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

5. Method Standardized* Anomalies (SSTA) obtained by:
Subtraction of the SST average
Division by 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. Search for the Analogue
Each month in [ ] 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
Best Analog year
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
ANOMALIES

8. IBIMET Seasonal Products

9. Seasonal Rainfall Forecasts
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
IBIMET
Remote Data
Server
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.