Meteorological Research Articles

Tropical Aviation Turbulence Induced by the Interaction Between a Jet Stream and Deep Convection

AbstractOn 18 December 2022, Hawaiian Airlines flight HA35 encountered severe turbulence without warning in a cloud‐free height. We reproduced this incident using the Weather Research and Forecasting Model (WRF) at a convection‐permitting resolution. We found that this case of tropical upper‐level turbulence occurred primarily due to the fast‐growing convective tower in the unstable layer created by gravity wave breaking. At lower altitudes, a mesoscale convective system (MCS) caused a decrease in wind speed in both upstream and downstream regions. At upper levels, a large‐scale jet descended and accelerated after flowing over the top of the MCS, which acted like a barrier and produced a situation similar to a downslope windstorm due to mountain terrain. Upper‐level turbulence is 2–3 km higher than the top of the MCS. The critical level above the jet and the locally self‐induced critical level created the locally enhanced descending jet stream, which destabilized the flow through Kelvin–Helmholtz instabilities. The convective tower existed near the flight route and played an important role in triggering turbulence in the unstable environment through its convective gravity waves.

Mapping the Sun's coronal magnetic field using the Zeeman effect.

Regular remote sensing of the magnetic field embedded within the million-degree solar corona is severely lacking. This reality impedes fundamental investigations of the nature of coronal heating, the generation of solar and stellar winds, and the impulsive release of energy into the solar system via flares and other eruptive phenomena. Resulting from advancements in large aperture solar coronagraphy, we report unprecedented maps of polarized spectra emitted at 1074 nm by Fe+12 atoms in the active corona. We detect clear signatures of the Zeeman effect that are produced by the coronal magnetic field along the optically thin path length of its formation. Our comparisons with global magnetohydrodynamic models highlight the valuable constraints that these measurements provide for coronal modeling efforts, which are anticipated to yield subsequent benefits for space weather research and forecasting.

Ozone sensitivity to high energy demand day electricity and onroad emissions during LISTOS

ABSTRACT Using a high-resolution, 1.33 km by 1.33 km coupled Weather Research and Forecasting-Community Multi-scale Air Quality Model (WRF-CMAQ), we quantify the impact of emissions of nitrogen oxides (NOx) from high energy demand day (HEDD) electricity generating units (EGU) and onroad vehicles on ambient ozone air quality in the Long Island Sound Tropospheric Ozone Study (LISTOS) region covering New York City (NYC); Long Island, NY; coastal Connecticut; and neighboring areas. We test sensitivity scenarios to quantify HEDD EGU NOx contributions to ozone: (1) zero out HEDD EGU emissions, (2) dispatch HEDD EGUs starting with the lowest NOx emitting units first, (3) reduce onroad emissions by 90%, (4) combine zero out HEDD EGU emissions and reducing onroad emissions by 90%, and (5) dispatch HEDD EGUs starting with the lowest emitting units coupled with a reduction in onroad emissions by 90%. Results determine that HEDD EGUs lead to highly localized impacts on ambient concentrations of ozone while onroad emission reductions lead to large-scale regional concentration impacts. Further, reducing onroad emissions by 90% leads to spatially smaller VOC-limited regions and spatially larger transitional and NOX-limited regions around NYC. Despite the limited scale at which the EGU emission reductions occur, modifying HEDD EGU NOX emissions still provides substantial benefits in reducing ozone concentrations in the region, particularly at elevated ozone concentrations above 70 ppb. Implications: High-resolution coupled meteorology-chemistry modeling was used to quantify the impacts of high energy demand day (HEDD) electricity generating units (EGUs) and onroad transportation emissions changes on ozone air quality in the LISTOS region. Despite being highly localized and variable, HEDD EGUs NOX emissions sensitivity tests led to quantifiable changes in ozone. Further, reducing onroad emissions by 90% produced large decreases in ozone concentrations and led to a more NOX-sensitive ozone photochemical regime. With a transition to greater NOX-sensitivity, urban NOX-titration weakens and ozone is more likely to decline with the removal of additional NOX from sources like HEDD EGUs.

