Understanding the impact of heatwave on urban heat in greater Sydney: Temporal surface energy budget change with land types

The impact of heatwaves (HWs) on urban heat island (UHI) is a contentious topic with contradictory research findings. A comprehensive understanding of the response of urban and rural areas to HWs, considering the underlying cause of surface energy budget changes, remains elusive. This study attempts to address this gap by investigating a 2020 HW event in the Greater Sydney Area using the Advanced Weather Research and Forecasting (WRF) model with 250-m high resolution. Findings indicate that the HW intensifies the nighttime surface UHI by approximately 4 °C. An analysis of surface energy budgets reveals that urban areas store more heat during the HW due to receiving more solar radiation and less evapotranspiration compared to rural areas. The maximum heat storage flux in urban during the HW can be around 200 W/m2 higher than that during post-HW. The stored heat is released at nightime, raising the air temperature in the urban areas. Forests and savannas have relatively lower storage heat fluxes due to high transpiration and albedo, and the maximum heat storage flux is only around 50 W/m2 higher than that during post-HW. In contrast, a negative synergistic effect is detected between the 2-m UHI and HW. This may be because other meteorological conditions including wind have substantial impacts on the air temperature pattern. The strong hot and dry winds coming from the west resulted in a higher air temperature in the western urban district, and intra-city disparities are higher. Meanwhile, the western forest area also experiences higher temperatures due to the westward winds. In addition, changes in wind direction alter the temperature distribution in the northern rural region. The findings of the present study may provide some insights into urban heat mitigation during HW.

High-resolution dynamical downscaling experiment outputs data over Reunion and Mauritius islands in the South-West Indian Ocean

The present article describes a dataset encompassing model outputs generated by the Weather Research and Forecasting (WRF) regional climate model. A high-resolution (1km) downscaling simulation was performed over two tropical islands, Reunion and Mauritius, situated in the South-West Indian Ocean (SWIO), with initial and boundary conditions provided by the ERA5 reanalysis with a global resolution of 0.25° × 0.25°. The simulation used three nested domains sequentially configured with spatial resolutions of 9, 3, and 1km, respectively, with a downscaling ratio of 3. The physical configurations of this simulation were determined through previous modeling studies and sensitivity tests. The published simulation data currently covers a period of 10 years, starting from 1991 (with the possibility to be extended to 30 years). Over 60 output variables were selected for publication with open access, including those related to the intermittent energy resources (e.g., surface solar radiation and its direct/diffuse components, wind speed/direction at multiple vertical levels, and precipitation, of interest for the run-off-river hydropower), as well as the widely used climatic/meteorological variables (e.g., temperature, pressure, humidity, etc.) at a temporal resolution varying from a day up to 30 minutes. All the data are available through an open-access data server, where an intelligent algorithm is applied to simplify the download process for data users. For the first time, a long-term, high-resolution climate/meteorological dataset covering Reunion and Mauritius has been simulated and published as open-access data, yielding substantial benefits to studies on climate modeling, weather forecasting, and even those related to climate change in the SWIO region. In particular, this dataset will enable a better understanding of the temporal and spatial characteristics of intermittent climate-related energy resources, consequently facilitating their implementation towards a green and low-carbon future.

Prediction of the Overseas Migration of the Fall Armyworm, Spodoptera frugiperda, to Japan

(1) Background: The fall armyworm, Spodoptera frugiperda, is an invasive migratory insect pest that first arrived in Japan in early July 2019. Since then, the species has immigrated to Japan mainly in the summer monsoon season and inflicted damage mainly on the maize used as animal feed in the western region, where major immigrations occur. In this study, to know the precise arrival timing and area of S. frugiperda for purposes of pest management, a prediction method for its overseas migration from neighboring source areas was developed. (2) Methods: The method uses the Weather Research and Forecast model to give numerical weather predictions and the GEARN-insect model to predict migration. Emigration source areas on the Chinese mainland and the island of Taiwan and the insect's take-off and flight behaviors were input to the GEARN-insect model to calculate the daily migration prediction figures. (3) Results: In a prediction evaluation using 2-year six-point trapping data in Japan, the prediction method achieved an average hitting ratio of 78%. (4) Conclusions: The method has sufficient prediction quality for operational use. The technique may be applicable to other migratory moths immigrating to Japan, such as the oriental armyworm, Mythimna separata.

Forecast of Fine Particles in Chengdu under Autumn-Winter Synoptic Conditions

We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.

Evaluation of the danger of a tailings pile belonging to an active mine through its characterization and a dispersion model

Mining is one of the principal economic activities in Mexico, which in addition to bringing benefits to the population, causes health and environmental problems. This activity produces a lot of wastes, but the main one is tailings. In Mexico, these wastes are disposed of in the open air, and there is no control over them, so the particles of these wastes are dispersed by wind currents to the surrounding population. In this research, tailings were characterized, finding in them particles smaller than 100 microns; in this way, tailings can enter into the respiratory system and hence can cause diseases. Furthermore, it is important to identify the toxic components. The present work does not have previous research in Mexico, and it shows a qualitative characterization of the tailings from an active mine using different analytical techniques. In addition to the data obtained from the characterization of the tailings, as well as the concentration of the toxic elements found, which were Pb and As, a dispersal model was generated and used to estimate the concentration of particles in the wind generated at the studied area. The air quality model used in this research is AERMOD, where it uses emission factors and available databases provided by Environmental Protection Agency (USEPA); Moreover, the model was coupled with meteorological information from the latest generation WRF model. The modeling results estimated that the dispersion of particles from the tailings dam can contribute up to 10.15 µg/m³ of PM₁₀ to the air quality of the site, which, according to the characterization of the samples obtained, could be dangerous for human health and can be estimated up to a concentration of 0.04 µg/m³ of Pb and 10.90 ng/m³ of As. It is very important to make this kind of research to know the risk which people around this disposal sites are exposed to.

