[PPT] - Estimation of Oceanic Rainfall using Passive Estimation of Oceanic PowerPoint Presentation

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Estimation of Oceanic Rainfall using Passive Estimation of Oceanic Rainfall using Passive and Active Measurements from and Active Measurements from SeaWinds SeaWinds Spaceborne Spaceborne Microwave Sensor Microwave Sensor

Khalil Khalil Ali Ahmad Ali Ahmad

Doctoral Dissertation Defense Doctoral Dissertation Defense

October 26 October 26th

th 2007

2007

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Presentation Outline: Presentation Outline:

Introduction

Introduction

Dissertation objectives

Dissertation objectives

Why measure rainfall from space ?

Why measure rainfall from space ?

Background

Background

Microwave

Microwave scatterometry scatterometry

Microwave radiometry

Microwave radiometry

SeaWinds

SeaWinds sensor sensor

SeaWinds

SeaWinds sampling contribution sampling contribution – – GPM mission GPM mission

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Presentation Outline: Presentation Outline:

SeaWinds

SeaWinds Rain Algorithm Rain Algorithm

QRad

QRad Rain Rate Algorithm Rain Rate Algorithm

Passive excess brightness

Passive excess brightness – – rain rate relationship rain rate relationship

Validation: TRMM 3B42RT, 2A12

Validation: TRMM 3B42RT, 2A12

Modeling

Modeling SeaWinds SeaWinds backscatter in presence of rain backscatter in presence of rain

Combined passive / active rain retrievals

Combined passive / active rain retrievals

Methodology

Methodology

Performance comparison with

Performance comparison with QRad QRad

Validation:

Validation: TRMM 2A12, JPL IMUDH flag TRMM 2A12, JPL IMUDH flag

Summary & Conclusions

Summary & Conclusions

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Dissertation Objectives: Dissertation Objectives:

Utilize the rain sensitivity of passive T

Utilize the rain sensitivity of passive TB

B / active

/ active σ σº º measurements acquired by measurements acquired by SeaWinds SeaWinds sensor sensor to infer global oceanic rainfall to infer global oceanic rainfall

Characterize the effects of rain on passive / active

Characterize the effects of rain on passive / active measurements measurements

Develop a statistical inversion algorithm

Develop a statistical inversion algorithm

Validate the quality of

Validate the quality of SeaWinds SeaWinds oceanic rain

ceanic rain

retrievals retrievals

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Why measure rainfall from space ? Why measure rainfall from space ?

Essential source for fresh water

Valuable for a wide range of research

Valuable for a wide range of research areas and related applications: areas and related applications:

Earth's hydrological cycle

Earth's hydrological cycle

Earth's energy cycle

Earth's energy cycle

Weather forecasting / climate change

Weather forecasting / climate change

Rainfall tend to be random in

Rainfall tend to be random in character and also evolve very rapidly character and also evolve very rapidly

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Why measure rainfall from space ? Why measure rainfall from space ?

Radar / rain gauges can provide reliable measurements

ver small land areas
ver small land areas
Difficult to quantify on regional / global scale

Difficult to quantify on regional / global scale

Impractical over ocean surface

Impractical over ocean surface

Space

Space-

based microwave

based microwave remote sensing instruments remote sensing instruments are indispensable tools in are indispensable tools in providing useful regional / providing useful regional / global scale precipitation global scale precipitation measurements measurements

Wide (global) coverage

Wide (global) coverage

Frequent / uniform sampling

Frequent / uniform sampling

SeaWinds daily coverage ~ 90%

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Background Background

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Ocean Surface

∫∫

=

A

t

r

dA R G P P σ π λ

4 2 3 2

) 4 (

σ o: Normalized Radar Cross Section

(NRCS) of the ocean surface

Microwave Microwave Scatterometry Scatterometry

… , 3 , 2 , 1 , sin 2 = = n n L θ λ

Bragg scattering from short waves

( )

, , , , p σ υ χ θ = … M

ϕ α χ − =

Scatterometer

Scatterometer : : A special purpose radar to measure

A special purpose radar to measure σ σº º

M : Geophysical Model

Function (GMF)

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Microwave Microwave Scatterometry Scatterometry

30º 40º 50º

Wind speed m/s Azimuth angle Radar Backscatter σº (dB)

