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This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small‐scale striated structures (150–200 m) and large‐scale convergence. The other modes are time‐varying (oscillating and decaying) and represent smaller spatial‐scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low‐frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales. [ABSTRACT FROM AUTHOR]

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city's land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city. [ABSTRACT FROM AUTHOR]

The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement: As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the influence of the urban environment of the Paris region on an isolated convective cell that formed downwind of the city on May 7, 2022, using the Meso‐NH research atmospheric model at a horizontal scale of 300 m. To account for all sources of forecast uncertainty, the initial and lateral boundary conditions of the simulations are provided by an ensemble prediction system. A multi‐layer urban scheme is used to represent the influence of buildings on the airflow accurately. Two sets of ensemble simulations are performed: the first set (URB) uses a fine‐scale surface description of the city, while the second set (NOURB) replaces urban surfaces with vegetation. This sensitivity test shows that, despite the high variability of simulated precipitation within the ensemble, the city of Paris plays a statistically significant role in the initiation of convection in this case. Convective cells are initiated over the city for several members of the URB ensemble, while almost no precipitation is simulated for the same members of the NOURB ensemble. The mean 6‐h rainfall accumulation of the URB ensemble is increased by 70% over Paris (compared with the NOURB ensemble) and no statistically significant trend is found around the city. The analysis reveals that the capital experiences a higher sensible heat flux due to drier and warmer air, resulting in enhanced vertical velocities and an increase in boundary‐layer height in the URB ensemble. Additionally, the total water content and cloud fraction over Paris are intensified, leading to more precipitation. These findings suggest that urbanisation has a notable impact on convection and precipitation processes in this case. [ABSTRACT FROM AUTHOR]

Abstract This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small‐scale striated structures (150–200 m) and large‐scale convergence. The other modes are time‐varying (oscillating and decaying) and represent smaller spatial‐scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low‐frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales.

A study was carried out in Santiago de Chile, located in a geographic basin, on the sustainability and diffusion of the recent SARS-CoV-2 pandemic. Hourly measurements were used (carried out for 3.25 years in seven communes of the city) to quantify the accumulated sick (AS) population, urban meteorology variables (MVs) (temperature (T), relative humidity (RH), and magnitude of wind speed (WS)), and air pollution (P) (PM10, PM2.5, 03). Time series (TS) were constructed for each commune, which related AS to MVs, called AS/VM, and to P, noted AS/P. Chaos theory was applied to each TS, requiring the following variables: the Lyapunov exponent (λ > 0), the correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), the Hurst exponent (H, such that 0 < H < 1), Lempel–Ziv complexity (LZ > 0), and information loss (<ΔI> < 0). Every TS complied with chaos theory. For each commune, CK was calculated as a quotient between the sum of AS/T, AS/WS, and AS/RH entropies and the sum of AS/PM10, AS/PM2.5, and AS/O3 entropies. The results show that the entropy for the AS/P ratio is lower than that of the AS/VM ratio in three of the seven communes, since between 2020 and early 2022, the population was confined, reducing pollution. The TS of the AS/P ratio is more persistent and complex. The predictability times of the ratios are comparable in four of the seven communes. The TS of the AS/MV ratios shows greater information loss and chaos. According to the calculated CK values, it is possible to relate it to anomalous diffusion (sub/super-diffusion) and the context that favored the expansion of the pandemic: urban densification, pollution, urban meteorology, population density, etc. Using Fréchet heavy-tailed probability, the compatibility of the results with CK is verified. [ABSTRACT FROM AUTHOR]

Hong Kong, renowned for its densely packed urban areas, poses unique challenges for understanding the effects of buildings on local meteorological conditions. To address this, the Hong Kong Observatory has started building a network of urban meteorological monitoring stations since 2017 for monitoring, analysing and studying urban microclimate. This paper presents an observational and numerical study focusing on wind measurements obtained from wind sensors installed on two smart lampposts in Tsim Sha Tsui, a major urban area in Hong Kong. Two representative high wind conditions in Hong Kong, Super Typhoon Saola in 2023 and a strong monsoon case characterized by prevailing easterly winds, are considered. With the use of high resolution computational fluid dynamic simulations, major features of actual observations can be reproduced. This suggests that district scale or even street scale weather services could be possible in the future with sufficient computational power.

This study examines ground-based precipitation observations recorded by a high-density gauge network located within approximately 40 km of the urban center of Louisville, Kentucky. An analysis of April–October events reveals that precipitation is significantly greater on the downwind side of Louisville than on the upwind side, particularly when precipitation systems have a westerly component to their motion. The mean difference between downwind and upwind precipitation across all events is 20%. This value is smaller for widespread precipitation events (i.e., most or all gauges detect precipitation) and is larger for isolated events (i.e., rain detected by one-half of the gauges or fewer). The largest and most significant differences between upwind and downwind precipitation amounts occur in association with moist moderate, moist tropical, and transitional air masses. [ABSTRACT FROM AUTHOR]

The surface sensible heat flux induced by precipitation (QP) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components. [ABSTRACT FROM AUTHOR]