Sensitivity of Cumulus Physics in ARW Model on Track of Tropical Cyclones ‘Amphan’ and ‘Bulbul’ over the Bay of Bengal

The Advanced Research Weather Research and Forecasting (ARW) model performance can be improved by analyzing the sensitivity of the physical parameterization schemes. In order to revalidate the performance of the ARW model in relation to the selection of Cumulus Physics (CP) schemes, the sensitivity of CP schemes for Tropical Cyclones (TCs) over the Bay of Bengal (BoB) region has been examined. National Centre for Environment Prediction (NCEP)’s Final Reanalysis (FNL) data (1010) have been used as lateral and initial conditions in the ARW model. The model has been configured in a single domain and runs for four different initial conditions in simulating TC ‘Amphan’ and for three different initial conditions in simulating TC ‘Bulbul’. In this study, average track errors have been found between 40-50 km for Kain-Fritsch Cumulus Potential (KFCP) and Multi-Scale Kain-Fritsch (MSKF) schemes for TC Amphan and below 60 km for the Kain Fritsch (KF) scheme for TC Bulbul. Lower landfall position error has been found below 60 km for the KF scheme for TC Amphan, and 40 km for KFCP and MSKF schemes for TC Bulbul. Journal of Engineering Science 15(1), 2024, 119-129

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

AbstractUsing the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka

ABSTRACT Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from −0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM2.5 simulations significantly, with NMB decreasing from −20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from −27% to −22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

A Comparative Study of Cloud Microphysics Schemes in Simulating a Quasi-Linear Convective Thunderstorm Case

An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes.

Integrating physics-based WRF atmospheric variables and machine learning algorithms to predict snowfall accumulation in Northeast United States

Accurate snowfall prediction is crucial for enhancing preparedness and resilience in the Northeast United States during winter weather events. This study introduces a novel approach that integrates Machine Learning (ML) models with atmospheric variables from the Weather Research and Forecasting (WRF) model to improve snowfall forecasts in the region. The significance lies in bridging the gap between physics-based Numerical Weather Prediction (NWP) models and the versatility of ML models, offering a promising advancement in winter storm predictions. Atmospheric variables related to 32 winter storms simulated by WRF, were used to feed Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms to predict 24 h accumulations of snowfall using the National Snowfall Analysis (NSA) product as reference. The comprehensive results revealed that the integrated approach provided more accurate snowfall prediction than the WRF and Air Force Weather Agency (AFWA) diagnostic, but faced challenges in the precision estimated by under-dispersion of the results. The WRF/XGBoost reduced the RMSE by 10.34 %, whereas WRF/RF reduced RMSE by 9.72 %, compared to WRF/AFWA. Similarly, WRF/XGBoost and WRF/RF increased the correlation coefficient by 12 % and 11 %. The most important variables in both ML algorithms were liquid water equivalent precipitation (LWE), snow ratio, wet-bulb temperature, temperature, and humidity at different heights, pressure tendency and wind speed, validating their ability to discern crucial factors influencing snowfall. Specific cases highlight the integrated approach’s effectiveness in addressing challenges faced by traditional diagnostics, including LWE overestimation and warm temperature bias. However, limitations emerge in capturing storms characterized by anomalous atmospheric conditions and high snowfall values, most likely attributed to underrepresentation in the training data. By highlighting strengths and acknowledging limitations, this integrated approach holds the potential for improved snowfall prediction for future storms in the region compared to the traditional approach.

Contraction of the radius of maximum symmetric rotational kinetic energy during the intensification of Tropical Cyclone Khanun (2017)

Tropical cyclone (TC) Khanun in 2017 was simulated in this study by the Weather Research and Forecasting (WRF) model. The observation-validated simulation data were used to examine how asymmetric rotational and environmental flows played the roles in determining the contraction of the radius of maximum kinetic energy of symmetric rotational flow (Kψs). The radius of maximum Kψs was contracted rapidly before rapid intensification (RI) and moved inward slowly, then barely moved, and moved inward slowly again during RI.The conversion from kinetic energy of asymmetric rotational flow (Kψa) to Kψs was induced by advection of asymmetric rotational tangential wind by asymmetric divergent radial wind. The conversion and convergence of inward flux of Kψs led to the rapid contraction before RI. During RI, in additional to horizontal and vertical flux convergence of Kψs, the conversion from environmental kinetic energy (Ke) to Kψs through the interaction between Kψs and symmetric radial environmental flow was important for the second slow contraction.

