Planetary Boundary Layer Height Research Articles

Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends.

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Assessing the ducting phenomenon and its potential impact on Global Navigation Satellite System (GNSS) radio occultation refractivity retrievals over the northeast Pacific Ocean using radiosondes and global reanalysis

Abstract. In this study, high-resolution radiosondes from the Marine Atmospheric Radiation Measurement (ARM) Global Energy and Water Experiment (GEWEX) Cloud System Study (GCSS) Pacific Cross-section Intercomparison (GPCI) Investigation of Clouds (MAGIC) field campaign and ECMWF Reanalysis version 5 (ERA5) global reanalysis data are used to assess characteristics of the elevated ducting layer along a transect over the northeastern Pacific Ocean from Los Angeles, California, to Honolulu, Hawaii. The planetary boundary layer (PBL) height (PBLH) increases as the strength of the refractivity gradient decreases westward along the transect. The thickness of the prevailing ducting layer remains remarkably consistent (∼ 110 m) in the radiosonde data. On the other hand, the ERA5 reanalysis generally resolves the ducting features well, but it underestimates the ducting height and strength, especially over the trade cumulus region near Hawaii. A simple two-step end-to-end simulation is used to evaluate the impact of the elevated ducting layer on radio occultation (RO) refractivity retrievals. A systematic negative refractivity bias (N bias) below the ducting layer is observed throughout the transect, peaking (−5.42 %) slightly below the PBLH and gradually decreasing towards the surface (−0.5 %). The N bias shows a strong positive correlation with the ducting strength. The ERA5 data underestimate the N bias, with the magnitude of the underestimation increasing westward along the transect.

Analysis of the dependence of the observed urban air pollution extremes in the vicinity of coal fuelled power plants on combined effects of anthropogenic and meteorological drivers

In this paper we assessed effects of changes of meteorological drivers, taken from datasets of observational records and modelling outputs, and human-made pollution, derived from records of energy production, on the mainly wintertime extreme observed values of urban particulate matter (PM) concentrations in the relative vicinity of coal fuelled thermoelectric power plants (TPPs) in Montenegro and Serbia. We used wavelet transform analysis, together with the dependency analysis and analysis of averages of climatic conditions, to study temporal dynamics of urban air pollution extremes in the vicinity of TPPs, the coincidence of their changes with observed levels of SO2 and NO2 concentrations in the air, and dependence of PM changes on several possible meteorological and anthropogenic drivers. We found that PM variations in urban areas are most probably caused by PM-SO2/NO2 coincidences that appear after a 2- to 3-hour time lags needed for transformation of SO2/NO2 TPP emissions into PM particles, if pollution is caused by TPP emissions alone. When other causes of PM variations than the TPP production exist, we found that PM-SO2/NO2 correlations appear at time ranges from several hours to several days. In our analysis only the changes in the planetary boundary layer height (PBLH) coincided with the drive to extremes in PM values, at PBLH levels lower than 300m. Following these findings, we suggested that PM extremes in our sample could be viewed as preconditioned compound events, where TPP and urban heating emissions provide preconditions for PM extremes and PBLH serves as a major meteorological driver to such events.

Weakened Aerosol‐PBL Interactions Enhance Future Air Quality Benefits Under Carbon Neutrality in China: Insights From the Advanced Variable‐Resolution Global Model

