Trajectory validation using tracers of opportunity such as fire plumes and dust episodes

Narendra Adhikari

March 26, 2007ATMS790 Seminar (Dr. Pat Arnott)

Outline

• Introduction

• Data and Methods

• Case Studies

-Fire episodes

-Dust episodes

Introduction

• Problem with model trajectories

• Errors associated– Physical (inadequate data: spatial and

temporal)– Computational (numerical truncation)– Forecast (error from forecast data)

• HYSPLIT trajectory model –for evaluation

Introduction- contd.HYSPLIT

• The HYSPLIT(Hybrid Single-Particle Lagrangian Integrated Trajectory) created and maintained by NOAA ARL

• The HYSPLIT model is a complete system for computing trajectories, complex dispersion and deposition simulations using either puff or particle approaches

Introduction- contd. Objectives

• Main objective of the study is the model trajectory validation, accuracy evaluation – Model trajectory evaluation using tracer of

opportunity like wildfire smoke plumes, wind blown dust trails etc.

• The major effort will be given to estimate plume height estimation for model input

Introduction- contd. Importance?

• Model trajectory can be used for forecasting – Accidental toxic chemical release plume path/time– Wildfire plume path, downwind dispersion and level

of pollution that might risk the personal health• How accurate are the model trajectory

calculation/forecasting?• Accurate estimation of trajectory run starting

position is important issue to evaluate trajectory model

• Quantify errors associated with model trajectory

Focus In this presentation

• Tracers of opportunity

• Dust and smoke plumes

Data

Model satellite data

EDAS MODIS

FNL GOES

MM5 MISR

CALIPSO

GOES & MODIS visible imagery

• GOES (Geostationary Operational Environmental Satellite)– Image every 15 minutes– Ground resolution of 1 km

• MODIS (MODerate resolution Imaging Spectroradiometer)– Twice Daily from two satellites (Terra and

Aqua)– Ground resolution 250 m

Use of GOES to identify dust plumes

• 15 minute time resolution is helpful for fire/dust episodes

• GOES longwave IR difference: channel 4 (10.7 um) minus channel 5 (12um) is helpful to identify dust plumes

GOES Aerosol Data

• GOES Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD)

• A measure of the aerosol column amount (ground to space)

• Data available: every 30 minutes

• GASP AOD can be used to identify fire plumes that are not visible in GOES visible images

MISR Data• Multi-angle Imaging SpectroRadiometer• MISR collects imagery simultaneously at 9

different views toward the earth’s surface

• Cloud/aerosol height information• Temporal resolution ~9days

CALIPSO Data

• Satellite lidar operated in 532nm and 1064nm

• Measures attenuated backscatter from atmosphere

• Shows vertical cross-section of atmospheric aerosols and clouds

• Vertical resolution of profiles between 30 and 60m

Examples of Fire plume satellite Image

MODIS visible image

Satellite visible image of fire plume

GASP, Aerosol Optical Depth

Smoke plume from Northern CA fire

Analyzing fire and dust plumes View from satellite

Illustrates complexity in estimating plume’s leading edge Shows problems in estimating

plume height

Fire, Dust Plume separation- an example

Image processing to isolate plume

Estimate plume extent & centerline

HYSPLIT trajectory run from different position/Height

Backtrajectory starting positions

Fire location

MISR Satellite image of fire plume- an example

Visible Image of Dust episode

El Paso

Dust plumes

Clouds

Clouds

Dust trails compare with HYSPLIT model trajectory

262 km

44 km

Green 500m

Blue 1000m

Background image produced by subtracting GOES channel 4 from channel 5

Web Cam picture of El Paso looking south

Clear conditions around noon

Web Cam picture of El Paso

Dust storm around 3 PM

Haze attributed to blowing dust

Wind Gusts during this episode in the El Paso Area

Peak gust of 56 mph

Hourly PM10 for El Paso Area

African Dust

Example

CALIPSO LIDAR Passing through dust region

Focus on this region

CALIPSO LIDAR vertical cross section through dust region

Over Ocean

AfricanCoastline

Over Ocean

30km

20km

10km

surface

CALIPSO vertical cross-section magnified

Challenges in Wind Trajectory Evaluations

Trajectory Validation

usingtracer of

opportunity

Using GIS tools to

Test accuracy

Results of trajectory Accuracy

Assign accuracy index

Outcome /applications

Satellite data / images

of fire plumes

Estimation of Plume top height

MISR StereographicImage to derive height

CALIPSO LIDAR forPlume height and

Vertical spread

Final Plume centerline And centroid height

Assign height and time for

the trajectory model

Trajectory-HYSPLIT

Dispersion-HYSPLIT

Model Run Part

Statistical Method

of Accuracy Test

Model Run Part

Acknowledgement

Thanks for the Advisors:

Prof. Mark Green

A. Prof. Dave DuBois

Questions/comments?

Thank you