1. Climate Modeling LaboratoryMEASNC State University Predictability of the Moisture Regime During the Pre-onset Period of Sahelian Rains Robert J. Mera Marine, Earth and Atmospheric Sciences North Carolina State University Seminar, April 3rd 2009

2. Climate Modeling LaboratoryMEASNC State University Motivation Why is the moisture regime important? Prediction of Monsoon rainfall Agriculture African Easterly Waves Public health: Meningitis Outbreaks

3. Climate Modeling LaboratoryMEASNC State University Outline The Application – Background – Health-climate link Our Study – Importance of Downscaling – Predictability of Pre-onset Conditions – Ensemble Prediction and Evaluation of Model Skill

4. Climate Modeling LaboratoryMEASNC State University The Application Meningitis is a serious infectious disease affecting 21 countries 300 million people at risk across the Sahel 700,000 cases in the past 10 years 10-50 % fatality rate 256,000 people lost to the disease in 1996 SAHEL

5. Climate Modeling LaboratoryMEASNC State University Meningitis-Climate link Outbreaks coincide with dry, dusty conditions over the Sahel due to the Harmattan winds flowing south from the Sahara (Jan-May) Largest correlation occurs between low humidity and disease outbreaks (Molesworth et al., 2006) Disease occurrence drops dramatically with the onset of humidity JanuaryJuly SH SHL Harmattan Moisture ITCZ

6. Climate Modeling LaboratoryMEASNC State University Meningitis-Climate link The most actionable case involves the link between humidity onset and cessation of disease Pink: # of casesOrange: Relative Humidity (%) 19982004

7. Climate Modeling LaboratoryMEASNC State University Current Efforts University Corporation for Atmospheric Research (UCAR) and the Google Foundation are funding efforts to explore climate-meningitis dynamics Global scale models will be employed for operational purposes

8. Climate Modeling LaboratoryMEASNC State University Our study: Importance of Downscaling WRF at 30km resolutionNCEP/NCAR Reanalysis at 2.5° 65 60 55 45 50 40 35 30 25 20 Ghana Relative Humidity (%)

9. Climate Modeling LaboratoryMEASNC State University The Scientific Question: Predictability of Moisture What are the dynamics governing the northward progression of the moisture regime? How well does the model represent the physical processes? What is the skill of the model in predicting the dynamics and statistics of the physical processes?

10. Climate Modeling LaboratoryMEASNC State University In the literature The West Africa summer monsoon is characterized by two steps: preonset and onset (Sultan and Janicot, 2003) The preonset stage corresponds to the arrival of the Inter Tropical Front (ITF) at 15 ° N From Sultan and Janicot (2003) Rain (mm/day) ITF

11. Climate Modeling LaboratoryMEASNC State University Schematic Cross Section of the West African Monsoon Sahara Sahel 10 N 20 N Equator 600 hPa 200 hPa 1000 hPa ITCZ Deep dry convection Deep moist convection AEJ Slide from John Marsham, U. of Leeds

12. Climate Modeling LaboratoryMEASNC State University Our Study The northward progression of moisture is related to the preonset stage of the monsoon and the position of the ITF Two important factors at work: –Interannual variability is dictated by fluxes in sea surface temperatures (SST), interaction with mid- latitude systems (teleconnections) –Intraseasonal variability is related to east-west transient disturbances, African Easterly Jet

13. Climate Modeling LaboratoryMEASNC State University Data and Methods NCEP/NCAR, ECMWF Reanalysis, In-situ observations & satellite data: Statistics of Relative Humidity, etc We use the Advanced Research WRF (WRF- ARW) Model for downscaling of reanalysis and operational forecasts, sensitivity analyses *NCEP: National Centers for Environmental Prediction *NCAR: National Center for Atmospheric Research *WRF: Weather Research and Forecasting Model *ECMWF: European Centre for Medium-Range Weather Forecasts