AbstractChina's pursuing the carbon neutrality goal could affect future air quality not only by reducing anthropogenic emissions but also by modulating aerosol‐planetary boundary layer (PBL) interactions. However, contributions of aerosol‐PBL interactions to future air quality benefits remain unclear. Here we conduct ensemble experiments using the variable‐resolution (VR) Community Atmosphere Model with full chemistry based on the scalable spectral element (SE) dynamical core with East Asia refined to ∼28 km (SE_VR). Additional simulations at a uniform resolution of ∼111 km (SE_UR) are conducted to help demonstrate SE_VR's advantages in projecting future air quality. Results of SE_VR show that the mean PM2.5 concentrations in China would drop to below 10 μg/m3, especially in Sichuan Basin (SCB) where the frequencies of moderate and severe air pollution events are predicted to decrease from 60.7% and 11.3% to nearly zero respectively when achieving carbon neutrality. The aerosol‐PBL interactions would be substantially weakened with anthropogenic emission reductions. At SCB and Eastern China (EC), the weakened radiative effects of black carbon (BC) would contribute 34.3% and 71.6% to the increase in PBL height (PBLH). Consequently, these weakened BC‐PBL interactions reduce the surface PM2.5 concentrations by 16.1 μg/m3 (18.9%) in SCB and 4.6 μg/m3 (16.4%) in EC. Notably, SE_VR outperforms SE_UR in projecting the frequency decrease of future air pollution events for its better reproducing current levels, particularly those caused by BC aerosols. This study highlights the importance of weakened aerosol‐PBL feedbacks on future air quality improvement and demonstrates the added values of variable‐resolution global models in air quality projections.

Influence of seasonal variation on spatial distribution of PM2.5 concentration using low-cost sensors.

Fine particulate matter (PM2.5) is one of the major airborne pollutants in urban environments and is associated with severe health impacts. In this study, a dense network of low-cost sensor (LCS) is used to cover large spatial area and detect ambient PM2.5 concentration in Guwahati city. The measurements were conducted at multiple sites in different seasons between July 2022 and June 2023. Seasonal variability significantly influences regional meteorology, aerosol optical depth (AOD), and PM2.5 concentration. The seasonal average PM2.5 concentration was highest during winter (113.05µgm -3), followed by post-monsoon (56.11µgm-3), then pre-monsoon (46.60µgm-3), and least for monsoon (32.36µgm-3) season. The elevated PM2.5 concentrations may be attributed to environmental conditions (low ambient temperature, calm wind, and low planetary boundary layer height) that resulted in the least dispersion of PM2.5. The concentration-weighted trajectory (CWT) analysis identifies the effect of regional (Indo-Gangetic Plain and northeast region) and transboundary (Bay of Bengal, Bangladesh, and northwest Asian countries) transported air masses on urban air quality. Post-monsoon and winter season has a high influence on long-range transported aerosols, whereas the monsoon and pre-monsoon seasons are affected by ocean and land air masses. Changes in surrounding activities and meteorology influence spatial distribution of PM2.5 particles. Elevated PM2.5 concentrations were recorded at in-city and outskirt sites because of the nearby activities (industry and traffic) and build-up area. In meteorology, wind significantly affects spatial dispersion of PM2.5 concentration to the sites located in upwind and downwind directions.

Influence of clouds on planetary boundary layer height: A comparative study and factors analysis

Clouds are one of the key factors influencing the evolution of the planetary boundary layer (PBL). Understanding the complex interactions between clouds and PBL height (PBLH) is essential for accurately simulating and predicting PBL processes. This study investigates the impact of clouds on PBLH evolution based on the lidar, radiosonde, ceilometer, and meteorological parameters observations at the Southern Great Plains site during the period January 2013 to December 2020. The findings indicates that the presence of clouds has an impact on the evolution of the PBLH. During the daytime, PBLH is lower under cloudy conditions than clear conditions, whereas during nighttime, PBLH is higher under cloudy conditions. This phenomenon arises because the intense solar radiation on clear days and strong turbulent mixing on cloudy nights contribute to the formation and maintenance of PBLH. Furthermore, during the daytime, clouds scatter and absorb solar radiation, leading to lower net radiation (NetR), sensible heat flux (SHF), surface temperature (TEM), and soil temperature (SoilT). These conditions, coupled with weaker turbulence intensity and high relative humidity (RH), leading to lower PBLH under cloudy conditions. Although TEM and SoilT are relatively high during clear nights, rapid surface radiative cooling and strong atmospheric stability inhibit the development of the PBLH. Consequently, during cloudy nights, clouds absorb and reflect longwave radiation from the surface, reducing surface radiative cooling rates, enhancing atmospheric instability and turbulence intensity. Furthermore, higher NetR and SHF, along with decreased RH, result in slightly deeper PBLH compared to clear conditions. Overall, this study systematically elucidates the influence of clouds on PBLH evolution and contributes to the understanding of the modulation of cloud on PBL structure.

Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height.

Inversion of the planetary boundary layer height from lidar by combining UNet++ and coordinate attention mechanism

The Arctic plays a significant role in global climate, and the planetary boundary layer height (PBLH) is one of the important parameters for studying Arctic climate. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory is an important location for studying the Arctic. However, the weather at the NSA site is complicated and varied. Arctic Haze frequently appears in this region from late autumn to early summer, while low clouds are prone to occur in summer. Meanwhile, due to the consistently low temperatures on the Arctic surface, the frequency of stable boundary layer occurrence is much higher than that in mid-latitude regions. All of these will increase the difficulty of PBLH detection. To address these challenges, we propose a PBLH inversion method based on deep-learning called Coord-UNet++. This method is based on UNet++ and introduces coordinate attention mechanism which can gather features in both horizontal and vertical directions, so it can more effectively capture spatial information in images to cope with complex weather conditions. The training set for the algorithm comes from the micropulse lidar at the NSA site, and the PBLH is labeled by using the microwave radiation profiler at the same site. This algorithm can achieve accurate inversion of the PBLH in complex weather conditions such as cloudy, haze and aerosol layer interference, R2 reaches 0.87, and it performs well in long-term inversion, with much higher stability and accuracy than traditional methods.

Effects of valley topography on ozone pollution in the Lanzhou valley: A numerical case study

Valley topography is recognized for its role in constraining pollutant dispersion, which frequently results in elevated pollutant concentrations within valley regions. However, the specific mechanisms by which valley topography influences daytime ozone (O3) production, nighttime O3 depletion, and diurnal variations in O3 concentrations remain inadequately understood. This study employs the online WRF-Chem air quality model to conduct sensitivity analyses, examining the effects of valley topography on summer O3 concentrations in Lanzhou, a valley city in western China. The results indicate that valley topography generally reduces surface temperature and wind speed while also lowering the planetary boundary layer height (PBLH), which ultimately leads to increased concentrations of primary pollutants. Nevertheless, the impact of valley terrain on O3 is time-dependent, with concentrations decreasing during the day and increasing at night. In Lanzhou, valley topography contributes to a 4.4% reduction in daytime O3 concentrations. This reduction is largely due to the blocking of solar radiation by surrounding mountains, which results in a 7% decrease in shortwave radiation (SR) and a 17% decline in PBLH, subsequently limiting O3 chemical production. Although valley topography restricts pollutant dispersion, the overall effect during the day is a net decrease in O3 levels. In contrast, nighttime valley topography leads to a notable 19.8% increase in O3 concentrations. This increase is driven by mountain breezes transporting O3-rich air from the slopes into urban areas, along with a 67.4% reduction in nitrogen oxide (NO) levels. Despite intensified NO titration within the valley, the combined effect of these local wind patterns contributes to elevated O3 concentrations at night. These findings offer valuable insights into the unique factors contributing to urban O3 pollution in areas with complex valley topography.

Investigating the Impact of Multiphysics on Modeled Planetary Boundary Layer Height Estimates during the PECAN Field Campaign