Coupled Fire–Atmosphere Simulations of the Split, Croatia, Wildfire

Abstract The Weather Research and Forecasting Spread FIRE (WRF-SFIRE) model was used to simulate the evolution of the fire perimeter for the Split wildfire in Croatia, evaluated in our previous detailed reconstruction of the event. A four-domain configuration was applied, with the innermost domain having a resolution of 500 m with a planetary boundary layer parameterization. The coupled fire grid was further downscaled to 33.3-m resolution, using the best available estimates of topography and fuel load data. WRF-SFIRE simulated slightly smaller fire areas in the four observed fire perimeter stages of the Split wildfire event, especially the northwestward spread in the last stage along the Adriatic coast. Additional experiments with multiple ignitions in the WRF-SFIRE model, representing the spotting mechanism, successfully simulated the northwestward spread in the event. The study also included sensitivity experiments that turned off the heat and moisture flux coupling between WRF and the fire grid. It was found that the simulated fire area is larger than the model run that included heat and moisture feedback from the fire grid to the atmosphere, which is contrary to some previous studies. The large wind speed and convergence along the fire line in our feedback-on simulation, which constrain the fire when there are feedback processes, were likely the cause of the smaller fire area. Last, areas of pine that burnt in the Split fire event favored crown fire spread, which is not represented in the current modeling framework of WRF-SFIRE.

CWRF Downscaling with Improved Land Surface Initialization Enhances Spring–Summer Seasonal Climate Prediction Skill in China

Abstract This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate–Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during 7 of 10 years with relatively high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, the substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia–northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian high alongside a strengthened East Asian jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where the accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

Reproducing vortex-induced vibrations of rooftop twin-mast by multi-scale coupled simulation of urban wind fields

The Shenzhen Electronics Group (SEG) Plaza in Shenzhen, China, experienced visible vibrations attributed to significant vortex-induced vibrations of the large spacing twin-mast atop the building. The vibration event occurred under complex urban wind environments. In order to reproduce wind fields around SEG Plaza, a multiscale simulation was carried out by integrating the Weather Research and Forecasting (WRF) and Computational Fluid Dynamics (CFD) models. The aerodynamic characteristics and wake modes of twin-mast are investigated by a downscale CFD simulation in detail. Finally, vortex-induced vibration (VIV) of twin-mast was analyzed considering fluid-structure interaction. This work reveals that wind conditions during the SEG Plaza vibration incident were appropriate for the VIV occurrence of twin-mast, and the results of VIV simulation agree well with the field monitoring results by computer vision identification. This study effectively reproduces the full pictures of VIVs of the rooftop twin-mast during this rare event, offering a critical view for the analysis of this event.

Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic.

Although clouds are a major factor influencing atmospheric environments in the Arctic, numerical simulations of Arctic clouds are uncertain. In this study, the effects of microphysics scheme and data assimilation (DA) on the simulation of clouds, hydrometeors and radiative fluxes in the Arctic were investigated using the polar weather research and forecasting (WRF) model and three-dimensional variational DA. Compared with the WRF 5-class (WSM5) microphysics scheme, when the Morrison double-moment (Morrison) scheme was used, the simulated amount of cloud ice water decreased by approximately 68%. In contrast, the amount of water vapour, cloud liquid water, snow and rain in the atmosphere increased. With DA, the amount of water vapour increased, leading to increased hydrometeors. The cloud liquid water increased in the middle and low atmospheres when Morrison was used, whereas it increased in the low atmosphere when DA was used. The increase in cloud liquid water by using Morrison resulted in a decrease in the downward short-wave radiative flux at the surface, whereas using DA increased the downward long-wave radiative flux. Changing the microphysics scheme induced redistribution of the region and amounts of hydrometeors, whereas DA induced an increase in hydrometeors in specific regions by adding observation information to the model states.

Effect of atmospheric response induced by preceding typhoon on movement of subsequent typhoon over Northwestern Pacific

This study investigates the atmospheric interactions between two closely located typhoons in 2019. Typhoons in the Western Pacific significantly impact Eastern and Southeastern Asian countries, leading to various damages. As global warming is expected to increase typhoon intensity, accurate track forecasting becomes crucial for coastal disaster management. Despite the existing knowledge about the influence of typhoon activities on the atmospheric background, limited research addresses the atmospheric response between two typhoons. The study focuses on the cases of LEKIMA and KROSA, occurring simultaneously in 2019, and utilizes the Weather Research and Forecasting (WRF) model for simulations. The experimental setup involves comparing two scenarios: one with both typhoons and one with LEKIMA removed. Results reveal LEKIMA-induced distinctive atmospheric responses, including the closure of the western North Pacific subtropical high (WNPSH) boundary and the formulation of a wave train, influencing KROSA’s stagnation. The absence of LEKIMA allows KROSA to move more freely along the steering flow. Furthermore, the study highlights the potential of atmospheric models for understanding typhoon effects at regional to mesoscale levels. A comprehensive analysis of similar cases could enhance typhoon predictions, contributing to better damage mitigation strategies.

