NRT (Near Real Time) Data Application to Air Quality...

NRT (Near Real Time) Data

Application to Air Quality

Forecasts

Sunling Gong

4th International Workshop on Air Quality Forecasting Research (IWAQFR)

Geneva, 12-14 December 2012

A CMA-WMO/GURME Pilot Project

– January-2-13

WMO Leads

The Atmospheric Research and Environment

Programme (AREP)

GURME Programme:

GONG, Sunling, Project/sciences Lead

CARMICHAEL, Gregory, SAG Chair

JALKANEN, Liisa, WMO/AREP

– January-2-13

CMA Leads

The Chinese Academy of Meteorological Sciences

Center for the Atmospheric Watch and Services

(CAWAS)

ZHANG, Xiaoye, Project/sciences Lead

– January-2-13

Objectives

– Develop and establish a NRT chemical data

transfer system to collect and process both

ground based and satellite observations,

based on the WMO data transfer protocols

for conventional weather data;

– Develop an AQ forecasting system and

integrate it with the NRT system to

illustrate the capacity of NRT data to

enhance the accuracy of AQ forecasts in

China;

– January-2-13

– Develop an emission estimating system

using the NRT data and inverse modeling

methodology;

– Exchange and transfer research results

with other national and international

agencies.

Objectives

– January-2-13

Expected Results

The pilot project will demonstrate the

potential of the NRT data in improving

the AQ forecast. The main product of

the project will be an integrated system

and associated methodology that can

be used to improve the AQ forecasting

ability in other regions (countries) under

WMO GURME.

– January-2-13

NRT Data in China

– January-2-13

CAWNET

Hourly PM10

(some include

PM2.5, PM1)

visibility

aerosol-light

absorption

aerosol-light

scattering

meteorology

37

Science and Technology Branch

Environment Canada

Data Checking systemThe data quality monitoring system can help the network staff find the problem of the instruments in time to decrease the possibility of bad data:

Green color means the instrument is OK.Blue color means the instrument is also OK, but needs to be take care of the power supply.Red color means serious problems occurred and need to resolve ASAP.

CARSNET (CMA Aerosol Remote Sensing NETwork)

CARSNET

26 sites with CIMEL

sunphotometer

kept running

operationally

The calibration

system including

solar and sky

measurements

26

– January-2-13

MODIS

Satellites - AOD

FY-3A

– January-2-13

AQ and Chemical Weather Forecasting System

– January-2-13

Chinese Chemical Weather Forecasting

System – CUACE

CUACE (CMA Unified Atmospheric Chemistry Environment)

Meteorological Frame: GRAPES, MM5, RCM, NCC-GCM

Emissions

various reactiontransformationSO2,NOx, PM, CO, NO2,O3

PM (Dust, BC-OC, SO4, NO3, NH4, SSalt), O3

Data assimilation

– January-2-13

CUACE/Haze-Fog Forecasting System

Yangzi River Delta

Perl River Delta

National 54 km

Regional 9 km

– January-2-13

Data assimilations

– January-2-13

3D-Var is to minimize the following function

( Lorenc,1986 )：

))(())(()()(2

1)( 1T1T

oobb xHxHxxxxxJ yOyB

Using the observational data y0 to find the

solution of x that satisfies the min J(x)

xa

3D-Var Method

– January-2-13

Model vs Obs.

Models:

12 size bins of 6 types

of aerosols:

SF, BC, OC, SD, NT

and SS

AODs

Vertical profiles

NRT Observations:

AOD

PM10, PM2.5

Dust

– January-2-13

Assimilation Scheme

• Modeled AOD Assimilated with obs. AOD

Assume the modeled

composition,

size distributions,

vertical profiles

If dust occurs

Dust DAS

with IDDI

Revised the SD

12 size bins of 6 aerosol types

Other NRT

Observations:

Lidar

PM

– January-2-13

With DASNo DAS

CUACE/Dust : WMO SDS-WAS

Niu et al 2008

2006 Spring Forecasts: threat Score (TS) increased from 0.22 to 0.31, a

41% enhancement.

– January-2-13

AOD (FY-3A) Assimilationa b

c d

Satellite Initial

Corrections Final

2009-9-29

– January-2-13

Emission Inversions

– January-2-13

FeedbackRadiation, Cloud

Emissions

Geophysical data

Meteorological obs. Gas Phase

Chemistry

Aerosol Module

O3 CO NOx

PM2.5

PM10Acid Deposition

ISORROPIAAerosol Equilibrium Scheme

GRAPES/M

M5

Mete

oro

logy T

ransp

ort

Data

Assimilation

NH4, NO3

CMA AQ Forecasting System

– January-2-13

Adjoint Model

– January-2-13

Ensemble Kaman Filter Model

EnKF

– January-2-13

CAWNET

Hourly PM10

(some include

PM2.5, PM1)

visibility

aerosol-light

absorption

aerosol-light

scattering

meteorology

BC

– January-2-13

Gridded Emissions

Total: 1.4 Tg/a

Unit:t/km2

Total: 2.95 Tg/a

0.5°×0.5°

BC

OC

– January-2-13

Original After inversed

BC Emi. Strength（mg m-2 s-1）

Ensemble Kaman Filter Model

– January-2-13

Improvement by EnKF

2008-7-2:02 2:14 3:02 3:14 4:02 4:140

2

4

6

8

10

12

BC

co

nce

ntr

atio

n (

ug

/m3

)

obs

Forecast

Forecast with inversing emission

– January-2-13

Technology Exchange/Transfer

– January-2-13

GEM-MACH - Structure

SMOKERegional Data

Canada & US

Gas Phase

Chemistry

CAMCanadian Aerosol Module

ISORROPIAEquilibrium Scheme

GE

MM

ete

oro

log

y T

ran

sp

ort

Em

issi

on

In

terfa

ce

Ch

emis

try I

nte

rfa

ce

Global

Emissions

– January-2-13

"Multiscale" Examples:

Three GEM Grid Configurations

globalregional

limited area

– January-2-13

A Daily AOD from MODIS

Deep Blue”

AOD product

over bright

land surface

0.55 μm

band

from both

Terra and

Aqua.

M O D - Terra

M YD - Aqua

– January-2-13

NRT AOD from GOES Satellite

– January-2-13

AeroNET AOD Sites

– January-2-13

AOD Assimilation for GEM-

MACH Globe

– January-2-13

Conclusions

• Data Assimilation (DA) can substantially improve

the AQ forecasts for PM, especially for strong,

episodic events, such as soil dust and bio-mass

burning;

• The NRT data can improve the aerosol

emissions to enhance the AQ forecasts;

• More speciated NRT data are needed to further

enhance the DA;

• NRT vertical profiles from surface or air-born

lidar would be useful to the DA.

– January-2-13

Thank you!

NRT (Near Real Time) Data Application to Air Quality...

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