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The limited understanding of the structure of the urban surface atmospheric boundary layer can be attributed to its inherent complexity, as well as a deficiency in comprehensive measurements. We analyzed one year of meteorological data and Doppler wind lidar measurements in Beijing to explore how atmospheric stability is influenced by wind speed, radiation, turbulence, and pollution levels. Results indicate that the predominant state of the urban boundary layers in Beijing is an active condition (characterized by strong unstable and unstable stability regimes) throughout the day, attributed to the significant heat storage capacity of the urban canopy. Strong stable regimes are more frequently observed during winter and autumn, peaking during transitions from night to day. Furthermore, both strong unstable and strong stable regimes occur under very weak wind conditions (indicating weak dynamic instability), with strong instability associated with high net radiation levels while strong stability correlates with low net radiation conditions (indicative of robust thermal stability). The unstable regime manifests under strong winds (reflecting strong dynamic instability) alongside moderate net radiation environments, characterized by elevated values of turbulence kinetic energy and urban boundary height, highlighting the critical role of mechanical turbulence generation during periods of high wind activity. Additionally, six instances of pronounced stable conditions observed during daytime can be partially attributed to low radiation coupled with high pollutant concentrations near the surface, resulting from prolonged temperature inversions due to intense radiative cooling effects and weak dynamic forcing. Our findings presented herein are expected to have urban boundary layer climate and environment implications for other cities with high pollution and dense urban infrastructure all over the world. [ABSTRACT FROM AUTHOR]

The limited understanding of the structure of the urban surface atmospheric boundary layer can be attributed to its inherent complexity, as well as a deficiency in comprehensive measurements. We analyzed one year of meteorological data and Doppler wind lidar measurements in Beijing to explore how atmospheric stability is influenced by wind speed, radiation, turbulence, and pollution levels. Results indicate that the predominant state of the urban boundary layers in Beijing is an active condition (characterized by strong unstable and unstable stability regimes) throughout the day, attributed to the significant heat storage capacity of the urban canopy. Strong stable regimes are more frequently observed during winter and autumn, peaking during transitions from night to day. Furthermore, both strong unstable and strong stable regimes occur under very weak wind conditions (indicating weak dynamic instability), with strong instability associated with high net radiation levels while strong stability correlates with low net radiation conditions (indicative of robust thermal stability). The unstable regime manifests under strong winds (reflecting strong dynamic instability) alongside moderate net radiation environments, characterized by elevated values of turbulence kinetic energy and urban boundary height, highlighting the critical role of mechanical turbulence generation during periods of high wind activity. Additionally, six instances of pronounced stable conditions observed during daytime can be partially attributed to low radiation coupled with high pollutant concentrations near the surface, resulting from prolonged temperature inversions due to intense radiative cooling effects and weak dynamic forcing. Our findings presented herein are expected to have urban boundary layer climate and environment implications for other cities with high pollution and dense urban infrastructure all over the world.

The coupling relationship between land surface processes and atmospheric boundary layer is one of the key links and difficulties in understanding the thermal effect over the Qinghai-Xizang Plateau.Based on the surface and radiosonde observation data of Nagqu Plateau Climate and Environment Observation and Research Station in May， July and October 2019， this paper analyzes the surface energy budget， the daily and seasonal variations of vertical profile of atmospheric temperature and humidity， in Nagqu Area of the Qinghai-Xizang Plateau， and discusses the evolution law of the atmospheric boundary layer height in different seasons in this region.The results show that the convective boundary layer is 2842 m high in sunny days due to the influence of diurnal net radiation intensity during the observation period in May.It is 1481 m high in cloudy day， which is relatively low， and the strong convective weather may change it into a stable boundary layer.In the meanwhile， the exchange between the sensible heat and the latent heat in the near surface atmosphere provides energy support for the maintenance and development of the atmospheric boundary layer.The vertical profile of potential temperature and specific humidity can correctly reflect the seasonal difference of atmospheric boundary layer height in gqu Area.The height of the convective boundary layer is highest in May， less high in October and the lowest in July while the stable boundary layer is highest in July， less high in May and lowest in October.

The coupling relationship between land surface processes and atmospheric boundary layer is one of the key links and difficulties in understanding the thermal effect over the Qinghai-Xizang Plateau. Based on the surface and radiosonde observation data of Nagqu Plateau Climate and Environment Observation and Research Station in May, July and October 2019, this paper analyzes the surface energy budget, the daily and seasonal variations of vertical profile of atmospheric temperature and humidity, in Nagqu Area of the Qinghai-Xizang Pla‐ teau, and discusses the evolution law of the atmospheric boundary layer height in different seasons in this region. The results show that the convective boundary layer is 2842 m high in sunny days due to the influence of diurnal net radiation intensity during the observation period in May. It is 1481 m high in cloudy day, which is relatively low, and the strong convective weather may change it into a stable boundary layer. In the meanwhile, the ex‐ change between the sensible heat and the latent heat in the near surface atmosphere provides energy support for the maintenance and development of the atmospheric boundary layer. The vertical profile of potential temperature and specific humidity can correctly reflect the seasonal difference of atmospheric boundary layer height in gqu Area. The height of the convective boundary layer is highest in May, less high in October and the lowest in July while the stable boundary layer is highest in July, less high in May and lowest in October. [ABSTRACT FROM AUTHOR]

In this study, we utilize a novel approach to solve the Ekman equations for eddy-viscosity profiles in the stable boundary-layer. By doing so, a well-known expression for the stable boundary-layer height by Zilitinkevich (Boundary-Layer Meteorology, 1972, Vol. 3, 141–145) is rediscovered. [ABSTRACT FROM AUTHOR]

Many studies have been conducted on air pollution in Ulaanbaatar city. However, most have focused on the sources of pollutants and their characteristics and distribution. Although the location of the city subjects it to unavoidable natural conditions where air pollution accumulates during the cold season, nature-based solutions have not yet been considered in the projects implemented to mitigate air pollution levels. Therefore, this study aims to determine the combined influence of geography and atmospheric factors on cold season air pollution. The spatiotemporal variations in the variables were investigated using meteorological observation data from 1991 to 2020 in the different land-use areas. Then, atmospheric stagnation conditions and air pollution potential parameters were estimated from daily radiosonde data. Subsequently, the temporal variations in air pollutants were studied and correlated with estimates of the above parameters. In the Ulaanbaatar depression, the stable cold air lake (colder than −13.5 °C), windless (34–66% of all observations), and poor turbulent mixing conditions were formed under the near-surface temperature inversion layer in the cold season. Moreover, due to the mountain topography, the winds toward the city center from all sides cause polluted air to accumulate in the city center for long periods. Air pollution potential was categorized as very high and high (<4000 m2·s−1), in the city in winter, indicating the worst air quality. Thus, further urban planning policy should consider these nature factors. [ABSTRACT FROM AUTHOR]

