CC BY
15Математические модели физики атмосферы, океана и окружающей среды
73
концентраций используются модели распространения легкой моно- и полидисперсной примеси от точечных, линейных и площадных источников [1, 2]. Степень загрязнения снежного покрова оценивается значением снежного индекса (NDSI) или яркостной градацией панхроматического канала [3, 4]. В результате исследований выявлены корреляционные связи между концентрациями взвешенных веществ в материале проб и яркостными характеристиками снежного покрова на космоснимке. Разработаны технологии численного восстановления полей концентрации атмосферного загрязнения территорий, оценивания суммарного выброса примеси. Разработка ведется на языке Python 3, Java/JavaScript.
Работа выполнена при финансовой поддержке Российского фонда фундаментальных исследований и Правительства Новосибирской области в рамках научного проекта № 19-47-540008, в рамках Госзадания (№ 0315-20190004) и программы РАН № 51 (№ 0315-2018-0016).
Список литературы
1. Рапута В. Ф. Модели реконструкции полей длительных выпадений аэрозольных примесей // Оптика атмосферы и океана. 2007. Т. 20, № 6. С. 506-511.
2. Ярославцева Т. В., Рапута В. Ф. Закономерности длительного загрязнения атмосферы и снежного покрова г. Новосибирска // Интерэкспо Гео-Сибирь. 2015.
3. Ярославцева Т. В., Рапута В. Ф. Использование космоснимков и наземных наблюдений для анализа полей длительного загрязнения снежного покрова города // Интерэкспо Гео-Сибирь. 2016.
4. Василевич М. И., Щанов В. М., Василевич Р. С. Применение спутниковых методов исследований при оценке загрязнения снежного покрова вокруг промышленных предприятий в тундровой зоне // Современные проблемы дистанционного зондирования Земли из космоса. 2015. Т. 12. № 2. С. 50-60.
Advances in air quality modeling and forecasting
A. Baklanov
Science and Innovation Department, World Meteorological Organization (WMO), Geneva, Switzerland
Email: abaklanov@wmo.int
DOI: 10.24411/9999-017A-2020-10362
Advance approaches in AQF combine an ensemble of state-of-the-art models, high-resolution emission inventories, space observations and surface measurements of most relevant chemical species to provide hindcasts, analyses and forecasts from global to regional air pollution and downscaling for selected countries and urban areas.
The importance of and interest to research and investigations of atmospheric composition and its modeling for different applications are substantially increased (see e.g. WWOSC, 2015; CCMM, 2016; GAW IP, 2017). Air quality forecast (AQF) and assessment systems help decision makers to improve air quality and public health, mitigate the occurrence of acute air pollution episodes, particularly in urban areas, and reduce the associated impacts on agriculture, ecosystems and climate.
Proceeding from the pioneering fundamental works of Acad. G.I. Marchuk and based on published reviews (e.g. Carmichael et al. 2008, Hollingsworth 2008, Zhang 2008, Menut and Bessagnet 2010, Grell and Baklanov 2011, Kukkonen et al. 2012, Zhang et al. 2012a,b, Baklanov et al. 2014, 2017, Ryan 2016, Benedetti et al. 2018, Bai et al. 2018, Kumar et al. 2018, Sokhi et al. 2019) and recent analyses, the presentation discusses main gaps, challenges, applications and advances, main trends and research needs in further developments of atmospheric composition and air quality modeling and forecasting, including the following trends in the development of modern atmospheric composition modelling and AQF systems: (i) Seamless prediction of the Earth system approach; (ii) Online coupling of atmospheric dynamics and chemistry models; (iii) Multi-scale prediction approach; (iv) Emission modeling for improved emission data; (v) Bias correction techniques and machine learning methods; (vi) Multi-platform observations and data assimilation; (v) Ensemble approach; (vi) Subseasonal to seasonal forecast; (vii) Fit for purpose approach; (viii) Impact based forecast.
Main trends and research priorities in seamless AQF as well as capacity building, training and education aspects of modern AQF systems and applications, following Zhang et al. (2019), are highlighted and discussed.
A number of WMO experts and authors of the Best Practices & Training Materials for CW-AQF book are acknowledged.
74
Секция 5
References:
1. Bai, L., et al., 2018: Air Pollution Forecasts: An Overview, IJERPH, 15, 780; doi:10.3390/ijerph15040780.
2. Baklanov A., et al., 2014: Online coupled regional meteorology chemistry models in Europe: current status and prospects. Atmos. Chem. Phys., 14, 317-398, doi:10.5194/acp-14-317-2014.