Assessment of meteorological settings on air quality modeling system-a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO₂ dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO₂ concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Modeling impacts of mining activity-induced landscape change on local climate

As a major energy source, coal has been mined on an increasingly larger scale as the social economy has continuously developed, resulting in drastic land type changes. These changes in turn cause changes in the local climate and affect the local ecological environment. Therefore, for coal cities, mining activities are an important factor influencing the local climate, and clarifying the impact of mining activities on the ecological environment is important for guiding regional development. In this paper, the impact of land use/cover changes (LUCCs) on local temperature in the spring and summer seasons from 1980 to 2018 was simulated using the Weather Research and Forecasting (WRF) model with Xilinhot city as the study area, and the regional distribution of local surface energy was analyzed in conjunction with the ground-air energy transfer process. The results show that the grassland area in Xilinhot remained above 85% from 1980 to 2018, so mining activities had a small impact on the average temperature of the whole region. However, in the mining area, the warming effect caused by mining activities was more obvious, with an average temperature increase of 0.822 K. Among other land transformation types, the conversion to water bodies had a very obvious cooling effect, lowering the temperature by an average of 2.405 K. By comparing the latent heat flux (LH), sensible heat flux (SH), and ground heat flux (GRD) under different land use types, it was found that in 2018, the LH decreased by 0.487 W/m², the SH decreased by 0.616 W/m², and the GRD decreased by 0.753 W/m². The conversion to built-up urban land caused a significant decrease in the LH in the corresponding area, allowing more energy to be used to increase SH values, which resulted in significantly higher urban temperatures than in other areas.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Targeted implementation of cool roofs for equitable urban adaptation to extreme heat

Cities are facing the twin pressures of greenhouse gas driven climatic warming and locally induced urban heating. These pressures are threatening populations that are sensitive to extreme heat due to sociodemographic factors including economic means. Heat-reducing infrastructure adaptation measures such as reflective "cool" materials can reduce urban temperatures. Here we examine the needs-based equity implications associated with heat-reducing cool roofing in Maricopa County, Arizona through application of high-resolution urban-atmospheric simulations. We simulate heatwave conditions and evaluate the air temperature reduction arising from uniform cool roof implementation (i.e., the entire urbanized county), and contrast results against simulated cooling impacts of needs-based targeted cool roof implementation in sociodemographically heat sensitive areas. We find that installing cool roofs uniformly, rather than in a targeted fashion, provides on average 0.66 °C reduction in the highest heat sensitivity area and 0.39 °C temperature reduction in the lowest heat sensitivity area due in part to a higher roof area density in the heat sensitive area. Targeting cool roof implementation yields 0.45 °C cooling in the most sensitive areas compared to 0.22 °C cooling in the least sensitive areas, meaning that needs-based targeted cool roofs in high sensitivity areas provide more relief than cool roofs targeted at low sensitivity areas, thus providing more cooling where it is most needed. Needs-based targeted implementation has the dual benefits of concurrently producing more than twice as much cooling and reducing heat exposure for the largest absolute number of individuals in the densely populated, highly heat sensitive areas. Targeting cool roof implementation to high heat sensitivity areas, however, does not achieve thermally equal temperatures in Maricopa County because the high sensitivity areas were substantially warmer than low sensitivity areas prior to implementation. This study illustrates the utility of a new "Targeted Urban Heat Adaptation" (TUHA) framework to assess needs-based equity implications of heat-reducing strategies and underscores its importance by examining the impacts of cooling interventions across sociodemographically heterogeneous urban environments.

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid

This article presents the weather and power data files from renewable sources used to solve the economic dispatch problem of a microgrid that operates in the isolated and grid-connected modes. Methodology is used in the research article "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data" (Silva et al., 2020). Automatic stations located in the Brazil's south and northeast furnished the weather data (global horizontal irradiance, temperature, and wind speed). A script generates files containing weather forecasts from one-day ahead using the geographical coordinates of the weather stations. Hybrid models, characterized by real and binary variables, use the weather forecasting data to calculate the photovoltaic and wind power forecasts. A microgrid management algorithm uses these forecasts to solve the optimal economic dispatch problem. This data-in-brief paper presents five datasets for each weather station: (i) Weather dataset downloaded from the website of the National Meteorological Institute, (ii) Weather research and forecasting (WRF) dataset derived from the raw data generated by the weather research and forecasting model, (iii) Weather dataset that joins the forecasted data with the measured data in a single file, (iv) Handled dataset that treats some gaps in the weather dataset and converts it to other formats, (v) Files containing only the temperature, global horizontal irradiance, and wind speed data, (vi) Files containing the measured and forecasted wind and solar power.

Real-time simulation of accidental passive transport of radioactive pollutant from a proposed nuclear power plant

Power plant's site selection is a complex task and involves through analyses of multi-disciplinary processes which are interlinked with each other. The site selection for nuclear power plants additionally requires an assessment of radiation doses to the environment and public during normal operation and in the case of an accident. This demands the problem of radioactive particles' dispersion in atmosphere to be analysed in real time for a comprehensive set of radioactive release scenarios in prevailing meteorological conditions in the plant surroundings. In this study, a local scale atmospheric dispersion problem, considering a hypothetical accidental release (1 Bq s^-1of I-131) from a nuclear power plant is simulated with a combination of weather forecasting and particle dispersion codes on a multiprocessor computer system. The meteorological parameters are predicted with a weather research and forecasting (WRF) model and used in Lagrangian particle dispersion model based code FLEXPART to calculate the trajectory of released particles, and thereby, the estimation of spatial I-131 dose distribution. The concentration of particles and radiation doses were calculated for release heights of 10, 57, and 107 m and found in a reasonable agreement with the observed data and better than an earlier investigation done with regional atmospheric modelling system (RAMS) code. A comparison between the results of WRF and RAMS for various meteorological parameters revealed that better space-time predictions of wind speeds and directions by WRF had a profound effect on tracing the trajectories of particles and thereby the spatial dose distribution. The particles followed the changes in wind direction predicted by WRF that were known to prevail in the region.