Precipitation induced by tropical cyclones (TCs) over cities is associated with both TC duration and urbanization; however, observational evidence of the impacts of TC duration and urbanization on precipitation in megalopolises is limited. In this study, the Yangtze River Delta (YRD) of eastern China is taken as a typical region because this region has been experiencing both rapid urbanization processes and frequent TC attacks. During 1979-2018, we find reduced translation speed and increased meandering of TCs over the YRD, resulting in increased TC duration and the proportion of TC stalling in this region. The correlation between TC duration and TC-induced precipitation amount is significant across the YRD region but is relatively weak in areas with faster urbanization expansion rates. Long-term increases in TC-induced precipitation are found in both rural and urban areas but are larger for urban areas. Urbanization plays an important role in enhancing TC-induced precipitation over urban areas of the YRD region. Areas with faster urbanization expansion rates and longer TC durations have larger TC-induced precipitation, suggesting that urban expansion and TC duration jointly amplify TC-induced precipitation. Our findings suggest that urban planners, in areas potentially affected by TCs, should consider adaptation measures to mitigate the impacts of urban rainstorms amplified by the combined effects of TCs and urbanization. [ABSTRACT FROM AUTHOR]

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban‐focussed land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information‐limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short‐wave radiation, sensible and latent heat fluxes, but little or no improvement in long‐wave radiation and momentum fluxes. Models with a simple urban representation (e.g., 'slab' schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid‐complexity models (e.g., 'canyon' schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three‐dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation‐only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban‐PLUMBER will evaluate models across 20 sites in different urban and regional climate zones. [ABSTRACT FROM AUTHOR]

For over 75 years, the National Oceanic and Atmospheric Administration's Air Resources Laboratory (NOAA ARL) has been at the forefront of federal meteorological and climate research. As the Special Projects Section (SPS) of the U.S. Weather Bureau (USWB), the laboratory pioneered the development of atmospheric trajectory modeling, initially used in studies related to nuclear weapons following World War II. Model development was guided by observations following weapons tests, assisted by later experiments using a wide variety of atmospheric tracers. Today's familiar Gaussian plume dispersion model, previously in nascent form, was developed and promoted with ARL research, as was the much later and widely used HYSPLIT model. Much of ARL's early research was focused on the challenges presented by the complex terrain surrounding nuclear installations, often addressed with high-spatial-resolution meteorological measurements, atmospheric tracers, and site-specific models. ARL has since extended boundary layer research to increasingly complex landscapes, such as forests, agricultural lands, and urban areas, and has expanded its research scope to air quality, weather, and climate applications based on the knowledge and experience developed throughout its long history. Examples of these research endeavors include the establishment of the U.S. Climate Reference Network, fundamental contributions to the development of the National Air Quality Forecast Capability, and foundational participation in the National Atmospheric Deposition Program. ARL looks forward to continuing to refine scientific understanding from field experiments, including coupling ground-based experimentation with modeling, and sustained observations, in order to facilitate the transfer of knowledge into practical applications of societal relevance. [ABSTRACT FROM AUTHOR]

This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle-rescale (R-R) and the digital filter-based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices. SIGNIFICANCE STATEMENT: Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle-rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter-based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs. [ABSTRACT FROM AUTHOR]

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. SIGNIFICANCE STATEMENT: Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location. [ABSTRACT FROM AUTHOR]

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies. Significance Statement: Urban climate simulations are subject to spatially heterogeneous biases in urban air temperatures. Common validation methods using official weather stations do not suffice for detecting these biases. Using a dense set of personal weather sensors in London, we detect these biases before proposing an innovative way to correct them with machine learning techniques. We argue that any urban climate impact study should use such a technique if possible and that urban climate scientists should continue investigating paths to improve our methods. [ABSTRACT FROM AUTHOR]

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions. [ABSTRACT FROM AUTHOR]

The urban climate is a modified boundary layer climate that is directly and indirectly influenced by anthropogenic activity and characterized by phenomena such as urban warming, reduced evapotranspiration, and increased emission of pollutants. As more than half of the global population is urban and the impacts of climate change will increase pressure on cities, a thorough understanding of the urban climate-related processes and phenomena is of vital importance. The present contribution gives a contemporary overview of the characteristics and phenomena of the urban climate addressed to non-specialists in the field. Starting with an analysis of the general differences between large cities and their surrounding environments, important aspects such as the radiation, energy and water balance as well as the temperature distribution in cities are presented in detail. Additionally, the situation of urban air quality and human–biometeorological aspects are described and the impact of climate change are addressed. Finally effective local adaptation and mitigation measures to reduce urban warming and flooding due to heavy rainfall are discussed.

Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates' performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Significance Statement: Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not. [ABSTRACT FROM AUTHOR]

This study quantifies the vegetation impact on urban meteorology by means of the numerical model WRF (Weather Research and Forecasting model). The assessment was made for two months: July and January. These were considered as representative for the summer and winter seasons, for the reference year 2015 in three European cities: Bologna, Milano, and Madrid. Two simulations at 1 km resolution were conducted over the cities with and without the actual urban vegetation, called VEG and NOVEG, respectively, in the model input. Then, the impact of vegetation was evaluated as the difference between the two simulations (VEG-NOVEG) for temperature, relative humidity, and wind speed fields. In general, we found that, as can be expected, urban vegetation tends to cool the atmosphere, enhance the humidity, and reduce the wind speed. However, in some cases, areas with the opposite behaviour exist, so that no a priori results can be attributed to the presence of urban vegetation. Moreover, even when major impact is confined around grid cells where urban vegetation is present, changes in meteorological quantities can be observed elsewhere in the city's area. The magnitude of urban vegetation impact is higher in summer than in winter and it depends on the city's morphological peculiarities, such as urban texture and vegetation types and distribution: average July temperature variations due to the presence of urban vegetation reach peaks of −0.8 °C in Milano, −0.6 °C Madrid, and −0.4 °C in Bologna, while in January, the values range between −0.3 and −0.1 °C. An average heating effect of ca. +0.2 °C is found in some parts of Madrid in January. For relative humidity, we found increments of 2%–3% in July and 0.5%–0.8% in January, while a decrease in wind speed was found between 0.1 and 0.5 m/s, with the highest occurring in Madrid during July. [ABSTRACT FROM AUTHOR]

Daily atmospheric concentrations of the pollutants PM10 and O3 vary according to weather types. This study aims to identify the weather patterns associated with PM10 and O3 pollution episodes from 2009 to 2020. Episodes characterized by exceedance of World Health Organization standards were identified, and their duration and persistence were studied. The results show that air pollution days are associated with three atmospheric patterns for PM10 and four for O3. The dominant weather pattern corresponds to an anticyclonic situation in central and Eastern Europe with a ridge of high pressure over France at the surface and 500-hPa geopotential height. For PM10, the persistent high-concentration sequences were found to be associated with a thermal inversion constraining the vertical dispersion of pollutants. For O3, the four weather types responsible for ozone pollution all have a higher occurrence in summer. The highest percentage (46% of days) is associated with the presence of a ground-level barometric marsh (an area of the atmosphere between two weather systems where the pressure varies slightly but is slightly low) and a ridge at 500 hPa (weather type T1). Similarly, thermal inversions and thermal winds cause pollution to persist beyond 8 consecutive days. Significance Statement: Air quality is not only influenced by ground-level emissions, but also by complex meteorological processes that can contribute to pollutant accumulations. The importance of this research is that the prediction of these processes helps to prevent the development of extreme concentrations near the surface. The results of this study provide a better understanding of how characteristic weather patterns in the Cergy-Pontoise conurbation impact PM10 and O3 pollutant levels. These impacts are expressed by the intensity and frequency of pollution episodes. [ABSTRACT FROM AUTHOR]

The paper summarizes many of the scientific achievements of Professor Sergej S. Zilitinkevich (1936–2021). It first focuses on his basic and applied atmospheric boundary layer research contributions. It then reviews their applications within research and operational numerical weather prediction and air quality modeling, showing their contribution to solving modeling problems related to extremely-stable and -unstable boundary layers. [ABSTRACT FROM AUTHOR]

This work analyzes the evolution of an urban thermal environment using measurements, in the form of time series, of atmospheric pollutants (PM10, PM2.5, CO) and meteorological variables (temperature (T), relative humidity (RH) and magnitude of wind speed (WS)) of three periods, each of 3.25 years: 2010–2013, 2017–2020 and 2019–2022. The study region is the capital of Chile, Santiago de Chile, located in a rugged basin geography. Of the total communes that make up the capital, six communes that are at different heights from sea level were selected for this study, providing 3,074,004 data records. These communes have been subject to an intensive urban densification process. The time series are analyzed through the chaos theory, demonstrating that they are chaotic through the calculation of the parameters: Lyapunov exponent (λ > 0), correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), Hurst exponent (0.5 < H < 1), Lempel–Ziv complexity (LZ > 0). Based on these parameters, the following is constructed for each commune: the CK ratio, which results from the ratio between the entropies of the meteorological variables and the entropies of the pollutants; the loss of information (<ΔI> < 0) using the Lyapunov exponent; the fractal dimension (D) using the Hurst exponent. It is verified, when comparing the three periods for the six communes, that: CK evolves declining with height, with a greater influence of pollutants; the loss of information is faster in urban meteorology; an increase in fractality. The estimation of the entropic flows, based on the Clausius equation, confirm the trend. The descriptive framework shows the weakness of the mitigation measures. [ABSTRACT FROM AUTHOR]

