Andy Morse

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Andy Morse University of Liverpool A.P.Morse_at_
liv.ac.uk WP2 WAM microclimate and
applications (Micrometeorology for health
applications)
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Thanks Moshe Hoshen, Liverpool School of
Tropical Medicine Phil McCall, Anne
Jones ECWMF Paco Doblas-Reyes and Tim Palmer

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• Disease background
• Recent malaria model work
• Motivation
• WP2 details
• OWP2 details

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WP2 WAM microclimate and applications. We
aim to quantify the microclimate of the region in
the sub-canopy layer in order to downscale global
model predictions and earth observation products
to the scales and parameters required for disease
prediction.
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Malaria Background

• Malaria kills more than 2,000,000 people per year
• 90 deaths sub-Saharan Africa
• -mostly children
• Mechanisms of the disease known for over 100
  years
• Anopheline mosquitoes and parasite Plasmodium
  spp. with P. falciparum most dangerous and cause
  of African epidemics

Slide 5 of 14
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Malaria Model malaria life cycle
biting/laying temperature dependent
sporogonic cycle temperature dependent
larval stage rainfall dependent
Slide 6 of 14
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Malaria Model comparison new dynamic and
existing rules based models
MARA
Mapping Malaria Risk in Africa
Prevalence proportion of human population
infected with malaria
Slide 7 of 14
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Probabilistic Seasonal Forecasting
Slide 8 of 14
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Probabilistic Seasonal Forecasting

• EU FP5 DEMETER multi-model ensemble system
  www.ecmwf.int/research/demeter
• Seven modelling groups running AOGCMs in full
  forecast mode, 4 start dates per year running out
  to 6 months, hindcasts 1959 to 2000
• Data available from data.ecmwf.int/data/
• EU FP6 ENSEMBLES www.ensembles-eu.org

DEMETER - hindcast biases
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Brier Skill Scores Feb 2-4 and 4-6 LT is the
lower tercile event, AM the above the median
event and UT the upper tercile event
After Morse et al. 2005
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Meningitis Model Spatial Distribution Meningitis
Epidemics 1841-1999 (n c.425) 1

• Statistical Model to produce a map of risk
• Epidemiological data and climatic and
  environmental variables
• Second model under development to predict
  location, onset and size of epidemics
• Initial results promising needs to be revisited

1 Molesworth A.M., Thomson M.C., Connor S.J.,
Cresswell M.P., Morse A.P., Shears P., Hart C.A.,
Cuevas L.E. (2002) Where is the Meningitis Belt?,
Transactions of the Royal Society of Hygiene and
Tropical Medicine, 96, 242-249.
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Meningitis Model Model of Predicted Risk

• Risk factors
• Land cover type
• Seasonal absolute humidity
• profile
• Seasonal dust profile
• Population density
• Soil type
• Significant but not included in final model
• Human factors not included
• Molesworth, A.M., Cuevas,L.E., Connor, S.J.,
• Morse A.P., Thomson, M.C. (2003).
• Environmental risk and meningitis epidemics in
  Africa, Emerging Infectious Diseases, 9 (10),
  1287-1293.

