predictability of the moisture regime during the pre onset period of sahelian rains
Download
Skip this Video
Download Presentation
Predictability of the Moisture Regime During the Pre-onset Period of Sahelian Rains

Loading in 2 Seconds...

play fullscreen
1 / 35

Predictability of the Moisture Regime During the Pre-onset Period of Sahelian Rains - PowerPoint PPT Presentation


  • 100 Views
  • Uploaded on

Predictability of the Moisture Regime During the Pre-onset Period of Sahelian Rains. Robert J. Mera Marine, Earth and Atmospheric Sciences North Carolina State University Seminar, April 3rd 2009. Motivation. Why is the moisture regime important?. Prediction of Monsoon rainfall.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Predictability of the Moisture Regime During the Pre-onset Period of Sahelian Rains' - lali


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
predictability of the moisture regime during the pre onset period of sahelian rains

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

Robert J. Mera

Marine, Earth and Atmospheric Sciences

North Carolina State University

Seminar, April 3rd 2009

motivation
Motivation
  • Why is the moisture regime important?

Prediction of Monsoon rainfall

African Easterly Waves

Agriculture

Public health: Meningitis Outbreaks

outline
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
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
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

SH

SHL

Harmattan

ITCZ

Moisture

ITCZ

January

July

meningitis climate link1
Meningitis-Climate link
  • The most actionable case involves the link between humidity onset and cessation of disease

1998

2004

Pink: # of cases

Orange: Relative Humidity (%)

current efforts
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
Our study: Importance of Downscaling

65

60

55

50

45

40

35

Ghana

Ghana

30

25

20

WRF at 30km resolution

NCEP/NCAR Reanalysis at 2.5°

Relative Humidity (%)

the scientific question predictability of moisture
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
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

ITF

Rain (mm/day)

From Sultan and Janicot (2003)

schematic cross section of the west african monsoon
Schematic Cross Section of the West African Monsoon

200 hPa

Deep moist convection

AEJ

600 hPa

Deep dry

convection

1000 hPa

10 N

20 N

Equator

ITCZ

Sahel

Sahara

Slide from John Marsham, U. of Leeds

our study
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
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

historical data reanalysis
Historical Data: Reanalysis

JUN 24

  • 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

JUN 14

APR 15

Two distinct slopes

model simulations
Model simulations

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

700 mb

AEJ

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

20N

EQ

ensemble prediction
Ensemble Prediction
  • We will use the ensemble prediction approach to generate probabilistic forecasts that will also allow us to analyze model skill
slide18

An ensemble forecast run was tested against interpolated observations

Interpolated Observations

Ensemble Simulation

slide19

An ensemble forecast run was tested against interpolated observations

-10 -8 -6 -4 -2 2 4 6 8 10

Relative Humidity Anomaly (%)

The error (anomaly) is much smaller than the signal

analyzing model skill
Analyzing Model Skill

Observations

EPS Forecast

the relative operating characteristic roc
The Relative Operating Characteristic (ROC)
  • 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
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:

Shift in baselines

According to user

Semazzi & Mera, 2006

model skill for end user
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
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
Acknowledgements
  • Dr Semazzi
  • CML crew
  • Google/UCAR group
  • NOAA ISET
  • Dr Arlene Laing, Dr Tom Hopson
historical data reanalysis1
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
criteria for issuing a forecast
Decision to issue a forecast of an event (E) to occur is probabilistically based on the criteria:Criteria for Issuing a forecast

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

ad