Weather Research And Forecasting Research Articles

Investigation of the Development of Tropical Storm Nicholas based on Global and Regional Climate Data

This paper studies the simulation of Cyclone Nicholas that occurred close to the coastal area of Western Australia and fell on the mainland of Southwestern Australia. The simulation was conducted via a dynamical downscaling model, Weather Research and Forecasting (WRF), to obtain a higher resolution with reference to the regional climate data. The model simulation is generated using a global reanalysis of climate data for the initial and lateral boundary conditions. We investigated the response of the tropical storm to the model regarding the track and intensity using a modified Kyklop method that appears more appropriate for a landfall cyclone. Our study suggests that the regional climate data computed by the model deviates from the storm track of the global climate data forcing field. In this study, the track of the simulated storm is parallel to the satellite data, but it is shifted slightly to the east, closer to the mainland. Nevertheless, the model simulation can implement the intensity of the storm as strongly as the observation, while the forcing data delivers substantial underestimation.

Intense Tropical Cyclones in the Western North Pacific Under Global Warming: A Dynamical Downscaling Approach

AbstractThis study aims to assess the global warming's impact on intense tropical cyclones (TCs) over the western North Pacific (WNP) through dynamical downscaling. 379 and 179 TCs reaching Category 1 in the High‐Resolution Atmospheric Model (HiRAM) are downscaled for use in the Weather Research and Forecasting (WRF) model at 5‐km horizontal resolution in the current climate (1979–2015) and for use in the Representative Concentration Pathways 8.5 (RCP8.5) in the future climate (2074–2100) scenarios, respectively. Inclusion of the downscaling simulations by WRF helps better reproduce the probability distribution of the TC's lifetime maximum intensity (LMI). In the warmer climate, the LMI of very intense TCs in WNP are projected to be stronger. Such an increase in intensity is statistically significant, and can be primarily explained by enhanced intensification rate. Meanwhile, TCs among the top 5% in LMI can reach higher intensities which cannot be attained in the current climate. After downscaling, the probability of WNP TCs reaching Category 4–5 increases by 6.5 percentage points in the late 21st century. This increase is 1.7 percentage points higher than the increase projected exclusively by HiRAM. Moreover, for TCs among the top 5% in LMI, a 233‐km and 300‐km westward shift of LMI locations is identified in the late 21st century for simulations with and without the downscaling approach, respectively. Both results suggest that very intense TCs would pose a higher threat to the WNP lands under global warming, as they become substantially stronger, and as their LMI locations migrate toward the coast.

Comprehensive sensitivity analysis of the WRF model for meteorological simulations in the Arctic

Accurate meteorological simulations are important in response to the rapid climate change and insufficient meteorological observations in the Arctic. In this study, we assessed the performance of the Weather Research and Forecasting (WRF) model in simulating meteorological parameters at two Arctic stations (Barrow and Summit) in April 2019, by using measurements and statistical parameters. Sensitivity tests for different planetary boundary layer (PBL) schemes, four-dimensional data assimilation (FDDA) and sea surface temperature (SST) were also performed to reveal their impacts on the accuracy of model simulations. The results demonstrated that the WRF model performs the best in predicting the surface pressure but the worst in predicting the winds at these two stations. The sensitivity tests showed that among the four tested PBL schemes (ACM2, MYJ, BL and YSU), the model equipping with the MYJ scheme behaves the best in predicting the meteorological parameters especially the winds at these two Arctic stations. Applying FDDA nudging methods can also significantly increase the accuracy of simulations. In addition, we found that updating a time-varying SST in the model may bring a two-sided influence on meteorological simulations in the Arctic, especially at coastal stations.

Impacts of Climate Change and Regional Variations on Future Rainfall Patterns in Thailand by Downscaling Method

In this study, we investigated the impacts of climate change on rainfall patterns in Thailand using the downscaling method. The simulation data was obtained from the Weather Research and Forecasting (WRF) model, using the Community Earth System Model (CCSM) as a boundary condition. The characteristics of rainfall were analyzed in terms of the total annual rainfall, rainfall intensity, the number of days with heavy rain, and the total amount of rainfall in each season in the future compared to the base periods. It was found that the simulation of the climate in upper Thailand was consistent with the reanalysis values, with TCC ranging from 0.6 to 0.9. The simulated annual rainfall amount is underestimated throughout the country. There are indications that rainfall will increase in average and extreme terms in some regions, including the eastern region of the Northeast, the western side of the North, and the upper part of the West. In the southern part of the country, the overall rainfall indices are expected to decrease with low confidence in almost the entire region.