The lack of sounding observations in the western Tibetan Plateau (TP), the highest terrain in the world, has resulted in few efforts to evaluate the quality of the atmospheric reanalysis results in this region. Using the sounding observations from the Third Tibetan Plateau Atmospheric Scientific Experiment during 2013–2015, the NCEP and ERA-Interim reanalysis temperature and humidity fields in the TP are evaluated and the characteristics of the reanalysis Atmospheric Boundary Layer (ABL) height are utilized to explain the reasons for the differences in temperature and humidity between the western and eastern TP. The results show that the NCEP and ERA-Interim reanalysis temperature and humidity products generally have larger errors at low level (such as 500 hPa) in the western TP (WTP) than in the eastern one (ETP) at 12:00 UTC. However, this difference is small at 00:00 UTC. Further analysis reveals that the temporal and spatial variations of temperature and humidity errors at low level are closely associated with the differences in the terrain and ABL between the western and eastern TP. In the early morning when the ABL height is low over the TP, the 500 hPa pressure level in both the WTP and ETP is significantly above the top of ABL, with weak spatial variations of temperature and humidity errors. However, in the late afternoon when there is a larger increase in ABL height over the WTP than over the ETP, the 500 hPa pressure level is located inside the ABL in the WTP and is still above the ABL in the ETP, which causes significant regional differences in these errors.

The planetary boundary layer height is a very important parameter in the atmosphere because it determines the range where the most effective dispersion processes take place, and it serves as a medium for the vertical transport of heat, moisture, and pollutants. The accurate estimation of boundary layer height (BLH) is vital for air pollution prediction. In this paper, the BLH estimated by AD-Net was compared with that from the ECMWFs over East Asia from September 2015 to August 2018. A continuous 24 h BLH estimation from AD-Net generally matched with the aerosol vertical structures. Diurnal and seasonal variation and spatial variation of BLH can also be shown, suggesting the good performance of AD-Net BLH. The comparison of seasonal mean BLH between AD-Net and ECMWFs was conducted at 20 lidar sites. On average, there was an underestimation of the ECMWFs, mostly in summer and winter. A significant disagreement between AD-Net and the ECMWFs was noted, especially over coastal areas and mountain areas. In order to investigate the difference between them, two BLHs were compared under different land cover types and climate conditions. In general, the BLH of the ECMWFs was less than that of AD-Net over most of the land cover types in summer and winter. The smallest differences (0.26 km) existed over water surfaces in winter compared with AD-Net, and the largest underestimation (1.42 km) occurred over grassland surfaces in summer. Similarly, all the BLHs of the ECMWFs were lesser than those of AD-Net under different climatological conditions in summer and winter. The mean difference between AD-Net BLH and ECMWFs BLH was 1.05, 0.71, and 0.48 km for arid regions, semi-arid and semi-wet regions, and wet regions, respectively. The largest underestimation occurred over arid regions in winter, with a value of 1.42 km. The smallest underestimation occurred over wet regions, with a value of 0.27 km. The present research provides better insight into the BLH performance in the ECMWFs reanalysis data. The new continuous PBL dataset can be used to improve the model parameterization of PBL and our understanding of the atmospheric transport of pollutants which affect air quality and human health. [ABSTRACT FROM AUTHOR]

Boundary layer development has a significant influence on dust emission and transportation. The effects of surface heat flux on distribution of dust related to BLH (boundary layer height) during a dust event in Northwest China are discussed by numerical simulation. The results show that: the dust emissions start in Dunhuang and strengthen in Minqin with large dust emission fluxes (as high as 3000 μg m−2 s−1) and surface wind speed (up to 20 m s−1). The dust at the early stage mainly concentrates below 500 m above the ground, then lifts over 3000 m above the ground at the strengthening stage, and the concentration in high value center is more than 24,000 μg m−3 at the dust source Dunhuang. The high dust concentration in Minqin maintains at about 1000 m above the ground. Weak dust emission in Minqin makes the dust concentration near the ground less than that in the upper air. The BLH in the high dust concentration center is higher than that in the surrounding area at the emission stage in Dunhuang. Sensitivity experiment with turning off surface heat flux shows obvious decrease of the dust concentration in Dunhuang, with more than four times reduction of the surface dust concentration. The BLH near the high dust concentration area reduces significantly in the sensitivity experiment. However, the gap between the two experiments reduces with the dust development. The BLH in the sensitivity experiment has small spatial differences with uniform distribution in Dunhuang. It means that the thermodynamic contribution is main reason for the distribution differences. The two experiments in Minqin also show that the height of high concentration dust has a difference of about 500 m. The dust lifts with the boundary layer development, but shows different distribution characteristics and influencing factors between Dunhuang and Minqin. [ABSTRACT FROM AUTHOR]

Abstract Boundary layer development has a significant influence on dust emission and transportation. The effects of surface heat flux on distribution of dust related to BLH (boundary layer height) during a dust event in Northwest China are discussed by numerical simulation. The results show that: the dust emissions start in Dunhuang and strengthen in Minqin with large dust emission fluxes (as high as 3000 μg m−2 s−1) and surface wind speed (up to 20 m s−1). The dust at the early stage mainly concentrates below 500 m above the ground, then lifts over 3000 m above the ground at the strengthening stage, and the concentration in high value center is more than 24,000 μg m−3 at the dust source Dunhuang. The high dust concentration in Minqin maintains at about 1000 m above the ground. Weak dust emission in Minqin makes the dust concentration near the ground less than that in the upper air. The BLH in the high dust concentration center is higher than that in the surrounding area at the emission stage in Dunhuang. Sensitivity experiment with turning off surface heat flux shows obvious decrease of the dust concentration in Dunhuang, with more than four times reduction of the surface dust concentration. The BLH near the high dust concentration area reduces significantly in the sensitivity experiment. However, the gap between the two experiments reduces with the dust development. The BLH in the sensitivity experiment has small spatial differences with uniform distribution in Dunhuang. It means that the thermodynamic contribution is main reason for the distribution differences. The two experiments in Minqin also show that the height of high concentration dust has a difference of about 500 m. The dust lifts with the boundary layer development, but shows different distribution characteristics and influencing factors between Dunhuang and Minqin.

