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Two‐meter temperature (°C) at the automated weather stations (dots) where (left) both the 1 km and 3 km WRF grid points are urban types and where the (right) 1 km and 3 km WRF grid points have the same nonurban vegetation type. Also shown are the simulated temperatures in the operational 1 km (orange) and 3 km (red) model runs, and the 1 km model run with the 3 km physics (blue). The results are the average of three 24 h periods: 10–12 July 2014.
Source publication
The 1km Institute of Urban Meteorology (IUM) operational model has a high-temperature bias, especially at night, and a high wind speed bias in urbanized areas, limiting the ability of IUM to provide accurate, high-resolution prediction of thermal stress and air quality for the densely populated Beijing-Tianjin metro region. This study provides an a...
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Citations
... These elucidate that the LW emissivity values adopted in our experiments fall within a reasonable range. All the experiments utilize the same specific heat and thermal conductivity parameters, with some modifications from the default WRF values to be more suitable for China (Table 1), for example, reducing the specific heat and thermal conductivity of buildings and the ground (Barlage et al., 2016). Note that the wall LW emissivity modified here refers to the emissivity of the outermost wall surface, the side directly exposed to outdoor air. ...
This study utilizes the Weather Research and Forecasting model coupled with an atmospheric chemistry model, a multi‐layer urban canopy model (UCM), and a building energy model to simulate the extreme rainfall event influencing the Guangzhou city in South China on 7 May 2017. By employing small variations in the longwave emissivity of buildings within the UCM, 11 convective‐permitting experiments are conducted. The maximal 18‐hr and hourly rainfall accumulation vary from a 2‐year return period to as high as a 20 or 40‐year return period with notable spatial differences. Comparisons between the more accurate group (GOOD) and less accurate group (POOR) simulations highlight that some minor differences in the near‐surface air thermodynamic conditions in urban area could lead to substantial differences in local convection and its impacts on subsequent convective systems. With persistent transportation of warm, moist airflows from the northern South China Sea, formation of a slow‐moving mesoscale outflow boundary to the north of the urban agglomeration leads to the development of a quasi‐stationary, compactly structured meso‐γ‐scale rainstorm in the GOOD simulations. The stronger low‐level to near‐surface convergence and mid‐level cyclonic shear within this system substantially enhance low‐level updrafts, leading to increased microphysical production and stronger horizontal advection of rainwater within the system. These findings offer some process‐based understanding about the uncertainties in simulating urban extreme rainfall and underscore the need to develop ensemble forecasting methods for convection‐permitting numerical models that incorporate increasingly complicated representations of anthropogenic influences.
... The WRF-Chem model adeptly captured the WS trend in Shenyang, although the simulated values were slightly over the observed ones. The WD simulation, however, was less accurate, which could be attributed to the parameterization schemes in the WRF mode leading to systematically higher urban WS, as discussed by Barlage et al. (2016) . In Changchun, the T simulation was the most accurate among the three cities, showing both precise trend representation and values. ...
Meteorological conditions The Northeast Plain WRF-Chem a b s t r a c t The Northeast Plain in China ranks among the top five regions that have been significantly impacted by haze pollution. To effectively control pollution, it is crucial to accurately assess the effects of emission reduction measures. In this study, we analyzed surveillance data and found substantial decreases (ranging from 19.0% to 50.1%) in average annual mass concentrations of key pollutants (such as CO, SO 2 , NO 2 , and PM 2.5) in the Northeast Plain from 2016 to 2020. To precisely determine the contributions of meteorological conditions and emission reductions to the improvement of air quality in the Northeast Plain, we conducted three scenario simulations. By comparing source emissions in December 2016 and 2020 using the WRF-Chem model (except for SO 2), we observed significant reductions of 21.3%, 8.8%, and 9.8% in mass concentrations of PM 2.5 , NO 2 , and CO, respectively, from 2016 to 2020. This highlights the essential role that meteorological conditions play in determining air quality in the Northeast Plain. Moreover, further reducing source emissions by 30% in December 2016 resulted in subsequent reductions of 25.3%, 29.0%, 4.5%, and 30.3% in mass concentrations of PM 2.5 , SO 2 , NO 2 , and CO, respectively, under the same meteorological conditions. Notably, source emission reduction was effective for PM 2.5 , SO 2 , and CO, but not for NO 2. The improvement in air quality in the Northeast Plain from 2016 to 2020 can be attributed to the combined effects of improved meteorological conditions and reduced pollution sources.
... The SLUCM is a single-layer model used to parameterize the effects of urban canopy geometry on the surface energy balance and low-level wind shear (Kusaka and Kimura, 2004;Kusaka et al., 2001). As reflected by previous studies, SLUCM is recommended for the long-term urban simulations (Liao et al., 2014), because multi-layer UCMs such as BEP + BEM often cause higher temperature bias than SLUCM (Barlage et al., 2016;Karlický et al., 2018), while consume much more time (approximately 30-40%) due to the increased complicatedness in computation (Jandaghian and Berardi, 2019). Such coupled models have been widely applied in urban-related simulations because they can capture the complex interactions between urban land surface characteristics and atmospheric processes, (Cao et al., 2018;Liu et al., 2017;Yu et al., 2021). ...
... Given the deficit of in situ meteorological data in cities, hydrodynamic mesoscale models of the atmosphere are nowadays one of the main tools for obtaining spatially and temporally detailed data on the UHI. Such models coupled to urban canopy parameterizations [10,11] with a grid spacing of a few kilometers to hundreds of meters are able to reproduce the majority of urban-induced meteorological effects [12] and are routinely used in numerical weather prediction [13,14], regional heat stress assessments [15,16] and refinements of the climate change scenarios [17,18] for urban areas. However, such models demand computing resources and require complex software and hardware infrastructure (data storage, input and output data processing, etc.). ...
This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.
... To simulate more accurate WRF LST values, the urban canopy model (UCM) is also used as one of the physical schemes in this paper because the WRF combined with UCM can describe the surface radiation and other processes in more detail (Chen et al. 2011). According to previous research on WRF LST (Barlage et al. 2016;Liu et al. 2018;Wang et al. 2020), the selection of other physical schemes and their corresponding codes are shown in Table 1. ...
At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1 km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1 K (< 2 K), RMSE < 2 K (< 2 K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80 K, 1.09 K, and 0.94 K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values).
... 02 Banks and Baldasano, 2016;Avolio et al., 2017;Huang et al., 2019), several other studies reported a daytime warm bias over the United Arab Emirates and India (Chaouch et al., 2017;Gunwani and Mohan, 2017). For a single parametrization scheme, it may perform better than other schemes according to the cases and the variables being evaluated (e.g., Borge et al., 2008;Hariprasad et al., 2014;Banks and Baldasano, 2016;Barlage et al., 2016;Li et al., 2016;Chaouch et al., 2017;Gunwani and Mohan, 2017;Ferrero et al., 2018;Wang and Ying, 2020;Martilli et al., 2021;Wang et al., 2021;Huang, 2020). It is thus necessary to investigate the applicability of PBL schemes over a specific climate regime, which would be helpful to determine the optimized parameterizations for regional mesoscale modeling, especially over the highly populated urban agglomerations. ...
... Recent studies have suggested the importance of UHI effects on nocturnal convection initiation over the GBA (Yin et al., 2020), the regulation of extreme rainfall (Li et al., 2021), and the distribution of extreme hourly rainfall (Sun Y. et al., 2021). Although the urban-canopy model in WRF has been developed and evaluated (e.g., Barlage et al., 2016;Huang et al., 2019;Sun X. et al., 2021), the expensive cost of computational resources prevents it from being the routine operational system for daily forecasts. Therefore, investigating the characteristics among the PBL schemes for the early summer rainy season over the GBA is an important step to identifying potential weaknesses, which may lead to substantial model inaccuracy. ...
Urbanization of large cities exerts significant changes in surface air temperature, which subsequently lead to inadvertent local weather and climate changes. The exchanges of momentum, moisture and heat within the planetary boundary layer (PBL) impact the urban atmosphere representation. The applicability of PBL schemes in regional modeling varies with different climate regimes and underlying surface. As the first step to gaining a better forecast of urban climates, the performances of multiple PBL schemes (YSU, MRF, ACM2, BL, MYNN, UW and GBM) on reproducing the low-level urban atmosphere are evaluated over the Greater Bay Area, South China, during April–June 2018. With the aid of observations from in situ weather stations and radiosondes, the urban environmental characteristics, including surface air temperature and humidity, temperature profile, urban heat island (UHI), and PBL height, are assessed. The results show a cool/moist bias near the surface during nighttime and a warm/dry bias during daytime for all PBL schemes. The daytime bias is significant on weak-UHI days while the nocturnal bias appears to be significant on strong-UHI days. The so-called best scheme depends on the exact meteorological variables, diurnal cycles, and thermodynamic conditions that are of interest. Specifically, the MYNN and MRF schemes perform the best for the daytime and nighttime air temperature, respectively. The MRF scheme also presents the best performance in simulating the observed UHI, with a good agreement with the observed diurnal variation. The numerical exercises in this study may serve as a reference for an efficient operational way that is readily accessible to forecast air temperature in the urban agglomeration over South China.
... In the current version of the WRF model with a single layer canopy model, the AH input is provided only for three distinct types of urban grids: low dense residential, high dense residential, and commercial/ industry, as shown in Fig. 1c depicting various types of land use land cover. According to Barlage et al. (2016), the default urban canopy parameterization are more appropriate for American cities and not appropriate for any given cities (Li et al., 2022). The default WRFUCM considers the AH emissions for the three urban land use classes (i.e., 1: low-res residential, 2: high-res residential, 3: Commercial). ...
