Utilisation of satellite and in-situ data in the FMI air quality forecasting system Mikhail Sofiev 1, Roman Vankevich 2, Marje Prank 1, Julius Vira 1, Pilvi Siljamo 1, Tatjana Ermakova 2,Milla Lanne 1, Joana Soares 1 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 Aerobiological observations Regional AQ forecasting system of FMI Satellite observations Phenological observations SILAM AQ model EVALUATION: NRT model-measurement comparison Aerobiological observations Meteorological data: ECMWF Online AQ monitoring Phenological models Fire Assimilation System HIRLAM NWP model Final AQ products UN-ECE CLRTAP/EMEP emission database

Remote-sensing data in current SILAM data flow Static Dynamic Global land cover Landsat Emission categories for FAS Other info Pollen source areas Flowering model SILAM TA (Rapid Responce Sys. NASA) MODIS AVHRR ATSR FRP MODIS AOD MODIS Tropspheric column NO2 OMI FAS-TA FAS-FRP Fire emission fluxes Concentrations AOD Model – Measurement comparison Model quality assessment Birch map Grass 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 Power per-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 Dispersio n of plumes CALIPSO orbits Merged Modelled X-Y-T series for smoke at receptor SILAM source term for adjoint run: X-Y- Z -T SILAM Sensitivity distributio n 3D at source In: injection height, fire parameters models, … out: plume-rise parameterization CALIPSO profiles 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]

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

Case study April-May 2006 Case description  April-May 2006, the most-interesting episode  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

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!