Abstract Accurate representation of the planetary boundary layer (PBL) in numerical weather prediction is crucial to air quality, weather forecasting, and climate change research and operations. Here, we evaluate the estimates of PBL height (PBLH) from a set of convective-permitting simulations conducted using the Weather Research and Forecasting (WRF) Model during the Plains Elevated Convection at Night (PECAN) campaign (from June to mid-July 2015). Our analysis aims to quantify the variability in PBLH, along with its associated surface variables and atmospheric profiles, to gauge the influence of model physics on the accuracy of simulated PBL properties. Specifically, we conducted twenty-four 45-day retrospective simulations encompassing different combinations of three PBL and two microphysics schemes, two initial and boundary condition (ICBC) datasets, and two model vertical grid spacings. We compare these simulations with measurements from surface sites and radiosonde launches across four sites in the southern Great Plains during PECAN. Our findings indicate that deriving PBLH from modeled atmospheric profiles yields a narrower spread (200–300 m) compared to PBLH calculated from the model’s PBL scheme (400–500 m) relative to radiosonde-derived PBLH under fair weather conditions. The choice of PBL scheme and ICBCs exerts the most significant impact (by up to 400 m) on the spread of modeled PBLH and associated atmospheric profiles, primarily influencing the representation of surface heating, transport, and mixing processes in the model. The model has difficulty reproducing the correct thermodynamic profile under cloud-intense conditions. These results underscore the benefit of using the physically consistent approach in retrieving, evaluating, and assimilating PBLH estimates, as well as the utility of multiphysics to better address model uncertainties. Significance Statement Studies on weather forecast models typically involve running the model multiple times and adjusting model inputs slightly each time to produce a range of predicted variables. This range is crucial for evaluating forecast probabilities and integrating observations into the model. We found that using different model physics schemes can result in an equal or greater spread for predicted variables (here, we focus on boundary layer height and related variables), compared to input tweaks alone. Additionally, a particular scheme can introduce biases. These findings underscore the importance of employing multiple physics schemes in the model, as they widen the coverage (potentially doubling) of outputs, and thus better address forecast uncertainties.

Numerical simulation and its optimization of cold air pools in the Lanzhou Valley

AbstractPersistent cold air pools (CAPs) trap pollutants in valleys for extended periods, leading to reduced visibility and increased air pollution within these valleys. The structure of the persistent cold air pool that occurred in the Lanzhou Valley in December 2016 was simulated using different Planetary Boundary Layer (PBL) scenarios of the Weather Research and Forecasting (WRF) model, and the simulation of the persistent cold air pool was further optimized in these PBL scenarios. The simulation results indicated that weather‐scale dry subsidence and nighttime ground radiation cooling were significant factors contributing to the accumulation of persistent CAPs and pollutants in the Lanzhou Valley. In contrast, convective lifting from the ground led to the dissipation of persistent CAPs and a reduction in pollution within the valley. During persistent CAPs, the PM2.5 concentration and valley heat deficit (Q) were 66.7% and 62% higher, respectively, than during non‐CAP. In the original MYNN scheme, the average PBL height, double turbulent kinetic energy (QKE), and turbulence length scale during persistent CAPs decreased by 30.79%, 50.5%, and 34.4%, respectively, compared to non‐CAP. Compared with the original MYNN scheme, the optimized MYNN scheme shows a significant improvement in the turbulence simulation during the sustained CAPs, resulting in a more stable atmosphere. The PBL height during the sustained CAPs is reduced by 28 m, the diurnal turbulence length scale is reduced by 31.62%, the stability parameter is reduced by 39%, the diurnal mean QKE is reduced by 27.45%, and the QKE impact height is reduced by 100–400 m.

Investigating uncertainties in air quality models used in GMAP/SIJAQ 2021 field campaign: General performance of different models and ensemble results

The international field campaign, GMAP/SIJAQ 2021, was conducted in Korea from October 18th to November 25th to enhance the performance and validation of the Geostationary Environment Monitoring Spectrometer (GEMS) products algorithm and obtain a better understanding of the current air pollution status of the Korean Peninsula. Five chemical transport models (CTMs), including CMAQ, CMAQ-GIST, CAMx, WRF-Chem, and WRF GEOS-Chem, were utilized during the campaign to assist in organizing the observation plan and identifying changes in pollutant concentrations and their spatiotemporal distribution in Korea following the Korea–United States Air Quality (KORUS-AQ) 2016. In this study, we evaluated the forecasting performance, strengths, and limitations of these five CTMs and their ensemble in simulating air quality. Intensive measurement data and intercomparisons were employed to explain discrepancies between observed and simulated results. A comparison of the CTM ensemble results for PM2.5 and various gaseous pollutants between the current GMAP/SIJAQ 2021 and previous KORUS-AQ 2016 campaigns showed the R-value for the total mass PM2.5 concentration increased from 0.88 to 0.94. This improvement is related to CTM updates, including the emission inventory and better reproductions of the concentrations of gaseous species. However, the models consistently underestimated carbon monoxide (CO) concentrations, similar to the results from KORUS-AQ. This finding still suggests a further challenge that requires consideration of missing anthropogenic sources. The results of the ensemble model agreed well with the chemical composition of PM2.5 observed at the intensive monitoring station. However, for NO3− and NH4+, discrepancies were primarily due to inaccuracies in the meteorological inputs, such as precipitation, relative humidity (RH), and nighttime planetary boundary layer height (PBLH) in the CTMs. Hence, all models overestimated the concentration of elemental carbon (EC), therefore, it is necessary to revise EC emissions in the SIJAQv2 inventory, as these apply to unusual levels recorded in Seoul during the reference year of 2018.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Nonlinear changes in urban heat island intensity, urban breeze intensity, and urban air pollutant concentration with roof albedo