Confronting Uncertainties of Simulated Air Pollution Concentrations during Persistent Cold Air Pool Events in the Salt Lake Valley, Utah

Air pollutant accumulations during wintertime persistent cold air pool (PCAP) events in mountain valleys are of great concern for public health worldwide. Uncertainties associated with the simulated meteorology under stable conditions over complex terrain hinder realistic simulations of air quality using chemical transport models. We use the Community Multiscale Air Quality (CMAQ) model to simulate the gaseous and particulate species for 1 month in January 2011 during the Persistent Cold Air Pool Study (PCAPS) in the Salt Lake Valley (SLV), Utah (USA). Results indicate that the temporal variability associated with the elevated NO_x and PM_2.5 concentrations during PCAP events was captured by the model (r = 0.20 for NO_x and r = 0.49 for PM_2.5). However, concentrations were not at the correct magnitude (NMB = -35/12% for PM_2.5 during PCAPs/non-PCAPs), where PM_2.5 was underestimated during PCAP events and overestimated during non-PCAP periods. The underestimated PCAP strength is represented by valley heat deficit, which contributed to the underestimated PM_2.5 concentrations compared with observations due to the model simulating more vertical mixing and less stable stratification than what was observed. Based on the observations, the dominant PM_2.5 species were ammonium and nitrate. We provide a discussion that aims to investigate the emissions and chemistry model uncertainties using the nitrogen ratio method and the thermodynamic ammonium nitrate regime method.

Radon transport events associated with the impact of a NORM repository in the SW of Europe

Two radon measurement stations located to the north and south of a NORM (Naturally Occurring Radioactive Materials) repository of phosphogypsum (southwest of Europe) were used to monitor radon behavior during 2018. The stations are located at opposing sides of the repository, one in Huelva City to the north and other one in a rural area to the south. This setup aimed to identify the influence of the NORM repository on each station and use radon levels as a marker of atmospheric transport in the local area. To achieve this, a comparison was carried out with other coastal stations in the south of Spain, finding higher average concentrations in Huelva City, ~3.3 Bq m^-3. Hierarchical clustering was applied to identify days with different radon patterns at each Huelva station, detecting possible local radon transport events from the repository. Three events were investigated with WRF (Weather Research and Forecasting) and FLEXPART-WRF (FLEXible PARTicle dispersion model). It was found that both sampling sites required atmospheric stagnant conditions to reach high radon concentration. However, under these conditions the urban station showed high radon regardless of wind direction while the rural station also required radon transport from the repository, either directly or indirectly.

Comparing temperature-related mortality impacts of cool roofs in winter and summer in a highly urbanized European region for present and future climate

Human health can be negatively impacted by hot or cold weather, which often exacerbates respiratory or cardiovascular conditions and increases the risk of mortality. Urban populations are at particular increased risk of effects from heat due to the Urban Heat Island (UHI) effect (higher urban temperatures compared with rural ones). This has led to extensive investigation of the summertime UHI, its impacts on health, and also the consideration of interventions such as reflective 'cool' roofs to help reduce summertime overheating effects. However, interventions aimed at limiting summer heat are rarely evaluated for their effects in wintertime, and thus their overall annual net impact on temperature-related health effects are poorly understood. In this study we use a regional weather model to simulate the winter 2009/10 period for an urbanized region of the UK (Birmingham and the West Midlands), and use a health impact assessment to estimate the impact of reflective 'cool' roofs (an intervention usually aimed at reducing the UHI in summer) on cold-related mortality in winter. Cool roofs have been shown to be effective at reducing maximum temperatures during summertime. In contrast to the summer, we find that cool roofs have a minimal effect on ambient air temperatures in winter. Although the UHI in summertime can increase heat-related mortality, the wintertime UHI can have benefits to health, through avoided cold-related mortality. Our results highlight the potential annual net health benefits of implementing cool roofs to reduce temperature-related mortality in summer, without reducing the protective UHI effect in winter. Further, we suggest that benefits of cool roofs may increase in future, with a doubling of the number of heat-related deaths avoided by the 2080s (RCP8.5) compared to summer 2006, and with insignificant changes in the impact of cool-roofs on cold-related mortality. These results further support reflective 'cool' roof implementation strategies as effective interventions to protect health, both today and in future.

The winter urban heat island: Impacts on cold-related mortality in a highly urbanized European region for present and future climate

Exposure to heat has a range of potential negative impacts on human health; hot weather may exacerbate cardiovascular and respiratory illness or lead to heat stroke and death. Urban populations are at increased risk due to the Urban Heat Island (UHI) effect (higher urban temperatures compared with rural ones). This has led to extensive investigation of the summertime UHI and its effects, whereas far less research focuses on the wintertime UHI. Exposure to low temperature also leads to a range of illnesses, and in fact, in the UK, annual cold-related mortality outweighs heat-related mortality. It is not clearly understood to what extent the wintertime UHI may protect against cold related mortality. In this study we quantify the UHI intensity in wintertime for a heavily urbanized UK region (West Midlands, including Birmingham) using a regional weather model, and for the first time, use a health impact assessment (HIA) to estimate the associated impact on cold-related mortality. We show that the population-weighted mean winter UHI intensity was +2.3 °C in Birmingham city center, and comparable with that of summer. Our results suggest a potential protective effect of the wintertime UHI, equivalent to 266 cold-related deaths avoided (~15% of total cold-related mortality over ~11 weeks). When including the impacts of climate change, our results suggest that the number of heat-related deaths associated with the summer UHI will increase from 96 (in 2006) to 221 in the 2080s, based on the RCP8.5 emissions pathway. The protective effect of the wintertime UHI is projected to increase only slightly from 266 cold-related deaths avoided in 2009 to 280 avoided in the 2080s. The different effects of the UHI in winter and summer should be considered when assessing interventions in the built environment for reducing summer urban heat, and our results suggest that the future burden of temperature-related mortality associated with the UHI is likely to increase in summer relative to winter.