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Microwave Radiometry Microwave Radiometry

Square Law Detector Low-Pass Filter

+

Receiver Noise Noise-free receiver Gain=G Bandwidth=B Lossless Antenna

Vout

Prec

Psys

Vd

Receiver

Power collected by antenna is

Power collected by antenna is:

K is

K is Boltzmann Boltzmann’ ’s s constant constant

B is receiver bandwidth

B is receiver bandwidth

T

TAP

AP is the scene brightness temperature

is the scene brightness temperature

B T k P

ap

ut =

Radiometer sensitivity or radiometric resolution (ΔT):

sys

T T B τ ∆ = ⋅

τ

τ is the integration time is the integration time

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Microwave Radiometry Microwave Radiometry

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SeaWinds SeaWinds Microwave Sensor Microwave Sensor

SeaWinds

SeaWinds is a Ku is a Ku-

band microwave

band microwave scatterometer scatterometer flown onboard two satellite flown onboard two satellite missions: missions:

QuikSCAT

QuikSCAT (June (June ‘ ‘99 ~ present) 99 ~ present)

ADEOS

ADEOS-

II (Dec.

II (Dec. ‘ ‘02 ~ Oct. 02 ~ Oct. ‘ ‘03) 03)

Instrument description:

Instrument description:

Radar: 13.4 GHz / 110 W pulse / 189 Hz PRF

Radar: 13.4 GHz / 110 W pulse / 189 Hz PRF

Mass / power: 200 kg / 220 W

Mass / power: 200 kg / 220 W

Antenna: 1

Antenna: 1-

meter

meter-

diameter parabolic dish

diameter parabolic dish Dual Dual Pol Pol (H / V) (H / V)

SeaWinds SeaWinds on

n QuikSCAT

QuikSCAT

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SeaWinds SeaWinds Microwave Sensor Microwave Sensor

Originally designed to measure marine wind vector by

Originally designed to measure marine wind vector by relating the measured surface backscatter to a GMF. To relating the measured surface backscatter to a GMF. To get an accurate backscatter measurement the instrument get an accurate backscatter measurement the instrument utilizes: utilizes:

Echo channel

Echo channel

Noise channel (~ 1 MHz)

Noise channel (~ 1 MHz)

Q

QuikSCAT uikSCAT / / S SeaWinds eaWinds Rad Radiometer ( iometer (QRad QRad / / SRad SRad) ) transforms observed noise into apparent brightness transforms observed noise into apparent brightness temperature: temperature:

Implemented through signal processing

Implemented through signal processing

Calibrated against TMI observations

Calibrated against TMI observations

Not an optimum

Not an optimum radiometer ( radiometer ( ∆ ∆T ~ 25 Kelvin /pulse ) T ~ 25 Kelvin /pulse )

Improved

Improved ∆ ∆T by averaging / spatial filtering T by averaging / spatial filtering

Be Bn

Frequency Power Received Spectrum

Radar Echo

Echo Channel Noise Channel

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SeaWinds SeaWinds Microwave Sensor Microwave Sensor

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Introduction

Introduction

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Oceanic instantaneous integrated rain rate,

Oceanic instantaneous integrated rain rate, 25 km 25 km resolution resolution

n WVC measurement grid
n WVC measurement grid
Data source:

Data source:

Polarized microwave brightness temperatures (L2A)

Polarized microwave brightness temperatures (L2A)

Polarized microwave backscatter (L2A)

Polarized microwave backscatter (L2A)

Collocated NCEP wind speeds (L2B)

Collocated NCEP wind speeds (L2B)

Statistical retrieval algorithm

Statistical retrieval algorithm: :

Empirical T

Empirical Tex

ex vs. IRR relationship

vs. IRR relationship
Empirical

Empirical σ σ0

ex ex vs. IRR relationship

vs. IRR relationship
Trained using near

Trained using near-

simultaneous collocations with TRMM

simultaneous collocations with TRMM Microwave Imager (TMI) oceanic rain rates Microwave Imager (TMI) oceanic rain rates

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Utility of

Utility of SeaWinds SeaWinds rain product: rain product:

Provides simultaneous, collocated precipitation measurements

Provides simultaneous, collocated precipitation measurements with with QuikSCAT QuikSCAT ocean surface wind vectors for rain

cean surface wind vectors for rain-
flagging

flagging contaminated wind vector retrievals contaminated wind vector retrievals

Increase oceanic rain sampling ~10%

Increase oceanic rain sampling ~10%

NASA

NASA’ ’s GPM mission: s GPM mission:

OBJECTIVES

Provide sufficient global sampling

to reduce uncertainties in short-term rainfall accumulations.