Using K-means cluster method, the whole country is divided into four regions (Qinghai-Tibet region, northwest region, central region and eastern region) by boundary layer height (BLH) derived from potential temperature gradient method based on L-band radar sounding secondly data of 119 stations from January 2010 to December 2018. Characteristics of BLH and frequency of different boundary layer state are investigated, including convective boundary layer (CBL), neutral boundary layer (NBL) and stable boundary layer (SBL), through their interannual, annual and diurnal variations respectively. Results show that there is no significant difference in the annual average BLH and the frequencies of different boundary layer states in four regions at 0800 BT and 2000 BT from 2010 to 2018. At 0800 BT, the annual average BLH is around 200-600 m and mainly in SBL. At 2000 BT, the annual average BLH in Qinghai-Tibet region is the highest (about 1500 m), followed by northwest region and central region (about 1000 m and 500 m), and that of eastern region is the lowest (about 400 m). Qinghai-Tibet region and northwest region are mainly with CBL and NBL, while central region and eastern region are mainly with NBL. Besides, the annual variation of BLH in four regions is similar at 0800 BT, but it's significantly different at 2000 BT. At 0800 BT, the difference of one-year monthly average BLH in four regions are not obvious, and there is no clear difference among these regions. But at 2000 BT, the monthly average BLH in each region reaches maximum in spring and summer, and minimum in autumn and winter. As for corresponding annual variation of different boundary layer state frequencies, SBL's frequency first increases then decreases while the frequencies of CBL and NBL first decrease then increase overall at 0800 BT and 2000 BT. And their turning point is in May to July. In general, the variation range of monthly average BLH and boundary layer state frequencies gradually descend from Qinghai-Tibet region, northwest region, central region to eastern region. The diurnal variations of BLH are different in four regions. In particular, the diurnal variations of Qinghai-Tibet region, northwestern region and central region show distinct seasonal difference. In Qinghai-Tibet region, the amplitude of diurnal variation can reach 2000-2300 m in spring and summer, but relatively weaker in autumn and winter. The diurnal variation in eastern region is similar in all seasons, and amplitudes are around 600 m.

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation. [ABSTRACT FROM AUTHOR]

Although coherent Doppler wind lidar (CDWL) is promising in detecting boundary layer height (BLH), differences between BLH results are observed when different CDWL measurements are used as tracers. Here, a robust solution for BLH detections with CDWL is proposed and demonstrated: mixed layer height (MLH) is retrieved best from turbulent kinetic energy dissipation rate (TKEDR), while stable boundary layer height (SBLH) and residual layer height (RLH) can be retrieved from carrier-to-noise ratio (CNR). To study the cause of the BLH differences, an intercomparison experiment is designed with two identical CDWLs, where only one is equipped with a stability control subsystem. During the experiment, it is found that the CNR could be distorted by instrument instability because the coupling efficiency from free-space to the polarization-maintaining fiber of the telescope is sensitive to the surrounding environment. In the ML, a bias up to 2.13 km of the MLH from CNR is found, which is caused by the CNR deviation. In contrast, the MLH from TKEDR is robust as long as the accuracy of wind is guaranteed. In the SBL (RL), the CNR is found capable to retrieve SBLH and RLH simultaneously and robustly. This solution is tested during an observation period over one month. Statistical analysis shows that the root-mean-square errors (RMSE) in the MLH, SBLH, and RLH are 0.28 km, 0.23 km, and 0.24 km, respectively. [ABSTRACT FROM AUTHOR]

This paper describes diurnal and seasonal variations of trace air pollutants include surface ozone (O3), oxides of nitrogen (NOx), carbon monoxide (CO), volatile organic compounds (Benzene, Toluene, Ethyl Benzene, Xylenes which are classified as BTEX)), ammonia (NH3), sulphur dioxide (SO2), and meteorological parameters observed at Kannur town (11.87° N, 75.37° E, 2 m msl) for a period of one year from September 2019. Seasonal variations of trace air pollutants exhibit a daytime maximum during winter due to the enhanced local emission and long-range transport, and minimum during the monsoon period. Surprisingly, air pollutants except O3 show a reduction in concentration in the months of April 2020 due to countrywide lockdown in the wake of restricting the spread of COVID 19. Weekday/weekend variations of air pollutants reveal that high concentrations of O3 are found on weekends compared to weekdays, unlike the concentrations of all other pollutants are found low during weekends. From the analysis of the chemical coupling between NO, NO2, NOx, O3, and OX (=O3+NO2), it is found that OX has both regional and local contributions on NOx. Intercorrelations between trace pollutants showed a strong positive correlation between O3 and CO, a negative correlation between O3 and NOx.

The planetary boundary layer height is a very important parameter in the atmosphere because it determines the range where the most effective dispersion processes take place, and it serves as a medium for the vertical transport of heat, moisture, and pollutants. The accurate estimation of boundary layer height (BLH) is vital for air pollution prediction. In this paper, the BLH estimated by AD-Net was compared with that from the ECMWFs over East Asia from September 2015 to August 2018. A continuous 24 h BLH estimation from AD-Net generally matched with the aerosol vertical structures. Diurnal and seasonal variation and spatial variation of BLH can also be shown, suggesting the good performance of AD-Net BLH. The comparison of seasonal mean BLH between AD-Net and ECMWFs was conducted at 20 lidar sites. On average, there was an underestimation of the ECMWFs, mostly in summer and winter. A significant disagreement between AD-Net and the ECMWFs was noted, especially over coastal areas and mountain areas. In order to investigate the difference between them, two BLHs were compared under different land cover types and climate conditions. In general, the BLH of the ECMWFs was less than that of AD-Net over most of the land cover types in summer and winter. The smallest differences (0.26 km) existed over water surfaces in winter compared with AD-Net, and the largest underestimation (1.42 km) occurred over grassland surfaces in summer. Similarly, all the BLHs of the ECMWFs were lesser than those of AD-Net under different climatological conditions in summer and winter. The mean difference between AD-Net BLH and ECMWFs BLH was 1.05, 0.71, and 0.48 km for arid regions, semi-arid and semi-wet regions, and wet regions, respectively. The largest underestimation occurred over arid regions in winter, with a value of 1.42 km. The smallest underestimation occurred over wet regions, with a value of 0.27 km. The present research provides better insight into the BLH performance in the ECMWFs reanalysis data. The new continuous PBL dataset can be used to improve the model parameterization of PBL and our understanding of the atmospheric transport of pollutants which affect air quality and human health.