An assessment of high-resolution wind speeds downscaled with the Weather Research and Forecasting Model for coastal areas in Ghana

Ghana produces over 50% of its electrical energy demands from fossil-fuelled thermal plants. To increase the proportion of renewable energy in the national energy generation, a Renewable Energy Master Plan (REMP) which seeks, among others, to shift the country's national energy generation capacity towards more renewable energy sources has been developed. The REMP noted that inadequate data on renewable energy sources such as wind is one of the challenges to achieving this target. In this regard, this paper assessed the open-source Weather Research and Forecasting Model, as a tool for generating wind resource data. The WRF model is often used to downscale meteorological datasets for wind resources assessments. However, due to diverse model options, performance assessments are required to establish the accuracy and suitability of a model configuration for an application in an area. This paper assessed the performance of a Weather Research and Forecasting Model configuration that is based on previous verification studies. In evaluation, data accuracy benchmarks were generally met by the downscaled wind data. A wind map that was generated was observed to be generally accurate and better than the previous 2001 wind map for Ghana. It is presumed that the configuration is suitable for wind mapping activities for the coastal areas in Ghana, and probably neighbouring countries. However, for downscaling time-series data, further studies are recommended.

Projected changes in the Iberian Peninsula drought characteristics

High spatial resolution drought projections for the Iberian Peninsula (IP) have been examined in terms of duration, frequency, and severity of drought events. For this end, a set of regional climate simulations was completed using the Weather Research and Forecasting (WRF) model driven by two global climate models (GCMs), the CCSM4 and the MPI-ESM-LR, for a near (2021-2050) and a far (2071-2100) future, and under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5). Projected changes for these simulations were analyzed using two drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), considering different time scales (3- and 12-months). The results showed that the IP is very likely to undergo longer and more severe drought events. Substantial changes in drought parameters (i.e., frequency, duration, and severity) were projected by both indices and at both time scales in most of the IP. These changes are particularly strong by the end of the century under RCP8.5. Meanwhile, the intensification of drought conditions is expected to be more moderate for the near future. However, the results also indicated key differences between indices. Projected drought conditions by using the SPEI showed more severe increases in drought events than those from SPI by the end of the century and, especially, for the high-emission scenario. The most extreme conditions were projected in terms of the duration of the events. Specifically, results from the 12-month SPEI analysis suggested a significant risk of megadrought events (drought events longer than 15 years) in many areas of IP by the end of the century under RCP8.5.

Spatiotemporal changes of surface solar radiation: Implication for air pollution and rice yield in East China

The changes of surface solar radiation (SSR) have significant implication for air pollution and rice yield. In this study, gridded SSR data, derived from multi-platform datasets and radiation model, were used to analyze its spatiotemporal changes over East China during 2000-2016. The results show SSR experiences dimming during 2000-2005, then turns into brightening till 2016. Both aerosol optical depth (AOD) and single scattering albedo (SSA) contribute to SSR trend. AOD dominates the spatiotemporal changes of SSR in East China, and this impact is higher in the North than the South. SSA has little impact on SSR with low AOD, but its contribution to SSR becomes important as AOD increases. Moreover, gridded planet boundary layer (PBL) was simulated by the Weather Research and Forecasting Model (WRF) and SSR-PBL relationship was also explored. Long-term evidence indicates PBL has a regulatory effect on SSR in the air pollution. Additionally, aerosol-induced radiation reduction can influence rice yield in East China, and it can result in about mean 6.74% reduction in rice yield over East China. Province-level changes of aerosol-induced reduction in rice production were also evaluated and it suggests the impact of aerosols on rice production is non-negligible, especially in Jiangsu and Anhui Province. Our study underscores the importance of aerosol pollution on surface radiation and the mitigation of aerosols is beneficial for crop production under climate change.

Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters

PM_2.5 concentrations in most of the Indian cities are in alarming levels. However, the current network of 308 monitoring stations are heterogeneously placed and do not cover many parts of the country. This limits the ability of agencies to measure the concentration which people are exposed to. In this study, ground level PM_2.5 concentrations and the associated risk and mortality in India using satellite based AOD data for the year 2015 was estimated to identify the state specific number of more monitoring sites required. Results indicate that average PM_2.5 concentrations were 89 μg/m³, which caused 1.61 million deaths including 0.34 million Chronic Obstructive Pulmonary Disease (COPD) deaths, 0.2 million Lung Cancer (LC) deaths, 0.53 million Ischemic Heart Disease (IHD) deaths and 0.70 million deaths due to Stroke. The years of life lost (YLL) per 1000 population due to exposure to PM_2.5 indicated Delhi (North-India) to be severely affected by PM_2.5 resulting in 227.47 years of life lost and was closely followed by Bihar (Eastern-India) (225.18), Rajasthan (Western-India) (225.05) and Uttar Pradesh (Northern-India) (213.16). Eastern India had the highest population weighted concentration (102.09 μg/m³) and contributed to 23.46% of premature mortality and was followed by Central (75.32 μg/m³) and Northern India (75.12 μg/m³), thus indicating severity of air pollution in India and need for its constant monitoring. As per Indian regulatory agency's guidelines, India still needs 1638 more air quality monitoring stations, of which North-Indian states require maximum number of additional stations i.e. 400, followed by 382 in eastern states.

Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands

A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O₃, PM_2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM_2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO₂, NO₂, PM_2.5, and CO and was often related to how well local emissions sources were characterized. SO₂ predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.

