- Program System for Analysis and Visualisation of Observational Network Data

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FLADIS is a program system for the analysis and visualisation of observational network data. FLADIS calculates the spatial distribution of air pollutants, such as PM10, NO2, NO, O3 and benzene, from point measurements and model simulation runs. It combines interpolated measured data and modelling results for each time step for which data is available.

  • Visualisation of air pollutant concentrations
  • EU Air Quality Framework Directive
  • Visualisation of meteorological data
  • Wide range of interpolation schemes
  • Combination with internal and external models
  • Interfaces to external models: e.g. LASAT, RCG, EURAD, IMMIS
  • Integration of elevation models
  • Uniform emission interface EES
  • Data assimilation
  • Cross validation
  • Various output formats
  • Graphics
  • Analysis tool for FLADIS results
  • Database link-up
  • GIS integration

Combination of interpolated observational data and modelling results

FLADIS calculates a weighted mean concentration of the interpolated measured data and the employed model for each grid point at each point in time for which data is available. The weighting factor is the coefficient of determination between the measurements and the model results. It assigns a higher influence to model results for points in time at which the model is able to explain the measurements.

Model results may be provided from internal or external models. Internal models are a balance approach and a linear statistical model. External models, e.g. LASAT, RCG, EURAD or IMMIS, can be used via predefined interfaces. FLADIS includes a data assimilation module that calibrates the model background fields prior to their combination with the interpolated measured data.

Parameters crucial to the distribution of air pollutants, such as the orography and the meteorology, are taken into account. The emission structure of individual pollutants can be processed directly from emission inventories using the uniform emission interface EES of FLADIS.

Integration into the geographic information system ArcView

FLADIS is available as an ArcView extension. Thus, all the settings for FLADIS projects may be controlled within the geographic information system (GIS). The numerous available GIS tools support the preperation of input files, consistency and plausibility checks and the creation of maps and graphics.

GIF files for web sites

FLADIS offers an export function for GIF graphics. The export function enables the user to create graphic files that can be linked directly to web pages as static or animated information sources. This is a perfect way to easily update public information systems on the Internet.

There are a couple of ways to control the GIF output:

  • standard output as single frames
  • output as an animation
  • legend settings
  • adding geo information

Seven interpolation schemes are currently implemented in FLADIS:

  • Shepard Interpolation
  • Hardy's Method of Multiquadratics
  • Duchon's Thin Plate Splines
  • Franke's Thin Plate Splines
  • Triangulation and bi-linear Interpolation
  • Beier/Doppelfeld Approach
  • Optimum Interpolation
Any of the methods has its advantages and disadvantages depending on the particular investigation. The following pictures show exemplarily the results of four of the methods based on the same data set

Entering emission data via the Einheitliche Emissionsschnittstelle (EES, uniform emission interface) Firstly, FLADIS summarises different emission inventories on a uniform grid. The emission inventories may include point, line and area sources.  

Secondly, a cluster analysis helps reducing the summarised emission inventory. The reduction is necessary to ensure that FLADIS calculations are performed in acceptable time.
Thirdly, a convolution is done on every single cluster using source specific emission cycles. Thus, a temporally distributed emission data base is available in FLADIS.

Including the orography

The distribution of air pollutants often shows a strong correlation to the terrain elevation. For example, ozone concentrations at night are occasionally higher at higher elevations than in the valleys.

In FLADIS, the terrain elevation may be taken into account explicitly by using a Digital Elevation Model for the area of interest.

The Delta Method implemented in FLADIS offers the visualisation of future exposure situations in a modelling area.

The approach is based on time series of measurements for a past (base) year and modelling results for the past and a future year. The measurement values to be expected for the future year are estimated with the Delta Method. The predicted values hold the structure of the base year observations and are used to calculate the spatial distribution for the future year according to the usual FLADIS scheme via interpolation and combination with modelling results.

NO2 annual mean 2002 based on measured and modelled data. Coloured circles: Measurement values.

NO2 annual mean 2010 based on forecasted measurement values and modelled data. Coloured circles: Forecasted measurement values.

Comprehensive statistical analysis

FLADIS is able to interpolate concentration fields using the temporal resolution of the underlying input data. Thus, all characteristics listed in the EU Air Quality Framework Directive 96/62/EC, such as hourly, daily and annual means, exceedance frequencies and uncertainties, are available. An evaluation according to the current Daughter Directives is performed automatically.

FLADIS allows to distinguish between local and regional limit value exceedances. Additionally, FLADIS offers the spatial evaluation of e.g. high percentiles.  
The number of measurements at any measurement time, the mean value of the measured data at any time step, the coefficient of determination of the employed model and some more information is provided in ASCII files.

Planning and analysis of observation networks - cross validation

FLADIS includes a statistical cross validation module that supports the planning and analysis of observational networks. Additionally, the cross validation results serve as an indicator for the quality of the calculated area values.

Visualisation of meteorological data

FLADIS visualises meteorological data like temperature, wind speed and wind direction from observation networks or external sources.

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