Cities have a significant impact on the environment, forming microclimatic features such as urban heat island, an increase in the intensity of convective weather events, etc. Numerical models of the atmosphere with an integrated block that describes the interaction between the urbanized surface and the atmosphere—urban parameterization—are good at reproducing the meteorological features of the urban environment. Reviews of urban parameterizations are mostly outdated, and recent ones do not fully cover aspects of the methods used in the models to describe physical processes. This paper is dedicated to updating information on urban parameterizations, comparing the approaches used in them to describe physical processes, and forming proposals for their improvement. Based on the most common urban parameterizations of various levels of complexity, the main groups of physical processes describing "urban surface-atmosphere" interaction are identified. They are the surface energy balance, radiation heat transfer, surface moisture balance, turbulent heat and moisture exchange in the urban canopy, anthropogenic influence on heat and moisture fluxes, radiation, and turbulent interaction with urban vegetation. The main approaches to the parameterization of physical processes are defined within each block. Modern trends in the development of urban parameterizations are highlighted: (1) over the past 10 years, parameterizations have become more complicated due to the addition of the building energy model, a three-dimensional structure of urban vegetation, and vertical resolution when calculating turbulent fluxes; (2) at the same time, not much attention is paid to revising the original empirical formulas, often obtained on the basis of single field or laboratory experiments. Ways to improve urban parameterizations are proposed by clarifying the basic dependencies used mainly in the calculation of turbulent fluxes, particularly using the results of highly detailed large-eddy simulation modeling, which, with growing computational power, is increasingly used to simulate explicit heat transfer between the atmosphere and individual elements of the urban environment. [ABSTRACT FROM AUTHOR]

A study was carried out in Santiago de Chile, located in a geographic basin, on the sustainability and diffusion of the recent SARS-CoV-2 pandemic. Hourly measurements were used (carried out for 3.25 years in seven communes of the city) to quantify the accumulated sick (AS) population, urban meteorology variables (MVs) (temperature (T), relative humidity (RH), and magnitude of wind speed (WS)), and air pollution (P) (PM10, PM2.5, 03). Time series (TS) were constructed for each commune, which related AS to MVs, called AS/VM, and to P, noted AS/P. Chaos theory was applied to each TS, requiring the following variables: the Lyapunov exponent (λ > 0), the correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), the Hurst exponent (H, such that 0 < H < 1), Lempel–Ziv complexity (LZ > 0), and information loss ( < 0). Every TS complied with chaos theory. For each commune, CK was calculated as a quotient between the sum of AS/T, AS/WS, and AS/RH entropies and the sum of AS/PM10, AS/PM2.5, and AS/O3 entropies. The results show that the entropy for the AS/P ratio is lower than that of the AS/VM ratio in three of the seven communes, since between 2020 and early 2022, the population was confined, reducing pollution. The TS of the AS/P ratio is more persistent and complex. The predictability times of the ratios are comparable in four of the seven communes. The TS of the AS/MV ratios shows greater information loss and chaos. According to the calculated CK values, it is possible to relate it to anomalous diffusion (sub/super-diffusion) and the context that favored the expansion of the pandemic: urban densification, pollution, urban meteorology, population density, etc. Using Fréchet heavy-tailed probability, the compatibility of the results with CK is verified.

The two-resistance mechanism (TRM) attribution method, which was designed to analyze the urban–rural contrast of temperature, is improved to study the urban–rural contrast of heat stress. The improved method can be applied to diagnosing any heat stress index that is a function of temperature and humidity. As an example, in this study we use it to analyze the summertime urban–rural contrast of simplified wet bulb globe temperature (SWBGT) simulated by the Geophysical Fluid Dynamics Laboratory land model coupled with an urban canopy model. We find that the urban–rural contrast of SWBGT is primarily caused by the lack of evapotranspiration in urban areas during the daytime and the release of heat storage during the nighttime, with the urban–rural differences in aerodynamic features playing either positive or negative roles depending on the background climate. Compared to the magnitude of the urban–rural contrast of temperature, the magnitude of the urban–rural contrast of SWBGT is damped due to the moisture deficits in urban areas. We further find that the urban–rural contrast of 2-m air temperature/SWBGT is fundamentally different from that of canopy air temperature/SWBGT. Turbulent mixing in the surface layer leads to much smaller urban–rural contrasts of 2-m air temperature/SWBGT than their canopy air counterparts. Significance Statement: Heat leads to serious public health concerns, but urban and rural areas have different levels of heat stress. Our study explains the magnitude and pattern of the simulated urban–rural contrast in heat stress at the global scale and improves an attribution method to quantify which biophysical processes are mostly responsible for the simulated urban–rural contrast in heat stress. We highlight two well-known causes of higher heat stress in cities: the lack of evapotranspiration and the stronger release of heat storage. Meanwhile, we draw attention to the vegetation types in rural areas, which determine the urban–rural difference in surface roughness and significantly affect the urban–rural difference in heat stress. Last, we find the urban–rural contrasts of 2-m air temperature/SWBGT are largely reduced relative to their canopy air counterparts due to the turbulent mixing effect. [ABSTRACT FROM AUTHOR]

