weather and climate monitoring for food risk management g. maracchi wmo, geneva, november 2004...
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Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Weather and climate monitoring for food risk
management
G. MaracchiIBIMET-CNR
Consiglio Nazionale delle Ricerche
WMO, Geneva, November 2004
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
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)
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring rainfallCalibration of IR Meteosat channel using SSM/I
SSM/I: 7 passages / day
+
Meteosat IR channel
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring rainfall Meteosat & SSM/I output
Temporal res: every six hours – Spatial res ~ 5 km
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring rainfall Meteorological Information Service for the area
touched by the Darfur crisis
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring rainfallIntegration of a Local Area Model in satellite
rainfall estimate
Simulations Domain:
1 Grid
Delta_x = Delta_y = 60km
NX = NY = 120
Top = 17 km, 36 levels
Model: RAMS 4.3.0.0
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring rainfallIntegration of a Local Area Model in satellite rainfall estimate
RAMS SimulationSatellite Estimate
Regional Reanalysis with RAMS
-use of satellite estimation to locate rainfall events
-use of RAMS simulation to extrapolate rainfall amount
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring NDVIMSG product
Advantage:
•15 minutes outputs used to compute daily and decadal images with Maximum Value Composite (MVC) technique in order to remove clouds effect
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Monitoring NDVIDerived product: vegetation development
Seasonal vegetation development in Burkina-Faso – AP3A Project
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Short term forecast Statistical Downscaling of Global Forecast System
Input
Output
Statistical Model
GFS 00 UTC run Variables: total precipitation, wind, pressure, relative humidity, temperatureLevels: surface, 1000mb, 925mb, 850 mbSpatial coverage: global – Resolution 1°
Kriging method
•Daily and comprehensive (180hrs) output of the choosen variables at 0.1° resolution distributed through Internet facilities – Spatial coverage: West and East Africa
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Forecast period: 00 - 180HrsResolution: 0.1°
Spatial coverage: 18W 49E – 3N 28NForecast period: 00 - 180HrsResolution: 1°Spatial coverage: Global
Kriging
Short term forecast Statistical Downscaling of Global Forecast System
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Other parameters downscaled: Relative Humidity 1000mb + Temperature 1000mb + Zonal and Meridional wind + PressureForecast period: 00 - 180HrsResolution: 0.1°Spatial coverage: 18W 49E – 3N 28N
Short term forecast Statistical Downscaling of Global Forecast System
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Medium term forecast Vertical Integrated Moisture Transport – VIMT
The moisture advection is
mainly meridional
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Medium term forecastOperative use of VIMT through
HOWI (Hidrological Onset and Withdrawal Index)
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Medium term forecastPredictive 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 20NSahel = 10W 10E – 10N 20NN Sahel = 10W 10E – 15N 20N
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Medium term forecastCurrent monsoon season
HOWI dynamics computed for each area of interest
Comparison withclimatological profile
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Medium term forecastSISP/ ZAR (Zones à Risque) Models
Input
ZAR ModelSISP Model
OutputMethodology
•Rainfall estimates derived from METEOSAT images
•Agroclimatic characterisation of the territory based on rainfall time series analysis and relevant cropping systems (millet, sorghum)
•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•…
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Estimation of the length of season
Comparison between the beginning of season respect to climatology
Medium term forecastZAR (Zones à Risque) Output
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Long term forecast – State of artmodel type output forecast period
ECMWF numerical anomaly % 6 months
Met Office numerical anomaly % 2-4 months
ECMWF Met Office
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
IRI
Long term forecast – State of artmodel type output forecast periodIRI statistical & numerical anomaly % 3 monthsPRESAO statistical & numerical anomaly % 4 monthsAfrican Desk statistical anomaly % 5 months
African Desk (NOAA/NCEP)
Presao ACMAD
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Long term forecastState of the art at IBIMET
Multidimensional space:SST Nino-3 std anomaliesSST Guinea std anomaliesSST Indian std anomaliesSST Nino-3 Growth rateSST Guinea Growth rateSST Indian Growth rate
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Forecast criterion: Proximity technique with euclidean distance for comparison with similar years
MultiDimensional Space
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
R1
R5
TARGET YEAR
Each year in [1979-2003] is defined by the esa vector = (SSTs1,…,GrowthRate1,…)
Long term forecastState of the art at IBIMET -
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
OUTPUT: Percentage anomaly respect to climatology
ISSUED: every month since April
VALIDITY: 3 months
Long term forecastState of the art at IBIMET – 2004 Result
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
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
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Long term forecast Statistical Model IBIMET - Predictors
Computation of Atlantic
and Guinean SST
anomalies thanks to
MSG
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Geopotential Height
Anomalies
Long term forecast Statistical Model IBIMET - Predictors
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Sahel spring soil humidity
anomalies
Long term forecast Statistical Model IBIMET - Predictors
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Previous SepOctNov
Guinean Precipitation
Long term forecast Statistical Model IBIMET - Predictors
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
Predictors
Long term forecast Statistical Model IBIMET
Output
Statistical Model
-SSTs Anomalies -Geopotential Heigth 500 mb -Soil Humidity -Previous SON Guinean preciptation
MultiLinear Regression MLR with Stepwise
•Percentage Anomalies respect to climatology•Forecast validity 3 months•Issued every month since April
Weather and climate monitoring for food risk management
G. MaracchiWMO, Geneva, November 2004
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