Boundary layer height (BLH) is an important parameter in climatology and air pollution research, especially in urban city. We calculated the BLH with a bulk Richardson number (Ri) method over urban cities of China during 1980–2018 using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim data after carefully validation with sounding data obtained from two meteorology stations in eastern China during 2010–2018. The values of BLH between these two types of data have correlation coefficients in the range of 0.65–0.87, which indicates that it is reasonable to analyze long-term trends of the BLH from ERA data sets. Using ERA-interim calculated BLH, we found that there is an increasing trend in the daytime BLH in most cities of eastern China, particularly during the spring season. A correlation analysis between the BLH and temperature, wind speed, relative humidity and visibility revealed that the variability in meteorological parameters, as well as in aerosol concentrations over highly polluted eastern China, play important roles in the development of the BLH.

Energy consumption in Bangladesh increased for economic, industrial, and digitalization growth. Reductions in conventional sources such as natural gas (54%) and coal (5.6%) are calls to enhance renewable resources. This paper aims to investigate the atmospheric variables for potential wind zones and develop a statistical power-forecasting model. The study-site is Bangladesh, focusing on eight divisions across two regions. First, the southern zone includes Dhaka (Capital), Chittagong, Barishal, and Khulna. The northern regions are Rajshahi, Rangpur, Mymensingh, and Sylhet. This investigation illustrates wind (m/s) speeds at various heights (m) and analyzes the boundary layer height (BLH) from the European Center for Medium Range Weather Forecast reanalysis 5th generation (ERA5). The data is from a period of 40 years from 1979 to 2018, assessing with a climatic base of 20 years (1979 to 2000). The climatological analysis comprises trends, time series, anomalies, and linear correlations. The results for the wind speed (BLH) indicate that the weakest (lower) and strongest (higher) regions are Sylhet and Barishal, respectively. Based on power-curve relationships, a simple power predictive model (SPPM) is developed using global wind atlas (GWA) data (sample: 1100) to estimate the power density (W/m2) and found an accuracy of 0.918 and 0.892 for Exponential (EXP) and Polynomial (PN) with mean absolute percentage errors (MAPE) of 22.92 and 21.8%, respectively. For validation, SPPM also forecasts power incorporating historical observations for Chittagong and obtains correlations of 0.970 and 0.974 for EXP and PN with a MAPE of 10.26 and 7.69% individually. Furthermore, calculations for annual energy production reveal an average megawattage of 1748 and 1070 in the southern and northern regions, with an MAPE of 15.71 and 5.85% for EXP and PN models, except Sylhet. The SPPM's predictability can be improved with observed wind speeds and turbine types. The research wishes to apply SPPM for estimating energy in operational power plants. [ABSTRACT FROM AUTHOR]

Monthly means, seasonal variances, and trends of a global climatology boundary layer height (BLH) over West Africa are presented based on 36 years (1979 - 2014) of six-hourly ERA-Interim reanalysis. In this region, we found that there is a link between the West Africa Monsoon (WAM) and the monthly means of BLH where largest values of BLH variances are developed in the tropics close to the Inter Tropical Convergence Zone (ITCZ). High temperatures and sufficient moisture are also available close this area. Seasonal trend magnitudes vary from -20 to 20 m per decade during the period 1979 - 2014 and characterize by negative trend over east of the Sahara region. Trends are only included in the analysis, if their probability exceeds the 95% significance level. Case study of diurnal variation was done during the African monsoon multidisciplinary analyses Special Observing Period 3 (AMMA S0P3) experiment (August 2006). We found that the lower boundary-layer appears around 875 hPa in the monsoon layer where the wind decreases at midday in interaction through exchange processes with air originating from above the boundary layer. In the same time, the dust in Saharan Air Layer (SAL) seems to modify the atmospheric boundary layer (ABL) thermodynamic attributes by altering the shortwave and longwave radiative budget. [ABSTRACT FROM AUTHOR]

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

Energy consumption in Bangladesh increased for economic, industrial, and digitalization growth. Reductions in conventional sources such as natural gas (54%) and coal (5.6%) are calls to enhance renewable resources. This paper aims to investigate the atmospheric variables for potential wind zones and develop a statistical power-forecasting model. The study-site is Bangladesh, focusing on eight divisions across two regions. First, the southern zone includes Dhaka (Capital), Chittagong, Barishal, and Khulna. The northern regions are Rajshahi, Rangpur, Mymensingh, and Sylhet. This investigation illustrates wind (m/s) speeds at various heights (m) and analyzes the boundary layer height (BLH) from the European Center for Medium Range Weather Forecast reanalysis 5th generation (ERA5). The data is from a period of 40 years from 1979 to 2018, assessing with a climatic base of 20 years (1979 to 2000). The climatological analysis comprises trends, time series, anomalies, and linear correlations. The results for the wind speed (BLH) indicate that the weakest (lower) and strongest (higher) regions are Sylhet and Barishal, respectively. Based on power-curve relationships, a simple power predictive model (SPPM) is developed using global wind atlas (GWA) data (sample: 1100) to estimate the power density (W/m2) and found an accuracy of 0.918 and 0.892 for Exponential (EXP) and Polynomial (PN) with mean absolute percentage errors (MAPE) of 22.92 and 21.8%, respectively. For validation, SPPM also forecasts power incorporating historical observations for Chittagong and obtains correlations of 0.970 and 0.974 for EXP and PN with a MAPE of 10.26 and 7.69% individually. Furthermore, calculations for annual energy production reveal an average megawattage of 1748 and 1070 in the southern and northern regions, with an MAPE of 15.71 and 5.85% for EXP and PN models, except Sylhet. The SPPM’s predictability can be improved with observed wind speeds and turbine types. The research wishes to apply SPPM for estimating energy in operational power plants.

The top of the boundary layer, referred to as the planetary boundary layer height (BLH), is an important physical parameter in atmospheric numerical models, which has a critical role in atmospheric simulation, air pollution prevention, and climate prediction. The traditional methods for determining BLHs using Doppler lidar vertical velocity variance (σw2) can be classified into the variance and peak methods, which depend on atmospheric conditions due to their use of a single threshold, hence limiting their ability to estimate diurnal BLHs. Edge detection (ED) was later introduced in BLH estimation due to its ability to identify the 2D gradient of an image. A key step in ED is automatically identifying the edge of BLHs based on the peaks of the profile, hence avoiding the influence of extreme atmospheric conditions. Two cases in the diurnal cycle on 4 March 2019 and 8 July 2019 reveal that ED outperforms both the variance and peak methods in nighttime and extreme atmospheric conditions. The retrieved BLHs from 2018 to 2020 were compared with radiosonde (RS) measurements for the same time at the neutral, stable, and convective boundary layers. The correlation coefficient (R: 0.4 vs. 0.05, 0.14; 0.26 vs. −0.10, −0.16; 0.35 vs. 0.01, 0.16) and root mean square error (RMSE (km): 0.58 vs. 0.82, 0.90; 0.37 vs. 1.01, 0.50; 0.66 vs. 0.98, 0.82) obtained by the ED method were higher and lower than those obtained by the variance and peak methods, respectively. The mean absolute error (MAE) of the ED method under the NBL, SBL, and CBL conditions are lower than the variance and peak methods (MAE (km): 0.44, 0.14, 0.50 vs. 0.62, 0.34, 0.64; 0.59, 0.75, 0.74), respectively. The mean relative error (MRE) of the ED method is lower than the variance and peak methods under the NBL condition (MRE: −8.88% vs. −18.39%, 13.91%). Under the SBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−38.64%, vs. −152.23%; 14.02%). Under the CBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−15.07% vs. 2.24%; 5.64%). In addition, the comparison between ED and wavelet covariance transform (WCT) method and RS measurements showed that the ED method has a similar performance with the WCT method and is even better. In the long-term analysis, the hourly and monthly BLHs in the diurnal and annual cycles, respectively, as obtained by ED, were highly consistent with the RS measurements and obtained the lowest standard error. In the annual cycle, the retrieved BLHs in summer and autumn were higher than those retrieved in spring and winter.