Characteristics and sources of PM2.5 with focus on two severe pollution events in a coastal city of Qingdao, China

In this study, the seasonal mean PM_2.5 concentration in Qingdao, a coastal city, during 2014-2018 was first analyzed and the winter, in particular of 2015, showed the highest concentration. To elucidate the sources and control factors of PM_2.5, three dimensional model Weather Research and Forecasting (WRF), Community Multiscale Air Quality model (CMAQ), as well as Flexible Particle model (FLEXPART), were used. During December 2015 and January 2016, modeling results showed that the mean contribution to PM_2.5 mass concentrations from local emissions in Qingdao was 25%, and the transport from north and west accounted for almost half. Over the two episodically polluted periods (29-31 December 2015; 15-17 January 2016), the local emissions in Qingdao surprisingly contributed to only 18% and 24% to PM_2.5 mass concentrations, respectively, indicating the dominant contributions from other regions, such as areas outside Qingdao in Shandong and Beijing-Tianjin-Hebei (BTH). The results show the sources region and contribution may vary remarkably along with the change in the pathways of the air parcel, inferred by the FLEXPART, while the near-surface PM_2.5 enhancement is largely caused by downward vertical advection and enhanced aerosol chemistry reactions, accompanied by simultaneous drop in the boundary layer height. This study also reveals that the transport contribution is sensitive to the air parcel trajectories. We, therefore, recommend the efficient emission control based on transport trajectories in short-term air quality improvement in Qingdao.

Comparison of four different types of planetary boundary layer heights during a haze episode in Beijing

The planetary boundary layer (PBL) height mainly determines the environmental capacity for the diffusion of atmospheric pollutants, and has always been a hot issue in the study of air pollution. However, there still remains great uncertainty, partly because different PBL heights definitions and the PBL heights are obtained by various measurement instruments. Pollutants are the substances emitted, different from the atmospheric background physical properties such as wind, temperature and turbulence flux that always exist even without pollution. It is very important to distinguish PBL heights obtained from wind, temperature, turbulence quantities and the concentration of pollutants. In this paper, we express the PBL heights determined on the above four parameters as Hu, H_θ, Ht and Hc respectively, and compare them during a heave haze pollution process in Beijing using observation data and simulation results. The comparison results show that: (1) H_θ, namely the inversion layer height, decreased from approximately 1250 m to 450 m from 26 to 30 December, resulting in deteriorating pollution situation. Hc, calculated by lidar and characterizes the maximum depth of vertical diffusion of particulates, also dropped below 500 m, and on the whole, the values of Hc estimated by gradient method and H_θ were in good agreement; (2) Generally, Hc was relatively lower than H_θ and Hu, despite a high bias caused by the existence of the residual layer, multilayer aerosol structure, or lower inversion; (3) Ht estimated from turbulence quantities simulated by WRF model mainly approximated Hu, H_θ and Hc in the daytime during haze pollution, however for the nocturnal boundary layer height in the winter, Ht was seriously underestimated. The averaged PBL heights according to the pollution level showed that Hc, H_θ, Hu and Ht differed greatly on clean days, and the maximum PBL height Hu exceeded 1400 m. On clean days, the inversion intensities observed were lower, so the blocking effect of the inversion layer to pollutant diffusion was not strong enough, H_θ (886 m) deviated from Hc (1111 m)_. However, Hc and Ht were very close, approximately 1100 m. The decrease of PBL height led to heavy pollution, Hc, H_θ and Ht were almost 700 m. Hu was slightly higher and reduced by about 450 m during heavy pollution. The detailed analyses and comparisons of the PBL height from different variables can help improve the rational application of different methods in the determination of PBL height.

Metropolitan Areas

Extreme precipitation events influence watershed, agriculture, and urban management. The probability of extreme precipitation is estimated for storm water management using precipitation intensity-duration-frequency (PIDF) curves. This study explores developing PIDF curves from dynamically downscaled 36- and 12-km simulations using the Weather Research and Forecasting (WRF) model. Three modeled data sets are examined: 36-km WRF model forced with 2.5° (~275-km) NCEP-DOE AMIP-II Reanalysis (R2); 36-km WRF model forced with 0.75° (~80-km) ERA-Interim; and 12-km WRF model forced with ERA-Interim. The WRF outputs are verified against historical observations for three cities in the Eastern United States using a 23-year period (1988-2010). The 36-km WRF data set driven by R2 produced PIDF curves that were acceptable at the 12- to 24-hr durations, but those WRF data could not realistically simulate extremes represented by the high-intensity, short-duration precipitation events. Increasing the resolution of WRF's driving data from R2 to ERA-Interim did not improve WRF's representation of precipitation events. Using 12-km grid spacing enhances WRF's ability to reproduce PIDF curves developed from observations. Finer grid spacing dramatically improves the frequency and intensity of the 1- to 3-hr events and improves the 6- to 24-hr events. However, improvements with the 12-km WRF data did not apply equally to all study sites, suggesting further modifications to the WRF configuration and/or methodology are necessary. Although imperfect, the results here lend confidence to using modeled data to construct PIDF curves, which could be valuable for projecting changes to parameters used in urban and environmental planning.

Estimation of thermal comfort felt by human exposed to extreme heat wave in a complex urban area using a WRF-MENEX model

Thermal comfort could indicate human thermal sensation when exposed to a local meteorological condition. Because humans can suffer illness when exposed to heat or even die, it is essential to assess human comfort levels to increased temperature and to provide this information to the public. This study aims to estimate thermal comfort using the human heat balance model combined with a numerical meteorological model in Seoul mega city during the heat wave periods experienced during 2016. The gridded thermal comfort index of physiological subjective temperature (PST) was calculated based on the Man-Environment Heat Exchange (MENEX) model, which used as inputs the meteorological parameters, clothing insulations, and metabolic rates. High-resolution meteorological parameters were obtained by coupling Weather Research and Forecasting (WRF) model with Building Effect Parameterization (BEP) + Building Energy Model (BEM) using detailed urban classification. The modeling results showed that the PST distribution has a clearly heterogeneous spatial distribution during the heat wave period. The high PST values were largely found in the residential area during the day, due to the high temperature and low wind speed associated with high-density buildings, and the daily maximum PST reached a very hot level (44.1-54.0 °C). Our study suggested that the human heat balance model combined with the numerical meteorological model could be used to provide more reliable information about thermal comfort to groups that may be vulnerable to the effects of heat waves in complex urban environments.

