SEASONAL PREDICTIONS AND MONITORING FOR SAHEL REGION

1. Consiglio Nazionale delle Ricerche SEASONAL PREDICTIONS AND MONITORING FOR SAHEL REGION WMO, Geneva, May 2005 G. Maracchi IBIMET-CNR

2. Seasonal Forecasting Motivations: • Why a “new” seasonal forecasting method is needed? • New insights on African – Monsoon physical mechanism and SST role on precipitation (Vizy&Cook2001, Giannini et al 2003). • A monthly anomaly data is needed, at least, for any agrometeorological application: seeding time and early warning systems. • Ongoing Activity on Seasonal Forecasting: • Setting up a map server – based data dissemination tool for end-users: • qualitatively browsing of available maps; • simple extraction of data for end-users applications: agrometeorological, risk management, hydrology; • Spatial Downscaling techniques;

3. Seasonal Forecasts: The Analogue Method

4. Feed Asian Monsoon Most variability during ENSO Water Vapour for African Monsoon • OUTPUT: Precip. Anomaly vs. 1979-2003 Clim. • ISSUED: every month • VALIDITY: Quarterly and Monthly Analogues method at Ibimet SST as Predictors over : • Niño-3 (5S-5N;150W-90W) • Guinea Gulf (10S-5N;20W-10E) • Indian Ocean (5S-15N;60E-90E)

5. Method • Standardized* Anomalies (SSTA) obtained by: • Subtraction of the 1979-2003 SST average • Division by 1979-2003 SST standard deviation • Standardized Change Rates to consider the trend of the predictors defined as: difference between current and previous standardized SSTA *Standardization is used to have the same order of magnitude of all the predictors

6. Best Analog year Search for the Analogue Each month in [1979-2003] is defined by a vector in a 6 dimentional space: • Predictors Pi : • SST Nino-3 std anomalies • SST Guinea std anomalies • SST Indian std anomalies • SST Nino-3 Change rate • SST Guinea Change rate • SST Indian Change rate Analog criterion: Minimization of the Euclidean distance in the 6-dimensional space of predictors Pi:

7. Seasonal Forecast: Step by Step CURRENT MONTH e.g.: April 2005 ANALOGUE YEAR e.g.: April 1989 MONTH+1 e.g.: May 2005 ≡ May 1989 MONTH+2 e.g.: June 2005 ≡ June 1989 MONTH+3 e.g.: July 2005 ≡ July 1989 CLIMATOLOGICAL AVERAGE e.g.: May, June, July 1979-2003 ANOMALIES

8. IBIMET Seasonal Products http://www.ibimet.cnr.it/Case/sahel/

9. Seasonal Rainfall Forecasts http://www.ibimet.cnr.it/Case/sahel/ AMJ - Anomaly May – Percent Anomaly

10. Qualitative Comparison: 1998 Good Accordance JAS – issued on June 1999

11. Qualitative Comparison: 2001 Good Accordance 2003 JAS – issued on June

12. Qualitative Comparison: Good Accordance 2004 JAS – issued on June

13. Qualitative Comparison: 2000 Bad Accordance 2002 JAS – issued on June

14. Monitoring Tools: • HOWI (Hydrological Onset and Withdrawal Index) • Satellite Rainfall Estimates based on Meteosat &SSM/I • NDVI based on Meteosat Second Generation

15. HOWI Dynamics To diagnose onset and withdrawal vertically integrated moisture transport (VIMT) is used 2005

16. Monsoon seasons for each year identified using HOWI 1984 no season !!

17. Monsoon seasons for each year identified using HOWI

18. Monitoring rainfall – Meteosat & SSM/I Output: every six hours – Resolution ~ 5 km

19. Monitoring NDVI using MSG Output: daily Resolution ~ 3 km near Equator

20. DATA DISSEMINATION

21. A new data dissemination tool: The Map Server • Advantages of Map Server • Simple and Efficient Map Displaying • Map Browsing • Data Query and Manipulation • Scale Dependent layers drawing Remote Data Server IBIMET End - User Possible ingestion of spatial downscaling modules in the Map Server.

22. Conclusion • The improving of seasonal forecasts on Sahel region, especially for agrometeorological applications, is based on a full comprehension of physical mechanism including Hadley Cell dynamics. • Geographical information scale would be coherent with agrometeorological models ( < 10km ). • Dissemination of seasonal forecast information should take into account the new web-based tools such as Map Server.