Weather and climate monitoring for food risk management

1. Weather and climate monitoring for food risk management Consiglio Nazionale delle Ricerche WMO, Geneva, November 2004 G. Maracchi IBIMET-CNR

2. Critical tools for food risk management in West Africa: • The activities of Ibimet are: • Monitoring(rainfall, vegetation) • Short term forecast(rainfall, temperature, humidity) • Medium term prediction(advection of humidity, beginning and length of the cropping season in the Sahel) • Long term prediction(2-3 months rainfall prediction)

3. Monitoring rainfall Calibration of IR Meteosat channel using SSM/I + SSM/I: 7 passages /day Meteosat IR channel

4. Monitoring rainfall Meteosat & SSM/I output Temporal res: every six hours – Spatial res ~ 5 km

5. Monitoring rainfall Meteorological Information Service for the area touched by the Darfur crisis

6. Monitoring rainfall Integration of a Local Area Model in satellite rainfall estimate Model: RAMS 4.3.0.0 Simulations Domain: 1 Grid Delta_x = Delta_y = 60km NX = NY = 120 Top = 17 km, 36 levels

7. Monitoring rainfall Integration of a Local Area Model in satellite rainfall estimate Satellite Estimate RAMS Simulation Regional Reanalysis with RAMS -use of satellite estimation to locate rainfall events -use of RAMS simulation to extrapolate rainfall amount

8. Monitoring NDVI MSG product • Advantage: • 15 minutes outputs used to compute daily and decadal images with Maximum Value Composite (MVC) technique in order to remove clouds effect

9. Monitoring NDVI Derived product: vegetation development Seasonal vegetation development in Burkina-Faso – AP3A Project

10. Short term forecast Statistical Downscaling of Global Forecast System GFS 00 UTC run Variables: total precipitation, wind, pressure, relative humidity, temperature Levels: surface, 1000mb, 925mb, 850 mb Spatial coverage: global – Resolution 1° Input Statistical Model Kriging method Output • Daily and comprehensive (180hrs) output of the choosen variables at 0.1° resolution distributed through Internet facilities – Spatial coverage: West and East Africa

11. Short term forecast Statistical Downscaling of Global Forecast System Kriging Forecast period:00 - 180Hrs Resolution:0.1° Spatial coverage:18W 49E – 3N 28N Forecast period:00 - 180Hrs Resolution:1° Spatial coverage:Global

12. Short term forecast Statistical Downscaling of Global Forecast System Other parameters downscaled:Relative Humidity 1000mb + Temperature 1000mb + Zonal and Meridional wind + Pressure Forecast period:00 - 180Hrs Resolution:0.1° Spatial coverage:18W 49E – 3N 28N

13. Medium term forecast Vertical Integrated Moisture Transport – VIMT The moisture advection is mainly meridional

14. Medium term forecast Operative use of VIMT through HOWI (Hidrological Onset and Withdrawal Index)

15. Medium term forecast Predictive meaning of HOWI When HOWI>0 we can predict that monsoon onset will take place from 6 weeks (WAM) up to 2 weeks after (North Sahel) WAM = 10W 10E – 5N 20N Sahel = 10W 10E – 10N 20N N Sahel = 10W 10E – 15N 20N

16. Medium term forecast Current monsoon season HOWI dynamics computed for each area of interest Comparison with climatological profile

17. Medium term forecast SISP/ ZAR (Zones à Risque) Models Input Methodology Output • forecast of the length of the current season • evaluation of the possibility to sow in zones that are not yet sown • comparison between the actual onset with the average onset of the agricultural season • the average growing season onset, length, end • … • Rainfall estimates derived from METEOSAT images • Agroclimatic characterisation of the territory based on rainfall time series analysis and relevant cropping systems (millet, sorghum) ZAR Model SISP Model

18. Medium term forecast ZAR (Zones à Risque) Output Comparison between the beginning of season respect to climatology Estimation of the length of season

19. Long term forecast – State of art ECMWF Met Office

20. Long term forecast – State of art IRI African Desk (NOAA/NCEP) Presao ACMAD

21. Long term forecast State of the art at IBIMET Multidimensional space: SST Nino-3 std anomalies SST Guinea std anomalies SST Indian std anomalies SST Nino-3 Growth rate SST Guinea Growth rate SST Indian Growth rate

22. Long term forecast State of the art at IBIMET - Each year in [1979-2003] is defined by the esa vector = (SSTs1,…,GrowthRate1,…) Forecast criterion: Proximity technique with euclidean distance for comparison with similar years

23. Long term forecast State of the art at IBIMET – 2004 Result OUTPUT: Percentage anomaly respect to climatology ISSUED: every month since April VALIDITY: 3 months

24. Long term forecast Development of a new statistical model at IBIMET • New predictors: • Atlantic and Guinean SST Anomalies • Geopotential heigth 500 mb • Soil moisture • Previous (SepOctNov) Guinean 2° rainfall season

25. Long term forecast Statistical Model IBIMET - Predictors Computation of Atlantic and Guinean SST anomalies thanks to MSG

26. Long term forecast Statistical Model IBIMET - Predictors Geopotential Height Anomalies

27. Long term forecast Statistical Model IBIMET - Predictors Sahel spring soil humidity anomalies

28. Long term forecast Statistical Model IBIMET - Predictors Previous SepOctNov Guinean Precipitation

29. Long term forecast Statistical Model IBIMET Predictors -SSTs Anomalies -Geopotential Heigth 500 mb -Soil Humidity -Previous SON Guinean preciptation Statistical Model MultiLinear Regression MLR with Stepwise Output • Percentage Anomalies respect to climatology • Forecast validity 3 months • Issued every month since April

30. CONCLUSION • IBIMET activities cover all steps of meteo and climate informations for feeding food crises prevention process • Innovative tools have been developed to improve monitoring and forecasting techniques • Operational products are available and quasi real time diffusion of informations • Effort in the next future will be focused on operational production of long term predictions