Hydro-thermal boundary conditions at different underlying surfaces in a permafrost region of the Qinghai-Tibet Plateau

Hydro-thermal properties of permafrost and its distribution are sensitive to climate changes and human activities. Accurate and reasonable prediction on aforementioned information is important for eco-environment construction and vital infrastructures development. To model the current and future states of permafrost, it is a key challenge to effectively determine the upper hydro-thermal boundary conditions for permafrost models under changing climate and different underlying surfaces at proper spatial and temporal scales. An approach, combined regional climate downscaling method with model output statistics method, was developed to produce a time series of air temperature, surface temperatures, and surface unfrozen water contents for different underlying surfaces. It provided various climate and surface parameters at a spatial scale on the order of 10² m² for engineering designs, which was used to predict boundary conditions under possible climate scenarios. The predicted and simulated models were calibrated and validated by the monitored data at an experimental site in Chumar, China, close to the Qinghai-Tibet Railway and the Qinghai-Tibet Highway. Results show that the multiple linear regression model (MLRM) can predict the current states and future changes of upper hydro-thermal boundary conditions for permafrost while the original states of natural surface are modified by natural or human factors on the condition of complicated climatic and complex topography regions. The statistical regression model (SRM) based on the outputs of regional climate model (RCM) and MLRM provides a simple method for the convenience of numerical calculation. These results also indicate the possible applications to other areas and situations.

Potential benefits of cool roofs in reducing heat-related mortality during heatwaves in a European city

Hot weather can exacerbate health conditions such as cardiovascular and respiratory diseases, and lead to heat stroke and death. In built up areas, temperatures are commonly observed to be higher than those in surrounding rural areas, due to the Urban Heat Island (UHI) effect. Climate change and increasing urbanisation mean that future populations are likely to be at increased risk of overheating in cities, although building and city scale interventions have the potential to reduce this risk. We use a regional weather model to assess the potential effect of one type of urban intervention - reflective 'cool' roofs - to reduce local ambient temperatures, and the subsequent impact on heat-related mortality in the West Midlands, UK, with analysis undertaken for the summer of 2006, as well as two shorter heatwave periods in 2006 and 2003. We show that over a summer season, the population-weighted UHI intensity (the difference between simulated urban and rural temperature) was 1.1 °C on average, but 1.8 °C when including only night times, and reached a maximum of 9 °C in the West Midlands. Our results suggest that the UHI contributes up to 40% of heat related mortality over the summer period and that cool roofs implemented across the whole city could potentially offset 18% of seasonal heat-related mortality associated with the UHI (corresponding to 7% of total heat-related mortality). For heatwave periods, our modelling suggests that cool roofs could reduce city centre daytime 2 m air temperature by 0.5 °C on average, and up to a maximum of ~3 °C. Cool roofs reduced average UHI intensity by ~23%, and reduced heat related mortality associated with the UHI by ~25% during a heatwave. Cool roofs were most effective at reducing peak temperatures during the daytime, and therefore have the potential to limit dangerous extreme temperatures during heatwaves. Temperature reductions were dependent on the category of buildings where cool roofs were applied; targeting only commercial and industrial type buildings contributed more than half of the reduction for heatwave periods. Our modelling suggested that modifying half of all industrial/commercial urban buildings could have the same impact as modifying all high-intensity residential buildings in the West Midlands.

Using a Balloon-Launched Unmanned Glider to Validate Real-Time WRF Modeling

The use of small unmanned aerial systems (sUAS) for meteorological measurements has expanded significantly in recent years. SUAS are efficient platforms for collecting data with high resolution in both space and time, providing opportunities for enhanced atmospheric sampling. Furthermore, advances in mesoscale weather research and forecasting (WRF) modeling and graphical processing unit (GPU) computing have enabled high resolution weather modeling. In this manuscript, a balloon-launched unmanned glider, complete with a suite of sensors to measure atmospheric temperature, pressure, and relative humidity, is deployed for validation of real-time weather models. This work demonstrates the usefulness of sUAS for validating and improving mesoscale, real-time weather models for advancements toward reliable weather forecasts to enable safe and predictable sUAS missions beyond visual line of sight (BVLOS).

Comparative study on typhoon's wind speed prediction by a neural networks model and a hydrodynamical model

There are many models to predict natural phenomena around the world, but it is still difficult to accurately forecast the events. Many scientists, modeling professions, students, and researchers working on the tropical cyclones prediction, but they are encountered to many errors during compiling and configuring the models. Despite the increasing accuracy of weather forecasts, there is an element of uncertainty in all predictions. This paper reviews two methods used in my previous papers for predicting typhoon wind speed in the South China Sea, a dynamical model, Weather Research and Forecasting (WRF), and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The performances of the models are calculated using statistical parameters of the root mean square error (RMSE) and Correlation Coefficient (CC), and the advantages and disadvantages of both models are represented. Regarding the statistical parameters values, the ANFIS model in comparison with the WRF model showed higher accuracy for typhoon intensity prediction because of higher CC and lower RMSE. The development of methods has represented several advanced techniques that their strengths and weaknesses have not been well-documented. In fact, a qualitative assessment and points to several ways in which the methods may be able to complement each other. The paper suggests that the scientists should improve the concepts of the models.

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Investigating two different methods and their performance in predicting typhoon intensity.

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Representing the strengths and weaknesses of both models.

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Suggesting some solutions for future researches.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.

