Planetary Boundary Layer Height Research Articles

Using clustering to understand intra-city warming in heatwaves: insights into Paris, Montreal, and Zurich

We introduce a novel methodological advancement by clustering paired near-surface air temperature with the planetary boundary layer height to characterize intra-city clusters for analytics. To illustrate this approach, we analyze three heatwaves (HWs): the 2019 HW in Paris, the 2018 HW in Montreal, and the 2017 HW in Zurich. We assess cluster-based characteristics before, during, and after heatwave events. While the urban clusters identified by this clustering align well with built-up areas obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data, additional local hot spots spanning several kilometers can also be recognized, extending outside the built-up areas. Using the objective hysteresis model, we further determine the overall strength coefficient of the hysteresis loop between ground storage flux and all-wave downward radiative flux, ranging from 0.414 to 0.457 for urban clusters and from 0.126 to 0.157 for rural clusters during the heatwave periods. Across all cities, we observe a consistent refueling-restoration mode in the cumulative ground heat flux as the heatwaves progress. Future developments of this proposed two-component clustering approach, with the integration of more influential physics and advances in spatial and temporal resolutions, will offer a more comprehensive characterization of cities for urban climate analytics.

Interaction of black carbon surface mass with meteorological variables and spatial pattern across the 36 states of tropical Nigeria

Black carbon (BC) has been linked to cardio-pulmonary diseases and lung cancer. It is also the second largest contributor to climate change, after carbon dioxide (CO2), Effective mitigation of BC is of much benefits, but it requires detailed knowledge of its spatial distribution, as well as the influence of local meteorology on the spread of the pollutant. Many countries in sub-Sahara Africa including Nigeria, lack data on BC level. There is also limited information on meteorological impact on BC spatial distribution. This study therefore assessed the spatial and temporal distribution of BC across the 36 states of Nigeria and the influence of meteorology on the pollutant spread. Data on BC surface mass (BCMASS) and seven meteorological variables (air temperature, relative humidity, rainfall, wind speed, planetary boundary layer height (PBLH), atmospheric pressure and wind direction) were obtained from Modern Era Retrospective analysis for Research (MERRA-2). Spatial distribution of BCSMASS across 36 states of Nigeria, and interaction with meteorology was studied using correlation analyses, Bayesian ridge regression with marginal maximum likelihood estimation, and a Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. There was significant (p < 0.05) variation in BCSMASS across the states. The meteorological variables, except wind direction, significantly influenced BCSMASS and they accounted for 63% of the variation. Five-day backward trajectory modelled for each month of the year revealed that northern Nigeria is majorly under the influence of the dry and dusty tropical continental wind, and this could partially account for the lower BCSMASS concentration in the region, compared to southern Nigeria. Based on population-weighted exposure, states in southern Nigeria, especially the south-eastern part, should be prioritized in planning mitigation, for effective climate action.

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method.

Montana Statewide Google Earth Engine-Based Wildfire Hazardous Particulate (PM2.5) Concentration Estimation

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure.

Analyzing the influence of the Planetary Boundary Layer Height, Ventilation Coefficient, Thermal Inversions, and Aerosol Optical Depth on the concentration of PM2.5 in the city of São Paulo: A Long-term study

Cases of intense air pollution have been a recurring problem in most of urban centers in different regions of the world. Although actions to mitigate pollutant emissions are fundamental, it is also necessary to understand which factors can favor their dispersion process. In this scenario, this paper presents, for the first time, a long-term analysis of the Planetary Boundary Layer Height (), estimated through lidar and radiosounding data, Ventilation Coefficient (), Thermal Inversions (), and Aerosol Optical Depth (), for the city of São Paulo, demonstrating how these variables are related with concentration. The analyzes showed that and have a seasonal cycle, with higher values in summer and lower ones in winter. Furthermore, is affected by one local factor, the sea-breeze, which reduces the concentration of aerosols in the late afternoon, resulting in an underestimated obtained from lidar data. Furthermore, from AERONET data, a predominance of Black Carbon and small particles was observed in all seasons, which are associated with the feedback effect observed in winter. Such effect attenuates the increase of and in cases of high concentrations of . Finally, it was presented a Generalized Linear Model, which combines , and information as input and can estimate the concentration with a R2 = 0.93.

