Computational Aspects of Chemical Data Assimilation in Air Quality Models

Greg Carmichael (Dept. of Chem. Eng., U. Iowa)

Adrian Sandu (Dept. of Comp. Sci., Mich. Inst. Tech.)

John Seinfeld (Dept. Chem. Eng., Cal. Tech.)

Tad Anderson (Dept. Atmos. Sci., U. Washington)

Peter Hess (Atmos. Chem., NCAR)

Dacian Daescu (Inst. of Appl. Math., U. Minn.)

Tianfeng Chai, Center for Global & Regional Environmental Research, UIowa

Chemical Weather Forecasting

SatelliteProducts

Global Assimilation

RegionalPrediction

Public Impact

Requires Close Integration of Observations and Models

ICCS2003 Melbourne Australia, June 3, 2003

The University of Iowa, USA

Characterization of Urban Signals

Science Support to Policy

UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies DDDA

S

TRACE-P/Ace-Asia EXECUTION

Emissions-Fossil fuel-Biomass burning-Biosphere, dust

Long-range transport fromEurope, N. America, Africa

ASIA PACIFIC

Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLIS

3D chemical model forecasts: - x - GEOS-CHyEM - CFORS - z

FLIGHTPLANNING

Boundary layerchemical/aerosolprocessing

ASIANOUTFLOW

Stratosphericintrusions

PACIFIC

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How Well Do Models Capture the Observed Features?

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Predictability – as Measured by Correlation Coefficient

Met Parameters are Best

Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimilation of chemical

data.

Advantages of Adjoint Sensitivities

We are Developing General Software Tools to Facilitate the Close Integration of Measurements and Models

The framework will provide tools for: 1) construction of the adjoint model; 2) handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis

of results; 6) remote access to data and computational resources. (www.cs.mtu.edu/~asandu/Software/Kpp)

Advantage Of Adjoints Is That We Get Sensitivities; e.g., Influence Functions (top view)

(top:Mar.1-3, bottom:Mar.22-24; from left to right: O3, NO2, HCHO)

Summary• Nonlinear, stiff chemistry important part of

chemical transport models;• One-step methods (RK, Ros) seem preferable

w/ adjoint sensitivity;• KPP: efficient code for simulation and

direct/adjoint sensitivities;• Preliminary 3D assimilation results with parallel

adjoint STEM-III;• Developing/exploring new optimization

techniques (using 2nd order sensitivities), adaptive grids…..

Target Applications Air quality forecasting in

urban environments; Integration of measurements

and models to produce a consistent/optimal analysis data set for atmospheric chemistry field experiments (e.g., Ace-Asia);

Inverse analysis to produce a better estimate of emissions;

Design of observation strategies to improve chemical forecasting capabilities.

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

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MTC ALP

PTHGLS

GVD

PLP BXH

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AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

NORTH EAST

HOUR

IND

EX

NORTH EAST

HOUR

IND

EX

Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PTC

MTC ALP

PTHGLS

GVD

PLP BXH

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MODERATE

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AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

PTC

MTC ALP

PTHGLS

GVD

PLP BXH

5740

5760

5780

5800

5820

5840

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G(km)

LIGHT

MODERATE

HEAVY

AIR QUALITY FORECAST-MELBOURNE

AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE

NORTH EAST

HOUR

IND

EX

NORTH EAST

HOUR

IND

EX NORTH EAST

HOUR

IND

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NORTH EAST

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Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution

TWO-SCENARIO TWO-SCENARIO FORECASTFORECAST

• Ability of forecast models to represent the individual processes controlling air pollution formation and transport.

& intercontinental

regional

Application: The Design of Better Observation Strategies to Improve Chemical Forecasting Capabilities.

Data Assimilation Will help us Better Determine Where and When to Fly and How to More Effectively Deploy our Resources (People, Platforms, $$s)

We Plan to Test These Tools Including Adaptive Measurements in

the Summer of 2004