User Awareness & Training: The Helsinki Atmosphere Test Case Tallinn, Estonia 9 th April 2014

Transcription

1 User Awareness & Training: The Helsinki Atmosphere Test Case Tallinn, Estonia 9 th April 2014 Ari Karppinen, Joana Soares, Mikhail Sofiev, Rostislav Kouznetsov(FMI), Renske Timmermans(TNO)

2 Contents Pollution: sources and forecasting PASODOBLE -Airsheds Finnish AQ assessment and forecasting platform description services & users: Ground-breaking forecasts pollen wildland fires

3 Air pollution Sources: Anthropogenic Biogenic from vegetation Natural (e.g. sea salt and dust) Wildland fires Important facts: Marine traffic: 40% of NOx and 50% of SOx of total-european anthropogenic emission Wild-land fires: on average contribute 10-50% of European anthropogenic emission of PM and some gases (e.g. CO) AQ problems can have a regional/local or transboundary origin

4 Air quality forecasting/assessment Goals: information to public and authorities AQ forecast: decision support for short-term abatement AQ assessment: analysis for long-term decision-making Main regulated species in Europe: O 3, NO 2, SO 2, PM 2.5, PM 10 AQ forecasting/assessment: various numerical models applied from global to national level Ensemble forecasting: MACC-II (Monitoring of Atmospheric Composition and Climate)

5 European service chain Global MACC European PASODOBLE Regional Local/Urban

6 AIRSHEDS - domains FMI SILAM RIUUK EURAD ACRI-ST CHIMERE AUTH WRF-CAMx

7 AIRSHEDS - domains TNO/KNMI/RIVM LOTOS-EUROS Joint airshed Covering main parts of other four airsheds to allow comparison/ validation ACRI-ST CHIMERE

8 FMI AQ assessment & forecasting platform IS4FIRES AQ products Sea surface Temp. & salinity Satellite observations Geo-morfological data Sea salt emission model Wind-blow dust emission model SILAM WRF EVALUATION: NRT model-measurement comparison AIS data STEAM emission model AROME NWP model TVM Online AQ monitoring Phenological observations Aerobiological observations Physiography, forest mapping Phenological model CLRTAP/EMEP emission data Boundary conditions HIRLAM NWP model Meteorological data: ECMWF ECHAM,RCA G/RCM model Aerobiological observations Satellite observations

9 Data users/providers Relies on the availability of MACC 2 and PASODOBLE products related to different pollutants for assessment and decision making Boundary condition Emissions Ground and remote sensing data Forecast & reanalysis: Boundary conditions Concentrations, emissions AQ assessment: long-range pollution transport impact on AQ in remote areas, for local level decision makers Impact of AQ policies

10 Animations AQ Europe AQ Regional AQ Meso-scale Data users/providers IS4FIRES Pollen Public Univ. of Turku THREDDS User Uptake Validation Lithuanian EPA WMS, WCS, NetCDFsubset MACC Helsinki RESA NIMH, Bulgaria U.Tartu City of Vilnius EAN PollenInfo.org ZAMG

11 THREDDS SILAM operational forecast data availability via interfaces was setup within MACC and PASODOBLE projects Extraction of the variables, subdomain or individual points, time intervals according to user needs A server platform enriched with a set of applications for distributing cartographic information in numerical and graphical forms: e.g Godiva viewer (

12 THREDDS: Threaded distributed data server A server platform enriched with a set of applications for distributing cartographic information in numerical and graphical forms Developed and supported by UNIDATA group Java Can use NetCDF file format (GRIB, GRIB2, NetCDF4 etc) Incorporates Web Map Service style interface OpenDAP (remote NetCDF access) Supports NetCDF Subset that is capable of extracting sub-areas and sub-sets from NetCDF files, including single-point time series Applications include three browsers for WMS, GUI for NetCDF Subset

13 THREDDS

14 WMS example: Godiva viewer

15 How to access the PASODOBLE-Airshed data? DLR centralised WCS: extraction, NetCDF TNO/KNMI forecast for NO2 for DAY+1 calculated on : getcoverage&coverage=tno-knmi_fc_no2_vmr_0& time= &elevation=1&bbox=5,42,20,60