This study examines how organized lines of deep convective storms can be impacted by a large city with a prominent urban heat island and how low‐level environmental vertical wind shear may influence the outcomes of that interaction. Idealized simulations of squall lines are conducted in which a simplified urban area—defined by perturbations to skin temperature and surface roughness length—is placed in the center of an otherwise horizontally homogeneous domain. Simulations are conducted with three different magnitudes of low‐level vertical wind shear representing "weak," "medium," and "strong" shear environments. Results show that storms experience noticeable modification—including enhanced downwind precipitation—after interacting with a prominent urban heat island in all three shear configurations. However, the details of the modification are a function of the shear magnitude. In the medium and strong shear simulations, updrafts are enhanced via increased buoyancy after passing over a prominent urban heat island. In contrast, little updraft strengthening is evident in the weak‐shear simulations. Instead, near‐surface winds are enhanced downwind of the urban heat island due to a more prominent descending rear‐inflow jet. Plain Language Summary: Idealized computer model simulations are conducted in which a line of thunderstorms pass over a simplified representation of a large urban area. Two main characteristics of the urban area (skin temperature excess, and surface roughness length) are systematically altered. The storm environment is also altered by varying the change in wind speed with height, or "vertical wind shear." Results show that the presence of a city can modify the structure of the thunderstorms, but the type of modification varies depending on the vertical wind shear strength. Key Points: Organized lines of convective storms can strengthen after passing over a large city with an urban heat islandHeat island‐induced intensification to updrafts is more prominent in environments with stronger low‐level vertical wind shearStorms in weak‐shear environments experience an enhancement to the rear‐inflow jet after passing over an urban heat island [ABSTRACT FROM AUTHOR]

This study contributes to the body of current knowledge about the urban effect on extreme precipitation (EP) by investigating the city–EP interaction over Lagos, Nigeria. This is a unique, first-time study that adds a "missing piece" of this information about the African continent to the comprehensive global urban precipitation "puzzle." The convection-permitting Weather Research and Forecasting (WRF) Model is employed within an ensemble simulation framework using combinations of different physical schemes and boundary/initial conditions to detect the urban signal on an extreme rainfall event that occurred on 30 May 2006. WRF simulations are verified against satellite-estimated and in situ observations, and the results from the best-performing ensemble members are used for analysis. The results show that the control simulation with urban representation generated 20%–30% more rainfall over the urban area than the nonurban sensitivity simulation, in which the city is replaced by forest. Physical mechanisms behind the differences were revealed. We found that the urbanization in Lagos reduced evapotranspiration, resulting in the increase of sensible heating (by 75 W m−2). This further enhances the urban heat-island effect (+1.5 K of air surface temperature), facilitating horizontal convergence and boosting daytime sea breeze. As a result, more moisture is transported from the southern sea area to inland areas; the moisture then converges over Lagos city, creating favorable conditions for enhancing convection and extreme-rainfall-generating processes. [ABSTRACT FROM AUTHOR]

The city of Mumbai, India, frequently receives extreme rainfall (>204.5 mm day−1) during the summer monsoonal period (June–September), causing flash floods and other hazards. An assessment of the meteorological conditions that lead to these rain events is carried out for 15 previous cases from 1980 to 2020. The moisture source for such rain events over Mumbai is generally an offshore trough, a midtropospheric cyclone, or a Bay of Bengal depression. The analysis shows that almost all of the extreme rain events are associated with at least two of these conditions co-occurring. The presence of a narrow zone of high sea surface temperature approximately along the latitude of Mumbai over the Arabian Sea can favor mesoscale convergence and is observed at least 3 days before the event. Anomalous wind remotely supplying copious moisture from the Bay of Bengal adds to the intensity of the rain event. The presence of midtropospheric circulation and offshore trough, along with the orographic lifting of the moisture, give a unique meteorological setup to bring about highly localized catastrophic extreme rainfall events over Mumbai. The approach adopted in this study can be utilized for other such locales to develop location-specific guidance that can aid the local forecasting and emergency response communities. Further, it also provides promise for using data-driven/machine learning–based pattern analysis for developing warning triggers. Significance Statement: We have identified the meteorological conditions that lead to extreme heavy rains over Mumbai, India. They are that 1) at least two of these rain-bearing systems, offshore trough, midtropospheric circulation, and Bay of Bengal depression moving north-northwestward are concurrently present, 2) an anomalous high SST gradient is present along the same latitude as Mumbai, and 3) the Western Ghats orography favors the rainfall extreme to be highly localized over Mumbai. [ABSTRACT FROM AUTHOR]

Heavy precipitation (HP) events can be preceded by moist heatwaves (HWs; i.e., hot and humid weather), and both can be intensified by urbanization. However, the effect of moist HWs on increasing urban HP remains unknown. Based on statistical analyses of daily weather observations and ERA5 reanalysis data, we herein investigate the effect of moist HWs on urban-intensified HP by dividing summer HP events into NoHW- and HW-preceded events in the Yangtze River delta (YRD) urban agglomeration of China. During the period 1961–2019, the YRD has experienced more frequent, longer-lasting, and stronger intense HP events in the summer season (i.e., June–August), and urbanization has contributed to these increases (by 22.66%–37.50%). In contrast, urban effects on HP are almost absent if we remove HW-preceded HP events from all HP events. Our results show that urbanization-induced increases in HP are associated with, and magnified by, moist HWs in urban areas of the YRD region. Moist HWs are conducive to an unstable atmosphere and stormy weather, and they also enhance urban heat island intensity, driving increases in HP over urban areas. Significance Statement: The contribution of urbanization to increases in heavy precipitation has been widely reported in previous studies. HP events can be preceded by moist heatwaves (hot and humid extremes); however, it is unknown whether moist HWs enhance urban effects on HP. We choose the Yangtze River delta urban agglomeration to explore this question and find that urbanization contributes to the increasing frequency, duration, maximum intensity, and cumulative intensity of HP events in the summer season. However, this urban signal is not detectable if we remove HW-preceded events from all HP events. In other words, moist HWs play a key role in magnifying urbanization-induced increases in HP. Given that urban areas are projected to continue expanding and moist HWs are projected to occur with increasing frequency and intensity in the future, the role of HWs in the urban water cycle merits further investigation. [ABSTRACT FROM AUTHOR]

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions.

