Impact of Highly Reflective Materials on Meteorology, PM10 and Ozone in Urban Areas: A Modeling Study with WRF-CHIMERE at High Resolution over Milan (Italy)

Falasca, Serena; Curci, Gabriele

doi:10.3390/urbansci2010018

Cited by 15 publications

(8 citation statements)

References 27 publications

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“…In addition, built-up surfaces that are mainly composed of artificial buildings and cement pavement which are clearly distinguished from natural surfaces favor heat accumulation [2,3]. Recent research shows that in urban regions, Urban Heat Islands (UHIs) can be observed by a difference of about 1.5-2 • C compared to rural area temperatures, and about 2.0-2.5 • C difference can be observed over densely built-up areas [4]. The UHI has a negative effect on the planetary boundary layer (PBL) height [5] and surface wind speed, which affects the transport and dispersion of pollutants.…”

Section: Introductionmentioning

confidence: 99%

Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

et al. 2020

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The accurate simulation of meteorological conditions, especially within the planetary boundary layer (PBL), is of major importance for air quality modeling. In the present work, we have used the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region. A lower first model layer with a 4 m surface layer could better reproduce the transport and diffusion of pollutants in a real urban environment. Three canopy models could better reproduce a 2 m temperature (T2) in the daytime but present a positive bias from 1 to 5 °C during the nighttime; the multi-urban canopy scheme “building effect parameterization” (BEP) underestimates the 10 m windspeed (W10) around 1.2 m s−1 for the whole episode, indicating the city cluster plays an important role in the diffusion rate in urban areas. For the simulation of pollutant concentrations, large differences were found between three canopy schemes, but with an overall overestimation during the pollution episode, especially for NO2 simulation, the average mean biases of NO2 prediction during the pollution episode were 40.9, 62.2, and 29.7 µg m−3 for the Bulk, urban canopy model (UCM), and BEP schemes, respectively. Meanwhile, the vertical profile of the diffusion coefficients and pollutants indicated an important impact of the canopy model on the vertical diffusion. The PBL scheme sensitivity tests displayed an underestimation of the height of the PBL when compared with observations issued from the Lidar. The YonSei University scheme YSU and Boulac PBL schemes improved the PBL prediction compared with the Mellor–Yamada–Janjic (MYJ) scheme. All the sensitivity tests, except the Boulac–BEP, could not fairly reproduce the PBL height during the pollution episode. The Boulac–BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and pollutants, especially for the NO2 and PM2.5 (particulate matter 2.5 micrometers or less in diameter) simulations. The mean bias of the NO2, PM2.5, and PM10 (particulate matter 10 micrometers or less in diameter) prediction were −5.1, 1.2, and −8.6 µg m−3, respectively, indicating that both the canopy schemes and PBL schemes have a critical effect on air quality prediction in the urban region.

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Section: Introductionmentioning

confidence: 99%

Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

et al. 2020

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“…The vertical grid had 33 levels for all domains, with the lowest one at about 23 m and the top at 50 hPa. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58].…”

Section: Analysis Of Climatic Inputs and Evaluation Of The Wrf Modelmentioning

confidence: 99%

“…Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Figure 6 shows the comparison between the results of the WRF runs and the observations provided by two urban weather stations in Rome, for a winter month (January) and a summer month (July).…”

Section: Analysis Of Climatic Inputs and Evaluation Of The Wrf Modelmentioning

confidence: 99%

Influence of Input Climatic Data on Simulations of Annual Energy Needs of a Building: EnergyPlus and WRF Modeling for a Case Study in Rome (Italy)