Combined impacts of aerosols and urbanization on a highly threatened extreme precipitation event in Beijing, China

On July 21, 2012, a catastrophic precipitation event occurred in Beijing, highlighting the serious threat of extreme precipitation on socio-economic development and human health under climate change. Nevertheless, whether, how and to what extent aerosols and urbanization, as the two main influencing factors of urban extreme precipitation, have affected this highly damaging extreme event remains largely unexplored. Here, we employed the weather research and forecasting model coupled with chemistry (WRF-Chem) and a single-layer urban canopy model to investigate the influences of urbanization, aerosols and their interactions on this extreme precipitation event. We found that the joint intensification effects of urbanization and aerosols on extreme precipitation events greatly enhance its negative influence on megacities. The results indicate that aerosols are enhanced by increasing cloud droplet numbers, thereby intensifying the feedback between precipitation and latent heating. Consequently, the total precipitation increased by 22.6%, raising the precipitation in the Beijing area increase by at least 50 mm. By stimulating atmospheric instability and strengthening vertical air motion (over 0.25 m s−1), the urban heat island effect considerably influences the temporal and spatial distributions of extreme precipitation events, resulting in an increase in warm cloud precipitation (80%) and a decrease (30%) in frontal precipitation. Consequently, joint intensification effects resulted in more concentrated precipitation in the southwest of Beijing, leading to a substantial increase (more than 40%, ∼80 mm). This condition may be an important reason for the most severe disasters in the southwest of Beijing.

Prioritizing social vulnerability in urban heat mitigation.

We utilized city-scale simulations to quantitatively compare the diverse urban overheating mitigation strategies, specifically tied to social vulnerability and their cooling efficacies during heatwaves. We enhanced the Weather Research and Forecasting model to encompass the urban tree effect and calculate the Universal Thermal Climate Index for assessing thermal comfort. Taking Houston, Texas, and United States as an example, the study reveals that equitably mitigating urban overheat is achievable by considering the city's demographic composition and physical structure. The study results show that while urban trees may yield less cooling impact (0.27 K of Universal Thermal Climate Index in daytime) relative to cool roofs (0.30 K), the urban trees strategy can emerge as an effective approach for enhancing community resilience in heat stress-related outcomes. Social vulnerability-based heat mitigation was reviewed as vulnerability-weighted daily cumulative heat stress change. The results underscore: (i) importance of considering the community resilience when evaluating heat mitigation impact and (ii) the need to assess planting spaces for urban trees, rooftop areas, and neighborhood vulnerability when designing community-oriented urban overheating mitigation strategies.

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

A Comprehensive Study on Winter PM2.5 Variation in the Yangtze River Delta: Unveiling Causes and Pollution Transport Pathways

To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM2.5, meteorological changes exert a significant impact on PM2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM2.5 pollution in the region (up 0~50 µg·m−3). Our findings also shed light on the potential sources and transport pathways of PM2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions.

Impacts of urban expansion on air temperature and humidity during 2022 mega-heatwave over the Yangtze River Delta, China

The Yangtze River Delta (YRD) experienced record-breaking heat in the summer of 2022. However, the urban heat pattern and the role of urban expansion over the last two decades in this hot summer have not been explored. Using the advanced mesoscale Weather Research and Forecasting (WRF) model, we reproduced the fine spatial features and investigated the urban heat island (UHI) and dry island (UDI) effects during the two identified heatwaves in 2022. We further replace the current (2020) land use with the historical (2001) land use in WRF to evaluate the impacts of urban expansion from 2001 to 2020 on air temperature and moisture. Our finding revealed that the conversion of land use resulted in near-surface warming and drying, with pronounced diurnal variations, especially during the July heatwave. The analysis of surface energy balance demonstrated that the substantial decrease in evapotranspiration (ET) was the primary driver of daytime warming, elevating temperatures by 7 °C (July heatwave) and 2 °C (August heatwave). This ET reduction also led to the strong daytime coupling of warming and drying effects over new urban areas. At night, the release of stored heat resulted in the temperature increase of 2 °C (1 °C) during July (August) heatwave, highlighting the nighttime as a critical period for heightened thermal risk. Additionally, urban expansion at the periphery contributed modestly to the warming of urban cores, exacerbating conditions in an already hot environment. This study enhances understanding of the impacts of urban expansion on air temperature and humidity during extreme heatwaves, thereby supporting targeted adaptation and mitigation for extreme events within large cities.