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.