Temperature is the most used meteorological variable for a large number of applications in urban resilience planning, but direct measurements using traditional sensors are not affordable at the usually required spatial density. On the other hand, spaceborne remote sensing provides surface temperatures at medium to high spatial resolutions, almost compatible with the needed requirements. However, in this case, limitations are represented by cloud conditions and passing times together with the fact that surface temperature is not directly comparable to air temperature. Various methodologies are possible to take benefits from both measurements and analysis methods, such as direct assimilation in numerical models, multivariate analysis, or statistical interpolation. High-resolution thermal fields in the urban environment are also obtained by numerical modelling. Several codes have been developed to resolve at some level or to parameterize the complex urban boundary layer and are used for research and applications. Downscaling techniques from global or regional models offer another possibility. In the Milan metropolitan area, given the availability of both a high-quality urban meteorological network and spaceborne land surface temperatures, and also modelling and downscaling products, these methods can be directly compared. In this paper, the comparison is performed using: the ClimaMi Project high-quality data set with the accurately selected measurements in the Milan urban canopy layer, interpolated by a cokriging technique with remote-sensed land surface temperatures to enhance spatial resolution; the UrbClim downscaled data from the reanalysis data set ERA5; a set of near-surface temperatures produced by some WRF outputs with the building environment parameterization urban scheme. The comparison with UrbClim and WRF of the cokriging interpolated data set, mainly based on the urban canopy layer measurements and covering several years, is presented and discussed in this article. This comparison emphasizes the primary relevance of surface urban measurements and highlights discrepancies with the urban modelling data sets.

Accurate characterization of land use and land cover changes (LULCC) is essential for numerical models to capture LULCC-induced effects on regional meteorology and air quality, while outdated LULC dataset largely limits model capability in reproducing land surface parameters, particularly for complex terrain. In this study, we incorporate land cover data from MODIS in 2019 into the Weather Research and Forecasting (WRF) model to simulate the impacts of LULC on meteorological parameters over the Sichuan Basin (SCB). Further, we conduct Community Multiscale Air Quality (CMAQ) simulations with WRF default LULC and MODIS 2019 to probe the effects on regional air quality. Despite consistency found between meteorological observations and WRF-CMAQ simulations, the default WRF land cover data does not accurately capture rapid urbanization over time compared with MODIS. Modeling results indicate that magnitude changes trigged by LULCC are highly varied across SCB and the impacts of LULCC are more pronounced over extended metropolitan areas due to alteration by urbanization, featured by elevating 2-m temperature up to 2°C and increased planetary boundary layer height (PBLH) up to 400 m. For air quality implications, it is found that LULCC leads to basin-wide O3 enhancements with maximum reaching 21.6 μg/m3 and 57.2 μg/m3 in the daytime and nighttime, respectively, which is mainly attributed to weakening NOx titration effects at night. This work contributes modeling insights into quantitative assessment for impacts of LULCC on regional meteorology and air quality which pinpoints optimization of the meteorology-air quality model.

Replacing dark conventional roofs with more reflective “cool” roofs has been proposed as a method to lower urban air temperatures. Many meteorological studies have simulated potential cool roof air temperature reductions. However, economic and logistical challenges make it difficult to perform the large-scale demonstrations needed to verify these model results. This work assesses whether a neighborhood-scale cool roof demonstration could yield an observable air temperature change. We use both an idealized theoretical framework and a meteorological model to estimate the air temperature reduction that could be induced by increasing roof albedo over ~ 1 km2 area of a city. Both the idealized analysis and model indicate that an air temperature reduction could be detected, with the model indicating a reduction of 0.5 °C and the idealized analysis indicating a larger reduction of 1.3 °C. Follow-on modeling is recommended prior to design of a neighborhood-scale demonstration.

This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement: Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments. [ABSTRACT FROM AUTHOR]

Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society. [ABSTRACT FROM AUTHOR]

Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city. Significance Statement: Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations. [ABSTRACT FROM AUTHOR]

The Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step toward this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties, including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY, reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY, offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort. [ABSTRACT FROM AUTHOR]

