Utilisation of satellite and in-situ data in the FMI air quality forecasting system

Mikhail Sofiev1, Roman Vankevich2, Marje Prank1, Julius Vira1, Pilvi Siljamo1, Tatjana Ermakova2 ,Milla Lanne1, Joana Soares1 1 Finnish Meteorological Institute 2 Russian State Hydrometeorological University

Content

• Introduction Use of remote-sensing data FMI air quality forecasting system

• Utilization of satellite information: a few examples Forecasting of allergenic pollen

– Static data used– Possibilities and difficulties of using NDVI

Wild-land Fire Assimilation System– Static and dynamic satellite data in FAS– FAS injection height parametrization

… CALIPSO, MISR

Data assimilation for AQ forecasting– Model state initialization vs emission correcton– Observation operator

Model evaluation– Case study for spring, 2006

• Summary

Introduction• Three most-common ways of utilizing the satellite-retrieved

information for the needs of air quality evaluation and forecasting: model-measurement comparison

– limited temporal resolution, good spatial coverage and resolution

– complementary to the in-situ information (the one usually with high temporal resolution but limited spatial coverage).

static datasets

– land-use, surface features etc.

dynamic information, including NRT

– directly as model input

– via data assimilation

Physiography,forest mapping

Aerobiologicalobservations

Regional AQ forecasting system of FMI

Satelliteobservations

Phenologicalobservations SILAM

AQ model EVALUATION:NRT model-measurement

comparison

Aerobiologicalobservations

Meteorological data: ECMWF

Online AQmonitoring

Phenologicalmodels

Fire AssimilationSystem

HIRLAMNWP model

Final AQ products

UN-ECE CLRTAP/EMEPemission database

Remote-sensing data in current SILAM data flow

Static DynamicGlobal land coverLandsat

Emission categories for FAS

Other info

Pollen source areas

Flowering model SILAM

TA(Rapid Responce Sys. NASA)MODISAVHRRATSR

FRPMODIS

AODMODIS

Tropspheric column NO2OMI

FAS-TA

FAS-FRP

Fire emissionfluxes

ConcentrationsAOD

Model – Measurementcomparison

Model quality assessment

Birch mapGrass map

Static remote sensing data in SILAM

• Land use (LandSat) Broad-leaf forest

– Birch forest (national inventories, where exist; latitudewise extrapolaton)

Grass map

• FAS burning vegetation categories Emission for unit FRP,

speciation same for all categories (varies more due to the state of the vegetation)

– Emission coefficients: total PM (Ichoku, 2005), relative speciation (Andreae & Merlet 2001)

Dynamic information: Normalized Difference Vegetation Index

• Averaged in time and space

• Birch leafs unfold 3-4 days after flowering starts

Dynamic information on fires: TA vs FRP

Temperature Anomaly Fire Radiative Powerper-pixel statistical database (time-integrated May-August 2006)

Mark size is proportional to tempr.anomaly Dot size is proportional to FRP

CALIPSO assimilation: injection height of fire plumes

Fire maps

Dispersion of

plumes

CALIPSOorbits

Merged ModelledX-Y-Tseries

for smokeat

receptor

SILAM sourceterm for

adjoint run:X-Y-Z-T

SILAM

SILAMSensitivity distribution 3D at source

In: injection height, fire parameters

models, …out:

plume-rise parameterization

CALIPSOprofiles

MODIS TA/FRP

CALIPSO profiles vs MODIS fires: Aug.2006

Obs: only smoke-declared profiles are considered

CALIPSO aerosol-type recognition

MISR data for fire injection height

• Change in reflectance with angle distinguishes different types of aerosols, and surface structure

• Stereo imaging provides geometric heights of clouds and aerosol plumes

• Height accuracies for low clouds have been validated to a few hundred meters (Naud et al., 2004);

22 AUG 2006, single plume injection, [m]

Stereo30300 - 400400 - 600600 - 10001000 - 12001200 - 16091610 - 16531654 - 16751676 - 16831684 - 22112212 - 22452246 - 27652766 - 28542855 - 34663467 - 44554456 - 58085810 - 83408341 - 19968No Data

Stereo30300 - 400400 - 600600 - 10001000 - 12001200 - 16091610 - 16531654 - 16751676 - 16831684 - 22112212 - 22452246 - 27652766 - 28542855 - 34663467 - 44554456 - 58085810 - 83408341 - 19968No Data

Direct data assimilation in regional AQ modelling

• What to assimilate? Where to assimilate? How to assimilate?

• What: Assimilated information should constrain maximum number of dimensions

of the model freedom

– should be available and reliable

• Where to: initialize the concentration fields

– short model memory

corrections to input data, such as emission

– extrapolation in time problematic

• How: Kalman filtration, optimal interpolation, 3D-VAR, etc.

– However, for strongly time-dependent fields 4D-VAR seems to be the right choice despite costs of adjointization of the model.

Assimilating initial contitions• 2 runs with the same setup of SILAM model

• strongly different initial conditions imitating the effect of intialization via data assimilation

• results are looked at +1 and +2 days

Assimilating

emission• First and seventh day of the assimilation Top: concentration of

SO2 (mol m−3) in the reference run.

Center: deviation (reference-assimilated, mol m−3) from the reference run.

Bottom: emission correction factor

• Negative correction to Etna; some corrections positive for the first and negative for the 7th day

Observation operator• The remotely measured variables are related to optical features of the

atmosphere and surface: optical depth, backward scattering, albedo, radiance, etc.

• Their conversion to concentrations is an ill-posed inverse problem, which requires strong assumtions for regularization.

• Solution for DA: model should provide the measured quantity

SILAM observation operator for remote-sensing measurements

For aerosols: wavelength and relative humidity dependent extinction efficiencies are computed from particle size parameter x = r / λ and complex refractive index m = n + i k using Mie theory (m = m(λ,Rh); r = r(Rh))

For gases: wavelength and temperature dependent extinction cross sections from experimental data are used

levels

sizesubstsizesubstsizesubst

TOA

sizesubstsizesubst zExtlevCdzzN **)(**)( ,,,,,

0

,,,

Case study April-May 2006• Case description

April-May 2006, the most-interesting episode 25.04-10.05. Low-wind conditions resulted in build-up of contamination over eastern

Europe Widespread wild-land fires over western Russia Synchronization of otherwise uncorrelated phenomena by meteorological

developments

• Model setup HIRLAM meteo data Resolution 0.2 deg; vertical 10 layers up to ~8 km Emissions:

– PM and gases from fires FAS – TA

… PM, SOx, NOx, VOCs

– Anthropogenic and natural emissions from TNO & EMEP

… PM, reactive gases

– Sea salt

Comparison with MODIS AOD

SILAM vs MODIS AOD

00.050.1

0.150.2

0.250.3

0.350.4

0.45

MODIS mean

SILAM mean

SILAM vs MODIS AOD-1

-0.8

-0.6

-0.4

-0.2

0

absolute deviationrelative deviation

SILAM vs MODIS AOD

00.10.20.30.40.50.60.70.8

Figure of merit in space

Spatial correlation

RMSE

Comparison with OMI NO2

Satellite-data from giovanni.gsfc.nasa.gov

-OMI Tropospheric column NO2

Summary

• High demand on remote sensing data Complementary to other sets of information

• Specific features of data decide the way to use them Static data

Time-resolving data

NRT data

• Minimum ad-hoc assumptions, clear communication of the data features and uncertainties are important If some assumptions are made in data retrieval algorithm, it should

be made clear, where these assumptions are applicable!