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Convective Systems in the 2006 West African Monsoon:

A Radar Study

Nick Guy

MS Research SJSU

PhD Research CSU

17 February 2009

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• African Monsoon Multidisciplinary Analyses

• Cooperative international project

• Science Objectives:– Improve understanding of

WAM

– Create strategy for monitoring and prediction of WAM

– Relate underlying science to socioeconomic issues

• NASA AMMA• Collaboration with AMMA• Primary Scientific Interests

– Relationship between AEWs and tropical cyclogenesis in the Atlantic basin

– role of the Saharan Air Layer (SAL) in modulating the intensity of the waves and tropical cyclone growth

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MCS

• Definition for this study– Organized t-storms with contiguous precipitation

region with horizontal scale >100 km

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SLMCS

• Linear organization and propagation

• Large impact of thermodynamic and dynamic structure of environment

• High prevalence of this type throughout season for MIT and NPOL radar sites

• Large contributor of precipitation totals in some areas

• Large trailing stratiform region

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African Precipitation

• Northward progression of rainfall

• Banded structure

ObservationalData GCM GCM

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West African Monsoon• Seasonally dependent thermally-induced

low over African continent

• Migration northward during boreal summer

Ferreira (2007)

10 N 20 N 30 NEQ

600

200

Pre

ssur

e (m

b)

Latitude

ITC

Z (

Mo

ns

oo

n R

ain

)

Sahara Warm DryAir

African Easterly Jet

Cool Gulf of Guinea SSTs

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WAM Characteristics I

• Two distinct phases (Sultan and Janicot 2003b)– Preonset – migration of southwesterly winds

and ITF past 15˚N– Onset - abrupt northward shift of the ITCZ

from 5˚N to 10˚N

• Time-frame: April – October precipitation– Results in 99% of annual rainfall (Shinoda et

al. 1999)– mid-June – September generally defines

WAM period

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WAM Characteristics II• MCSs account for estimated 80-90% of annual

rainfall in Sahel (Mathon et al. 2002)– Convective portion of total rainfall: average of 65% in

tropics (Schumacher and Houze 2006)

– Convective portion of total area : average of 10% in tropics (Houze 1993)

• Formation of MCSs (largest contributor of rainfall) is highly correlated to AEWs– SLMCS - AEJ coupling (Ferreira et al. 2009)

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Section 2

Radar Data Analysis

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Radar Locations• MIT – Niamey, Niger (13.49ºN, 2.17ºE)

• C-band Doppler radar• Operated 5 July – 27 September 2006• ~11250 scans for analysis• 37 MCS-scale events observed

• TOGA – Praia, São Tiago (14.92ºN, 23.48ºW)• C-band Doppler radar• Operated 15 August – 16 September 2006• ~4300 scans for analysis• 6 MCS-scale events observed

• NPOL – Dakar, Senegal (14.66ºN, 17.10ºW)• S-band, dual polarized Doppler radar• Operated 19 August – September 30 2006 • ~3500 scans for analysis• 12 MCS-scale events observed

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Data & System Classification• Data Resolution

– MIT & TOGA : 10-minute– NPOL : 15 minute

• Rmax = 150 km (130 km used for data analysis)

• Feature classification structure based on a simplified version of that used by Rickenbach and Rutledge (1998)– Sub-MCS and MCS-scale events– Visual inspection - subjective

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MIT Radar Data Quality Control• GVS software package employed for QC

– Removal of non-meteorological data

– Maximize meteorological echo retained– Algorithm based on a modified approach developed by

Rosenfeld et al. (1995)

• Generally favorable results from the QC operation

• Attenuation correction for MIT site (Russell and Williams 2009) [GATE correction used for data set]

• Comparison to TRMM PR showed good agreement – bias adjusted in radar data

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Section 3

Rainfall Estimation & Analysis

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Convective-Stratiform Map

SLMCS event plotted in terms of convective-stratiform components

SLMCS event plotted in terms of reflectivity

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Z-R Relationships

• MIT• Z = 364R1.36 (Sauvageot and Lacaux 1995)

• NPOL• Z = 368R1.24 (Nzeukou et al. 2004)

• TOGA• Z = 230R1.25 (Hudlow 1979)

• Global used, may use newer Conv-Strat components in future, caveat: does not exist for TOGA

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Rainrate Timeseries

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MCS Contributions to Seasonal Totals

Rain Fraction Area Fraction

TOGA

MCS 0.670 0.394

Sub-MCS 0.279 0.428

NPOL

MCS 0.670 0.576

Sub-MCS 0.223 0.305

MIT MCS 0.919 0.872

Sub-MCS 0.071 0.106

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MCS Statistics

Precipitation

Area Coverage

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Sub-MCS Statistics

Precipitation

Area Coverage

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Diurnal Composites

MCS-scale systems Sub- MCS-scale systems

Note the difference in vertical scales MCS component dominates total

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TRMM Diurnal Signal

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Vertical Structure

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Current Research Avenues• Include additional radar data

– Dialogue with French group (RONSARD C-band and XPORT X-band radar)

• TRMM data integration– Rainfall, OLR, and lightning flash density climatology– Vertical reflectivity profiles– Conv/Strat compositions

• Aerosol (MODIS) and Lightning (WWLLN & TRMM) data integration

• Reanalysis fields• Focus on disturbances that become TCs

– Case study comparison– 7 (Zipser et. al 2008) or 8 (NHC, NOAA) waves – 5 of

which possibly seed TCs• Add WRF modeling component – TBD after initial

results

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TRMM ClimatologyJune

July

August

September

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2006 WAM vs. Climatology

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WWLLN Lightning Distribution

TOGA NPOL

MIT

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Wave 5 Case Study

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MODIS Aerosol Distribution

TOGA

MIT

NPOL

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Acknowledgements

• Drs. Steve Rutledge, Rob Cifelli, Tom Rickenbach, Tim Lang

• Bart Kelley, Jason Pippitt, Dave Wolfe at GSFC

• Paul Kucera, Earle Williams, and Brian Russell

• CEAS Fellowship for making this possible