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Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform
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作者 Alexander Mahura Alexander Baklanov +46 位作者 Risto Makkonen Michael Boy Tuukka Petäjä Hanna KLappalainen Roman Nuterman Veli-Matti Kerminen Stephen R.Arnold Markus Jochum Anatoly Shvidenko Igor Esau Mikhail Sofiev Andreas Stohl Tuula Aalto Jianhui Bai Chuchu Chen Yafang Cheng Oxana Drofa Mei Huang Leena Järvi Harri Kokkola Rostislav Kouznetsov Tingting Li Piero Malguzzi Sarah Monks Mads Bruun Poulsen Steffen M.Noe Yuliia Palamarchuk benjamin foreback Petri Clusiu Till Andreas Soya Rasmussen Jun She Jens Havskov Sørensen Dominick Spracklen Hang Su Juha Tonttila Siwen Wang Jiandong Wang Tobias Wolf-Grosse Yongqiang Yu Qing Zhang Wei Zhang Wen Zhang Xunhua Zheng Siqi Li Yong Li Putian Zhou Markku Kulmala 《Big Earth Data》 EI CSCD 2024年第2期189-230,共42页
The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research Programme.The PEEX MP has more than 30 models and is directed towards seamless envir-onmental prediction.The main fo... The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research Programme.The PEEX MP has more than 30 models and is directed towards seamless envir-onmental prediction.The main focus area is the Arctic-boreal regions and China.The models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and resolutions.This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical domain.The employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are described.Several virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are introduced.The over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,etc.for population,envir-onment and climate in the PEEX domain.This work was also one of the last projects of Prof.Sergej Zilitinkevich,who passed away on 15 February 2021.Since the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him. 展开更多
关键词 Multi-scale and-processes modelling concept seamless coupling high-performance computing data infrastructure virtual research platforms
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A new implementation of FLEXPART with Enviro-HIRLAM meteorological input,and a case study during a heavy air pollution event
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作者 benjamin foreback Alexander Mahura +18 位作者 Petri Clusius Carlton Xavier Metin Baykara Putian Zhou Tuomo Nieminen Victoria Sinclair Veli-Matti Kerminen Tom V.Kokkonen Simo Hakala Diego Aliaga Risto Makkonen Alexander Baklanov Roman Nuterman Men Xia Chenjie Hua Yongchun Liu Markku Kulmala Pauli Paasonen Michael Boy 《Big Earth Data》 EI CSCD 2024年第2期397-434,共38页
We integrated Enviro-HIRLAM(Environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical w... We integrated Enviro-HIRLAM(Environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical weather prediction(NWP)model.The publicly available version of FLEXPART can utilize either ECMWF(European Centre for Medium-range Weather Forecasts)Integrated Forecast System(IFS)forecast or reanalysis NWP data,or NCEP(U.S.National Center for Environmental Prediction)Global Forecast System(GFS)forecast or reanalysis NWP data.The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields.We compared backward trajectories gener-ated with FLEXPART using Enviro-HIRLAM(both with and without aerosol effects)to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs,for a case study of a heavy haze event which occurred in Beijing,China in November 2018.We found that results from FLEXPART were considerably different when using different meteorological inputs.When aerosol effects were included in the NWP,there was a small but noticeable differ-ence in calculated trajectories.Moreover,when looking at potential emission sensitivity instead of simply expressing trajectories as lines,additional information,which may have been missed when looking only at trajectories as lines,can be inferred. 展开更多
关键词 Atmospheric and chemical transport modelling trajectory and particle dispersion modelling severe air pollution episode FLEXPART Enviro-HIRLAM
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