Retrieval of Annual Air Quality Statistics from a Limited Number of LES Model Simulations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00604658" target="_blank" >RIV/67985807:_____/24:00604658 - isvavai.cz</a>
Result on the web
<a href="https://palm.muk.uni-hannover.de/trac/raw-attachment/wiki/conference/pmc24_book_of_abstracts.pdf" target="_blank" >https://palm.muk.uni-hannover.de/trac/raw-attachment/wiki/conference/pmc24_book_of_abstracts.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Retrieval of Annual Air Quality Statistics from a Limited Number of LES Model Simulations
Original language description
ZÁKLADNÍ ÚDAJE: PMC 24 Book of Abstracts. Offenbach: Leibniz University Hannover (LUH) and the German Weather Service (DWD), 2024. s. 7-7. [PMC 24: PALM Model Conference 2024. 17.09.2024-20.09.2024, Offenbach] ABSTRAKT: Legislative air quality limits are based on annual statistics, like annual mean or n-th highest hourly or daily concentration. Complex CFD models may provide air quality simulations at the street level. However, these simulations are computationally too expensive for large time periods like a year. For RANS models this is usually solved by calculation of steady-state concentration fields for different wind directions, which are then scaled by the wind speed to provide concentrations for a particular hour. However this approach is not suitable for the LES models, which count for time-evolving resolved turbulence. With these models usually several periods of time extent of days are calculated. Annual statistics have to be constructed from a limited number of ’typical’ days, which guarantee a reasonable coverage of different scenarios during the year. We propose a method for identification of ’typical’ days based on k-medoids clustering. The method was validated on monitoring stations. We also demonstrate its performance on pilot PALM simulations. Target of this pilot experiment is to prove the potential to retrieve the fields of annual statistics from LES models.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů