainfall over the region and underestimates the observed rainfall (but also gives false alarms for some ASALs areas)

• The accuracy of the model-generated rainfall and maximum and minimum temperature decreases with increasing prediction lead time

• The skill for rainfall beyond 5 days is unreliable


37

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Summary of Results• GFS generally captures the locations of

highest and lowest maximum and minimum temperatures but exaggerates their areal extent

• GFS underestimates maximum temperature but overestimates minimum temperature

• GFS has better skill in predicting daily maximum temperature than it does with rainfall, and worst for minimum temperature


38

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Conclusions• GFS is a useful tool for predicting the cycle of 7-

day rainfall and maximum temperature, but not minimum temperature over the domain

• GFS has better skill in predicting rainfall, maximum and minimum temperature for seven day averaged forecasts than for daily forecasts over a seven-day period

• Seven-day averaged quantities are not superior to daily forecasts within the first two to four days of the forecasts, but may be useful for predicting mean quantities on extended NWP range


39

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

Recommendation

• The model needs some fine tuning to improve its ability to predict the maximum temperature and rainfall. The model, in its current form, is not suitable for predicting minimum temperatures over the domain

• There is need to recalibrate RFE and improve the quality of reanalysis data


40

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

• Thank you for your attention

• Merci boucoup

• Ahsante sana


41

University of N

airobi, Nairobi, K

enya

Departm


ww

.uonbi.ac.ke

university of nairobi, nairobi, kenya department of meteorology, august 15-19, 201111th...

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