High-precision and -resolution atmospheric boundary layer height (BLH) has received increasing attention in air pollution research in recent years. The low time resolution of sounding data is the main challenge to validate BLH retrieval from lidar observations. To resolve this issue, we conducted simultaneous tethered balloon sounding and lidar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during winter 2019–2020. The BLHs derived from the tethered balloon sounding data were 170, 210, 393, 676, 423, and 190 m at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 (Beijing time), respectively. The diurnal evolution of BLH was reasonably captured by lidar observation-based wavelet covariance transform and ideal profile fitting methods, which exhibited correlation coefficients of 0.91 and 0.89, respectively, with the BLHs determined from tethered balloon sounding data. The lidar results slightly overestimated the BLHs, though all results were acceptable when considering both the absolute and relative errors with respect to BLHs from the tethered balloon data. Our results revealed high-precision and -resolution diurnal variations in BLH at SACOL in Northwest China and suggest the importance of validating lidar-based BLHs using simultaneous sounding data.

The knowledge regarding how the boundary layer height (BLH) changes over time is still poor. Here we analyze the spatial and temporal changes in radiosonde‐derived measurements of BLH over China from 1979 to 2016. A qualitatively robust and abrupt change of BLH occurred in 2004. Over the former period (from 1979 to 2003) a spatially uniform increase was found in the BLH, while over the latter period BLH decreased in a spatially nonuniform way. Second, the meteorological influence on the rising BLH was determined to have a negative association with soil moisture, lower tropospheric stability and relative humidity, and a positive association with the near‐surface temperature. Yet, there was a different influence of meteorology on the BLH over the latter period, where a negative association was revealed with Tsfc and relative humidity. These findings shed new light on the long‐term changes in air pollution across China. Key Points: Long‐term (1979‐2016) trend in the boundary layer height (BLH) over China was examined using newly released radiosonde dataRobust trend analyses revealed a significant temporal disparity in the trend of BLH, with a robust reversal occurring in 2004The meteorological influences on the rising and declining BLH were determined to be quite different [ABSTRACT FROM AUTHOR]

The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles.

The diurnal evolution of the atmospheric boundary layer—the lowermost part of the atmosphere where the majority of human activity and meteorological phenomena take place—is described by its depth. Additionally, the boundary layer height (BLH) and the turbulence intensity strongly impact the pollutant diffusion, especially during transition periods. Based on integrated observations from a 325-m meteorological tower and a Doppler Wind lidar in the center of Beijing, the entire diurnal cycle of urban BLH in December 2016 was characterized. Results highlight that the Doppler lidar exhibited it is well suited for monitoring convective BLH while it trudges in monitoring stable BLH, while a 325-m meteorological tower provided an important supplement for Doppler lidar under nocturnal boundary layer and heavily polluted conditions. For the diurnal cycle, under light wind condition, the pattern of urban BLH was largely modulated by thermal forcing of solar radiation and may partly be affected by wind speed. While under strong wind condition, the pattern of urban BLH was largely modulated both by thermal forcing and dynamical forcing. The present work also presented evidence for several new features in the morning and afternoon transitions of the urban boundary layer, showing the duration of the morning transition varied between 1 and 5 h, with the largest value occurring under weak wind with high PM2.5 concentration; while the afternoon transition ranged from 3 to 6 h, which was positively (negatively) correlated to wind speed (PM2.5 concentration). Our work highlights that weak wind speed (weak dynamic motion) and heavy aerosol pollution (weak thermal forcing due to the effect of cooling) can dramatically affect the evolution of the boundary layer.

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales.

The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

Some upper air atmospheric parameters measured during period of 2011-2016 by means of radiosonde located at Hasanuddin International Airport were examined for characterization of boundary layer over Makassar, Indonesia. These data, combined with surface atmospheric parameters were used to calculate some boundary layer parameters using AERMET model which based on Monin-Obukhov similarity theory. The obtained Monin-Obukhov length which reflecting atmospheric stability then converted into traditional Pasquill-Gifford stability classification. Examination of wind characteristics of wind showing clearly their dependence of the day, season and height. Winds dominantly flows from the southeast during the daytime with the relatively larger velocity and from the northwest with smaller velocity during the nighttime. Interpretation of monin-obukhov length using Pasquill-Gifford stability classification showing that the atmosphere was dominantly unstable during the daytime and dominantly stable during the nighttime. These atmospheric stabilities were also varied during seasons. The height of convective boundary layer (CBL) was start to rise in the morning and reaching its maximum in the afternoon (18:00) at the mean value of 2 km. Meanwhile, the height of mechanical boundary layer (MBL) during the day time forming parabolic curve with its maximum value of 1.2 km at noon. These indicated that any released pollution from the stack will be less dispersed during the nighttime due to the fact of lower mixing height, lower wind speed, atmosphere become more stable, and it dispersed in different direction compare to the daytime.