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Selected Recent Papers Molesworth, A.M.,
Cuevas, L.E., Connor, S.J., Morse A.P., Thomson,
M.C. (2003). Environmental risk and meningitis
epidemics in Africa, Emerging Infectious
Diseases, 9 (10), 1287-1293. Hoshen, M.B.,
Morse, A.P. (2004) A weather-driven model of
malaria transmission, Malaria Journal, 332 (6th
September 2004)  doi10.1186/1475-2875-3-32 (14
pages) Morse, A.P., Doblas-Reyes, F., Hoshen,
M.B., Hagedorn, R.and Palmer, T.N. (2005) A
forecast quality assessment of an end-to-end
probabilistic multi-model seasonal forecast
system using a malaria model, Tellus A, ( in
press)
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Recent Work
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Malaria Model Rainfall dependence
Analysis and diagram from Anne Jones
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Malaria Model Temperature dependence
Mosquito survival after Martens (1995)
At T 25C sporogonic cycle length 15.9 days
2.9 survive to infectious stage
Analysis and diagram from Anne Jones
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Applying malaria models key questions and
motivation (non exhaustive list)
Human Questions immunity, dry season
transmission, clinical records, intervention,
early warning systems etc. Mosquito and Parasite
Questions development rates, survivability,
pesticide and drug resistance, dry season
transmission Physical Environment Questions
local temperature and humidity regimes (in
and out), breeding sites and water
temperature, - downscaling, rainy season
onset, cessation and break cycle timing,
prediction of WAM, heterogeneity of rainfall and
vegetation as refuges.
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WP2 WAM microclimate and applications Liverpool
PDRA (Morse, Taylor, Parker) WP2.1 To make
sub-canopy observations alongside the flux
station array, and thereby to quantify the
microclimates of the region, in relation to
spatial patterns inferred by satellite and
aircraft data. Microclimate measurements
temperature RH plus soil and water
temperature, radiation, wind speed. Reference to
local surface heat towers and aircraft
soundings. Link to satellite and aircraft
radiometry (Links to WP1 plus WP3 and OWPs 1
and 4) Field experiment EOP
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WP2.2 To use mesoscale model simulations (at
smallest spatial resolutions) to simulate the
control of the microclimate by spatial
inhomogeneities of surface properties (as
provided by WP1 remote sensing). This modelling
will be in the form of case studies and
idealised simulations. Comparisons made where
observations exist. Define spatial variability
of variables T RH etc. across large
areas. Link to surface schemes or UCD Advanced
Canopy-Atmosphere-Soil-Algorithm ACASA (Pyles et
al. 2000) Links to WP1, WP3 and OWP 4
(radiometry) Model lead study linked to remote
sensing and measurements
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WP2.3 To use the observations, along with
global, mesoscale / microscale model (UMs)
results to explore the sensitivity of
environmental malaria development parameters to
the model resolution. (i) examine local scale
temperature distribution and rainfall
variability, particularly with regard to
landscape and land use factors, to determine
suitability for sustained breeding sites, (ii)
examine the daily and seasonal humidity
cycles. Links very closely to WP1, WP3 and UEA
studentship Observation and model produced
drivers to drive an application model Nested
models downscaling vs. observations
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WP2.4 To develop sub-canopy and dwelling
microclimate models to use in association with
satellite data. To allow extension after
detailed observations i. statistical model
between microclimate observations and flux
stations/regional models ii. statistical models
between observations and radiance derived surface
temperatures iii Surface schemes JULES 2.5km
WP1 iv Canopy models (Challinor 1D drag and
ACASA drive from mesoscale models link WP2.2) can
this relink R/S? v. Hut model simple energy
balance model All link to WP2.3, WP1,
OWP1. Local models driven by Remote Sensing/
regional models. Modelling studies linked to
observations
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Models Nested Met. Office UM Mesoscale
UM Maximising interactions with WP1 and WP3 ?
Use only WP1 products or add additional model
products? Depends on PDRA. Liverpool job spec.
needs to be clear Support of AMMA modelling
group (Leeds PDRA, Matthews, Morse, Parker,
Pyle, Taylor). Training and support
CGAM Support of Leeds and CEH Dynamic RD
malaria model for sensitivity studies Development
of different local scale canopy and dwelling
modelling techniques statistical and dynamic
energy budget
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Comment WPs 2.2 and 2.3 link very closely to WPs
1.3 and 1.4 Links to AMMA-EU and
AMMA-Africa EU links include EU WP1.2
Surface-atmosphere feedbacks CEH EU WP1.4 Scaling
Issues IRD EU WP2.3 Physical and Biological
Processes over Land Surfaces (FZK) Impacts
Studies EU WPs 3.1, 3.2, 3.3, 3.4 - Land
Productivity, Human processes, Water Resources,
Health CIRAD, IGUC, AGHYMET, Liverpool EU WP
4.2 Field Campaigns EOP/LOP IRD WP4.3 Remote
Sensing CNRS
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Liverpool Project Partner Dr Phil McCall,
Liverpool School of Tropical Medicine (LSTM).
Involvement in planning observations and the
conduct of data analysis, ensure our activities
meet requirements of the research community
working on development and distribution of
disease vectors. Liverpool PDRA Oct 05 36
months Work on the data collection and modelling
studies, working closely with Leeds, CEH and the
LSTM.
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Observational Work OWP2 Micrometeorology
(Liverpool Morse, Lloyd) EOP activity Micromete
orological measurements included at/near selected
flux stations. Temperature, humidity vertical
and horizontal profiles within canopy Soil and
puddle temperatures with soil moisture Limited
windspeed and radiation At least one dwelling
will be instrumented for temperature and
humidity. Site characterisation for use in
surface schemes and canopy models Links to OWP 1
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Microclimate Sites 2 sites (major and minor)
plus and a simple satellite or roving set
Primary site Banizoumbou (1326? N 241? E), to
the east of Niamey, Niger to include an
instrumented straw roof dwelling. Second site
Djougou, Benin (940? N 134? E) Rover either
nearby one of the sites or embedded with AMMA-EU
health group
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Equipment Configuration wish list 1. Basic T
RH (Rover)   10 paired TRH solid sate sensors,
1 reference TRH e.g. Vaisala all with mini
beehive radiation screens logger and power.
Mini tower and / or means of attachment to
vegetation etc. 2. T RH secondary site. 1 x
Rover extra TRH ref (1 vertical profile
through plant canopy with a few spatial
measurements) plus 1 wind speed (basic
instrument), 1 net radiation, 2 off soil heat
flux, 2 off soil temperature, tipping bucket rain
gauge. Infrastructure for deployment within
and through plant canopy. 3. T RH main
site 3 x Rover extra TRH ref - Justification
paired inside and outside measurements on
dwelling plus spatial and vertical variability
vegetation microclimate. Plus 2 wind speed,
two net radiometers, two PAR, 4 soil heat flux, 5
soil temperature one for use in shallow water
pool, tipping bucket rain gauge. Assume solar
will be OK from flux station.
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• Links to projects and related activities
• FP6 EU ENSEMBLES 15MEu
• joint leader RT6 applications and impacts
  1.96MEu 28 partners
• leader WP 6.3 and WP 5.5 Probabilistic
  prediction at seasonal to interannual timescales
  11 partners working on variety applications
• FP6 EU AMMA 12.5MEu
• - Leader WP 3.4 Health Impacts Climate/health -
  Benin, Niger, Senegal - malaria, RVF, meningitis
• NERC e-Science Ph.D. Anne Jones DEMETER hindcasts
  - malaria model GRID Liverpool Cluster jointly
  with Physics
• WCRP CLIVAR WG Seasonal to Interannual
  Predictions - applications
• AMMA ISSC WG 4 Impact and Applications joint
  leader
• Washington, R., Harrison, M, Conway, D., Black,
  E., Challinor, A., Grimes, D., Jones, R., Morse,
  A. and Todd, M (2004). African Climate Report - A
  report commissioned by the UK Government to
  review African climate science, policy and
  options for action, DFID/DEFRA, London, December
  2004, pp45 http//www.defra.gov.uk/environment/cli
  matechange/ccafrica-study/index.htm