WRF-SMOKE-CMAQ modeling system for air quality evaluation in São Paulo megacity with a 2008 experimental campaign data

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over São Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in São Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM₁₀ and ozone average concentration of the entire period was well represented, with correlation coefficients for PM₁₀, varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM_2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of São Paulo.

Diurnal and seasonal variations in surface methane at a tropical coastal station: Role of mesoscale meteorology

In view of the large uncertainties in the methane (CH₄) emission estimates and the large spatial gaps in its measurements, studies on near-surface CH₄ on regional basis become highly relevant. This paper presents the first time observational results of a study on the impacts of mesoscale meteorology on the temporal variations of near-surface CH₄ at a tropical coastal station, in India. It is based on the in-situ measurements conducted during January 2014 to August 2016, using an on-line CH₄ analyzer working on the principle of gas chromatography. The diurnal variation shows a daytime low (1898-1925ppbv) and nighttime high (1936-2022ppbv) extending till early morning hours. These changes are closely associated with the mesoscale circulations, namely Sea Breeze (SB) and Land Breeze (LB), as obtained through the meteorological observations, WRF simulations of the circulations and the diurnal variation of boundary layer height as observed by the Microwave Radiometer Profiler. The diurnal enhancement always coincides with the onset of LB. Several cases of different onset timings of LB were examined and results presented. The CH₄ mixing ratio also exhibits significant seasonal patterns being maximum in winter and minimum in pre-monsoon/monsoon with significant inter-annual variations, which is also reflected in diurnal patterns, and are associated with changing synoptic meteorology. This paper also presents an analysis of in-situ measured near-surface CH₄, column averaged and upper tropospheric CH₄ retrieved by Atmospheric Infrared Sounder (AIRS) onboard Earth Observing System (EOS)/Aqua which gives insight into the vertical distribution of the CH₄ over the location. An attempt is also made to estimate the instantaneous radiative forcing for the measured CH₄ mixing ratio.

WRF modeling of PM2.5 remediation by SALSCS and its clean air flow over Beijing terrain

Atmospheric simulations were carried out over the terrain of entire Beijing, China, to investigate the effectiveness of an air-pollution cleaning system named Solar-Assisted Large-Scale Cleaning System (SALSCS) for PM_2.5 mitigation by using the Weather Research and Forecasting (WRF) model. SALSCS was proposed to utilize solar energy to generate airflow therefrom the airborne particulate pollution of atmosphere was separated by filtration elements. Our model used a derived tendency term in the potential temperature equation to simulate the buoyancy effect of SALSCS created with solar radiation on its nearby atmosphere. PM_2.5 pollutant and SALSCS clean air were simulated in the model domain by passive tracer scalars. Simulation conditions with two system flow rates of 2.64 × 10⁵ m³/s and 3.80 × 10⁵ m³/s were tested for seven air pollution episodes of Beijing during the winters of 2015-2017. The numerical results showed that with eight SALSCSs installed along the 6^th Ring Road of the city, 11.2% and 14.6% of PM_2.5 concentrations were reduced under the two flow-rate simulation conditions, respectively.

Substantial impacts of landscape changes on summer climate with major regional differences: The case of China

China's rapid socioeconomic development during the past few decades has resulted in large-scale landscape changes across the country. However, the impacts of these land surface modifications on climate are yet to be adequately understood. Using a coupled process-based land-atmospheric model, therefore, we quantified the climatic effects of land cover and land management changes over mainland China from 2001 to 2010, via incorporation of real-time and high-quality satellite-derived landscape representation (i.e., vegetation fraction, leaf area index, and albedo) into numerical modeling. Our results show that differences in landscape patterns due to changes in land cover and land management have exerted a strong influence on summer climate in China. During 2001 and 2010, extensive cooling of up to 1.5°C was found in the Loess Plateau and 1.0°C in northeastern China. In contrast, regional-scale warming was detected in the Tibetan Plateau (0.3°C), Yunnan province (0.4°C), and rapidly expanding urban centers across China (as high as 2°C). Summer precipitation decreased in the northeastern region, with patchy reduction generally <1.8mm/day, but increased in the Loess Plateau, with local spikes up to 2.4mm/day. Our study highlights that human alterations of landscapes have had substantial impacts on summer climate over the entire mainland China, but these impacts varied greatly on the regional scale, including changes in opposite directions. Therefore, effective national-level policies and regional land management strategies for climate change mitigation and adaptation should take explicit account of the spatial heterogeneity of landscape-climate interactions.

Impacts of future urban expansion on summer climate and heat-related human health in eastern China

China is the largest and most rapidly urbanizing nation in the world, and is projected to add an additional 200 million city dwellers by the end of 2030. While this rapid urbanization will lead to vast expansion of built-up areas, the possible climate effect and associated human health impact remain poorly understood. Using a coupled urban-atmospheric model, we first examine potential effects of three urban expansion scenarios to 2030 on summer climate in eastern China. Our simulations indicate extensive warming up to 5°C, 3°C, and 2°C in regard to low- (>0%), high- (>75%), and 100% probability urban growth scenarios, respectively. The partitioning of available energy largely explains the changes in 2-m air temperatures, and increased sensible heat flux with higher roughness length of the underlying urban surface is responsible for the increase of nighttime planetary boundary layer height. In the extreme case (the low-probability expansion pathway), the agglomeration of impervious surfaces substantially reduces low-level atmospheric moisture, consequently resulting in large-scale precipitation reduction. However, the effect of near-surface warming far exceeds that of moisture reduction and imposes non-negligible thermal loads on urban residents. Our study, using a scenario-based approach that accounts for the full range of urban growth uncertainty by 2030, helps better evaluate possible regional climate effects and associated human health outcomes in the most rapidly urbanizing areas of China, and has practical implications for the development of sustainable urban regions that are resilient to changes in both mean and extreme conditions.