Abstract In this study, large-eddy simulations (LES) are conducted to investigate the interaction between the urban heat island (UHI) circulation and the sea breeze (SB) circulation. An idealized city, which has higher surface heat flux and lager roughness length than the rural surface, is located adjacent to or at a distance from the coastline. The heat flux over both rural and urban surfaces varies sinusoidally with time to mimic the diurnal variation of surface heating. Five urban cases (with differences in city size, location, surface heating strength, and surface roughness) and one non-urban case are simulated. The results show that the SB circulation is enhanced by stronger surface heating in urban area, which leads to a larger SB velocity, a deeper SB thickness, and a stronger ascending motion over the SB front. Moreover, the ascending motion over the SB front is significantly enhanced when it meets the updraft of the UHI circulation, and the maximum ascending motion occurs when the SB front enters the core of the updraft of the UHI circulation. Larger city size delays the occurrence of the maximum ascending motion. Higher surface drag contributes to the enhancement of the ascending motion of SB circulation by intensifying horizontal temperature gradient near the SB front. On the other hand, when the SB flow occupies the urban area, the boundary layer height rapidly decreases to a much lower level, and the UHI circulation is eliminated. [ABSTRACT FROM AUTHOR]

Satellite-based monitoring can retrieve ground-level PM2.5 concentrations with higher-resolution and continuous spatial coverage to assist in making management strategies and estimating health exposures. The Sichuan Basin has a complex terrain and several city clusters that differ from other regions in China: it has an enclosed air basin with a unique planetary boundary layer dynamic which accumulates air pollution. The spatiotemporal distribution of 1-km resolution Aerosol Optical Depth (AOD) in the Sichuan Basin was retrieved using the improved dark pixel method and Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The retrieved seasonal AOD reached its highest values in spring and had the lowest values in autumn. The higher correlation (r = 0.84, N = 171) between the ground-based Lidar AOD and 1-km resolution MODIS AOD indicated that the high-resolution MODIS AOD could be used to retrieve the ground-level PM2.5 concentration. The Lidar-measured annual average extinction coefficient increased linearly with the Planetary Boundary Layer Height (PBLH) in the range of 100~670 m, but exponentially decreased between the heights of 670~1800 m. Both the correlation and the variation tendency of simulated PBLH from the Weather Research and Forecasting (WRF) model & Shin-Hong (SHIN)/California Meteorological (CALMET) model (WRF_SHIN/CALMET) were closer to the Lidar observation than that of three other Planetary Boundary Layer (PBL) schemes (the Grenier-Bretherton-McCaa (GBM) scheme, the Total Energy-Mass Flux (TEMF) scheme and the University ofWashington (UW) scheme), which suggested that the simulated the Planetary Boundary Layer Height (PBLH) could be used in the vertical correction of retrieval PM2.5. Four seasonal fitting functions were also obtained for further humidity correction. The correlation coefficient between the aerosol extinction coefficient and the fitted surface-level PM2.5 concentration at the benchmark station of Southwest Jiao-tong University was enhanced significantly from 0.62 to 0.76 after vertical and humidity corrections during a whole year. During the evaluation of the retrieved ground-level PM2.5 with observed values from three cities, Yibin (YB), Dazhou (DZ), and Deyang (DY), our algorithm performed well, resulting in higher correlation coefficients of 0.78 (N = 177), 0.77 (N = 178), and 0.81 (N = 181), respectively. [ABSTRACT FROM AUTHOR]

The distribution of hurricane boundary layer heights (HBLHs) relative to the hurricane center is investigated in a composite framework, using total 2674 dropsondes collected within four times the radius of maximum wind speed (RMW) of 61 hurricanes during 2002–2020. The HBLH is computed by the bulk Richardson number (Ri b) method, which is sensitive to the critical Ri b choice and more reasonable when the surface friction effect is considered. The sea surface friction velocity is derived from the 10-m wind speed through an air-sea drag relation. The results show that the HBLH is significantly low in the eye zone (0–0.5 RMW), out of which the HBLH is highest in the gale region (0.5–1.5 RMW) and decreases with the radius to the hurricane center. Similar to the hurricane wind field asymmetry, the HBLH is generally higher in the right than in the left in relation to the hurricane motion direction. ERA5 generally overestimates the HBLH, but substantially underestimates the higher HBLHs around the RMW, showing different variation of HBLHs along with the radius compared to dropsonde observations. According to linear regression analyses, the HBLH at least determined by the Ri b method is importantly affected by the hurricane boundary layer dynamic factor, while has no significant correlation with the thermal factor. • The HBLH by the Ri b method is sensitive to considering surface friction effect or not. • The HBLH distribution is asymmetric relative to the hurricane motion direction. • The HBLH is highly correlated with the hurricane boundary layer dynamic factor. [ABSTRACT FROM AUTHOR]

In the backdrop of the need for high spatio-temporal resolution data on PM 2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM 2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM 2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM 2.5 data (from US Consulate and Embassy’s air monitoring program) and MODIS AOD, spanning for almost four years. PM 2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM 2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship. [ABSTRACT FROM AUTHOR]

In this study, some key geometric and thermal parameters derived from recent field and satellite observations in Beijing were collected and incorporated into WRF-UCM (Weather Research and Forecasting) model instead of previous default ones. A series of sensitivity model simulations were conducted to investigate the influences of these parameters on radiation balance, meteorological variables, turbulence kinetic energy (TKE), as well as planetary boundary layer height (PBLH) in regions around Beijing in summer 2014. Model validation demonstrated that the updated parameters represented urban surface characteristics more realistically and the simulations of meteorological variables were evidently improved to be closer to observations than the default parameters. The increase in building height tended to increase and slightly decrease surface air temperature at 2 m (T2) at night and around noon, respectively, and to reduce wind speed at 10 m (WS10) through a day. The increase in road width led to significant decreases in T2 and WS10 through the whole day, with the maximum changes in early morning and in evening, respectively. Both lower surface albedo and inclusion of anthropogenic heat (AH) resulted in increases in T2 and WS10 over the day, with stronger influence from AH. The vertical extension of the impact of urban surface parameters was mainly confined within 300 m at night and reached as high as 1600 m during daytime. The increase in building height tended to increase TKE and PBLH and the TKE increase was larger at night than during daytime due to enhancements of both mechanical and buoyant productions. The increase in road width generally reduced TKE and PBLH except for a few hours in the afternoon. The lower surface albedo and the presence of AH consistently resulted in increases of TKE and PBLH through both day and night. The increase in building height induced a slight divergence by day and a notable convergence at night, whereas the increase in road width led to a remarkable divergence through the entire day. Both AH and lower surface albedo induced a wind convergence over the day, which tended to strengthen nighttime mountain downslope wind and daytime southerly wind to the south of Beijing, but to weaken daytime upslope wind in mountain areas. [ABSTRACT FROM AUTHOR]