Cities develop a specific climate related to their morphology and the materials that constitute them. The addition of vegetation in urban areas induces cooling and shading effects that can modify local climate and thermal comfort conditions. The Town Energy Balance (TEB) urban canopy model offers several configurations for a more or less fine-tuned consideration of natural covers and associated physical processes in the urban environment. This study aims to evaluate the sensitivity of TEB to the representation of vegetation and the resolution of the chosen databases in the simulation of microclimatic variables, at the scale of a heterogeneous urban neighborhood located in Toulouse, France. First, the effect of the improved description of the vegetation input to the model is highlighted by comparing the results obtained with a readily available national database and then with a very-high-resolution satellite-derived vegetation database. Second, the two vegetation parameterizations, with or without explicit tree stratum, that are available in the TEB model are evaluated and compared. Measurements carried out on specific routes and stop points in a neighborhood of Toulouse allowed microclimatic variables to be evaluated. Results show that refining the vegetation database can somehow improve the modeling of air temperature. As a result of enhancing the vegetation description in the model, that is, physical processes associated with the presence of trees in urban canyons, the air temperature, but also the wind and the thermal comfort index, are better simulated. These results are encouraging for the use of TEB as a decision support tool for urban planning purposes. [ABSTRACT FROM AUTHOR]

In mesoscale Weather Research and Forecasting (WRF) model, several urban‐modeling options exist, such as Single‐layer Urban Canopy Model, Building Effect Parameterization, and Building Energy Model. However, these models have limitations in terms of the choice of land surface models (LSMs) and planetary boundary layer (PBL) schemes, the associated computational expenses, and other constraints. Recently, Dy et al. (2019, https://doi.org/10.1029/2018JD029333) included the urban‐momentum drag effect for wind‐speed modeling by developing a new multilayer model based on a modified nonlocal Asymmetric Convective Model version‐2 (ACM2) PBL scheme. This urban‐based ACM2 (UACM) has demonstrated a significantly improved ability over the base‐ACM2 (BACM) to predict wind speed near the urban ground surface and shown an inflection point at roof level in the vertical profile of horizontal wind. In this study, urban thermal and moisture components are introduced into the Pleim‐Xiu (PX) LSM and coupled with UACM. The diurnal variation in street, walls, and roof‐surface temperatures is modeled using the two‐layer force‐restore algorithm. Simple radiation treatment is considered to account for shadowing on streets based on the solar zenith angle and building morphology. Heat and moisture flux evolution are considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The idealized and real urban data case WRF simulations are performed over the Pearl River Delta region in southern China to test the new UACM. The computationally efficient UACM is expected to perform faster for operational forecasting runs. Key Points: Urban thermal and moisture effects are included in the Pleim‐Xiu land surface model, and connected to the Urban Asymmetric Convective ModelAbility of new UACM is confirmed by performing idealized simulations to analyze model sensitivity to various urban morphological parametersImprovement in the urban wind, temperature, and moisture‐prediction is demonstrated with the new UACM by conducting real‐data case studies [ABSTRACT FROM AUTHOR]

In this study, we focused on the impacts of the planetary boundary layer (PBL) low-level jet (LLJ) on the horizontal distribution, vertical development, and 3D structure of urban heat island (UHI). Observational datasets were collected from 224 automatic weather stations (AWSs), and an intensive sounding experiment was conducted in Beijing from 28 August to 2 September 2016. Three-dimensional simulations were operated by the Weather Research and Forecasting (WRF) Model. The results show the following: Ri was smaller than 0.25 at both urban and suburban stations near the surface when the LLJ was present. Through turbulent mixing, the LLJ extended the horizontal distribution of the canopy UHI downwind and increased the total UHI area by approximately 1 × 103 km2. The temperature lapse rate in the urban area was 0.7°C (100 m)−1 with the LLJ, twice that in the absence of an LLJ. The jet enhanced the vertical mixing above the urban area, accompanied by a near-surface TKE up to 0.52 m2 s−2, elevating the vertical UHI development height to 200 m. The LLJ is capable of increasing the temperature of the downwind urban area by a maximum of 8.5°C h−1 through warm advection. The temperature advection in the upper air caused by the LLJ also tilted the 3D UHI structure as a plume. Results reproduced the process by which the LLJ affect the 3D UHI structure through turbulence and advection, and could also provide ideas regarding the influence of the LLJ in other PBL processes. [ABSTRACT FROM AUTHOR]

In this work, we investigate the effect of areawide building retrofitting on summertime, street-level outdoor temperatures in an urban district in Berlin, Germany. We perform two building-resolving, weeklong large-eddy simulations: one with nonretrofitted buildings and the other with retrofitted buildings in the entire domain to meet today’s energy efficiency standards. The comparison of the two simulations reveals that the mean outdoor temperatures are higher with retrofitted buildings during daytime conditions. This behavior is caused by the much smaller inertia of the outermost roof/wall layer in the retrofitting case, which is thermally decoupled from the inner roof/wall layers by an insulation layer. As a result, the outermost layer heats up more rigorously during the daytime, leading to increased sensible heat fluxes into the atmosphere. During the nighttime, the outermost layer’s temperature drops down faster, resulting in cooling of the atmosphere. However, as the simulation progresses, the cooling effect becomes smaller and the warming effect becomes larger. After 1 week, we find the mean temperatures to be 4 K higher during the daytime while the cooling effects become negligible. SIGNIFICANCE STATEMENT: Building retrofitting is taking place in Europe and other continents as a measure to reduce energy consumption. The change in the building envelope directly influences the urban atmosphere. Our study reveals that areawide retrofitting in a German city district can have negative effects on the outdoor microclimate in summer by causing higher air temperatures. [ABSTRACT FROM AUTHOR]