et al. 2018

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The simulation of the energy consumptions in an hourly regime is necessary in order to perform calculations on residential buildings of particular relevance for volume or for architectural features. In such cases, the simplified methodology provided by the regulations may be inadequate, and the use of software like EnergyPlus is needed. To obtain reliable results, usually, significant time is spent on the meticulous insertion of the geometrical inputs of the building, together with the properties of the envelope materials and systems. Less attention is paid to the climate database. The databases available on the EnergyPlus website refer to airports located in rural areas near major cities. If the building to be simulated is located in a metropolitan area, it may be affected by the local heat island, and the database used as input to the software should take this phenomenon into account. To this end, it is useful to use a meteorological model such as the Weather Research and Forecasting (WRF) model to construct an appropriate input climate file. A case study based on a building located in the city center of Rome (Italy) shows that, if the climatic forcing linked to the heat island is not considered, the estimated consumption due to the cooling is underestimated by 35–50%. In particular, the analysis and the seasonal comparison between the energy needs of the building simulated by EnergyPlus, with the climatic inputs related to two airports in the rural area of Rome and with the inputs provided by the WRF model related to the center of Rome, show discrepancies of about (i) WRF vs. Fiumicino (FCO): Δ = −3.48% for heating, Δ = 49.25% for cooling; (ii) WRF vs. Ciampino (CIA): Δ = −7.38% for heating, Δ = +35.52% for cooling.

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“…With the technological development in surface networks and spaceborne sensors, and the improvement in modeling, in the last decades, many studies were published describing the urban atmosphere of cities all over the world (Masson et al 2020). In the case of Milan, the target city chosen for this work, recent and less recent research covers surface observations (Bacci and Maugeri 1992;Mariani et al 2016) as well as remote sensing from space (Anniballe et al 2014) and modeling (Mariani et al 2016;Falasca and Curci 2018). All these works describe with increasing detail the UHI main characteristics in Milan.…”

Section: Introductionmentioning

confidence: 99%

High-resolution climatic characterization of air temperature in the urban canopy layer

Montoli¹,

Frustaci²,

Lavecchia³

et al. 2021

Bull. of Atmos. Sci.& Technol.

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Urbanized environments are of greater relevance because of the high and still rapidly increasing percentage of the world population living in and around cities and as the preferred location of human activities of every type. For this reason, much attention is paid to the urban climate worldwide. Among the UN 2030 17 Sustainable Development Goals, at least one concerns resilient cities and climate action. The WMO supports these goals promoting safe, healthy, and resilient cities by developing specially tailored integrated urban weather, climate, and environmental services. An unavoidable basis for that is an improved observational capability of urban weather and climate, as well as high-resolution modeling. For both the former and the latter, and of primary importance for the latter, urban meteorological surface networks are undoubtedly a very useful basis. Nevertheless, they are often unfit for detailed urban climatological studies and they are generally unable to describe the air temperature field in the urban canopy layer (UCL) with a spatial resolution which is sufficient to satisfy the requirements set by several professional activities and especially for local adaptation measures to climate change. On the other hand, remote sensing data from space offer a much higher spatial resolution of the surface characteristics, although the frequency is still relatively lower. A useful climatological variable from space is, for instance, the land surface temperature (LST), one of the WMO Essential Climate Variables (ECV). So often used to describe the Surface Urban Heat Islands (S-UHI), LST has no simple correlation with UCL air temperature, which is the most crucial variable for planning and management purposes in cities. In this work, after a review of correlation and interpolation methods and some experimentation, the cokriging methodology to obtain surface air temperature is proposed. The implemented methodology uses high quality but under-sampled in situ measurements of air temperature at the top of UCL, obtained by using a dedicated urban network, and satellite-derived LST. The satellite data used are taken at medium (1 × 1 km 2 ) resolution from Copernicus Sentinel 3 and at high resolution (30 × 30 m 2 ) from NASA-USGS Landsat 8. This fully exportable cokriging-based methodology, which also provides a quantitative measure of the related uncertainties, was tested and used to obtain medium to high spatial resolution air temperature maps of Milan (Italy) and the larger, much populated, but also partly rural, surrounding area of about 6000 km 2 . Instantaneous as well as long period mean fields of fine spatially resolved air temperature obtained by this method for selected weather types and different Urban Heat Island configurations represent an important knowledge improvement for the climatology of the urban Extended author information available on the last page of the article Bulletin of Atmospheric Science and Technology 1 3 and peri-urban area of Milan. It finds application not only in more detailed urban climat...

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Impact of Highly Reflective Materials on Meteorology, PM10 and Ozone in Urban Areas: A Modeling Study with WRF-CHIMERE at High Resolution over Milan (Italy)

Cited by 15 publications

References 27 publications

Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

Influence of Input Climatic Data on Simulations of Annual Energy Needs of a Building: EnergyPlus and WRF Modeling for a Case Study in Rome (Italy)

High-resolution climatic characterization of air temperature in the urban canopy layer

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