Present and future capabilities of the Sand and Dust Storm Warning System for North Africa to...
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Present and future capabilities of the Sand Present and future capabilities of the Sand and Dust Storm Warning System for North and Dust Storm Warning System for North Africa to provide knowledge on Africa to provide knowledge on environmental risk indicators of meningitis environmental risk indicators of meningitis epidemics epidemics Carlos Pérez 1 José M. Baldasano 1,2 , Emilio Cuevas 3 , Slobodan Nickovic 4 , Len Barrie 4 , Xavier Querol 5 (1) Earth Sciences Department. Barcelona Supercomputing Center (BSC; Spain) (2) Laboratory of Environmental Modeling. Universitat Politècnica de Catalunya (UPC, Spain) (3) National Institute of Meteorology (INM; Spain) (4) AREP, World Meteorological Organization (WMO; Switzerland) (5) Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC; Spain) [email protected]
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Transcript of Present and future capabilities of the Sand and Dust Storm Warning System for North Africa to...
Present and future capabilities of the Sand Present and future capabilities of the Sand and Dust Storm Warning System for North and Dust Storm Warning System for North
Africa to provide knowledge on environmental Africa to provide knowledge on environmental risk indicators of meningitis epidemicsrisk indicators of meningitis epidemics
Carlos Pérez 1
José M. Baldasano 1,2, Emilio Cuevas 3, Slobodan Nickovic 4, Len Barrie 4, Xavier Querol 5
(1) Earth Sciences Department. Barcelona Supercomputing Center (BSC; Spain)(2) Laboratory of Environmental Modeling. Universitat Politècnica de Catalunya (UPC, Spain)(3) National Institute of Meteorology (INM; Spain)(4) AREP, World Meteorological Organization (WMO; Switzerland)(5) Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC; Spain)
GEO Meningitis Environmental Risk Consultative Meeting, Geneva, 26-27 September 2007
Mineral Dust Impacts
Sand and Dust Storm Warning Systemfor North Africa, Europe and Middle East
PURPOSE: - Achieve comprehensive, coordinated and sustained observations and modelling capabilities of the sand and dust storm
- Increase the understanding of its formation processes
- Enhance and provide operational prediction capabilities and dust-related data
- Epidemics start during the dry season
- Certain environmental factors, such as low absolute humidity, land cover types and dusty atmospheric conditions, may play an important role (Lapeyssonnie, 1963; Cheesbrough et al., 1995; Greenwood, 1999; Molesworth et al., 2003; Thomson et al., 2006).
Districts crossing the Alert and Epidemic thresholds in African countries under enhanced surveillance 2006
Dust from MODIS
?
Dust and meningitis epidemics
Health-related GEMS-MACC project proposal work package: Sand and Dust forecasting to prevent meningitis epidemics
Objectives: Gain scientific knowledge about the relationship between atmospheric mineral dust, general atmospheric conditions and meningitis in the Sahel region Improve environmental prediction models for meningitis prevention
How can we contribute to improving the knowledge on environmental risk indicators of meningitis epidemics?
Activities:1- Refined short-term dust forecasts and dust surveillance in the Sahel region2- Retrospective analysis of dust with model and available satellites and its relationship with meningitis in the Sahel3- Explore links between dust, meningitis and large scale climate indexes
SINK
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SOURCE
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Cu
tC
Dust REgional Atmospheric Model (DREAM) (Nickovic et al., 2001)
Simulates all major processes of the atmospheric dust cycle.
Fully embedded as one of the governing prognostic equations in the atmospheric NCEP/Eta atmospheric model (Janjic 1994, 1996a,b, Janjic 1997)
4 transport particle sizes (0.73, 6.1, 18, 36 m) Dust production scheme with introduced viscous sublayer
(Shao 1993; Janjic 1994). Particle size distribution effects. Soil wetness effects on dust production (Fecan et al, 1999). Dry (Georgi, 1986) and wet deposition. Developed and operated at University of Athens, ICOD
Malta and Barcelona Supercomputing Center
(http://www.cgd.ucar.edu/tss/staff/mahowald/dust.html)
1- Refined short-term dust forecasts and dust surveillance in the Sahel region
SDS WS Operational products
Model predictions (72-h): Horizontal distribution
PM2.5, PM10, TSP at surface and height Total column mass (dust load) Dust aerosol optical depth Wet, dry, total deposition Visibility (soon available) Meteorological variables
Vertical distribution Cross sections Fixed point/time profiles
Fixed point (selected sites/cities) Dustgrams Meteograms
Request-only basis: Numerical data Climatology
http://www.bsc.es/projects/earthscience/DREAM/
1- Refined short-term dust forecasts and dust surveillance in the Sahel region
Observations real time or near real time:
15-minute RGB dust from Meteosat Second Generation (MSG) Seviri channels for North Africa
Weekly maps of Normalized Difference Vegetation Index (NDVI) obtained from 15-minute Seviri MSG channels (3km resolution)
Dust from MODIS, SeaWIFS, OMI
AERONET and Visibility data
Vegetation index derived from SEVIRI/MSG data over West Africa
SDS WS Operational products
1- Refined short-term dust forecasts and dust surveillance in the Sahel region
Meteosat Second GenerationMeteosat Second Generation
Model has shown very good agreement with observations in a number of studies of single events (e.g., Ansmann et al., 2003, Papayannis et al., 2005; Pérez et al., 2006a;b; Jiménez et al, 2006 ….)