14. Climate Modeling LaboratoryMEASNC State University Preliminary analysis and results

15. Climate Modeling LaboratoryMEASNC State University Historical Data: Reanalysis Mean 2000–2008 relative humidity time series (%) computed on the grid points located between 10 ° W and 10 ° E longitude, 14.5 ° N and 15.5 ° N latitude Two distinct slopes APR 15 JUN 14 JUN 24

16. Climate Modeling LaboratoryMEASNC State University April 1, 2006 relative humidity (%) at the surface, 925mb winds and u component at 0 to delineate ITF Cross section along the prime meridian from 0° to 20 ° N: Relative humidity (shaded) and u component at 0 EQ 20N 700 mb Model simulations AEJ

17. Climate Modeling LaboratoryMEASNC State University Ensemble Prediction We will use the ensemble prediction approach to generate probabilistic forecasts that will also allow us to analyze model skill

18. Climate Modeling LaboratoryMEASNC State University An ensemble forecast run was tested against interpolated observations Interpolated Observations Ensemble Simulation

19. Climate Modeling LaboratoryMEASNC State University -10 -8 -6 -4 -2 2 4 6 8 10 Relative Humidity Anomaly (%) An ensemble forecast run was tested against interpolated observations The error (anomaly) is much smaller than the signal

20. Climate Modeling LaboratoryMEASNC State University Analyzing Model Skill NoYes No No cost (  ) Miss (  ) Yes False Alarm (  ) Hit (  ) Observations EPS Forecast

21. Climate Modeling LaboratoryMEASNC State University The Relative Operating Characteristic (ROC) The ROC method is widely used for estimating the skill of ensemble prediction systems (EPS) (Marzban, 2004) A perfect forecast system would have a ROC area (ROCA) of 1

22. Climate Modeling LaboratoryMEASNC State University An Extended ROC Procedure ROC plots model skill only for an optimum user We developed an extended (EROC) procedure that caters to a particular user’s needs: Shift in baselines According to user Semazzi & Mera, 2006

23. Climate Modeling LaboratoryMEASNC State University Model Skill for End-user Additional analysis through EROC can help with current health efforts and the incurred costs: –Transportation of Supplies –Inoculation –Personnel

24. Climate Modeling LaboratoryMEASNC State University Looking Forward Understanding the moisture regime statistics: variance of 40% RH date and changes in slope of humidity trends Sensitivity studies using SSTs, land cover, meridional transient distrubances, teleconnections with mid-latitude systems Application of EROC for surface conditions pertinent to health efforts

25. Climate Modeling LaboratoryMEASNC State University Acknowledgements Dr Semazzi CML crew Google/UCAR group NOAA ISET Dr Arlene Laing, Dr Tom Hopson

26. Climate Modeling LaboratoryMEASNC State University Questions?

27. Climate Modeling LaboratoryMEASNC State University Auxiliary slides

28. Climate Modeling LaboratoryMEASNC State University Historical Data: Reanalysis Mean 2000–2008 relative humidity time series (%) computed on the grid points located between 10 ° W and 10 ° E longitude, 14.5 ° N and 15.5 ° N latitude

29. Climate Modeling LaboratoryMEASNC State University Large scale Climatology

30. Climate Modeling LaboratoryMEASNC State University Large Scale Climatology

31. Climate Modeling LaboratoryMEASNC State University

32. Climate Modeling LaboratoryMEASNC State University Criteria for Issuing a forecast Decision to issue a forecast of an event (E) to occur is probabilistically based on the criteria: Where: (N): size of the ensemble (n): number of the runs in the ensemble for which (E) actually occurs (p): probability given by the ratio (n/N) This is the threshold fraction above which the event (E) is predicted to occur based on the model forecast

33. Climate Modeling LaboratoryMEASNC State University

34. Climate Modeling LaboratoryMEASNC State University

35. Climate Modeling LaboratoryMEASNC State University