Understand horizontal & vertical

structure of rainfall, its associated latent heating.

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SeaWinds SeaWinds sampling sampling – – 3 hr window 3 hr window

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Daily average revisit time Daily average revisit time – – 3 hr window 3 hr window

TMI & SSMI (3 satellites) QRad, TMI & SSMI (3 satellites) Time

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Passive

Passive-

only (
nly (QRad

QRad) )

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Collocation Database Collocation Database

1st Quarter ~ 106 2nd Quarter~ 121 3rd Quarter~ 167 4th Quarter~ 27

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Excess Excess Brightness Temperature Model Brightness Temperature Model

The polarized microwave

The polarized microwave “ “excess brightness excess brightness” ” ( (Tex Texp

p) is

) is proportional to the integrated rain rate proportional to the integrated rain rate

T

Tb ocean

b ocean : Ocean background (includes atmospheric Emissions without ra

: Ocean background (includes atmospheric Emissions without rain) in)

based upon 7 year SSMI climatology

based upon 7 year SSMI climatology

T

Tb w.speed

b w.speed : Wind speed brightness bias

: Wind speed brightness bias

p b p b p b p

speed w T

cean

T meas T Tex . − − =

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Wind Speed Correction Wind Speed Correction

Calculate polarized wind speed correction

Calculate polarized wind speed correction: :

p b p b p

cean

T meas T Tex − =

T

Tb

b meas meas : rain free brightness

: rain free brightness temperature measurements temperature measurements

T

Tb ocean

b ocean : ocean background

: ocean background (includes atmospheric Emissions (includes atmospheric Emissions without rain) without rain)

based upon 7 year SSMI

based upon 7 year SSMI climatology climatology

Wind speed Tb correction (H-Pol) Wind speed (m/s)

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H H-

Pol

Pol “ “Tex vs. IRR Tex vs. IRR” ” Transfer Function Transfer Function

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QRad Rain Calculations QRad Rain Calculations

i n i p EX p i p

T a IRR ) (

, ,

∑

=

⋅ =

(n = 3)

Calculate Polarized Rain Rates

Calculate Polarized Rain Rates: :

Calculate Combined Rain Rates:

Calculate Combined Rain Rates:

( )

v h QRad

IRR IRR c c IRR ⋅ + ⋅ + = β α

1

Calculate Coefficients:

Calculate Coefficients:

N IRR IRR

QRad N i TMI 2 1

) ( min

∑

=

−

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Instantaneous Rain Rate Product

By orbit, 25 km resolution

QRad QRad Algorithm Block Diagram Algorithm Block Diagram

Calc. Polarized

Excess Brightness Tex @ 25 km Combine using a weighted average Using ( Tex- IRR )

Calc. Polarized Instantaneous

Rain Rate QRad Tb (L2A) Ocean Tb background NCEP wind Speed Spatial Filtering 3x3 Window Apply threshold

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Overall Rain Scatter Overall Rain Scatter

Correlation ~ 80% RMS ~ 6.5 [km*mm/hr]

TMI IRR [km*mm/hr] QRad IRR [km*mm/hr]

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QRad QRad Radiometric Response Radiometric Response

H-Pol V-Pol

Wind speed (m/s) Rain rate (km *mm/hr)

total rain wind

ex ex ex

T T T = +

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Validation of

Validation of QRad QRad

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TRMM 3B42RT Data Product TRMM 3B42RT Data Product

Provides near real

Provides near real-

time global precipitation

time global precipitation: :

3

3-

hour universal time windows

hour universal time windows

Spatial resolution: 0.25

Spatial resolution: 0.25º º x x 0.25 0.25º º

Rain estimates are derived from all available high

Rain estimates are derived from all available high quality (HQ) microwave merged with visible and quality (HQ) microwave merged with visible and infrared rain rate (VAR) infrared rain rate (VAR)

