User Awareness & Training: The Helsinki Atmosphere Test Case Lisbon, Portugal 13 th February 2014 Joana Soares

Transcription

1 User Awareness & Training: The Helsinki Atmosphere Test Case Lisbon, Portugal 13 th February 2014 Joana Soares

2 Contents Pollution: sources and forecasting 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: episodically entirely dominant, in average comprise 10-50% of European anthropogenic emission of PM and some gases, such as 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 FMI AQ assessment & forecasting platform IS4FIRES AQ products Sea surface Temp. & salinity Geo-morfological data AIS data Satellite observations Sea salt emission model Wind-blow dust emission model ships STEAM emission model fires natural SILAM CTM AROME NWP model WRF TVM EVALUATION: NRT model-measurement comparison Online AQ monitoring Phenological observations Aerobiological observations Physiography, forest mapping Phenological model biogenic CLRTAP/EMEP emission data Boundary conditions anthropogenic HIRLAM NWP model Meteorological data: ECMWF ECHAM,RCA G/RCM model meteorology Aerobiological observations Satellite observations

6 Data users/providers Focuses on long-range pollution transport impact on AQ in remote areas, targeting local level decision makers Relies on the availability of MACC 2 and PASODOBLE products related to different pollutants for assessment and decision making Demonstrate how local authorities can take advantage of the Copernicus atmosphere products on decision-making in case of pollution events

7 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

8 SILAM operational forecast data (MACC/Pasodoble products ) availability within #define project Expert User: Helsinki Region Environmental Services Authority Dedicated web portal displaying data relevant to the User was setup 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 Features: use products in their operational workflow overlap of KML and WMS proprietary layers on the map basic Decision Support system User Uptake Helsinki test case

9 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

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

11 Ground-breaking forecast: Pollen Goals Support pollen allergy forecasting groups and organizations Produce the best forecast: optimal combination of pollen monitoring and modelling By-products Demonstrate the feasibility of AQ forecasting approach for pollen Awareness by familiarizing users with existing opportunities Improvement model products according to user expectations 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)

12 Ground-breaking forecast: Pollen Vegetation map + pollen productivity Pollen concentration [#/m3] Meteorological forecast Where does it grow? Dispersion model When does it flower? SILAM How much pollen does it produce? release transport sinks Flowering intensity Multi-threshold model

13 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 Observational data from European Aeroallergen Network (EAN) Austria, Germany, France, Finland, Estonia

14 Example of forecasts Implementation of birch pollen emission module 6 models have some version (SILAM, MATCH, EMEP, CHIMERE, MOCAGE, EURAD) Season 2010 model intercomparison The agreed set of EAN data has been distributed to the modelling teams Signed non-distribution agreement by all groups Analysis is on-going model simulations will be iterated Discussions are going on with Meteo France Season 2010 re-analysis Birch, based on SILAM computations Season-long 4D-VAR data assimilation of EAN observations Season 2013 forecasting: first real-life application

15 First try some errors happen Vienna Forecast Re-analysis

16 Re-analysis time series Berlin Helsinki Nice Strassburg Zams 2km

17 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

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

19 Fire Assimilation System 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

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

21 Fire seasons 2003: prediction vs observed MODIS AQUA AOD

22 Summary Observations and modelling of both natural aerosols and chemical pollutants exist and can be used for information services Pollen : birch, grass, olive, ragweed Dust and sea salt Chemical pollutants covered (including fires): 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

23 Thank you!