The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.

The Importance of Wind Simulations over Dried Lake Beds for Dust Emissions in the Middle East

Dust storms are one of the major environmental hazards affecting the Middle East countries, and largely originate in vast deserts and narrow dried lake beds. This study analyzes the inter-annual variation in dust weather conditions from 2000 to 2020 using data obtained from ten meteorological stations located around dried (completely or partly) lakes in Northwest (Urmia Lake) and South (Bakhtegan Lake) Iran. Since the wind regime is one of the most important factors controlling dust emissions in the dust source areas, wind speed simulations from the Weather Research and Forecasting (WRF) Model for 134,113 grid points covering the Middle East area, with a resolution of 5 km, were analyzed and compared with wind measurements at the stations around Urmia and Bakhtegan Lakes from 2005 to 2015. The analysis shows that the annual number of dust days was highly variable, presenting a significant increase at the stations around Urmia Lake during 2008–2011 and at the stations around Bakhtegan Lake in 2007–2012. Eleven years of WRF simulations of the mean diurnal wind patterns revealed that the highest 10 m wind speed occurred mostly around the local noon (12 to 15 UTC), generally coinciding with the majority of the reported dust codes within this time frame, as a result of the association between wind speed and dust emissions (dust weather conditions) around these lake basins. Consequently, accurate wind simulation has high importance for unbiased numerical prediction and forecasting of dust conditions. The comparison between the measured mean monthly 10 m wind speed and WRF-simulated 10 m wind speed revealed that the model overestimated wind data in all the stations around the Bakhtegan Lake but performed better at reconstructing the wind speeds at stations around Urmia Lake. Furthermore, notable differences were observed between measured and simulated wind directions, thus leading to uncertainties in the simulations of the dust-plume transport.

Wind gust forecasting by post-processing the WRF model outputs using ANN

Strong and highly variable winds and gusts are major hazards to infrastructure, properties, and life. Consequently, accurate prediction and timely detection of wind gust intensity have always been a focus of interest for earth scientists and weather forecasters.In this study, The WRF (Weather Research and Forecasting) post-process diagnostic of wind gusts (WPD method) was utilized to predict non-convective wind gust speeds using the direct outputs of the WRF model. To improve the prediction accuracy of this method, the results were post-processed using an artificial neural network (ANN). Multiple different ANN algorithms were examined to achieve the most accurate predictions possible. The results were evaluated using observational data extracted from 32 synoptic stations across Iran during the time period from 2014 to 2018.The results indicate that employing a multilayer perceptron ANN with a hybrid structure, consisting of one input layer comprising five parameters (10 m wind speed, sea level pressure, temperature, relative humidity, and predicted wind gust speed obtained from the WPD method), one hidden layer with a sigmoid activation function and 12 neurons, one output layer with a linear activation function and using the BR (Bayesian Regularization) training algorithm, significantly improve the accuracy of the WPD wind gust speed prediction method. The RMSE for wind gust speed prediction has decreased from 3.68 m/s (WPD method) to 1.88 m/s for the validation dataset. Additionally, there were considerable improvements of 50 %, 74 %, and 17 % in the MAE, MSE, and R2, respectively.

The synergistic effects of urbanization and an extreme heatwave event on urban thermal environment in Paris

In late July 2019, an extreme heatwave swept across the European regions. This study focused on examining the collective impacts of urbanization and this extreme heatwave on Paris's thermal environment. To achieve this, high-resolution simulations were performed using the urbanized version of the Weather Research and Forecasting (WRF) model. The inclusion of enhanced urban canopy parameters and improved spatial resolutions significantly improved the near-surface meteorological variables and the spatial-temporal patterns of the urban heat island in Paris. The impervious surface sensitivity study revealed that during the full-day sunlight conditions and low wind scenarios that characterized the extreme heatwave, the urban underlayer contributed to a 0.4 °C rise in daytime temperature and 1 °C increase during nighttime hours in the downtown area. Among the various representative Local Climate Zones (LCZ) types in Paris, namely compact mid-rise, large low-rise, and sparsely built, the contributions to temperature increase were about 0.3 °C (0.7 °C), 0.3 °C (0.5 °C), and 0.3 °C (0.6 °C) for daytime (nighttime) temperatures, respectively. The surface energy budget analysis further indicated that the augmented heat storage within urban impervious surfaces is one of the main causes of the enhanced urban heat island effect, particularly during the nighttime periods during heatwaves.