Due to the limited spatial coverage of surface PM 2.5 monitoring sites, satellite AOD (aerosol optical depth) products have been widely used to estimate surface PM 2.5 in different parts of the world. A major difficulty as well as source of uncertainty in converting AOD to PM 2.5 is the determination of aerosol vertical distribution, usually represented by the boundary layer height (BLH). In this study, we evaluate the performance of different approaches of estimating aerosol vertical distributions in the AOD-PM 2.5 conversion process, using long-term and multi-source data acquired at a super station, Yuen Long, Hong Kong. The monthly climatology of aerosol vertical distribution and BLH products derived from lidar, radiosonde, and MERRA reanalysis data are respectively applied for converting AOD to surface aerosol extinction coefficients. Seasonal empirical hygroscopic growth functions are constructed to convert aerosol extinction to dry PM 2.5 mass concentration. Results indicate that different vertical distribution estimation approaches can have highly varying effect on the converted PM 2.5 concentration. Using lidar-derived BLHs shows the best agreement, with a correlation coefficient of 0.73 and a relative bias of 30.6% between retrievals and observations. Since continuous lidar measurements are not available for most regions, the climatology pattern of aerosol structure and radiosonde-derived BLHs are found to be suitable alternatives with a correlation coefficient of ∼0.6, and considerably outperform the results using BLHs derived from reanalysis data. Elevated aerosol layers appear to be the major source of uncertainty and result in an overestimate of satellite results, especially during the spring and summer seasons. [ABSTRACT FROM AUTHOR]

Based on the ERA-20C, climatic variations of the boundary layer height (BLH) over arid and semiarid areas in East Asia and North Africa that span 1900-2010 were analyzed. In East Asia, the BLH exhibited a descending trend from arid region centers to the periphery. Over the past 111 years, the BLH has had a rising trend of 14.0 m decade-1. in the representative region (EA1) of the eastern areas with the 111-year average of 725 m and a decreasing trend of -1.6 m decade-1. in the representative region (EA2) of the western areas with the 111-year average of 792 m. From the mid-1960s to 1970s, EA1's BLH had a sharp rise that caused the average to increase by 93 m after the 1980s. In North Africa, the BLH exhibited a high spatial distribution in the western and southern areas and a relatively low spatial distribution in the eastern and northern areas. Over the past 111 years, the BLH has had a rising trend of 9.7 m decade-1. in the representative region (NA1) of the southwestern region with the 111-year average of 915 m and a decreasing trend of -6.3 m decade-1. in the representative region (NA2) of other regions with the 111-year average of 882 m. In the 1940s and the 1970s, NA1's BLH had two obvious increases that caused the average to increase by 51 m and 22 m, respectively, while NA2's BLH had two obvious declines that caused the average to decrease by 48 m and 7 m, respectively. On the spatial distribution, the BLH, sensible heat flux, latent heat flux, and volumetric soil water had a good corresponding relationship. On the temporal change, the BLH in East Asia had a stronger correlation with thermodynamic factors, whereas the BLH in North Africa had a stronger correlation with dynamic factors. Besides, the upper-level stratification also has some influence on the BLH's change. [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs. [ABSTRACT FROM AUTHOR]

Global positioning system radio occultation (GPS RO) refractivity data obtained from the first Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for the years 2007 to 2012 were used to estimate an overall climatology for the height of the marine boundary layer (MBL) over the central North Pacific Ocean including the Hawaiian Island region (10°N–45°N; 125°W–175°W). The trade wind days are identified based on the six-year National Centers for Environmental Prediction (NCEP) global analysis for the same period. About 87% of the RO soundings in summer (June–July–August, JJA) and 47% in winter (December–January–February, DJF) are under trade wind conditions. The MBL height climatology under trade wind conditions is derived and compared to the overall climatology. In general, MBL heights are lowest adjacent to the southern coast of California and gradually increase to the south and west. During the summer (JJA) when the northeasterly trade winds are the dominant surface flow, the median MBL height decreases from 2.0 km over Kauai to 1.9 km over the Big Island with an approximate 2 km maximum that progresses from southwest to northeast throughout the year. If the surface flow is restricted to trade winds only, the maximum MBL heights are located over the same areas, but they increase to a median height of 1.8 km during DJF and 2.1 km during JJA. For the first time, the GPS RO technique allows the depiction of the spatial variations of the MBL height climatology over the central North Pacific. [ABSTRACT FROM AUTHOR]

Air pollution can endanger human health, especially in urban areas. Assessment of air quality primarily relies on ground-based measurements, but these provide only limited information on the spatial distribution of pollutants. In recent years, satellite derived Aerosol Optical Depth (AOD) has been used to approximate particulate matter (PM) with varying success. In this study, the relationship between hourly mean concentrations of particulate matter with a diameter of 10 micrometers or less (PM10) and instantaneous AOD measurements is investigated for Berlin, Germany, for 2001–2015. It is found that the relationship between AOD and PM10 is rarely linear and strongly influenced by ambient relative humidity (RH), boundary layer height (BLH), wind direction and wind speed. Generally, when a moderately dry atmosphere (30% < RH ≤ 50%) coincides with a medium BLH (600–1200 m), AOD and PM10 are in the same range on a semi-quantitative scale. AOD increases with ambient RH, leading to an overestimation of the dry particle concentration near ground. However, this effect can be compensated if a low boundary layer (

Satellite-based monitoring can retrieve ground-level PM2.5 concentrations with higher-resolution and continuous spatial coverage to assist in making management strategies and estimating health exposures. The Sichuan Basin has a complex terrain and several city clusters that differ from other regions in China: it has an enclosed air basin with a unique planetary boundary layer dynamic which accumulates air pollution. The spatiotemporal distribution of 1-km resolution Aerosol Optical Depth (AOD) in the Sichuan Basin was retrieved using the improved dark pixel method and Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The retrieved seasonal AOD reached its highest values in spring and had the lowest values in autumn. The higher correlation (r = 0.84, N = 171) between the ground-based Lidar AOD and 1-km resolution MODIS AOD indicated that the high-resolution MODIS AOD could be used to retrieve the ground-level PM2.5 concentration. The Lidar-measured annual average extinction coefficient increased linearly with the Planetary Boundary Layer Height (PBLH) in the range of 100~670 m, but exponentially decreased between the heights of 670~1800 m. Both the correlation and the variation tendency of simulated PBLH from the Weather Research and Forecasting (WRF) model & Shin-Hong (SHIN)/California Meteorological (CALMET) model (WRF_SHIN/CALMET) were closer to the Lidar observation than that of three other Planetary Boundary Layer (PBL) schemes (the Grenier-Bretherton-McCaa (GBM) scheme, the Total Energy-Mass Flux (TEMF) scheme and the University of Washington (UW) scheme), which suggested that the simulated the Planetary Boundary Layer Height (PBLH) could be used in the vertical correction of retrieval PM2.5. Four seasonal fitting functions were also obtained for further humidity correction. The correlation coefficient between the aerosol extinction coefficient and the fitted surface-level PM2.5 concentration at the benchmark station of Southwest Jiao-tong University was enhanced significantly from 0.62 to 0.76 after vertical and humidity corrections during a whole year. During the evaluation of the retrieved ground-level PM2.5 with observed values from three cities, Yibin (YB), Dazhou (DZ), and Deyang (DY), our algorithm performed well, resulting in higher correlation coefficients of 0.78 (N = 177), 0.77 (N = 178), and 0.81 (N = 181), respectively.

