Urban climate and air quality affects a large fraction of global population. It is however notoriously difficult to work with climate anomalies at such fine scales. The urban areas, even smaller ones, are characterized by very complex surface geometry, high concentrations of surface-level emission sources and significant urban climate anomalies. Those factors make the meteorological/air quality models under-performing, regular observations non-representative and non-covering, and pure statistical interpolation methods misleading.
But …, there are available much more data than presently in use. Diverse observational networks are complemented by citizen observations, e.g., from hundreds of the NETATMO stations. There are statistical methods of data fusion and interpolation enhanced by machine-learning algorithms (Varentsov et al., 2020; Venter et al., 2020), that can create a final user-oriented product – a map of environmental quality at very high resolution. The methods are computationally costly and require HPC-computing for operations. One might benefit from well-developed integrated modeling chains that detail the meteorological/ air quality conditions down to the spatial scale of a few kilometres (Baklanov et al., 2017a). Those chains are however missing the final link in the downscaling chain – street-level detailing of the information – that is required to make it useful for the stakeholders. This final link is in strong demand by the society (Baklanov et al., 2017b).
In our previous joint collaborative project funded by the Nordic Council of Ministers, we developed and demonstrated essential elements of the numerical modeling technology to close the knowledge gap and to create this missing link with application of the PALM modeling code and data kriging algorithms (Esau et al., 2021), see Figure 1.
Figure 1: Simulations of the air pollution episode in Apatity, Russia, computed with a mesoscale modelling chain (red shading of the high concentration polluted plume) and an LES (large-eddy simulation)-enhanced modelling chain (green shading). For more details refer to Esau et al. (2021).
However, neither connections to larger scales through regional-scale model chain nor access to citizen observations have been yet implemented. This project aims to finalize the work with the HPC-related part of the technology development. It will complete seamless integration of meteorological models and urban data within the UIS frameworks incorporating a large-eddy simulation turbulence-resolving model.
As a part of the PEEX collaboration, we join efforts in studies of urban climate and air quality with the enhanced UIS. More specifically, we focus on integration of the urban large-eddy simulation model PALM4U® that works on meter-scales and meteorological, km-scale Enviro-HIRLAM – HIgh Resolution Limited Area Model. This modelling chain will run on remote-sensing and citizen data to provide very high-resolution environmental assessment and prediction. The geographical target is Northern Fennoscandia and Kola Peninsula.
A HPC-Europa3 Transnational Access Programme grants technological and financial support to this project. Such individual grants allow to strengthen, expand and improve international collaboration among the PEEX partners on research and development of the common PEEX Modelling Platform. COVID-19 restrictions forced us first to postpone and then to break the project in two parts. At start, we obtained an access to CSC’s HPC Puhti supercomputer in September 2021. We begin with setting up both models and test them through a remote access. The second part hopes for a physical visit to the University of Helsinki (8 weeks during February-March 2022) to complete all necessary analysis and to achieve a real synergy, to prepare a joint manuscript and presentation at the University seminar.
Text by: Dr. Igor Esau, Nansen Environmental and Remote Sensing Centre, Bergen, Norway
Baklanov et al. (2017a). Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2). Geoscientific Model Development, 10, 2971–2999. https://doi.org/10.5194/gmd-10-2971-2017
Baklanov et al. (2017b). From urban meteorology, climate and environment research to integrated city services. Urban Climate. https://doi.org/10.1016/j.uclim.2017.05.004
Esau, I., Bobylev, L., Donchenko, V., Gnatiuk, N., Lappalainen, H.K., Konstantinov, P., Kulmala, M., Mahura, A., Makkonen, R., Manvelova, A., Miles, V., Petäjä, T., Poutanen, P., Fedorov, R., Varentsov, M., Wolf, T., Zilitinkevich, S., Baklanov, A., 2021. An enhanced integrated approach to knowledgeable high-resolution environmental quality assessment. Environ. Sci. Policy 122, 1–13. https://doi.org/10.1016/j.envsci.2021.03.020
Wolf, T., Pettersson, L. H., & Esau, I. (2019). A very high-resolution assessment and modelling of urban air quality. Atmospheric Chemistry and Physics Discussions, 1–43. https://doi.org/10.5194/acp-2019-294
Varentsov, M., Esau, I., & Wolf, T. (2020). High-resolution temperature mapping by geostatistical kriging with external drift from large-eddy simulations. Monthly Weather Review, https://doi.org/10.1175/MWR-D-19-0196.1
Venter, Z.S., Brousse, O., Esau, I., Meier, F., 2020. Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data. Remote Sens. Environ. 242, 111791. https://doi.org/10.1016/j.rse.2020.111791