A new method for detecting urban morphology effects on urban-scale air temperature and building energy consumption under mesoscale meteorological conditions

Impact of urban morphology on urban-scale building energy consumption under mesoscale climate has not been elucidated. A new model was developed to examine the impact using the mesoscale Weather Research and Forecasting (WRF) model. For the first time, local climate zone and building categories (LCZBCs) are integrated into WRF, handling landuse/landcover and building configuration. The urban morphology parameters are clustered into three groups: urban structure, vegetation fraction, and impervious surface thermal properties. Our results show that urban structure induces a higher urban heat island (UHI) intensity at nighttime than does in daytime. The difference in urban-rural building/street structure is the key factor in compact and open high-rise areas, especially commercial-dominant ones, where the UHI intensity and AC load are increased by 40.33% and 34.52%, respectively. Besides, greenery is most effective to mitigate UHI problems, lessening AC demand in middle-rise and low-rise areas. Its benefit is most prominent for residential-dominant areas that could reduce the UHI intensity and AC load as much as 106.26% and 53.13%, respectively. In contrast to rural pavement, urban impervious surfaces induce negligible increases in temperature (4.75%) and AC load (2.33%). The outcome provides insights into the dominant parameters in LCZBCs and the strategy for urban (re)development.

Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant role. In this regard, physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the Weather Research and Forecasting (WRF) model as CPM to the use of different physics schemes on Andalusia, a complex orography region in the southern part of the Iberian Peninsula (IP). To do that, a set of 1-year WRF simulations was completed based on two “one-way” nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, characterized to be a very wet year, were compared with observational datasets from different sources to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations (from annual to hourly). In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the “best” configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region.

Impacts of irrigation on a precipitation event during GRAINEX in the High Plains Aquifer Region

Land use land cover change, including irrigation, impacts weather and climate. In this paper a precipitation event that occurred on the morning of 23 July 2018 during the Great Plains Irrigation Experiment (GRAINEX) is investigated. Six Weather and Research Forecasting (WRF) model-based experiments were conducted, which involved the increase or decrease of soil moisture by 5 % and up to 15 % over the irrigated croplands. These changes approximated soil moisture content in response to different levels of irrigation applications. An additional experiment, where irrigated land use was changed to grassland, was conducted to capture pre-irrigation land use and its impacts. It was found that regardless of strength of irrigation, precipitation decreased. In addition, the model did not produce precipitation over non-irrigated land use. When grassland replaced irrigated agriculture, increases in precipitation were estimated. With increased irrigation, latent heat flux increased compared to the control simulation and decreased when irrigation decreased. On the other hand, sensible heat flux was decreased compared to control when irrigation increased. The planetary boundary layer over irrigated land use was shallower than over non-irrigated land use while over grassland it was higher than irrigated but lower than non-irrigated land use. The changes in precipitation, the surface energy balance, and the planetary boundary layer served as a reminder of irrigation's complex effects on the atmosphere. Additional analysis of other precipitation events during GRAINEX would be helpful to better understand the effects of irrigation.

