krishnamurti, chakraborty, v. mehta, a. mehtamonsoon and impacts workshop - ahmedabad, india7...
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Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Experimental Prediction of Climate-related Malaria Incidence
Outline* Climate Variability and Malaria in India
* The FSU Super-ensemble Technique with 13 Coupled Climate Models for Rainfall Prediction: An Experiment for Malaria Incidence Prediction in Botswana, Southern Africa * Next Steps for a Malaria Early Warning System in (Western) India
T.N. Krishnamurti and Arindam ChakrabortyFlorida State University
Tallahassee, Florida, U.S.A.
Vikram M. MehtaThe Center for Research on the Changing Earth System
Columbia, Maryland, U.S.A.
Amita V. MehtaNASA-Goddard Space Flight Center
andUniversity of Maryland-Baltimore County
Greenbelt, Maryland, U.S.A.
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Poverty and Health: Malaria, an Example of Vector-borne Diseases Influenced by Climate Variability and Change
Malaria around for 4,000 years, influenced human history to a great extent
According to the WHO’s World Malaria Report 2005, 3.2 billion people lived in areas at risk of malaria transmission at the end of 2004
350 to 500 million clinical episodes of malaria every year
At least one million deaths every year due to malaria
Potential destabilization of socio-economic-political systems, triggering national/international security problems
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Influence of El Niño-La Niña Climate Variability on Indian Rainfall and Malaria Incidence
More rain and more malaria cases in western and northwestern India during La Niña (1996; left)
Less rain and fewer malaria cases in western and northwestern India during El Niño (1998; right)
Rain and malaria prediction 2-3 months in advance possible
El Niño-La Niña Climate Index (gray) and annual number of malaria cases in India (blue)
March-April-May
June-July-August
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
3 million cases
1.8 million cases
Jun-Jul-Aug 1996 La Niña Jun-Jul-Aug 1998 El Niño
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Seasonal Rainfall and Malaria Incidence Prediction in Botswana in Southern Africa: A Case Study
• Malaria incidence dependent on rainfall, temperature, humidity, winds, land cover-use, topography, and other local conditions
• Accurate seasonal prediction of rainfall, temperature, other hydro-meteorological variables, and land cover-use very useful for early warning of malaria risk and decision-making about prevention/mitigation
• Application of the FSU multi-model synthetic super-ensemble technique with 13 coupled climate models in predicting malaria incidence a season in advance in Botswana, using only rainfall prediction
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Atmospheric Component Oceanic Component
Model Res. Initial Condition Model Res. Initial Condition
ANR (FSU) FSUGSM with Arakawa-Schubert convection and new radiation (band model) T63L14 ECMWF with Physical
Initialization HOPE global 5o longitude, 0.5-5o
latitude, 17 levels Coupled assimilation relaxed to observed SST
AOR (FSU) FSUGSM with Arakawa-Schubert convection and old radiation (emissivity/absorptivity based)
T63L14 ECMWF with Physical Initialization HOPE global 5o longitude, 0.5-5o
latitude, 17 levels Coupled assimilation relaxed to observed SST
KNR (FSU) FSUGSM with Kuo convection and new radiation (band model) T63L14 ECMWF with Physical
Initialization HOPE global 5o longitude, 0.5-5o
latitude, 17 levels Coupled assimilation relaxed to observed SST
KOR (FSU) FSUGSM with Kuo convection and old radiation (emissivity/absorptivity based) T63L14 ECMWF with Physical
Initialization HOPE global 5o longitude, 0.5-5o
latitude, 17 levels Coupled assimilation relaxed to observed SST
CCM3 (NCAR)
CCM3 atmospheric model T63L18 ECMWF SOM 2.4 o x 1.2 o -2.4o Coupled assimilation
POAMA1 (Australia)
BMRC Atmospheric Model (BAM3) R47L17 From latest atmosphere and ocean conditions from GASP
ACOM2 2o x 0.5o-1.5o, 25 levels
From ocean assimilation which was based on optimum interpolation (OI) technique.
