The Interaction of African Dust and Dryair Outbreaks - PowerPoint PPT Presentation

1 / 16
About This Presentation
Title:

The Interaction of African Dust and Dryair Outbreaks

Description:

Seasonal Variance. Aerosol Winter. Aerosol Spring. PW Winter. PW Spring. Seasonal Variance. Aerosol Summer. Aerosol Fall. PW Summer. PW Fall. Synoptic-Scale ... – PowerPoint PPT presentation

Number of Views:37
Avg rating:3.0/5.0
Slides: 17
Provided by: orcaRsm
Category:

less

Transcript and Presenter's Notes

Title: The Interaction of African Dust and Dryair Outbreaks


1
The Interaction of African Dust and Dry-air
Outbreaks
  • Xiaoyu Liu
  • Univ. of Miami/RSMAS

2
Outline
  • Motivation
  • Data and methodology
  • Preliminary results
  • Conclusion and future work

3
Motivation
  • Aerosol and water vapor affect clouds and
    convection (e.g., in tropical cyclones and
    African monsoon) differently.
  • Are African dust outbreaks always dry? Are
    African dry-air outbreaks always dusty?
  • Can we study their relationship using satellite
    data?

4
dataset
  • Aerosol
  • TOMS Earth Probe satellite data
  • 1996-present
  • 1 x 1.25
  • Water Vapor
  • NVAP(NASA Water Vapor Project) data
  • 1988-1997
  • 1 x 1

Jan - Dec 1997 1 x 1
5
Methodology
  • Aerosol
  • Aerosol Index
  • Column integrated
  • Water Vapor
  • Integrated precipitable water
  • L1 surface-700mb
  • L2 700-500mb
  • L3 500-300mb

6
Seasonal mean
Aerosol Winter
PW Winter
Aerosol Spring
PW Spring
7
Seasonal mean
Aerosol Summer
PW Summer
Aerosol Fall
PW Fall
8
Seasonal Variance
Aerosol Winter
PW Winter
PW Spring
Aerosol Spring
9
Seasonal Variance
Aerosol Summer
PW Summer
Aerosol Fall
PW Fall
10
Synoptic-Scale Correlation
Winter
Summer
Fall
Spring
11
Time-series
Winter
Spring
12
Synoptic-Scale Correlation
Winter
Summer
Fall
Spring
13
Time-series
Winter
Spring
14
Time-series
Summer
Fall
15
Conclusion
  • The character of African air is not simple. Dust
    and water vapor are related in certain ways, but
    can be independent of each other.
  • Data of a longer record are needed to quantify
    their relationships.

16
Future Work
  • Vertical Structure
  • Inter-annual variability
  • New satellite data (MODIS, AQUA)
  • Acknowledgment Chidong Zhang, Jeremy
    Pennington, J. Lin
Write a Comment
User Comments (0)
About PowerShow.com