predictability of the moisture regime during the pre-onset period of sahelian rains

Climate Modeling LaboratoryMEASNC State University

Predictability of the Moisture Regime During the Pre-onset Period of

Sahelian RainsRobert J. Mera

Marine, Earth and Atmospheric SciencesNorth Carolina State University

Seminar, April 3rd 2009


Motivation• Why is the moisture regime

important?Prediction of Monsoon rainfall

Agriculture

African Easterly Waves

Public health: Meningitis Outbreaks


Outline

• The Application– Background– Health-climate link

• Our Study– Importance of Downscaling– Predictability of Pre-onset Conditions– Ensemble Prediction and Evaluation of

Model Skill


The Application• Meningitis is a serious

infectious disease affecting 21 countries

• 300 million people at risk across the Sahel

• 700,000 cases in the past 10 years

• 10-50 % fatality rate • 256,000 people lost to the

disease in 1996

SAHEL


Meningitis-Climate link• Outbreaks coincide with dry, dusty conditions over the Sahel due to the

Harmattan winds flowing south from the Sahara (Jan-May)• Largest correlation occurs between low humidity and disease outbreaks

(Molesworth et al., 2006)• Disease occurrence drops dramatically with the onset of humidity

January July

SHSHL

Har

mat

tan

Moi

sture

ITCZ

ITCZ


Meningitis-Climate link• The most actionable case involves the link

between humidity onset and cessation of disease

Pink: # of cases Orange: Relative Humidity (%)

1998 2004


Current Efforts

• University Corporation for Atmospheric Research (UCAR) and the Google Foundation are funding efforts to explore climate-meningitis dynamics

• Global scale models will be employed for operational purposes


Our study: Importance of Downscaling

WRF at 30km resolution NCEP/NCAR Reanalysis at 2.5°

65

60

55

45

50

40

3530

25

20

Ghana Ghana

Relative Humidity (%)


The Scientific Question: Predictability of Moisture

• What are the dynamics governing the northward progression of the moisture regime?

• How well does the model represent the physical processes?

• What is the skill of the model in predicting the dynamics and statistics of the physical processes?


In the literature• The West Africa summer monsoon is characterized by

two steps: preonset and onset (Sultan and Janicot, 2003)

• The preonset stage corresponds to the arrival of the Inter Tropical Front (ITF) at 15°N

From Sultan and Janicot (2003)

Rain (mm/day)

ITF


Schematic Cross Section of the West African Monsoon

SaharaSahel10 N 20 NEquator

600 hPa

200 hPa

1000 hPa

ITCZ

Deep dry

convectionDe

ep

moi

st c

onve

ctio

n

AEJ

Slide from John Marsham, U. of Leeds


Our Study• The northward progression of moisture is related

to the preonset stage of the monsoon and the position of the ITF

• Two important factors at work:– Interannual variability is dictated by fluxes in sea

surface temperatures (SST), interaction with mid-latitude systems (teleconnections)

– Intraseasonal variability is related to east-west transient disturbances, African Easterly Jet


Data and Methods• NCEP/NCAR, ECMWF Reanalysis, In-situ

observations & satellite data: Statistics of Relative Humidity, etc

• We use the Advanced Research WRF (WRF-ARW) Model for downscaling of reanalysis and operational forecasts, sensitivity analyses

*NCEP: National Centers for Environmental Prediction*NCAR: National Center for Atmospheric Research*WRF: Weather Research and Forecasting Model*ECMWF: European Centre for Medium-Range Weather Forecasts


Preliminary analysis and results


0

20

40

60

80

1-Mar 15-Apr 30-May 14-Jul 28-Aug 12-Oct

Rel

ativ

e H

umid

ity (%

)Historical Data:

Reanalysis

• Mean 2000–2008 relative humidity time series (%) computed on the grid points located between 10°W and 10°E longitude, 14.5°N and 15.5°N latitude

Two distinct slopes

APR 15JUN 14

JUN 24


April 1, 2006 relative humidity (%) at the surface, 925mb winds and u component at 0 to delineate ITF

Cross section along the prime meridian from 0° to 20 ° N: Relative humidity (shaded) and u component at 0 EQ 20N

700 mb

Model simulationsAEJ


Ensemble Prediction

• We will use the ensemble prediction approach to generate probabilistic forecasts that will also allow us to analyze model skill


An ensemble forecast run was tested against interpolated observations

Interpolated Observations Ensemble Simulation


-10 -8 -6 -4 -2 2 4 6 8 10Relative Humidity Anomaly (%)

An ensemble forecast run was tested against interpolated observations

The error (anomaly) is much smaller than the signal


Analyzing Model Skill

No Yes

No No cost ()

Miss()

Yes False Alarm()

Hit()

ObservationsE

PS

For

ecas

t


The Relative Operating Characteristic (ROC)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1False Alarm Rate

Hit

Rat

e

• The ROC method is widely used for estimating the skill of ensemble prediction systems (EPS) (Marzban, 2004)

• A perfect forecast system would have a ROC area (ROCA) of 1


An Extended ROC Procedure• ROC plots model skill only for an optimum user

• We developed an extended (EROC) procedure that caters to a particular user’s needs:

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1False Alarm Rate

Hit

Rat

e

Base line for μ=ō(=0.33), Vmin=0

ROC

Base line for μ=0.25(<ō), Vmin=0

Base line for μ=0.40(>ō), Vmin=0

Shift in baselinesAccording to user

Semazzi & Mera, 2006


Model Skill for End-user

• Additional analysis through EROC can help with current health efforts and the incurred costs:– Transportation of Supplies– Inoculation– Personnel


Looking Forward

• Understanding the moisture regime statistics: variance of 40% RH date and changes in slope of humidity trends

• Sensitivity studies using SSTs, land cover, meridional transient distrubances, teleconnections with mid-latitude systems

• Application of EROC for surface conditions pertinent to health efforts


Acknowledgements

• Dr Semazzi• CML crew• Google/UCAR group• NOAA ISET• Dr Arlene Laing, Dr Tom Hopson


Questions?


Auxiliary slides


Historical Data: Reanalysis

• Mean 2000–2008 relative humidity time series (%) computed on the grid points located between 10°W and 10°E longitude, 14.5°N and 15.5°N latitude


Large scale Climatology


Large Scale Climatology


Criteria for Issuing a forecast

Decision to issue a forecast of an event (E) to occur is probabilistically based on the criteria:

p nN pt

Where:(N): size of the ensemble(n): number of the runs in the ensemble for which (E) actually occurs(p): probability given by the ratio (n/N)

This is the threshold fraction above which the event (E) is predicted to occur based on the model forecast

predictability of the moisture regime during the pre-onset period of sahelian rains

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