This study systematically examines how the urban heat island (UHI) and urban breeze circulation (UBC) respond to an increase in roof albedo (αr) and its influence on urban air pollutant dispersion. For this, idealized ensemble simulations are performed using the Weather Research and Forecasting (WRF) model. The increase in αr from 0.20 to 0.65 decreases the UHI intensity, UBC intensity, and urban planetary boundary layer (PBL) height in the daytime (from 1200 to 1700 LST) by 47%, 36%, and 6%, respectively. As both UBC intensity and urban PBL height decrease, the daytime urban near-surface passive tracer concentration increases by 115%. The daytime UHI intensity, UBC intensity, and urban tracer concentration nonlinearly change with αr: For 0.10 ≤ αr < 0.80, the rates of changes in the UHI intensity, UBC intensity, and urban tracer concentration with αr overall increase as αr increases. For αr ≥ 0.80, the daytime roof surface temperature is notably lower than the daytime urban near-surface air temperature, the UHI intensity, UBC intensity, and urban tracer concentration very slightly changing with αr. This study provides insights into the associations between changes in roof surface temperature and roof surface energy fluxes with αr and those in UHI intensity.

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises.

Enhancing Global Simulation of Smoke Injection Height for Intense Pyro‐Convection Through Coupling an Improved One‐Dimensional Plume Rise Model in CAM‐chem

AbstractThe impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.

Sensitivity of horizontal resolution and land surface model in operational WRF forecast for Online Nuclear Emergency Response System (ONERS)

Accurate Meteorological forecasts are crucial for the assessment of plume dispersion and dose prediction in nuclear power plant (NPP) sites. In this work the forecast sensitivity of the Weather Research and Forecasting (WRF) model is tested by running a series of forecast simulations for horizontal resolution, and land surface models (LSM) in the context of Online Nuclear Emergency Response System (ONERS) for Indian NPP sites. 72 h forecast simulations are made for three seasons viz. summer, southeast and northeast monsoon using the Global Forecast data. Three simulation experiments, namely 2 km-NOAH, 3 km-NOAH and 3 km-NOAHMP are conducted using two different nested domain configurations (18–6–2 km and 9–3 km) and two LSM schemes (NOAH and NOAH-MP) and tested at four different NPP sites. Forecast comparison of surface winds, relative humidity, temperature, heat fluxes and planetary boundary layer heights with data from meteorological tower, radiosonde and the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) shows 3 km-NOAH is equally capable in predicting surface parameters as well as vertical profiles compared to 2 km-NOAH with marginal differences. 3 km-NOAHMP shows less mean bias and better correlation for boundary layer height and heat fluxes. Comparison of spatial flow-field with 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data shows synoptic scale seasonal winds, sea level pressure systems and temperature hot-spots are better captured by 3 km-NOAHMP compared to 6 km coarse domain in the 18–6–2 km configuration. The daily accumulated rainfall by all simulations is overestimated compared to ERA5 data. The predictions by 3 km-NOAHMP better agree with Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERGE) data whereas 2 km-NOAH predicts delayed rainfall occurrence. Dispersion simulations of hypothetical plume release from a coastal NPP site with all three forecasts properly show the influence of local scale diurnal land-sea breeze and seasonal winds on the plume movement. Therefore the 9–3 km domain with NOAHMP LSM is found to be a suitable choice for operational weather forecast in ONERS for Indian NPP sites.

Multi-Scale Meteorological Impact on PM2.5 Pollution in Tangshan, Northern China.