Moisture sources for precipitation over the Pamirs Plateau in winter and spring

AbstractThe abundant precipitation over Pamirs Plateau (PP) in spring and winter plays a vital role in the water resources over the arid areas of Central Asia. Understanding the moisture sources and water vapour transportation associated with precipitation are very important, but there were few studies investigating the moisture sources of PP. We used a Lagrangian model driven with the Weather Research and Forecasting output to analyse the moisture sources at Northern Pamirs Plateau (NPP) and Southern Pamirs Plateau (SPP) in spring 2016, 2009 and 2001, and in winter 2016, 2010 and 2017, as the seasonal precipitation in PP was the largest, median and lowest during 2001 to 2018, and the precipitation in spring and winter was much higher than that in summer and autumn. The moisture sources from four regions were quantified: Atlantic‐Europe‐Africa, Arctic‐Northern Asia, Indian Ocean, and moisture recycling from the PP. Atlantic‐Europe‐Africa and Indian Ocean are the dominant moisture source regions in spring and winter, which contribute more than 70% to the total moisture affecting the precipitation at PP. The contributions of Indian Ocean are higher at SPP than those at NPP in spring and winter. The contributions from Arctic‐Northern Asia and PP are generally low, except that the moisture from PP region contributed 19% to the spring precipitation at NPP, indicating the importance of a local moisture source in enhancing the spring precipitation at NPP. The moisture contributions originating from the different source regions show great differences between winter and spring. The moisture transportation is affected by westerlies, and the zonal winds in spring affect the moisture transportation, while the meridional winds over the Arabian Peninsula mainly affect the moisture transportation in winter.

Impacts of maritime shipping on air pollution along the US East Coast

Abstract. Air pollution is considered a leading threat to human health in the US and worldwide. An important source of air pollution in coastal areas is the globally increasing maritime shipping traffic. In this study, we take a high-resolution modeling approach to investigate the impacts of ship emissions on concentrations of various atmospheric pollutants, under the meteorological conditions and emissions of the year 2018. We utilize the Comprehensive Air Quality Model with extensions (CAMx) to simulate transport, diffusion, and chemical reactions and the Weather Research and Forecasting (WRF) model to provide the meteorological inputs. We focus on four criteria pollutants – fine particulate matter with a diameter smaller than 2.5 µm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) – as well as nitrogen oxide (NO), and we calculate their concentrations in the presence and absence of ship emissions along the US East Coast, particularly in the proximity of major ports. We find that ship emissions increase the PM2.5 concentrations over the ocean and over a few areas inland. The 98th percentile of the 24 h average PM2.5 concentrations (the “design value” used by the US Environmental Protection Agency) increased by up to 3.2 µg m−3 in some coastal areas. In addition, ships contribute significantly to SO2 concentrations, up to 95 % over the Atlantic and up to 90 % over land in coastal states, which represents a ∼45 ppb increase in the SO2 design values in some states. The 98th percentile of the hourly NO2 concentrations also increased by up to 15 ppb at the major ports and along the shore. In addition, we find that the impact of shipping emissions on O3 concentrations is not uniform, meaning that ships affect ozone pollution in both positive and negative ways: over the ocean, O3 concentrations were significantly higher in the presence of ships, whereas O3 concentrations decreased in the presence of ships in major coastal cities. Our simulation results show that ships emit significant amounts of fresh NO in the atmosphere, which then helps scavenge O3 in volatile organic compound (VOC)-limited areas, such as major ports. By contrast, over the ocean (NOx-limited regime), enhanced NOx concentrations due to ships contribute to the formation of O3 and therefore enhance O3 concentrations. Overall, due to the dominant southwesterly wind direction in the region, the impacts of ships on air pollutants mainly remain offshore. However, in coastal states near major ports, the impacts are significantly important.

Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations

Abstract. Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top–down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO2 (XCO2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO2 enhancement (ΔXCO2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of ∼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO2 emissions and biogenic fluxes. Although some of the diurnal variation of ΔXCO2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO2 simulation results from the posterior CO2 fluxes were improved (mean bias, −0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO2 d−1 at the 95 % confidence level, respectively. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO2 emissions.

Evaluation of three radiation schemes of the WRF-Solar model for global surface solar radiation forecast: A case study in Egypt