CERFACS (France)
ARPEGE T63L31 ERA40 ERA40 2o x 2o, 31 Levels Forced by ERA40
ECMWF (Europe)
IFS T95L40 ERA40 HOPE-E 1.4o x 0.3o-1.4o, 29 levels Forced by ERA40
INGV (Italy) ECHAM-4 T42L19 Coupled AMIP type OPA 8.1 2o x 0.5o-1.5o, 31 levels Forced by ERA40
LODYC (France)
IFS T95L40 ERA40 OPA 8.2 2o x 2o, 31 levels Forced by ERA40
MPI (Germany)
ECHAM-5 T42L19 Coupled run relaxed to observed SST MPI-OMI 2.5o x 0.5o-2.5o, 23
levels Coupled run relaxed to observed SST
MetFr (France)
ARPEGE T63L31 ERA40 OPA 8.0 182 x 152 GP, 31 levels Forced by ERA40
UKMO (England)
ARPEGE 2.5 x 3.75, 19 levels ERA40 GloSea OGCM
HadCM3 based 1.25o x 0.3o-1.25o, 40 levels Forced by ERA40
Particulars of 13 Coupled Climate Models
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Total Forecast Data Sets Available for the Present Study
(13 x 4 x 9 x 6 =) 28086 (starting every 3 months)
9Each of 7 DEMETER models (CERFACS, ECMWF, INGV, LODYC, MPI, MeteoFrance, UKMO)
(13 x 12 x 9 = ) 14043 (starting every month)
1POAMA1
(13 x 12 x 3 = ) 4683 (starting every month)
1CCM3
(13 x 12 x 3 = ) 4683 (starting every month)
1Each of 4 FSU models (ANR, AOR, KNR, KOR)
Total forecasts during 1989-2001
Length of forecasts (in
months)
Forecasts per month
Model
Total Forecasts = 23400 ( = 468 x 5 + 1404 + 2808 x 7)
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Methodology
• Seasonal, super-ensemble rainfall forecasts from 13 coupled atmosphere-ocean models from March 1989 to February 2002 over 17.5o-30.0oE, 27.5oS-17.5oS
• Three months’ lead time forecasts for the peak malaria incidence month of March
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Adjusted Malaria Incidence and Rainfall
December to February 1981-82 to 2001-02
• Adjusted log (malaria incidence) (AMI, per 1000 people) in Botswana related to summer (December-February) rainfall (and other factors)
• Initial increase in AMI with increase in rainfall and decrease in very heavy rainfall because mosquito breeding areas washed away
Empirical relationship between AMI and rainfall P (mm/day) in Botswana:
AMI = -0.2541 P2 + 1.9558 P - 3.2823
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Super-ensemble (SSE) provides more accurate rainfall forecasts compared to the ensemble mean (EM).
Step 1: December Forecast of December-January-February Rainfall
Rainfall Forecasts made in December for December-January-February Season usingSuper-ensemble and Ensemble-mean Techniques
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
SSE Provides Better Malaria Prediction (Correlation = 0.19, RMSE = 0.52) Compared to EM (Correlation=-0.47, RMSE=0.68).
Step 2: Forecast of March Malaria Incidence from the December Rainfall Forecast
0.25
1
4
Malaria Cases per 1000People
More AccurateForecasts ofLarger Outbreaks
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Next Steps to Develop a Malaria Early Warning System in Western India
* A network of government and private health professionals, hydro-meteorological specialists, and climate prediction specialists
* A malaria observing system consisting of weather observing stations, malaria data gatherers, and a central/distributed data archive system
* A very-high resolution (~10 kms.) seasonal climate prediction system for Western India; synergy with agricultural and water resources impacts prediction
* Quantification of relationships between malaria incidence, hydro-meteorological variables, land cover-use, and other local factors at a very-high spatial resolution
* Quantitative assessments of monsoon climate variability’s, including extreme weather events’, impacts on vector-borne diseases, especially malaria, regional economies, and other societal matters
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Thank youThank you
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Multimodel FSU Conventional Super-ensemble The superensemble forecast is constructed as,
S ai(Fi F i) O i1
N
are the ith model forecasts.
are the mean of the ith model forecasts over the training period.is the observed mean of the training period.are the regression coefficient obtained by a minimization procedure during the training period. Those may vary in space but are constant in time.is the number of forecast models involved.
iF
iF
O
ia
where,
N
The coefficients ai are derived from estimating the minimum of function,
G (Si
i 1
Ntrain
Oi)2 the mean square error.
E 1
N(Fi F i) O
i1
N
Multimodel bias removed ensemble is defined as,
In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.
Krishnamurti, Chakraborty, V. Mehta, A. Mehta Monsoon and Impacts Workshop - Ahmedabad, India 7 February 2007
Generating Synthetic Data Using EOF
Multimodel Synthetic Ensemble/Superensemble Prediction System
N - Actual Data Sets
Fi(x,T) Fi, n(T).n
i, n(x)
Observed Analysis
O(x,T) Pn(T).n
n(x)
PC EOFTraining Forecast
i = model
n = mode
Estimating Consistent Pattern
What is matching spatial pattern in forecast data Fi(x,t), which evolves according to PC time series P(t) of observed data, O(x,t) ?
P(t) i,nFn,i
i, n(t) (t)
Fireg (T) i,n
n
Fi,n (T)Forecasts
Normalized Weights
N - Synthetic Data Sets )().(),( , xTFTxF nn
regni
syni
Observation
Obs