Air quality forecast using CAMS products in Hungary

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Atmosphere Monitoring Air quality forecast using CAMS products in Hungary Zita Ferenczi Emese Homolya István Ihász Ilona Krüzselyi Hungarian Meteorological Service

Transcript of Air quality forecast using CAMS products in Hungary

Page 1: 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

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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

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• 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

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• 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

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• 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

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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)

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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

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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 °

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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 °

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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)

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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

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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

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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

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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

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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

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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

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• 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

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