VAR estimates obtained from geostationary visible/infrared

VAR estimates obtained from geostationary visible/infrared

bservations
bservations

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Comparison of 108 Instantaneous Comparison of 108 Instantaneous QRad QRad – – TRMM 3B42RT HQ Collocated Rain Events TRMM 3B42RT HQ Collocated Rain Events

QRad HQ Normalized histogram Rain Rate [mm/hr] HQ Rain Rate [mm/hr] QRad Rain Rate [mm/hr]

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QRad / TRMM 3B42RT HQ Instantaneous QRad / TRMM 3B42RT HQ Instantaneous Collocated Rain Event Collocated Rain Event

QRad HQ

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QRad / TRMM 2A12 Instantaneous QRad / TRMM 2A12 Instantaneous Rain Rate Rain Rate

QRad TMI

Longitude Latitude

Land Land

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Rain Image Comparison

QRad TMI

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Rain Scatter Rain Scatter

TMI IRR [km*mm/hr] QRad IRR [km*mm/hr]

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QRad QRad / TMI Rain Pattern Classification / TMI Rain Pattern Classification

TMI > 0, QRad > 0 TMI = 0, QRad > 0 TMI > 0, QRad = 0 TMI = 0, QRad= 0 Agree = 89.03 % False alarm = 5.07% Missed = 5.90%

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Zonal Average Rain Rate Zonal Average Rain Rate

Average Weekly Rain Rate 10° N to 20° N

QRad TMI

Average Rain Rate (mm/hr) 0° N to 20° N Pentad number

QRad TMI

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Monthly Average Rain Rate Monthly Average Rain Rate

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Active Sigma

Active Sigma-

0 Model

0 Model

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Rain Effects on Rain Effects on SeaWinds SeaWinds σ σo

In the presence of Rain, three major factors affect the

In the presence of Rain, three major factors affect the measured ocean surface measured ocean surface σo: :

Two way path attenuation

Two way path attenuation

Reduces received power

Reduces received power

Volume backscatter

Volume backscatter

Enhances received power

Enhances received power

Surface perturbation

Surface perturbation “ “Splash Effect Splash Effect” ”

Alters ocean surface roughness structure

Alters ocean surface roughness structure

( , , , , ) ( , , ) ( , , , ) ( , , )

meas wind excess

r u p r p u p r p σ χ θ α θ σ χ θ σ θ = ⋅ +

σ0

meas

: Measured SeaWinds backscatter σ0

wind

: Wind induced backscatter σ0

Ex-rain : Excess-backscatter due to rain

α : Two-way path attenuation

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SeaWinds SeaWinds Rain Excess Backscatter Rain Excess Backscatter and Attenuation Models and Attenuation Models

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Sigma Sigma-

0 Forward Model Validation (1)

0 Forward Model Validation (1)

R = 1.0 km*mm/hr

H-Pol V-Pol

R = 1.5 km*mm/hr

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Sigma Sigma-

0 Forward Model Validation (2)

0 Forward Model Validation (2)

H-Pol V-Pol

R = 25.5 km*mm/hr R = 36.4 km*mm/hr

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Sigma Sigma-

0 Forward Model Validation (3)

0 Forward Model Validation (3)

H-Pol V-Pol

R = 79.7 km*mm/hr R = 116.2 km*mm/hr

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Scatterometer Scatterometer Response Response

( , , , , ) ( , , ) ( , , , ) ( , , )

meas wind excess

r u p r p u p r p σ χ θ α θ σ χ θ σ θ = ⋅ +

H-Pol V-Pol

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Combined Passive / Active Retrievals

Combined Passive / Active Retrievals

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Methodology Methodology

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SeaWinds SeaWinds Rain Calculations Rain Calculations

Calculate Polarized Excess Backscatter: Calculate Polarized Excess Backscatter:

( , , ) ( , , , , ) ( , , ) ( , , , )

ex m ws

r p r u p r p u p σ θ σ χ θ α θ σ χ θ = − ⋅

Minimize Objective Function: Minimize Objective Function:

mod 2 , , 2 1

( )

meas el N ex i ex i i i

J σ σ δ

=

− = ∑

δi

2 = f(IRRQRad)

Calculate Combined Rain Rates: Calculate Combined Rain Rates:

PA PA SeaWinds h h v v

IRR IRR IRR γ γ = ⋅ + ⋅

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SeaWinds SeaWinds / TMI Rain Scatter / TMI Rain Scatter

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SeaWinds SeaWinds / TMI Rain Scatter / TMI Rain Scatter

3 ≤ ws < 7 0 ≤ ws < 3 7 ≤ ws < 12 ws ≥ 12 TMI IRR [km*mm/hr] SeaWinds IRR [km*mm/hr]

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SeaWinds SeaWinds / / QRad QRad Comparison Comparison

,

, 1

( , , ) ( , , , , )

ex i

N ex meas i i

r p r u p σ θ η σ χ θ

=

=∑

80 %

ηex

(SeaWinds – QRad) Corr Coeff

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SeaWinds SeaWinds Rain Algorithm Rain Algorithm

Validation of

Validation of SeaWinds SeaWinds Rain Retrievals Rain Retrievals

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Validation Dataset Validation Dataset

72 Collocated events

72 Collocated events

Apr ~ Oct 2003

Apr ~ Oct 2003

±

± 30 minutes 30 minutes

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Rain Statistics Rain Statistics

TMI SeaWinds

pdf cdf

Rain Rate [km*mm/hr]

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Rain Error Statistics Rain Error Statistics

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SeaWinds SeaWinds / TMI Rain Image Comparison / TMI Rain Image Comparison

Land Mask

SeaWinds TMI

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SeaWinds SeaWinds / TMI Rain Pattern Classification / TMI Rain Pattern Classification

TMI > 0, QRad > 0 TMI = 0, QRad > 0 TMI > 0, QRad = 0 TMI = 0, QRad= 0 Agree = 92.89% False alarm = 4.46% Missed = 2.65%

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IMUDH / TMI Rain Pattern Classification IMUDH / TMI Rain Pattern Classification

TMI > 0, QRad > 0 TMI = 0, QRad > 0 TMI > 0, QRad = 0 TMI = 0, QRad= 0 Agree = 90.72% False alarm = 4.72% Missed = 4.56%

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SeaWinds SeaWinds Rain Detection Rain Detection

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SeaWinds SeaWinds Rain Flag Statistics Rain Flag Statistics

Wind speed regime Agreement % Missed rain % False Alarm % 0 ≤ ws < 3 91.20 2.40 6.40 3 ≤ ws < 7 90.38 3.46 6.15 7 ≤ ws < 12 89.49 4.22 6.29 ws ≥ 12 85.37 8.47 6.16 All Data 89.72 4.06 6.23

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Summary & Concluding Remarks Summary & Concluding Remarks

The

The SeaWinds SeaWinds microwave sensor has the simultaneous microwave sensor has the simultaneous capability to measure the active capability to measure the active σ σº º and the passive T and the passive Tb

b from

from the Earth the Earth’ ’s surface and atmosphere s surface and atmosphere

SeaWinds

SeaWinds measurements are sensitive to the presence of rain measurements are sensitive to the presence of rain

ver the ocean
ver the ocean
H

H-

pol

pol higher sensitivity (wider dynamic range) higher sensitivity (wider dynamic range)

The passive T

The passive Tb

b measurements are mainly rain dominant,

measurements are mainly rain dominant, while active while active σ σº º measurements can be either wind /rain measurements can be either wind /rain dominated dominated

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Summary & Concluding Remarks Summary & Concluding Remarks

Utilize measurement sensitivity to develop a passive

Utilize measurement sensitivity to develop a passive-

only
nly

and combined active / passive statistical rain retrieval and combined active / passive statistical rain retrieval algorithms for algorithms for SeaWinds SeaWinds sensor sensor

Excellent agreement with standard rain products

Excellent agreement with standard rain products

Nearly unbiased retrievals

Nearly unbiased retrievals

Powerful

Powerful “ “stand stand-

alone

alone” ” rain flag rain flag

SeaWinds

SeaWinds / / QRad QRad rain estimates can provide additional rain estimates can provide additional independent sampling of the oceanic rain and therefore, these independent sampling of the oceanic rain and therefore, these rain retrievals have the potential for contributing to NASA's rain retrievals have the potential for contributing to NASA's GPM Mission objectives of improving the global sampling of GPM Mission objectives of improving the global sampling of

ceanic rain within 3 hour windows
ceanic rain within 3 hour windows

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Summary & Concluding Remarks Summary & Concluding Remarks

