SEASONAL PREDICTIONS AND MONITORING FOR
SAHEL REGION
G. MaracchiIBIMET-CNR
Consiglio Nazionale delle Ricerche
WMO, Geneva, May 2005
Seasonal ForecastingMotivations:
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;
Seasonal Forecasts:The Analogue Method
Analogues method at IbimetSST as Predictors over :
1.Niño-3 (5S-5N;150W-90W)
2.Guinea Gulf (10S-5N;20W-10E)
3.Indian Ocean (5S-15N;60E-90E)
• OUTPUT: Precip. Anomaly vs. 1979-2003 Clim.
• ISSUED: every month
• VALIDITY: Quarterly and Monthly
Water Vapour for African Monsoon
Most variability during ENSO
Feed Asian Monsoon
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
Search for the AnalogueEach month in [1979-2003] is defined by a vector in a 6 dimentional space:
Analog criterion: Minimization of the Euclidean distance in the 6-dimensional space of predictors Pi:
Predictors Pi :1. SST Nino-3 std anomalies2. SST Guinea std anomalies3. SST Indian std anomalies4. SST Nino-3 Change rate5. SST Guinea Change rate6. SST Indian Change rate
6
1
2min past
icurr
i PP
Best Analog year
Seasonal Forecast: Step by StepCURRENT MONTH
e.g.: April 2005
ANALOGUE YEARe.g.: April 1989
MONTH+1e.g.: May 2005 ≡ May 1989
MONTH+2e.g.: June 2005 ≡ June 1989
MONTH+3e.g.: July 2005 ≡ July 1989
CLIMATOLOGICAL AVERAGEe.g.: May, June, July 1979-2003
ANOMALIES
IBIMET Seasonal Productshttp://www.ibimet.cnr.it/Case/sahel/
Seasonal Rainfall Forecastshttp://www.ibimet.cnr.it/Case/sahel/
AMJ - Anomaly
May – Percent Anomaly
Qualitative Comparison:
1998
1999
Good Accordance
JAS – issued on June
2003
2001
Qualitative Comparison:
Good Accordance
JAS – issued on June
2004
Good Accordance
Qualitative Comparison:
JAS – issued on June
2000
2002
Bad Accordance
Qualitative Comparison:
JAS – issued on June
Monitoring Tools:• HOWI (Hydrological Onset and Withdrawal Index)• Satellite Rainfall Estimates based on Meteosat &SSM/I• NDVI based on Meteosat Second Generation
HOWI DynamicsTo diagnose onset and withdrawal vertically integrated moisture
transport (VIMT) is used
2005
su rfa cemb
mb g
dpqVIMT U
1 0 0 0
8 5 0
w h e r eq i s t h e s p e c i f i c h u m i d i t y i n u n i t s o f g K g - 1
U i s t h e w i n d v e c t o r i n u n i t s o f m s - 1
p i s t h e p r e s s u r e i n u n i t s o f m bg i s t h e g r a v i t a t i o n a l a c c e l e r a t i o n i n u n i t s o f m s - 2
F o l l o w i n g F a s u l l o a n d W e b s t e r ( 2 0 0 3 ) t h e t i m e s e r i e s X o f V I M T i n t h e a r e a o f i n t e r e s t i s n o r m a l i z e d b y t h ec l i m a t o l o g i c a l a n n u a l c y c l e t h r o u g h t h e t r a n s f o r m a t i o n
w h e r e i s t h e n o r m a l i z e d t i m e s e r i e s ( 1 9 7 9 2 0 0 2 ) a r e t h e v a l u e s o f t h e c l i m a t o l o g i c a l a n n u a l c y c l e
1minmaxmin2 XX
su rfa cemb
mb g
dpqVIMT U
1 0 0 0
8 5 0
w h e r eq i s t h e s p e c i f i c h u m i d i t y i n u n i t s o f g K g - 1
U i s t h e w i n d v e c t o r i n u n i t s o f m s - 1
p i s t h e p r e s s u r e i n u n i t s o f m bg i s t h e g r a v i t a t i o n a l a c c e l e r a t i o n i n u n i t s o f m s - 2
F o l l o w i n g F a s u l l o a n d W e b s t e r ( 2 0 0 3 ) t h e t i m e s e r i e s X o f V I M T i n t h e a r e a o f i n t e r e s t i s n o r m a l i z e d b y t h ec l i m a t o l o g i c a l a n n u a l c y c l e t h r o u g h t h e t r a n s f o r m a t i o n
w h e r e i s t h e n o r m a l i z e d t i m e s e r i e s ( 1 9 7 9 2 0 0 2 ) a r e t h e v a l u e s o f t h e c l i m a t o l o g i c a l a n n u a l c y c l e
1minmaxmin2 XX
Monsoon seasons for each year identified using HOWI
1984 no season !!
Monsoon seasons for each year identified using HOWI
Monitoring rainfall – Meteosat & SSM/I
Output: every six hours – Resolution ~ 5 km
Monitoring NDVI using MSG
Output: daily Resolution ~
3 km near Equator
DATA DISSEMINATION
Legend
apcp_030505
High : 431
Low: 0
IBIMET Remote Data Server
End - User
Advantages of Map Server • Simple and Efficient Map Displaying• Map Browsing• Data Query and Manipulation• Scale Dependent layers drawing
A new data dissemination tool: The Map Server
Possible ingestion of spatial downscalingdownscaling modules in the
Map Server.
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.