Urban-scale Numerical Weather Prediction (NWP) systems will be important tools for decision-making in and around large cities in a changing climate exposed to more extreme weather events. Such a state-of-the-art real-time system down to 250-m grid spacing was implemented in the context of the Toronto 2015 Panamerican games, Canada (PanAm). Combined with the Global Environmental Multiscale (GEM) model, attention was brought to the representation of the detailed urban landscape, and to the inclusion of sub-daily variation of the Great Lakes surface temperature. Results show a refined representation of the urban coastal environment micro-meteorology with a strong anisotropy of the urban heat island reaching about 2 °C on average for the summer season, coastal upwelling, and mesoscale features such as cumulus clouds and lake-breeze flow. Objective evaluation at the surface with a dense observational network reveals an overall good performance of the system and a clear improvement in comparison to reference forecasts at 2.5-km grid spacing in particular for standard deviation errors in urban areas up to 0.3 °C for temperature and dew point temperature, and up to 0.5 m s − 1 for the wind speed, as well as for precipitation with an increased Equitable Threat Score (ETS) by up to 0.3 for the evening accumulation. The study provides confidence in the capacity of the new system to improve weather forecasts to be delivered to urban dwellers although further investigation of the initialization methods in urban areas is needed. [ABSTRACT FROM AUTHOR]

Established urban hydrometeorological literature shows the enhanced rainfall occurs generally near and downwind of urban centers. This information is often too generalized for operational use. Anomaly analysis in a spatial framework similar to operational forecast zones may provide utility in prediction of extreme events like localized flash flooding. This paper introduces a spatial analysis algorithm deploying 4-km NCEP Multisensor Precipitation Estimates Stage IV data to determine specific areas, or sectors, of anomalous precipitation across three southern United States cities (Atlanta, Houston, and Raleigh). Rainfall frequency and intensity are quantified during the afternoon and early evening across a 21-year period. Hotspot analysis and sector-based comparison of anomalies show that specific areas within each city are prone to isolated extreme rainfall events more often than other areas. Isolated precipitation in central and eastern Atlanta, specifically, has a high rate of recurrence and intensity during convectively active hours. 700 hPa wind deviations from the zonal mean are mapped to domain sectors as rainfall anomalies exceed ten standard deviations. These results are an application of the provided geospatial algorithm which combines gridded raster data with subdivisions defined by polygon features to assess the clustering of variables (environmental or otherwise) within domains of interest. • A geospatial algorithm application focuses on urban precipitation clustering. • The algorithm subdivides and aggregates any raster data for comparison within a domain. • Three SE United States cities are assessed for areas prone to excessive rainfall. • Hot spot analysis shows that certain areas within each city are prone to excessive rainfall. • Clustering in Atlanta is pronounced and accounts for a majority of summer rainfall. [ABSTRACT FROM AUTHOR]

This work analyzes the evolution of an urban thermal environment using measurements, in the form of time series, of atmospheric pollutants (PM10, PM2.5, CO) and meteorological variables (temperature (T), relative humidity (RH) and magnitude of wind speed (WS)) of three periods, each of 3.25 years: 2010–2013, 2017–2020 and 2019–2022. The study region is the capital of Chile, Santiago de Chile, located in a rugged basin geography. Of the total communes that make up the capital, six communes that are at different heights from sea level were selected for this study, providing 3,074,004 data records. These communes have been subject to an intensive urban densification process. The time series are analyzed through the chaos theory, demonstrating that they are chaotic through the calculation of the parameters: Lyapunov exponent (λ > 0), correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), Hurst exponent (0.5 < H < 1), Lempel–Ziv complexity (LZ > 0). Based on these parameters, the following is constructed for each commune: the CK ratio, which results from the ratio between the entropies of the meteorological variables and the entropies of the pollutants; the loss of information ( < 0) using the Lyapunov exponent; the fractal dimension (D) using the Hurst exponent. It is verified, when comparing the three periods for the six communes, that: CK evolves declining with height, with a greater influence of pollutants; the loss of information is faster in urban meteorology; an increase in fractality. The estimation of the entropic flows, based on the Clausius equation, confirm the trend. The descriptive framework shows the weakness of the mitigation measures.

The wind field over an urban lake may exhibit considerable variability resulting from wind-shielding effects from surrounding structures. Field measurements at an urban reservoir in Singapore were augmented by computational fluid dynamics (CFD) model results to develop a wind model over the reservoir surface via a data assimilation approach. The field measurements identified, depending on structure alignment with the prevailing wind direction, wind shielding that impacted wind direction and velocity over the reservoir surface. The wind model integrated the temporal response of the measurements and spatial distribution produced by the CFD modeling. The wind model was used to predict the spatiotemporal pattern of the wind field over the reservoir surface for a full year. The modeling results showed good agreement with measured wind data at three measurement locations on the reservoir surface. The wind model has been incorporated with a hydrodynamics and water quality model to provide the spatiotemporal wind forcing over the reservoir surface. [ABSTRACT FROM AUTHOR]