The passive

The passive-

only
nly QRad

QRad is implemented by NASA JPL as part is implemented by NASA JPL as part

f level 2B (L2B) science data product
f level 2B (L2B) science data product

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Publications Publications

Journal Papers:

Journal Papers:

Ahmad, K.A., Jones, W.L., Kasparis, T., Wiechecki Vergara, S., Adams,

I.S., Park, J.-D., “Oceanic rain rate estimates from the QuikSCAT Radiometer, A Global Precipitation Mission path finder,” J. Geophys. Res., Vol 110, June 2005.

Adams, I. S., Hennon, C. C., Jones, W. L., and K. Ahmad, “Evaluation

f hurricane ocean vector winds from WindSat,” IEEE Trans. Geosci.
Rem. Sens. Vol, 44, March 2006.
K. A. Ahmad, W. L. Jones, and T. Kasparis, “Estimation of Oceanic

Rainfall Retrievals using passive and active measurements from SeaWinds Remote Sensor," IEEE Trans. Geosci. Rem. Sens. - To be submited

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Publications Publications

Conference proceedings:

Conference proceedings:

K. A. Ahmad, W. L. Jones, and T. Kasparis, "Oceanic Rainfall Retrievals

using passive and active measurements from SeaWinds Remote Sensor," presented at IGARSS 07, Barcelona, Spain, 2007.

K. A. Ahmad, W. L. Jones, and T. Kasparis, “QRad Level 2B Data

product," presented at IGARSS 06, Denver, CO, 2006.

Vasud Torsekar, Takis Kasparis, W. Linwood Jones , Khalil A. Ahmad

and David G. Long,“ Oceanic Rain Identification using Multi-Fractal Analysis of QuikSCAT Sigma-0”, Oceans 05, Sept. 18-23, 2005, Washington, D.C.

Pet Laupattarakasem, W. Linwood Jones, Khalil A. Ahmad and Svetla

Veleva ”Calibration/Validation of the SeaWinds Radiometer Rain Rate Algorithm”, Oceans 05, Sept. 18- 23, 2005, Washington, D.C.

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Publications Publications

Conference proceedings:

Conference proceedings:

Khalil A. Ahmad , W. Linwood Jones and Takis Kasparis,"QuikSCAT

Radiometer (QRad) Rain Rates for Wind Vector Quality Control", Oceans 05, Sept. 18-23, 2005, Washington, D.C.

Adams, Ian s., Hennon, Christopher, Jones, W. Linwood and Khalil A.

Ahmad, “Hurricane Wind Vector Measurements from WindSat Polarimetric Radiometer” , Proc. IEEE IGARSS 05, July 25-29, 2005, Seoul, Korea.

K. A. Ahmad, W.L. Jones, J. Thomas-Stahle, and C. Kummerow,

“Oceanic rain rates from the WindSat Radiometer,” Proc. IEEE IGARSS 05, July 25-29, 2005. , Seoul, Korea.

Ahmad, K. A., Jones, W. L., and T. Kasparis, “Application of

QuikSCAT Radiometer Rain Rates to Near-Real-Time Global Precipitation Estimates”, Proc. IEEE IGARSS 04, Sept. 20-24, 2004, Anchorage, AK

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Publications Publications

Conference proceedings:

Conference proceedings:

Khalil A. Ahmad , W. Linwood Jones and Takis Kasparis,"QuikSCAT

Radiometer (QRad) Rain Rates for Wind Vector Quality Control", Oceans 05, Sept. 18-23, 2005, Washington, D.C.

Ahmad, K. A., Jones, W. L. and T. Kasparis, “Precipitation

Measurements using the QuikSCAT Radiometer”, Proc. IEEE IGARSS 03, Jul 21-25, 2003, Toulouse, France

Jones, W. L., Ahmad, K. A., Park, J. D. and J. Zec, “Validation of

QuikSCAT Radiometer Rain Rates using the TRMM Microwave Radiometer and the Special Sensor Microwave Imager”, Proc. IEEE IGARSS 02, Jun 24-28, 2002, Toronto, Ontario, Canada