Changing urban land use dynamics and associated anthropogenic heat (AH) has increased warming over urban centres. This warming is associated with the Urban Heat Island (UHI) effect and is mainly studied in major cities worldwide. However, predominantly industrial regions can also experience heat island effect due to heat emissions from industrial infrastructure and associated activities. The current study examines the role of the urban morphology and anthropogenic heat over the Angul-Talcher region (industrial region) of Odisha state, India. The region is also referred to as India's highly polluted industrial cluster due to open cast mining and industries. In the present study, Weather Research and Forecast model (WRF) coupled with Single-Layer Urban Canopy Model (WRFUCM) has been modified to examine the impact of urban morphology and anthropogenic heat on the heat island effect over the region. The study refers modified version of WRFUCM as the WRFAH [Gridded] model, which incorporates anthropogenic heat from different industries with stack height information. It was found that the WRF model capabilities have improved in simulating surface meteorological variables such as temperature at 2 m, wind speed, and relative humidity with the inclusion of gridded AH. Urban canopies were estimated to bring in an increase of up to 0.77 • C in local near-surface temperatures during daytime and up to 2.12 • C during nighttime. AH emissions further increased temperatures by up to 0.32 • C during daytime and 0.69 • C during nighttime. Maximum heat island intensity with urban canopies is estimated to be 5.62 • C which increases to 7.11 • C with the inclusion of average AH released at canopy level. However, this has significantly improved to 5.88 • C with inputs of industry-specific AH released at stack heights with the WRFAH[Gridded] model against observed maximum heat island intensity of 6.06 • C. Overall, the influence of urban morphology was found to be higher than the influence of AH over the region at ground level, and the inclusion of gridded AH in the model showed improved performance for the heat island assessment.
... WRF-GHG is an abbreviation for the Weather Research and Forecast model coupled with Chemistry (WRF-Chem) in its passive tracer option (Skamarock et al., 2021;Beck et al., 2011). WRF-Chem simulates the emission, transport, mix-ing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. ...
Réunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4, and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight to the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied.
A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics, and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a Pearson correlation coefficient of 0.90. These observations represent different air masses to those near the surface; they are influenced by processes from Madagascar, Africa, and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Réunion Island. Furthermore, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Furthermore, WRF-GHG is capable of simulating CO levels on Réunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column Fourier transform infrared (FTIR) observations on the other hand, a model resolution of 50 km might already be sufficient.
... The combination of WRF and urban canopy model can more accurately simulate the LST of urban areas. According to related studies (Barlage et al., 2016;Fu et al., 2019;Li et al., 2013b;Yan et al., 2021), three schemes are selected. Considering the static LULC as an input parameter, a total of 3 × 2 WRF configuration schemes are selected, as shown in Table 1, from which the optimal WRF configuration scheme is determined. ...
... Due to the lack of clear-sky LST pixels, indicating cloud contamination in the research process, the evaluation of the RS-WRF coupled model in this paper is based on clear-sky LST pixels. In addition, the LST simulated by WRF is sensitive to the WRF scheme (Fallmann et al., 2013), such as the PBL (Barlage et al., 2016), LULC (Li et al., 2018b), and URB (Kusaka et al., 2001) WRF schemes. However, completely analyzing the sensitivity to each of the WRF model schemes is expensive , so this paper selects six schemes from relevant studies and then selects the optimal scheme from among these six LST simulation schemes. ...
Land surface temperature (LST) is a key parameter in the physics of land surface processes. Currently, the technique most commonly used to obtain LST is thermal infrared (TIR) remote sensing (RS). However, this technique is affected by clouds and cannot obtain complete spatiotemporal LST images. To solve this problem, RS and weather research and forecasting (WRF) coupled model (RS-WRF coupled model) was developed to produce cloud-free MODIS-like LST data. (i) The WRF model is used to simulate the cloud-free LST with a 1 km resolution. (ii) The optimal machine learning model is utilized to fit the simulated LSTs and produce cloud-free MODIS-like LSTs. (iii) Combined with a median filtering algorithm, the salt and pepper noise in the fitted image is optimized. Taking Beijing as a test site. Under relatively little cloud cover and greater cloud contamination, the root mean square error of the LST constructed by the RS-WRF coupled model is approximately 1.2 and 1.8 K, respectively. The correlation coefficients under both conditions exceed 0.9. Overall, the RS-WRF coupled model can provide cloud-free time series MODIS-like LST images in areas with frequent cloud cover, thereby compensating for the disadvantage that satellite TIR images contaminated by clouds cannot obtain complete LST estimates.
... Besides the unified NOAH land surface model (see Sec. 3.2), no additional urban surface model was included in the simulations. Other studies using the WRF model often show wind speed overestimation above urban areas (Feng et al., 2016;Barlage et al., 2016;Zhang et al., 2009;Kim et al., 2013). ...
Reunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4 and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight in the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied. A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a coefficient of 0.90. These observations represent different air masses than those near the surface, they are influenced by processes from Madagascar, Africa and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season, but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Reunion Island. Further, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Further, WRF-GHG is capable of simulating CO levels on Reunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column FTIR observations on the other hand, a model resolution of 50 km might already be sufficient.
