Air quality forecast using CAMS products in Hungary

Atmosphere Monitoring

Air quality forecast

using CAMS products

in Hungary

Zita Ferenczi Emese Homolya István Ihász Ilona Krüzselyi

Hungarian Meteorological Service

AtmosphereMonitoring

O v e r v i e w

• AQ information system of OMSZ

– Current status

– Future plans

• Using Copernicus Atmosphere Monitoring Service products atOMSZ

• Validation of the first results


• Objectives: – to develop an air quality information website

– to inform the public and support the work of decision makers

• Current situation:– The air quality forecast for Budapest is needed to be revised

– Motivation: • CHIMERE model version 2008 - new model version available

• updated gridded emission data

• Future plans:– Complex website: emission, measurement, model results

– On the modeling page:• much more info, not only forecast

A Q i n f o r m a t i o n s y s t e m o f O M S Z


• What we would like to present on these pages:– Air quality forecast:

• Aim: to predict smog situations

• Air quality maps for 4 different domains

• Meteorological parameters, which have essential effects on air quality(PBL, SI index…)

• EPSgrams for different cities

– Analysis of the air quality of Hungary:• Assessment of the air quality of Hungary

– Maps and documents

M o d e l l i n g i n f o r m a t i o n


• CHIMERE: Eulerian off-line chemistry-transportmodel

• The code is completely written in Fortran90, and running scripts are written in shell

• Code version: 2017

• The model requires several numerical tools:

• a Fortran 95 compiler (e.g. gfortran)

• GNU bash Bourne shell, awk and make

• Unidata NetCDF library (free)

• PnetCDF library (free)

• Open MPI or LAM-MPI software (free)

• The NCO libraries (free)

• python libraries (free)

• The key processes are taken into account:

• Emission

• Transport (advection and mixing)

• Chemistry

• Deposition (dry and wet)

C h e m i s t r y t r a n s p o r t m o d e l

MACC LMDz-INCA GOCART

AROME, (WRF)

OMSZEMEP


P r e - p r o c e s s i n g o f t h e e m i s s i o n

• Emission sources:– Anthropogenic (EMEP, TNO…)

– Biogenic (MEGAN model)

– Mineral dust

– (Volcanoes, forest fires)

– (Resuspension)

• Activity sectors: SNAP code, no GNFR!!

• „Emisurf” preprocessor:– based on a top-down approach, calculates

hourly emission fluxes on the horizontalCHIMERE grid

– Proxies: population density

• Available emission data:– Emission inventory (2015) for Hungary

(OMSZ) and the Carpathian Basin (EMEP)(0.1° x 0.1 °)

– High resolution emission data for three major cities in Hungary (Budapest, Pécs, Miskolc) (0.05° x 0.05 °)

SNAP codes

Sector 1Combustion in the production and

transformation of energy

Sector 2 Non-industrial combustion plants

Sector 3 Industrial combustion plants

Sector 4 Industrial processes without combustion

Sector 5Extraction and distribution of fossil fuels and geothermal energy

Sector 6 Use of solvents and other products

Sector 7 Road Transport

Sector 8 Other mobile sources and machinery

Sector 9 Waste treatment and disposal

Sector 10 Agriculture

Sector 11 Other sources and sinks (nature)


A n t h r o p o g e n i c E m i s s i o n f l u x e s

• Using the emisurf pre-processor

• Horizontal and monthlydownscaling

– The seasonal factor: at first, a seasonal factor (country specified) for the annual data is applied.

• Weekly and hourly factors are alsoapplied

• Result:

– prepares NetCDF monthly datafiles, projected on the horizontal CHIMERE grid, containing fluxes for the CHIMERE chemical species


H u n g a r i a n g r i d d e d e m i s s i o n

0.1 °

0.05 °


I n p u t m e t e o r o l o g i c a l d a t a

Numerical weather predictiondata: AROME (WRF)• In the cases of the cities

(Budapest, Miskolc, Pécs): 0.015° x 0.02°

• Carpathian Basin: 0.1° x 0.1 °


U s i n g C A M S d a t a a t O M S Z

• Models: CHIMERE, EMEP, EURAD, LOTOSEUROS, MATCH, (MOCAGE, SILAM)

• Pollutants: O3, CO, NO2, SO2, PM2.5, PM10

• Domain: 15°W/45°N/25°W/50°N

• Spatial resolution: 0.1° x 0.1°

• Time resolution:

– 0-48 hours: 1 hour

– 51-96 hours: 3 hours

• Visualisation: HAWK (Hungarian Advanced WorKstation)


P r o c e s s i n g C A M S d a t a a t O M S Z

• Maps: using our own visualisation system (HAWK)

• Menu with CAMS models:

• Results of the visualisation:

CAMS-CHIMERECAMS-EMEP OMSZ-CHIMERE


E P S G R A M

• Aim: providing air quality forecast for different cities

• Original grib data files from CAMS– 6 pollutants– 7 model results– Forecast for 94 hours

• Postprocessing the grib files (script)– 14 Hungarian cities– Results are available on our intraweb– Results have to be reviewed only after a validation– In the near future: epsgrams on our web site


V a l i d a t i o n

• Comparing the model results with the measured data• Starting date: 06.13.2019.• 2.5 months data are available for the validation• Daily averages, daily maxima and hourly data were analysed• Correlation, BIAS and RMSE were calculated• The aim of the „validation”:

– Can we and the policy makers use this information? How?– How can we interpret this information?– Providing comprehensible information to everyday users

• Problems:– Comparing the gridded data with point measurements– City – which grid point should we use?

• The results of the validation are very interesting and useful


F i r s t r e s u l t s o f t h e v a l i d a t i o n - O 3


F i r s t r e s u l t s o f t h e v a l i d a t i o n - N O 2


F i r s t r e s u l t s o f t h e v a l i d a t i o n - P M 1 0


• Results of the validation– Results are only preliminary with great uncertainty – We need longer time series for a real validation

• O3

– good correlation (0.7)– overestimation– CHIMERE gives the best results

• NO2

– correlation is not bad (0.5)– underestimation– In the case of Miskolc extremely bad results (all models give bad results!)– LOTUSEUROS gives the best results

• PM10

– correlation is not bad (0.3-0.7), in some cases good – underestimation– EMEP gives the best results

C o n c l u s i o n


T h a n k y o u f o r y o u r a t t e n t i o n !

Documents

Air quality forecast using CAMS products in Hungary