This study aims to assess the performance of three different radiation schemes in Weather Research and Forecasting (WRF) mesoscale model against ECMWF fifth generation (ERA5) Reanalysis data over Egypt. Dudhia, rrtmg, and Goddard schemes were used to simulate Global Horizontal Irradiance (SR) during 2017. Sixteen stations were selected to represent the different climate conditions in Egypt. The observed SR data is only available at five stations and used to evaluate the accuracy of ERA5 and WRF output. SR data at the other eleven stations were extracted from ERA5 to evaluate the corresponding values from WRF simulation. Coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), monthly standard deviation (STDV) and mean absolute percentage error (MAPE%) are the implemented evaluation statistical parameters in the study. The results showed that ERA5 and WRF Dudhia scheme have reliable results as compared to the observations at the five stations. Also, the WRF Dudhia scheme simulates the SR at the other eleven stations better than rrtmg, and Goddard schemes as compared to ERA5 dataset. Where, the three WRF radiation schemes overestimate the SR along the eleven stations and underestimate at Alexandria station by −0.15 KWh/m2, which may due to the weather conditions over these sites. But the Dudhia scheme over the eleven stations has the lowest MBE (<0.34 KWh/m2), RMSE (<0.58 KWh/m2), and MAPE (<−2%), with R2 more than 0.985 at most stations.

Impacts of different boundary layer parameterization schemes on simulation of meteorology over Himalaya

The boundary layer plays a vital role in governing the local atmospheric dynamics and meteorology, including the vertical transport of moisture, momentum, energy, and air pollutants. Hence, accurate parameterization of the boundary layer processes is essential in atmospheric models such as the regional climate models (RCMs). However, evaluations of different boundary layer schemes within the Weather Research and Forecasting (WRF) model remain nearly non-existing over complex terrains of the Himalayan region. In this regard, six different planetary boundary layer (PBL) schemes: Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3), Shin-Hong Scale-aware (SHSS), Mellor-Yamada-Janjić (MYJ), Asymmetric Convective Model version 2 (ACM2), and Quasi Normal Scale Elimination (QNSE) in the WRF model have been evaluated against observations during the Ganges Valley Aerosol Experiment (GVAX). The evaluation is carried out for clear-sky conditions during spring (March 22–27, 2012) against surface-based, balloon-borne, and radar wind profiler (RWP) observations. The MYJ, YSU, and SHSS performed well in simulating the temperature and specific humidity, whereas the ACM2 (MYNN3) shows lower (higher) bias in T2 and higher (lower) bias in Q2. Model performance is limited in reproducing wind field (r ∼ 0.3) with different PBL schemes over this region. Model performance is seen to vary significantly with the choice of PBL schemes; nevertheless, all schemes could typically capture the daytime maxima in boundary layer height with some overestimation. The competing effects of synoptic versus local circulation were found to control the boundary layer evolution over the Himalayas, affecting the model performance. The model better reproduced the boundary layer evolution during strong surface flow regimes, while a prominent nocturnal peak in boundary layer height is observed during the weak mean flow conditions associated with topography-induced local circulations. The model performance is limited for reproducing such mountain meteorological features in contrast to the homogeneous terrains where such nocturnal peaks are absent. This study may serve as a reference for selecting a suitable PBL scheme for regional climate modeling studies in the future.

On the sources of ambient SOA in PM2.5: An integrated analysis over Jinan city of China

Secondary organic aerosol (SOA) accounts for a significant fraction of fine particulate matter (PM2.5) and is a crucial component that affects climate and public health. However, the formation of SOA in urban areas remains unclear and sources contributed to SOA are poorly characterized. In this study, we combined ambient measurements and the Weather Research and Forecasting (WRF)-Community Multi-scale Air Quality (CMAQ) modeling system to investigate the evolution of SOA during winter over Jinan city in China. Relative contributions of different anthropogenic sources to ambient SOA were subsequently probed by the Integrated Source Apportionment Method (ISAM) to explore the impact of anthropogenic emissions. The mean concentration of SOA was 11.2 μg m−3, accounting for 29.4% in PM2.5, and SOA concentrations on clean days (4.9 μg m−3) increased by 4.8 times on polluted days (23.3 μg m−3), and strong correlation between the elevated PM2.5 episodes and increased SOA concentrations was found (R2 > 0.5). In addition, SOA concentrations and the proportion of SOA in PM2.5 were higher during the daytime (24.9 μg m−3, 32.4%) compared to nighttime (21.9 μg m−3, 25.1%) on polluted days, which is primarily attributed to the intense atmospheric photochemical reactions and anthropogenic activities. Mobile sources act as the dominant contributor to SOA (57.6%) and road diesel emissions (30.9%) made the largest contribution to SOA, followed by non-road mobile sources (17.4%) among subsectors in mobile sources. This study sheds new insights into ambient SOA formation and SOA source apportionment for typical megacities in China, guiding the design of source-oriented regulation efforts on SOA mitigation.