3. Baklanov, A., et al., 2017: Key Issues for Seamless Integrated Chemistry-Meteorology Modeling. Bull. Amer. Meteor. Soc., 98, 2285-2292, https://doi.org/10.1175/BAMS-D-15-00166.1.
4. Benedetti, A., et al., 2018.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615-10643, https://doi.org/10.5194/acp-18-10615-2018.
5. Carmichael, G. R., et al., 2008: Predicting air quality: Improvements through advanced methods to integrate models and measurements, J. Comp. Phys., 227, 3540-3571, 2008.
6. CCMM, 2016: Coupled Chemistry-Meteorology/Climate Modelling (CCMM): status and relevance for numerical weather prediction, atmospheric pollution and climate research. WMO GAW Report No.226, WMO, Geneva, Switzerland.
7. GAW, 2017: WMO Global Atmosphere Watch (GAW) Implementation Plan: 2016-2023. WMO GAW Report No.228, 81 p.
8. Grell, G. & A. Baklanov, 2011: Integrated modelling for forecasting weather and air quality: A call for fully coupled approaches. Atmos. Environ., 45, 6845-6851, doi:10.1016/j.atmosenv.2011.01.017.
9. Hollingsworth, A. et al., 2008: Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project. Bull. Amer. Meteor. Soc., 89, 1147-1164, doi:10.1175/2008BAMS2355.1.
10. Kukkonen, J., et al., 2012: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1-87, doi:10.5194/acp-12-1-2012.
11. Kumar, R., V.-H. Peuch, J.H. Crawford, G. Brasseur, 2018: Five steps to improve air-quality forecasts. Nature, 561: 27-29.
12. Menut, L. and Bessagnet, B. 2010: Atmospheric composition forecasting in Europe. Annales Geophysicae, 28: 61-74, 2010.
13. Ryan, W.F., 2016: The air quality forecast rote: Recent changes and future challenges, Journal of the Air & Waste Management Association, 66:6, 576-596, DOI: 10.1080/10962247.2016.115146.
14. Sokhi et al. 2018: Mesoscale Modelling for Meteorological and Air Pollution Applications. Anthem, doi:10.2307/j. ctv80cdh5
15. WWOSC, 2015: Seamless Prediction of the Earth System: from Minutes to Months, WWOC book, WMO-No. 1156.
16. Zhang, Y. et al., 2019: Best Practices and Training Materials for Chemical Weather/Air Quality Forecasting (CW-AQF). WMO GAW and ETR. 1st edition: https://elioscloud.wmo.int/share/s/WB9UoQ5kQK-dmgERjSAqIA
17. Zhang, Y., 2008: Online coupled meteorology and chemistry models: history, current status, and outlook, Atmospheric Chemistry and Physics, 8, 2895-2932, doi:10.5194/acp-8-2895-2008.
18. Zhang, Y., et al., 2012a,b: Real-Time Air Quality Forecasting, In Two Parts. Atmospheric Environment, 60, 632655, doi:10.1016/j.atmosenv.2012.06.031 and 60, 656-676, doi:10.1016/j.atmosenv.2012.02.041.
From urban air quality forecasting and information systems to integrated urban hydrometeorology, climate and environment systems and services for smart cities
A. Baklanov1 and WMO GURME and IUS teams
1 Science and Innovation Department, World Meteorological Organization (WMO), Geneva, Switzerland
Email: abaklanov@wmo.int
DOI: 10.24411/9999-017A-2020-10363
This presentation is analysing a modern evolution in research and development from specific urban air quality systems to multi-hazard and integrated urban weather, environment and climate systems and services and provides an overview of joint results of large international WMO GURME, IUS, and EU FP FUMAPEX, MEGAPOLI and MarcoPolo projects teams.
Urban air pollution is still one of the key environmental issues for many cities around the world. A number of recent and previous international studies have been initiated to explore these issues. In particular relevant experience from the European projects FUMAPEX, MEGAPOLI, MarcoPolo will be demonstrated. MEGAPOLI studies aimed to assess the impacts of megacities and large air-pollution hotspots on local, regional and global air quality; to quantify feedback mechanisms linking megacity air quality, local and regional climates, and global climate change; and to develop improved tools for predicting air pollution levels in megacities (Baklanov et al., 2010). FUMAPEX developed for the first time an integrated system encompassing emissions, urban meteorology and population exposure for urban air pollution episode forecasting, for assessment of urban air