Long-term exposure to ambient ozone and adult-onset asthma: A prospective cohort study

BackgroundThe association between long-term exposure to ozone (O3) and adult-onset asthma (AOA) remains inconclusive, and analysis of causality is lacking. ObjectivesTo examine the causal association between long-term O3 exposure and AOA. MethodsA prospective cohort study of 362,098 participants was conducted using the UK Biobank study. Incident cases of AOA were identified using health administrative data of the National Health Services. O3 exposure at participants’ residential addresses was estimated by a spatio-temporal model. Instrumental variable (IV) modelling was used to analyze the causal association between O3 exposure and AOA, by incorporating wind speed and planetary boundary layer height as IVs into time-dependent Cox model. Negative control outcome (accidental injury) was also used to additionally evaluate unmeasured confounding. ResultsDuring a mean follow-up of 11.38 years, a total of 10,973 incident AOA cases were identified. A U-shaped concentration-response relationship was observed between O3 exposure and AOA in the traditional Cox models with HR of 0.916 (95% CI: 0.888, 0.945) for O3 at low levels (<38.17 ppb), and 1.204 (95% CI: 1.168, 1.242) for O3 at high levels (≥38.17 ppb). However, in the IV analysis we only found a statistically significant association between high-level O3 exposure and AOA risk, but not for low-level O3 exposure. No significant associations between O3 exposure and accidental injury were observed. ConclusionOur findings suggest a potential causal relationship between long-term exposure to high-level ambient O3 and increased risks of AOA.

Investigating the Interaction Between Transboundary Haze and Planetary Boundary Layer in Singapore

AbstractTransboundary air pollution is one of the critical environmental problems in Southeast Asia; nevertheless the interaction between transboundary haze and local planetary boundary layer (PBL) remains unclear due to a lack of sufficient observations and sophisticated simulations. This study applied LiDAR observations and model simulations to comprehensively evaluate the interaction between transboundary haze and local PBL during a recent transboundary haze episode in Singapore in October 2023. Results show that upper‐level southeasterly wind, especially the nocturnal low‐level jet, created favorable conditions for aerosol transport when fire hotspot numbers increased in Indonesia. The rapid diurnal increase in PBL height facilitated the entrainment of upper‐level haze into the PBL, interacting with ground‐level aerosols through turbulent mixing. Model simulations further show the significant contributions of fire emissions to the transboundary haze episode in maritime Southeast Asia. The impacts extended toward the southern region of Peninsular Malaysia, driven by the persistent prevailing southeasterly wind.

Role of air stagnation in determining daily average PM2.5 concentrations in areas with significant impact of long-range transport

In areas where the regional transport of air pollutants exerts a significant impact, ascertaining whether short-term air stagnation affects PM2.5 concentrations is crucial for accurate air quality forecasting and effective management planning. However, this research area remains underexplored. In this study, we analyzed the relationship between stagnant atmospheric conditions and daily average PM2.5 concentrations in areas with a substantial long-range transport impact (LRT). Specifically, we focused on days with elevated PM2.5 concentrations (daily average ≥ 35 μg/m3) from January to March 2019 in South Korea. The analysis was performed using the Weather Research and Forecasting and the Community Multiscale Air Quality Modeling System. Stagnant conditions were quantified using ventilation index (VI), calculated as the product of planetary boundary layer height and 10-m wind speed. The correlation coefficient between daily average VI and PM2.5 concentration (r = −0.23) was lower than that between VI and NO2 concentration (r = −0.60). This can be attributed to the fact that LRT was 2.9-fold higher than local emission impact (LEI) during days of elevated PM2.5 concentrations. Notably, the correlation coefficient between LRT and VI (r = −0.03) was considerably lower than that of LEI (r = −0.70). Hence, predicting the daily average PM2.5 concentration solely based on VI proved challenging in an area characterized by substantial LRT. For future research, in areas where LRT plays a substantial role, accurate prediction of PM2.5 concentrations requires distinguishing the effects of LRT and LEI on PM2.5 concentrations.

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Abstract In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a one-dimensional (1D) convolutional neural network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorological, and land-use/land-cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an index of agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal threefold cross validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley additive explanations analysis. The results revealed solar radiation reaching the surface, planetary boundary layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times as fast in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well. Significance Statement This work develops an emulator of the Community Multiscale Air Quality model, using a one-dimensional convolutional neural network to predict hourly surface NO2 concentrations across densely populated regions in Texas. Our emulator is capable of providing rapid and highly accurate NO2 estimates, enabling it to model diverse scenarios and facilitating informed decision-making to improve public health outcomes. Notably, this model outperforms traditional methods in computational efficiency, making it a robust, time-efficient tool for air pollution mitigation efforts. The findings suggest that key variables like solar radiation, planetary boundary layer height, and NOx (NO + NO2) emissions significantly influence the model’s NO2 predictions. By adding appropriate training data, this work can be extended to other regions and other pollutants such as O3, PM2.5, and PM10, offering a powerful tool for pollution mitigation and public health improvement efforts worldwide.