Exploring the natural ventilation potential for supertall buildings considering vertical meteorology: A case study in Harbin, China

The natural ventilation strategy is gaining increasing attention from architects due to its advantages in reducing energy consumption and maintaining a healthy indoor environment. For supertall buildings, the vertical variation in outdoor meteorology has inspired us to explore the energy-saving potential by adopting natural ventilation strategy. This study investigates the impact of vertical variation in urban meteorology on the natural ventilation potential (NVP) of supertall buildings based on numerical simulation and field observation. Firstly, the Weather Research and Forecasting (WRF) model is used to simulate the urban meteorology of the study region by integrating the local climate zone (LCZ) map. Next, meteorological data from sounding experiments by unmanned aerial vehicle (UAV) and monitoring stations are used to validate the simulated result, and thereby to explore the vertical meteorology in urban area. Finally, the vertical NVP in supertall buildings is quantitatively assessed considering the impact of LCZ in urban areas based on thermal comfort models. The findings reveal that atmospheric temperature decreases approximately linearly with increasing altitude, with a maximum temperature gradient of up to −0.82 °C/100 m. Static models underestimated the annual natural ventilation hours (NVHs) by at least 348 h compared to the adaptive thermal comfort model. Compared to low-density building regions, the higher NVP is indicated in high-density building regions. This study aims to illustrate the importance of considering the vertical variations of urban meteorology, and is expected to assist architects and policy makers in quantifying the energy saving potential of natural ventilation in supertalls.

Mathematical and computer modeling of atmospheric air pollutants transformation with input data refinement

&lt;p&gt;This article addresses the critical issue of harmful impurity dispersion in industrial facility atmospheres, considering point sources and factoring in photochemical changes. Conjugate equations are employed to assimilate pollutant data into the transfer equations' right side. Boundary conditions derive from global models like weather research and forecasting (WRF) and system for integrated modeling of atmospheric composition (SILAM), customized for the unique characteristics of an industrial city's pollutants. To encompass anthropogenic heat sources and surface heterogeneity, the model incorporates differential schemes for the atmospheric boundary layer, transport equations, and impurity transformation equations. Parameters for photochemical transformations, varying with weather and time of day, are derived from Ust-Kamenogorsk. A cloud-based geoinformation system (GIS) is developed for monitoring and forecasting air pollution. It assimilates data sources and accounts for photochemical transformations, enabling visualization of diverse weather and environmental scenarios. The article presents numerical modeling results of impurity spread and transformation influenced by mesometeorological processes, topography, and water resources within a specific city.&lt;/p&gt;

Cooling the Coldest Continent: The 4 December 2021 Total Solar Eclipse over Antarctica

Abstract Total solar eclipses (TSEs) are impressive astronomical events that have attracted people’s curiosity since ancient times. Their abrupt alterations to the radiation balance have stimulated studies on “eclipse meteorology,” most of them documenting events in the Northern Hemisphere while only one TSE (23 November 2003) has been described over Antarctica. On 4 December 2021—just a few days before the austral summer solstice—the moon blocked the sun over the austral high latitudes, with the path of totality arching from the Weddell Sea to the Amundsen Sea, thus producing a ∼2-min central TSE. In this work we present high-resolution meteorological observations from Union Glacier Camp (80°S, 83°W), the only location with a working station under totality, and South Pole station. These observations were complemented with meteorological records from 37 surface stations across Antarctica. Notably, the largest cooling (∼5°C) was observed over the East Antarctic dome, where obscurity was ∼85% while many sectors experienced insignificant temperature changes. This heterogenous cooling distribution, at odds with the seemingly homogeneous land surface of Antarctica, is partially captured by a simple radiative model. To further diagnose the effect of the eclipse on the surface meteorology, we ran multiple pairs of simulations (eclipse enabled and disabled) using the Weather Research and Forecasting (WRF) Model. The overall pattern and magnitude of the simulated cooling agree well with the observations and reveal that, in addition to the solar radiation deficit and cloud cover, low-level winds and the height of the planetary boundary layer are key determinants of the temperature changes and their spatial variability.