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University of Nairobi, Nairobi, Kenya Department of Meteorology, www.uonbi.ac.ke August 15-19, 201111th Inter'l RSM Workshop-National Central University,

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Presentation on theme: "University of Nairobi, Nairobi, Kenya Department of Meteorology, www.uonbi.ac.ke August 15-19, 201111th Inter'l RSM Workshop-National Central University,"— Presentation transcript:

1 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 1 Predictability of Weather on Extended NWP Timescales over Kenya Using the GFS Model Franklin J. Opijah University of Nairobi, Kenya

2 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 2 Early-Warning Systems may reduce vulnerability to floods, disease, pestilence, strong winds, hazardous air Dust Storms/Hazardous Air Malaria Epidemics Communication Impairment Strong Winds

3 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 3 EWS can reduce vulnerability to pests, drought and famine Food Insecurity Livestock Management Pest Invasions Food Availability Water Resource Management Heat Waves Hydropower Generation

4 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 4 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 5 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? IndicatorComing rains Dry spell Croaking frogs PronouncedReduced Migrant birdsAppearance Disappearance Indigenous trees Leaving, flowering Shedding CattleStampedes Bird nests (weaver birds ) More nestsFewer nests Red antsAppearance Human bodyDiscomfort (hot) Discomfort (cold)

6 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 6 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 7 Outline 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

8 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 8 Global Forecast System Horizontal 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 (O 3, H 2 O, CO 2, CH 4, N 2 O, 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 9 Outline 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

10 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 10 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 11 Domain of Study Topography & Homogeneous Climate Zones

12 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 12 Observed Weather Patterns over Kenya in the 2008 OND Season

13 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 13 Outline 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

14 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 14 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 15 Forecast ‘ Yes ’ Forecast ‘ No ’ Observed ‘ Yes ’ ab Observed ‘ No ’ cd

16 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 16 Outline 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

17 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 17 Station-Averaged Error Analysis: RFE and Observed Rainfall over Kenya April Corr coef (%) November Corr coef (%) April RMSE (mm) November RMSE (mm)

18 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 18 Spatial Distribution of Correlation Coefficients and RMSE between Observed and RFE Rainfall over Kenya April 2008November 2008 Root mean square error Correlation coefficients

19 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 19 Outline 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

20 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 20 Comparison of Observed and GFS Rainfall : November 2008

21 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 21 Rainfall Spatial Distribution in Kenya: 1 November 2008 and 3 November 2008 GFSObservedReanalysisRFE

22 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 22 Observed and GFS Rainfall, Maximum and Minimum Temperature (7 November 2008) Observed1-Day Lead Time4-Day Lead Time7-Day Lead Time

23 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 23 RMSE and Correlation Analysis: Rainfall, Maximum and Minimum Temperature Rainfall Maximum Temperature Minimum Temperature Station Average Area-Average

24 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 24 Averaged Hit Rate and Proportion Correct for Rainfall and Temperature Rainfall Temperature

25 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 25 GFS Skill Score Indices (%): Rainfall, Maximum and Minimum Temperature RainfallMaximum Temperature Minimum Temperature 2AFC ETS HSS TSS Two-alternative forced choice test score Equitable threat score Heidke skill score True skill statistic

26 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 26 Outline 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

27 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 27 Spatial Distribution of 7-day cumulative Rainfall : 1-7 November 2008 GFS ModelObservedReanalysisRFE

28 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 28 Spatial Distribution of 7-day Maximum and Minimum Temperature: 1-7 November 2008 GFS ModelObservedReanalysis Maximum Temperature Minimum Temperature

29 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 29 Rainfall Bias: GFS minus Observed Rainfall

30 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 30 Temperature Difference: GFS minus Observed Maximum and Minimum Temperature

31 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 31 7-day GFS and Observed Total Rainfall and Average Temperature RainfallTmaxTmin Eldoret Mombasa Narok

32 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 32 Station-averaged Temporal Variability Rainfall, Maximum and Minimum Temperature

33 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 33 Error 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 Area Average

34 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 34 Skill Score Indices (%) : Rainfall, Maximum and Minimum Temperature Maximum Temperature Minimum Temperature Rainfall Skill score 2AFCETSHSSTSS2AFCETSHSSTSS2AFCETSHSSTSS Station Mean Area Average

35 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 35 Outline 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

36 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 36 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 37 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 38 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 39 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 Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 40 Thank you for your attention Merci boucoup Ahsante sana

41 University of Nairobi, Nairobi, Kenya Department of Meteorology, August 15-19, th Inter'l RSM Workshop-National Central University, Jhongli, ROC 41


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