Expanding Mars’s Climate Modeling: Interpretable Machine Learning for Modeling Mars Science Laboratory Relative Humidity

For the past several decades, numerous attempts have been made to model the climate of Mars, with extensive studies focusing on the planet’s dynamics and climate. While physical modeling and data assimilation approaches have made significant progress, uncertainties persist in comprehensively capturing the complexities of the Martian climate. We propose a novel approach to Martian climate modeling by leveraging machine-learning techniques that have shown remarkable success in Earth climate modeling. Our study presents a deep neural network designed to model relative humidity in Gale crater, as measured by NASA’s Mars Science Laboratory “Curiosity” rover. By utilizing meteorological variables produced by the Mars Planetary Climate Model, our model accurately predicts relative humidity with a mean error of 3% and an R 2 score of 0.92 over the range of relative humidity compared. Furthermore, we present an approach to predict quantile ranges of relative humidity, catering to applications that require a range of values. To address the challenge of interpretability associated with machine-learning models, we utilize an interpretable model architecture and conduct an in-depth analysis of its decision-making processes. We find that our neural network can model relative humidity at Gale crater using a few meteorological variables, with the monthly mean surface H2O layer, planetary boundary layer height, convective wind speed, and solar zenith angle being the primary contributors. In addition to providing an efficient method for modeling climate variables on Mars, this approach can also be utilized to expand on current data sets by filling spatial and temporal gaps in observations.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Measurement report: Atmospheric ice nuclei in the Changbai Mountains (2623 m a.s.l.) in northeastern Asia

Abstract. Atmospheric ice nucleation plays an important role in modulating the global hydrological cycle and atmospheric radiation balance. To date, few comprehensive field observations of ice nuclei have been carried out at high-altitude sites, which are close to the height of mixed-phase cloud formation. In this study, we measured the concentration of ice-nucleating particles (INPs) in the immersion freezing mode at the summit of the Changbai Mountains (2623 m above sea level), northeast Asia, in summer 2021. The cumulative number concentration of INPs varied from 1.6 × 10−3 to 78.3 L−1 over the temperature range of −5.5 to −29.0 °C. Proteinaceous-based biological materials accounted for the majority of INPs, with the proportion of biological INPs (bio-INPs) exceeding 67 % across the entire freezing-temperature range, with this proportion even exceeding 90 % above −13.0 °C. At freezing temperatures ranging from −11.0 to −8.0 °C, bio-INPs were found to significantly correlate with wind speed (r = 0.5–0.8, p &lt; 0.05) and Ca2+ (r = 0.6–0.9), and good but not significant correlation was found with isoprene (r = 0.6–0.7) and its oxidation products (isoprene × O3) (r = 0.7), suggesting that biological aerosols may attach to or mix with soil dust and contribute to INPs. During the daytime, bio-INPs showed a positive correlation with the planetary boundary layer (PBL) height at freezing temperatures ranging from −22.0 to −19.5 °C (r &gt; 0.7, p &lt; 0.05), with the valley breezes from southern mountainous regions also influencing the concentration of INPs. Moreover, the long-distance transport of air mass from the Japan Sea and South Korea significantly contributed to the high concentrations of bio-INPs. Our study emphasizes the important role of biological sources of INPs in the high-altitude atmosphere of northeastern Asia and the significant contribution of long-range transport to the INP concentrations in this region.

Sensitivity analysis of the WRF simulated planetary boundary layer height to synoptic conditions over eastern China

Accurately representing the planetary boundary layer height (PBLH) and its thermal/dynamic structures within is essential for simulating meteorological and environmental conditions in numerical models. However, how accurately model reproduces PBL processes under diverse synoptic conditions remains under-explored. Using the Weather Research Forecasting model that configured with the widely-used YSU boundary layer scheme, the present study conducted a nearly three-year PBL simulation to examine its sensitivity to synoptic conditions in eastern China. Nine types of synoptic patterns were identified by combining T-mode principal component analysis with K-means clustering. The simulated PBLH varied significantly with synoptic patterns, with the PBLH magnitude over land following the order: quasi-summer monsoon > transition > quasi-winter monsoon. In terms of simulation biases, the model presented an overall overestimation of PBLH over most land and an underestimation over the ocean compared with two spaceborne lidars (Cloud Aerosol Transport System; Cloud Aerosol Lidar with Orthogonal Polarization) and radiosondes; The overestimation and underestimation were amplified under quasi-summer monsoon types. As the synoptic patterns were characterized by different levels of pressure, wind, temperature, and humidity. This study further explored the relationship between model biases and meteorological factors. The results showed that the simulated PBLH biases in unstable conditions were linearly dependent on thermal factors, and showed less relationship with dynamical factors. The negative PBLH biases under warm, dry, and unstable conditions can be attributed to the model's underestimation of virtual potential temperature at lowest model level. While the positive PBLH biases under cold, wet, and stable conditions were caused by warm bias of virtual potential temperature profile at lower levels.

Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights

Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.

Exploring the role of aerosol-ozone interactions on O3 surge and PM2.5 decline during the clean air action period in Eastern China 2014–2020

The China implemented the Clean Air Action Plan (CAAP) in September 2013 to address the pressing air pollution problem. This initiative yielded a noteworthy reduction in PM2.5 while simultaneously witnessing the O3 elevation. Here, we used a revised WRF-Chem model to study the drivers (anthropogenic emissions (EMI), meteorological changes (MET)) of decreasing PM2.5 and increasing O3 in eastern China across the four seasons from 2014 to 2020, focusing on the response of aerosol-ozone interaction (AOI) to emission reduction (AOI_ER). In contrast to the original version, the model provided more comprehensive feedback of aerosols on O3, including radiation effects and heterogeneous chemistry. Our survey showed that EMI was the main factor causing the PM2.5 decline and O3 increase. The influence of MET on both O3 and PM2.5 exhibited seasonal variations. Compared to 2014, emission reduction during CAAP weakened the inhibition of aerosol radiative effect on meteorological parameters and photolysis rate, causing a rise in surface shortwave radiation (SW), temperature (T2), planetary boundary layer height (PBLH), wind speed (WS10), JNO2, and JO1D throughout the CAAP period. This increased the O3 by 0.57–2.11 ppb in the four seasons and enhanced the emission-reduction-induced O3 increase by 22.2%–57.3%. Emission reduction alleviated the summer negative feedback of AOI on PM2.5, increasing by 4.61 μg m−3, thereby offsetting the 16.7% emission reduction efficacy. Oppositely, in spring, autumn, and winter, emission reduction weakened the promotion of AOI on PM2.5, resulting in a decrease by 2.5–3.12 μg m−3, amplified the emission-reduction-induced PM2.5 reduction by 8.3%–26.6%. Nitrate and sulfate aerosols generated by heterogeneous chemistry emerged as the primary components driving the alterations in PM2.5 within the context of emission reduction. Our research emphasized the importance of considering changes in AOI when formulating emission control policies and evaluating their effectiveness. More effective emission policies should focus on comprehensively considering the emissions and the weakened aerosol effects to achieve better air quality and health benefits.

Comparative Analysis of Aerosol Vertical Characteristics over the North China Plain Based on Multi-Source Observation Data

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.

Understanding the Roles of Aerosols and Clouds in Environment, Meteorology and Climate with Advanced Lidar Remote Sensing Techniques

This Special Issue lists nine publications, covering the topics of advanced atmospheric lidar techniques, lidar retrievals, and lidar applications. The investigations listed here are diverse, but they are all focused on atmospheric lidars. Some urgent issues, for instance low blind zone detection and polarization detection at a near-infrared wavelength band, were discussed and explored. The results are helpful for extending atmospheric lidar applications. In terms of lidar retrievals, a planetary boundary layer height retrieval and an automatic lidar retrieval for aerosol optical properties were investigated in some of the publications, which can strengthen the atmospheric lidar capabilities. For lidar applications, a detailed analysis of the evolution of stratospheric aerosol and dust–cloud interactions was presented. In this Editorial, the articles published within this Special Issue are reviewed to highlight their innovative contributions and main research findings.