Great difference exists in the aerosol optical depth (AOD) between summer and winter over the North China Plain (NCP). Monthly mean AOD at 550 nm derived from the MODIS (MODerate Resolution Imaging Spectroradiometer) products during 2000–2014 over the area of 30–40° N and 110–125° E exhibits an annual maximum in June (0.855 ± 0.130) and a minimum in December (0.381 ± 0.032). This seasonality of AOD is in the opposite phase with the surface particulate matter (PM) concentration (higher in winter and lower in summer). The possible causes for the higher AOD in June (compared with December) include (a) a higher boundary layer height (BLH) that results in more efficient transport and mixing of aerosol particles to a higher altitude (corresponding to a lower particle concentration near surface) as revealed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations profile, (b) a higher relative humidity (RH) due to the inshore monsoon circulation that leads to enhancement of aerosol extinction, (c) emission from the regional open stalk burning in the summer harvest season (as seen from MODIS fire products), and (d) the typical eastward open topographical basin over NCP. Under the assumption that the aerosol and water vapor are well mixed within the boundary layer, analysis on multi-year average shows that the differences in BLH, RH and surface PM concentration can explain up to 81% of the variance of monthly averaged AOD over NCP. A preliminarily hypothesis is also suggested to interpret the shift of AOD pattern from winter to summer with an abrupt increase of AOD from May to June, as well as an increase of surface PM 2.5 concentration over NCP during the early phase of northward progress of the East Asia summer monsoon front. [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs.

Ground-level ozone is an important pollutant regarding air quality and climate. Mexico City frequently experiences severe ozone episodes due to a combination of strong ozone precursor emissions and its specific topographical environment which critically impacts meteorological conditions. High ozone levels during these episodes cause harmful effects to the public health and the environment. This necessitates ranking air quality and meteorological variables according to their contributions towards the build-up of ozone. In this study, three machine learning models are used to learn a prediction function with hourly data of eight predictors as input and hourly ground-level ozone mixing ratios as output. One-year hourly data of eight predictors collected in Mexico-City from March 2015 to February 2016 is employed to train and test the models. The best model, capturing ozone peak levels with 92% accuracy during 6–18 March 2016, is used to rank the predictors according to their importance in the build-up of ozone applying a shapley additive explanations approach based on the game theory shapley values. This 6–18 March 2016 period encompassed different meteorological and emission conditions and included a severe ozone smog episode from 12 to 17 March 2016. Such ranking of the air quality and meteorological variables is crucial for policy-making decisions regarding the prevention and mitigation of ozone detrimental effects during severe ozone episodes and provides insight into the functional dependency of ozone on its predictors. The proposed approach showcases Mexico City, but its principles can be applied for ozone episodes at any other location. [Display omitted] • Machine learning models to simulate O 3 levels during Mexico City severe O 3 episode. • Planetary boundary layer height inclusion among machine learning models' predictors. • Deep Neural Network model captures best peak O 3 levels during 6–18 March interval. • SHAP ranks UV-A, NO, and NO 2 as the most important predictors regarding O 3 build-up. [ABSTRACT FROM AUTHOR]

The advantage provided by the unprecedented data density and deeper penetration of Global Navigation Satellite System (GNSS) Radio Occultation (RO) based refractivity profiles from COSMIC-2/FORMOSAT-7 mission over the tropical and subtropical regions is leveraged to get a seasonal picture of daytime planetary boundary layer height (PBLH) distribution from 2 years of data (from October 2019 to September 2021) using the traditional method and a new approach proposed in this study. Traditional PBLH retrieval relies on the minimum refractivity gradient (MRG). In the new method, preference is given to the lowest significant peak in refractivity gradient (if present) rather than the MRG alone for determining PBLH. Comparison with the MRG method shows the highest decrease in seasonal mean PBLH over the oceanic ITCZ regions (by 500–700 m) which is in line with studies reporting low PBLH from in situ observations. The lowest decrease was observed over subtropical oceanic subsidence regions with marine stratocumulus clouds (MSC region; ~100–150 m) where the MRG method was thoroughly validated. The seasonal scale PBLH uncertainty (standard deviation) decreased by ~300 m over the ITCZ regions in the proposed method; it partially increased over the offshore MSC regions by ~100 m indicating detection of infrequent cases of marine boundary layer decoupled from stratocumulus clouds aloft which is not possible in the traditional method. The overestimation and higher uncertainty in PBLH derived from the traditional method over deep convective regions are attributed to the erroneous detection of seasonally varying cloud tops, subsidence base, and elevated water vapour layers as PBL top. An eastward shift by more than 5o longitude is observed in the location of PBLH maxima (seasonal mean) over the southeastern Pacific Ocean in the proposed method (with respect to the traditional method) for all seasons. The proposed method shows a better comparison with daytime PBLH from ERA5 reanalyses over the oceans but there is a partial overestimation of PBLH by ERA5 over continental regions in the summer hemisphere. • Distinctly low RO-based seasonal mean PBLH observed over deep convective regions over the oceans • Detection of boundary layer top decoupled from stratocumulus cloud deck over the subtropical oceans • Indication of super refraction putting a physical limit on the retrieval of GNSS RO refractivity profiles below the PBL top • Distinct eastward shift observed in the location of the PBLH maxima over the southeastern Pacific Ocean in all seasons [ABSTRACT FROM AUTHOR]

Atmospheric boundary layer height is one of the most important parameters in atmospheric dispersion modelling because it has a large effect on predicted air quality. Comparisons between Atmospheric Dispersion Modelling System, version 4 (ADMS 4) and lidar data were carried out on boundary layer height data from central London. The comparison showed that the boundary layer height predicted by the ADMS 4 was, on average, lower than lidar for the subset of data taken. ADMS 4 has a very simple surface scheme which is not representative of complex urban environments and the results from this research imply that there is not sufficient surface roughness within the model to produce a large enough boundary layer height. The aim of this study is to create an improved model to better forecast the growth of the daytime urban boundary layer and predict boundary layer height, h , in an air quality dispersion model using lidar measurements. The combined model was developed by using a surface model and an atmospheric boundary layer height model. Measurements of atmospheric boundary layer height by lidar used vertical velocity variance and the overall conclusion was that the combined model improved the performance of ADMS in urban areas. [ABSTRACT FROM AUTHOR]