SeaWIFSSeaWIFS Lidars - EARLINETLidars - EARLINET AERONET - ONLINEAERONET - ONLINE
Operational verification
1- Refined short-term dust forecasts and dust surveillance in the Sahel region
1- Refined short-term dust forecasts and dust surveillance in the Sahel region
MareNostrum
- Peak performance of 94,21 Teraflops - 10240 IBM Power PC 970MP processors
2- Retrospective analysis of dust and opportunities for meningitis studies
Long term Saharan dust simulations 1958 – 2006 (under Long term Saharan dust simulations 1958 – 2006 (under progress)progress)
WHAT CAN WE PROVIDE TO THE HEALTH COMMUNITY ???
- 3D fields of dust and meterology- Validated with observations !!
Reanalysis data: NCEP/NCAR 1958-2006
Complementing and, at least, partially overcoming Satellite data (AI) limitations
Seasonal Average 1959-2006: surface dust concentration
2- Retrospective analysis of dust and opportunities for meningitis studies
Seasonal Average 1959-2006 Wet dust deposition
2- Retrospective analysis of dust and opportunities for meningitis studies
Izaña Station (Tenerife) dust record 1987-1999Model Validation – 12 h average total dust concentration
Izaña Station (Tenerife)28° 18' N, 16° 29' W, elevation 2367 meters a.s.l.
350 km west of Africa. A trade wind inversionlayer is usually present below 1800 meters a.s.l. avoiding the arrival of polluted air from the surrounding lowland areas.
2- Retrospective analysis of dust and opportunities for meningitis studies
0.0
10.0
20.0
30.0
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80.0
90.0
Seaso
nal D
ust
Concentratio
n (ug m
-3)
Observations (Izaña)
Model (DREAM)
SEASONAL CORRELATION TOTAL 0.680 1988 0.848 1989 0.912 1990 0.996 1991 0.912 1992 -0.731 1993 0.747 1994 0.783 1995 0.521 1996 0.431 1997 -0.915 1998 0.805
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01/01/1987 15/05/1988 27/09/1989 09/02/1991 23/06/1992 05/11/1993 20/03/1995 01/08/1996 14/12/1997 28/04/1999
Daily
Dust
Concentratio
n (ug m
-3)
Observations (Izaña)
DREAM model
DAILY CORRELATION TOTAL 0.449 1987 0.674 1988 0.609 1989 0.571 1990 0.680 1991 0.706 1992 0.502 1993 0.395 1994 0.541 1995 0.442 1996 0.207 1997 0.437 1998 0.553 1999 0.762
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01/87 01/88 01/89 01/90 01/91 01/92 01/93 01/94 01/95 01/96 01/97 01/98 01/99
Montly
Dust
Concentratio
n (ug m
-3)
Observations (Izaña)
Model (DREAM)
MONTHLY CORRELATION TOTAL 0.725 1987 0.990 1988 0.892 1989 0.917 1990 0.922 1991 0.888 1992 0.648 1993 0.594 1994 0.614 1995 0.707 1996 0.604 1997 0.759 1998 0.797 1999 0.965
2- Retrospective analysis of dust and opportunities for meningitis studies
Winter
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1986 1988 1990 1992 1994 1996 1998 2000YEAR
Sea
son
al D
ust
Co
nce
ntr
atio
n ( g
m-3
) n
m
Observations DJF (Izaña)
Model DJF (DREAM)
R=0.79
IZAÑA
R=0.62JFM DREAM AOD 550nm - AVHRR dust [%] 10-30 W 15-30 N
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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006YEAR
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8A
VH
RR
du
st [
%]
DREAM AVHRR
AVHRR 10-30W 15-30N Evan et al., 2006
2- Retrospective analysis of dust and opportunities for meningitis studies
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+ +
Correlations DJF NAO vs. DJF averages 1981-2006DJF NAO vs. DJF concentration DJF NAO vs. DJF AOD
DJF NAO vs. DJF Dry Dep DJF NAO vs. DJF Wet Dep
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3- Link between dust, meningitis and large scale climate indexes
NOV NAO – NOV dust
DEC NAO – DEC dust
JAN NAO – JAN dust
FEB NAO – FEB dust
Winter monthly correlations
3- Link between dust, meningitis and large scale climate indexes
Other possible indexes to look at:- TNA (Tropical Northern Atlantic Index) - NTA (North Tropical Atlantic SST Index)- Atlantic Tripole SST- Sahel Standardized Rainfall
- Dust forecasting and observations available for the health community through the SDS WS Regional Center
- Dust model retrospective analysis available for research
- More refined simulations and forecasts are planned
- Need for collaboration and feedback between atmospheric and health community within GEMS-MACC and other projects
Final aspects and further steps
THANKSTHANKS
Barcelona Supercomputing Center-Centro Nacional de Supercomputación
Earth Sciences Department. Barcelona.
GEO Meningitis Environmental Risk Consultative Meeting, Geneva, 26-27 September 2007
