utilisation of satellite and in-situ data in the fmi air quality forecasting system mikhail sofiev...
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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!