scholarly journals Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 669 ◽  
Author(s):  
Adrienn Varga-Balogh ◽  
Ádám Leelőssy ◽  
István Lagzi ◽  
Róbert Mészáros
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Prediction ◽  
Heating Season ◽  
Air Quality Prediction ◽  
Consistent Model ◽  
Urban Monitoring ◽  
Overall Performance ◽  
Monitoring Service ◽  
The City

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

Urbanization of Weather Forecast, Air-Quality Prediction and Climate Scenarios - Project URBI PRAGENSI

2021 ◽  
Author(s):  
Tomas Halenka ◽  
Michal Belda ◽  
Peter Huszar ◽  
Jan Karlicky ◽  
Tereza Novakova
Keyword(s):  
Air Quality ◽  
Weather Prediction ◽  
Regional Climate ◽  
Weather Forecast ◽  
Quality Prediction ◽  
Climate Scenarios ◽  
Air Quality Prediction ◽  
Air Quality Forecast ◽  
The City ◽  
Very High

<p>The ratio of population living in cities is growing and this is especially true for the largest ones, megacities. However, even smaller cities like the City of Prague  (about 1.5 M) can suffer significantly and the night time temperature difference under summer heat wave can achieve more than 5°C. To assess the impact of cities and urban structures on weather, climate and air-quality, modelling approach is commonly used and the inclusion of urban parameterization in land-surface interactions is of primary importance to capture the urban effects properly. This is especially important when going to higher resolution, which is common trend in operational weather forecast, air-quality prediction as well as regional climate modeling. This represents the rapidly developing research, motivated by specific risks in urban environment, with strong impacts on vulnerable communities there, leading to the tools to assess properly impacts within the cities and the effectiveness of adaptation and mitigation options applied there by the city authorities. Under the action towards the Smart Cities and within the framework for developing adequate climate services, such supporting tools for decission making are inevitable. It is valid not only for extreme heat waves impact prediction, but as well in air-quality forecast and in long term perspective in connection to climate change impacts assessment. This provides the background for the project within Operational Program Prague - The Pole of Growth “Urbanization of weather forecast, air-quality prediction and climate scenarios for Prague”, shortly URBI PRAGENSI.</p><p> </p><p>There are four main tasks within the project. First, urbanization of weather forecast, i.e. involving and testing the urban parameterization scheme in the weather prediction model can provide in very high resolution localized weather prediction and especially under the heat wave condition it can well capture the temperature differences in the city center with respect to the remote areas. There are applications, which can use such localized prediction for planning and decision making on e.g. public services for some specific groups of population in risks. Further, air-quality forecast based on such urbanized weather condition forecast can benefit from better estimates of temperature for chemical reactions, mixing height for dispersion conditions etc. Third, urbanized scenarios of climate change can provide better description of future conditions in the city for adaptation and mitigation options, moreover, in connection to urban heat island urbanized regional climate model in very high resolution is good tool for estimates of efficiency  of potential adaptation or mitigation measures which might be applied by the city administration. Last, but not least, microscale simulations using LES methods are supposed to be used for selected local hot-spots to solve them.</p>

On the urban effects in high resolution weather forecast and regional climate simulations

2020 ◽  
Author(s):  
Tomas Halenka ◽  
Michal Belda ◽  
Peter Huszar ◽  
Jan Karlicky ◽  
Tereza Novakova
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Heat Waves ◽  
Regional Climate ◽  
Weather Forecast ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
The Impact ◽  
The City ◽  
Urban Effects

<p>When downscaling to higher resolution, which is common trend in operational weather forecast, air-quality prediction as well as regional climate modeling, capturing the urban effects properly becomes of primary importance to describe the impact of cities and urban structures on weather, climate and air-quality. This is necessary for proper assessment of not only impacts in the cities, but the effectiveness of adaptation and mitigation options applied within cities. It is valid not only for extreme heat waves impact prediction, but as well in air-quality prediction and in long term perspective in connection to climate change impacts. This provides the background for the project within Operational Program Prague - The Pole of Growth “Urbanization of weather forecast, air-quality and climate scenarios for Prague”, shortly URBI PRAGENSI.</p><p>In the comparison of different urban parameterizations in WRF and RegCM we demonstrate the importance of urban models in the high resolution simulations, especially under conditions of heat waves. There are differences in the impacts of such parameterizations in different models, but basically all are able to capture the effects of urban heat island in these simulations, which can be quite significant and achieve up to about 8-10 °C difference between the city and its vicinity for large cities during night time, but even in smaller cities like the City of Prague (about 1.5M), it can be more than 5°C. More detailed analysis of the effects in terms of energy balance in the city and remote areas in high resolution simulations will be presented, as well as the impacts on other parameters, especially those connected to air-quality like mixing layer height, stability, etc., where the proper choice of the parameterization really matters and simplistic option like bulk in WRF rather fails.</p><p>CORDE FPS on urbanization, which is under preparation, will be introduced with its aims and potential tasks.</p>

scholarly journals Research on air quality prediction method in Hangzhou based on machine learning

2021 ◽  
Vol 2010 (1) ◽  
pp. 012011
Author(s):  
Zhongjie Fu ◽  
Haiping Lin ◽  
Bingqiang Huang ◽  
Jiana Yao
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Prediction Method ◽  
Quality Prediction ◽  
Air Quality Prediction

The use of MODIS data and aerosol products for air quality prediction

2004 ◽  
Vol 38 (30) ◽  
pp. 5057-5070 ◽  
Author(s):  
Keith D. Hutchison ◽  
Solar Smith ◽  
Shazia Faruqui
Keyword(s):  
Air Quality ◽  
Quality Prediction ◽  
Modis Data ◽  
Air Quality Prediction
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