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Predictability of Weather on Extended NWP Timescales over Kenya Using the GFS Model. Franklin J. Opijah University of Nairobi, Kenya www.uonbi.ac.ke. Strong Winds. Dust Storms/Hazardous Air. Malaria Epidemics. Communication Impairment.
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Predictability of Weather on Extended NWP Timescales over Kenya Using the GFS Model Franklin J. Opijah University of Nairobi, Kenya www.uonbi.ac.ke 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Strong Winds Dust Storms/Hazardous Air Malaria Epidemics Communication Impairment Early-Warning Systems may reduce vulnerabilityto floods, disease, pestilence, strong winds, hazardous air 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Livestock Management Pest Invasions Hydropower Generation Water Resource Management Food Insecurity Heat Waves Food Availability EWS can reduce vulnerability to pests, drought and famine 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Malaria is rife in humid, high temperature areas Meningitis is rife in dusty, low-humidity areas Strong Linkage between Weather Conditions and Disease 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Is it possible to forecast impending weather using indigenous knowledge (IK)? Modelling Challenge: Is NWP Superior to IK? Traditional Forecasting Techniques in Kenya (UNDP Report) 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Global Forecast System 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 Weather/Climate Controls over Kenya 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Domain of Study Topography & Homogeneous Climate Zones 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Observed Weather Patterns over Kenya in the 2008 OND Season 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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) 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Station-Averaged Error Analysis: RFE and Observed Rainfall over Kenya 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Spatial Distribution of Correlation Coefficients and RMSE between Observed and RFE Rainfall over Kenya 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Comparison of Observed and GFS Rainfall : November 2008 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Rainfall Spatial Distribution in Kenya: 1 November 2008 and 3 November 2008 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Observed and GFS Rainfall, Maximum and Minimum Temperature (7 November 2008) 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
RMSE and Correlation Analysis:Rainfall, Maximum and Minimum Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Rainfall Temperature Averaged Hit Rate and Proportion Correct for Rainfall and Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
GFS Skill Score Indices (%): Rainfall, Maximum and Minimum Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Spatial Distribution of 7-day cumulative Rainfall : 1-7 November 2008 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Spatial Distribution of 7-day Maximum and Minimum Temperature: 1-7 November 2008 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Rainfall Bias: GFS minus Observed Rainfall 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Temperature Difference: GFS minus Observed Maximum and Minimum Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
7-day GFS and Observed Total Rainfall and Average Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Station-averaged Temporal Variability Rainfall, Maximum and Minimum Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Error Analysis of 7-Day Total Rainfall (mm) and 7-Day Average Maximum and Minimum Temperature (C) 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Skill Score Indices (%) : Rainfall, Maximum and Minimum Temperature 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
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 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
Thank you for your attention • Merci boucoup • Ahsante sana 11th Inter'l RSM Workshop-National Central University, Jhongli, ROC
11th Inter'l RSM Workshop-National Central University, Jhongli, ROC