Irrigated Agriculture Significantly Modifies Seasonal Boundary Layer Atmosphere and Lower-Tropospheric Convective Environment

Abstract Modification of grasslands into irrigated and nonirrigated agriculture in the Great Plains resulted in significant impacts on weather and climate. However, there has been lack of observational data–based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX), conducted during the 2018 growing season, collected data over irrigated and nonirrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season. The data analyzed include latent and sensible heat flux, air temperature, dewpoint temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat relative to sensible heat over irrigated areas while average maximum air temperature was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas than over nonirrigated croplands. Relative to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world. Significance Statement To meet the ever-increasing demand for food, many regions of the world have adopted widespread irrigation. The High Plains Aquifer (HPA) region, located within the Great Plains of the United States, is one of the most extensively irrigated regions. In this study, for the first time, we have conducted a detailed irrigation-focused land surface and atmospheric data collection campaign to determine irrigation impacts on the atmosphere. This research demonstrates that irrigation significantly alters lower atmospheric characteristics and creates favorable cloud and convection development conditions during the growing season. The results clearly show first-order impacts of irrigation on regional weather and climate and hence warrant further attention so that we can minimize negative impacts and achieve sustainable irrigation.

Particulate Matter in the American Southwest: Detection and Analysis of Dust Storms Using Surface Measurements and Ground-Based LIDAR

This research study focuses on the coupling between particulate matter and the planetary boundary layer. Particulate matter affects human health and it is a complex mixture of suspended substances. Various sources of particulate matter include volcanic eruptions, soil lofted by strong winds, wildfires, and particles formed from chemical reactions of gas-phase emissions. Strong winds are one source of dust pollution when they loft soil particles. Particulate matter and the planetary boundary layer are closely linked. The planetary boundary layer plays a critical role in meteorology and particulate matter concentrations due to its involvement in energy, latent heat, and mass transfer with the free troposphere. Currently, there has been no research on the impact of dust events on the planetary boundary layer in our region, El Paso, Texas, which is located on one of the biggest sources of dust in the Western Hemisphere, the Chihuahuan Desert. In this study, we used PM10 concentrations to detect dust events during the 2016–2022 period in the El Paso region. During the study period, we observed 74 dust events. The dust events were categorized as synoptic or convective cases. Synoptic cases are associated with cold fronts, while convective cases are associated with local convective systems such as thunderstorms. We observed that synoptic cases occurred most frequently during springtime, while convective cases were more frequent during summer monsoon months. Synoptic cases tend to occur earlier in the afternoon with lower temperatures, while convective cases tend to occur in the late evening with higher temperatures. We also found that the planetary boundary layer height collapsed after the maximum hourly PM10 concentration and then the boundary layer returned to its original height.

Seasonal Variations and Spatial Patterns of Arctic Cloud Changes in Association with Sea Ice Loss during 1950–2019 in ERA5

Abstract The dynamic and thermodynamic mechanisms that link retreating sea ice to increased Arctic cloud amount and cloud water content are unclear. Using the fifth generation of the ECMWF Reanalysis (ERA5), the long-term changes between years 1950–79 and 1990–2019 in Arctic clouds are estimated along with their relationship to sea ice loss. A comparison of ERA5 to CERES satellite cloud fractions reveals that ERA5 simulates the seasonal cycle, variations, and changes of cloud fraction well over water surfaces during 2001–20. This suggests that ERA5 may reliably represent the cloud response to sea ice loss because melting sea ice exposes more water surfaces in the Arctic. Increases in ERA5 Arctic cloud fraction and water content are largest during October–March from ∼950 to 700 hPa over areas with significant (≥15%) sea ice loss. Further, regions with significant sea ice loss experience higher convective available potential energy (∼2–2.75 J kg−1), planetary boundary layer height (∼120–200 m), and near-surface specific humidity (∼0.25–0.40 g kg−1) and a greater reduction of the lower-tropospheric temperature inversion (∼3°–4°C) than regions with small (&lt;15%) sea ice loss in autumn and winter. Areas with significant sea ice loss also show strengthened upward motion between 1000 and 700 hPa, enhanced horizontal convergence (divergence) of air, and decreased (increased) relative humidity from 1000 to 950 hPa (950–700 hPa) during the cold season. Analyses of moisture divergence, evaporation minus precipitation, and meridional moisture flux fields suggest that increased local surface water fluxes, rather than atmospheric motions, provide a key source of moisture for increased Arctic clouds over newly exposed water surfaces during October–March. Significance Statement Sea ice loss has been shown to be a primary contributor to Arctic warming. Despite the evidence linking large sea ice retreat to Arctic warming, some studies have suggested that enhanced downwelling longwave radiation associated with increased clouds and water vapor is the primary reason for Arctic amplification. However, it is unclear how sea ice loss is linked to changes in clouds and water vapor in the Arctic. Here, we investigate the relationship between Arctic sea ice loss and changes in clouds using the ERA5 dataset. Improved knowledge of the relationship between Arctic sea ice loss and changes in clouds will help further our understanding of the role of the cloud feedback in Arctic warming.