Tangshan, a major industrial and agricultural center in northern China, frequently experiences significant PM2.5 pollution events during winter, impacting its large population. These pollution episodes are influenced by multi-scale meteorological processes, though the complex mechanisms remain not fully understood. This study integrates surface PM2.5 concentration data, ground-based and upper-air meteorological observations, and ERA5 reanalysis data from 2015 to 2019 to explore the interactions between local planetary boundary layer (PBL) structures and large-scale atmospheric processes driving PM2.5 pollution in Tangshan. The results indicate that seasonal variations in PM2.5 pollution levels are closely linked to changes in PBL thermal stability. During winter, day-to-day increases in PM2.5 concentrations are often tied to atmospheric warming above 1500 m, as enhanced thermal inversions and reduced PBL heights lead to pollutant accumulation. Regionally, this aloft warming is driven by a high-pressure system at 850 hPa over the southern North China Plain, accompanied by prevailing southwesterly winds. Additionally, southwesterly winds within the PBL can transport pollutants from the adjacent Beijing-Tianjin-Hebei region to Tangshan, worsening pollution. Simulations from the chemical transport model indicate that regional pollutant transport can contribute to approximately half of the near-surface PM2.5 concentration under the unfavorable synoptic conditions. These findings underscore the importance of multi-scale meteorology in predicting and mitigating severe wintertime PM2.5 pollution in Tangshan and surrounding regions.

Assessment of Multiple Planetary Boundary Layer Height Retrieval Methods and Their Impact on PM2.5 and Its Chemical Compositions throughout a Year in Nanjing

In this study, we investigate the planetary boundary layer height (PBLH) using micro-pulse lidar (MPL) and microwave radiometer (MWR) methods, examining its relationship with the mass concentration of particles less than 2.5 µm in aerodynamic diameter (PM2.5) and its chemical compositions. Long-term PBLH retrieval results are presented derived from the MPL and the MWR, including its seasonal and diurnal variations, showing a superior performance regarding the MPL in terms of reliability and consistency with PM2.5. Also examined are the relationships between the two types of PBLHs and PM2.5. Unlike the PBLH derived from the MPL, the PBLH derived from the MWR does not have a negative correlation under severe pollution conditions. Furthermore, this study explores the effects of the PBLH on different aerosol chemical compositions, with the most pronounced impact observed on primary aerosols and relatively minimal influence on secondary aerosols, especially secondary organics during spring. This study underscores disparities in PBLH retrievals by different instruments during long-term observations and unveils distinct relationships between the PBLH and aerosol chemical compositions. Moreover, it highlights the greater influence of the PBLH on primary pollutants, laying the groundwork for future research in this field.

Regional source contributions to summertime ozone in the Yangtze River Delta

Ozone (O3) pollution is increasing in the Yangtze River Delta (YRD), with significant influences from regional transport during pollution events. However, the specific contributions of different regions remain imprecisely quantified. This study estimated the regional contributions of eight regions to summer O3 in the YRD using the Community Multiscale Air Quality (CMAQ) model with a source-tagged photochemical mechanism. Non-background O3 is attributed to nitrogen oxides (NOX) and volatile organic compounds (VOCs) from different regions. Background O3 is the predominant contributor with average ratios of 75.0%, 65.3%, 64.8% and 63.0% for Shanghai, Nanjing, Hangzhou and the entire YRD, respectively. Locally formed O3 are 39.9, 42.0, 39.6, and 36.9 parts per billion (ppb) by volume in the above areas. North Zhejiang and south Jiangsu are the primary sources of heavy pollution episodes as the maximum daily 8-h average (MDA8) O3 concentrations in these regions increase the most. NOX is the predominant contributor to heavy pollution episodes with the maximum increment attributed to NOX (O3N) of 14.3 ppb, while VOCs only contribute to 2.1 ppb (O3V) in Jiangsu. Relative humidity is crucial in heavy pollution episodes while high temperature, low planetary boundary layer (PBL) height and the wind field are associated with regional transport. Diurnal variation underscores the importance of understanding O3 formation in the afternoon (12:00–16:00), which is essential for devising effective mitigation policies.