16 Animations, GE AQ Europe AQ Regional AQ Meso-scale FMI AQ services and users Public IS4FIRES Pollen Univ. of Turku THREDDS WMS, OpenDAP, NetCDFsub Validation Lithianian EPA NIMH, Bulgaria U.Tartu City of Vilnius EAN, PollenInfo.org Helsinki RESA ZAMG

17 User Uptake Helsinki test case operational forecast data (MACC/Pasodoble products ) availability Expert User: Helsinki Region Environmental Services Authority basic Decision Support system: dedicated web portal displaying data relevant to the User was set-up Expert User requirements improving regional information data for local scale air quality forecast PM2.5: spatial distribution of emissions and concentrations source-differentiated straightforward user interface for untrained users evaluation the number of citizens exposed to high concentrations

18 User Uptake - Prototype The main functionalities are: Browse for products according to predefined criteria Display single or animation of products Overlap of KML and WMS proprietary layers on the map

19 Ground-breaking forecast: Pollen Goals Support pollen allergy forecasting groups and organizations Produce the best forecast: optimal combination of pollen monitoring and modelling Questions Where does it grow? When does it flower? How much pollen does it produce? Tools SILAM (Europe, zoom to N.Europe: birch, grass, olive, ragweed) COSMO-Art (C. & W.Europe: birch, grass, ragweed) EnviroHIRLAM (N.Europe: birch). MACC 7-model ensemble (Europe: birch)

20 Ground-breaking forecast: Pollen Vegetation map + pollen productivity Pollen concentration [#/m3] Meteorological forecast Dispersion model SILAM release transport sinks Flowering intensity Multi-threshold model

21 30-years modelled trends grass, olive: mainly increase birch, ragweed: mixed, mainly decrease

22 1st ensemble pollen forecasting FMI pollen emission module for birch has been made available for MACC modelling teams MACC ENS modelling teams performed trial operational forecasting of birch pollen emission and dispersion over Europe in 2013

23 Re-analysis: predicted vs measured Nice Strassburg the season started two weeks later than usually Helsinki The seasonal integral of pollen concentrations was ~100 times lower than 2012 season

24 Ground-breaking forecast: Fire Goals of a fire system are to forecast the fire emissions and plume dispersion and support AQ assessments detecting major wildland fires: remote sensing and ground-based observations (location, intensity, spread, and height) modelling both the spread of the fires in the terrain and dispersion of the fire plumes in the atmosphere

25 NRT Fire systems GFAS project_structure/input _data/d_fire/?op=get EFFIS applications/current-situation IS4FIRES (

26 Fire Assimilation System IS4Fires v1.5 Boreal Crop Peat Grass Shrub Temperate Tropical USGS Offline Land use MODIS TA FRP NRT & offline Emission flux MISR AOD FRP Offline calibration (Sofiev et al, 2009, Akagi 2011) Emission factors Injection height profile CTM (SILAM) SEVIRI FRP Plume top Offline (Sofiev et al, 2012) Plume rise param. FRP FRP FRP FRP FRP Offline Diurnal variation FRP ECMWF Meteo parameters

27 Fire Assimilation System: products Emissions Concentrations U(z) w(t p,t a,w 0,..) Aerosol Optical Depth Injection height

28 Fire seasons 2003: prediction vs observed urban sites only (AeroNet) Sahara dust: max 20 µg/m3 MODIS AQUA AOD

29 Summary Observations and modelling of both natural aerosols and chemical pollutants exist and can be used for information services All SILAM numerical products are presently distributed via THREDDS Services. Animations are provided via original FMI interface, Google Earth, and WMS. Rolling archive ~2 TB Pollen : birch, grass, olive, ragweed Dust and sea salt Chemical pollutants covered NOx, SOx, NHx, O 3, VOC PM: anthropogenic and fires Inter-annual variability and trends are available Pollen & chemical pollutants Impact on AQ due to climate change is available

30 Thank you!