Case study of dust event sources from the Gobi and Taklamakan deserts: An investigation of the horizontal evolution and topographical effect using numerical modeling and remote sensing

A severe dust event occurred from April 23 to April 27, 2014, in East Asia. A state-of-the-art online atmospheric chemistry model, WRF/Chem, was combined with a dust model, GOCART, to better understand the entire process of this event. The natural color images and aerosol optical depth (AOD) over the dust source region are derived from datasets of moderate resolution imaging spectroradiometer (MODIS) loaded on a NASA Aqua satellite to trace the dust variation and to verify the model results. Several meteorological conditions, such as pressure, temperature, wind vectors and relative humidity, are used to analyze meteorological dynamic. The results suggest that the dust emission occurred only on April 23 and 24, although this event lasted for 5days. The Gobi Desert was the main source for this event, and the Taklamakan Desert played no important role. This study also suggested that the landform of the source region could remarkably interfere with a dust event. The Tarim Basin has a topographical effect as a "dust reservoir" and can store unsettled dust, which can be released again as a second source, making a dust event longer and heavier.

From hygroscopic aerosols to cloud droplets: The HygrA-CD campaign in the Athens basin - An overview

The international experimental campaign Hygroscopic Aerosols to Cloud Droplets (HygrA-CD), organized in the Greater Athens Area (GAA), Greece from 15 May to 22 June 2014, aimed to study the physico-chemical properties of aerosols and their impact on the formation of clouds in the convective Planetary Boundary Layer (PBL). We found that under continental (W-NW-N) and Etesian (NE) synoptic wind flow and with a deep moist PBL (~2-2.5km height), mixed hygroscopic (anthropogenic, biomass burning and marine) particles arrive over the GAA, and contribute to the formation of convective non-precipitating PBL clouds (of ~16-20μm mean diameter) with vertical extent up to 500m. Under these conditions, high updraft velocities (1-2ms^-1) and cloud condensation nuclei (CCN) concentrations (~2000cm^-3 at 1% supersaturation), generated clouds with an estimated cloud droplet number of ~600cm^-3. Under Saharan wind flow conditions (S-SW) a shallow PBL (<1-1.2km height) develops, leading to much higher CCN concentrations (~3500-5000cm^-3 at 1% supersaturation) near the ground; updraft velocities, however, were significantly lower, with an estimated maximum cloud droplet number of ~200cm^-3 and without observed significant PBL cloud formation. The largest contribution to cloud droplet number variance is attributed to the updraft velocity variability, followed by variances in aerosol number concentration.

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency.

Numerical simulation of the impact of reforestation on winter meteorology and environment in a semi-arid urban valley, Northwestern China

Since 1999 Chinese government has made great effort to reforest the south and north mountains surrounding urban Lanzhou - a city located in a river valley, Northwestern China. Until 2009 obvious land use change occurred, with 69.2% of the reforested area been changed from grasslands, croplands, barren or sparsely vegetated land to closed shrublands and 20.6% from closed shrublands, grasslands, and croplands to forests. Reforestation changes land-surface properties, with possible impact on the evolution of atmospheric variables. To understand to what extent the local meteorology and environment could be affected by reforestation in winter, and through what processes, two sets of simulations were conducted using the Weather Research and Forecasting model (WRF) and the FLEXible PARTicle (FLEXPART) dispersion model for a control case with high-resolution remotely sensed land cover data for 2009 and a scenario assuming no reforestation since 1999. Results suggested that the changes in albedo, surface exchange coefficient and surface soil heat conductivity related to reforestation led to the changes in surface net radiation and surface energy partitioning, which in turn affected the meteorological fields and enhanced the mountain-valley wind circulation. Replacement of shrublands and grassland with forest in the south mountain through reforestation play a dominant role in the enhancement of mountain-valley wind circulation. Reforestation increased the amount of air exchanged between the valley and the outside during the day, with the largest hourly increase of 10% on calm weather days and a monthly mean hourly increase of 2% for the study period (Dec. 2009). Reforestation affected the spatial distribution of pollutants and slightly improved the urban air quality, especially in the eastern valley. Results from this study provide useful information for future urban air quality management and reforestation plan, and some experience for cities with similar situations in the world.

A high resolution hydrodynamic model system suitable for novel harmful algal bloom modelling in areas of complex coastline and topography

Fjordic coastlines provide sheltered locations for finfish and shellfish aquaculture, and are often subject to harmful algal blooms (HABs) some of which develop offshore and are then advected to impact nearshore aquaculture. Numerical models are a potentially important tool for providing early warning of such HAB events. However, the complex topography of fjordic shelf regions is a significant challenge to modelling. This is frequently compounded by complex bathymetry and local weather patterns. Existing structured grid models do not provide the resolution needed to represent these coastlines in their wider shelf context. In a number of locations advectively transported blooms of the ichthyotoxic dinoflagellate Karenia mikimotoi are of particular concern for the finfish industry. Here were present a novel hydrodynamic model of the coastal waters to the west of Scotland that is based on unstructured finite volume methodology, providing a sufficiently high resolution hydrodynamical structure to realistically simulate the transport of particles (such as K. mikimotoi cells) within nearshore waters where aquaculture sites are sited. Model-observation comparisons reveal close correspondence of tidal elevations for major semidiurnal and diurnal tidal constituents. The thermohaline structure of the model and its current fields are also in good agreement with a number of existing observational datasets. Simulations of the transport of Lagrangian drifting buoys, along with the incorporation of an individual-based biological model, based on a bloom of K. mikimotoi, demonstrate that unstructured grid models have considerable potential for HAB prediction in Scotland and in complex topographical regions elsewhere.

Weekly variability of precipitation induced by anthropogenic aerosols: A case study in Korea in summer 2004

We examine the effect of anthropogenic aerosols on the weekly variability of precipitation in Korea in summer 2004 by using Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models. We conduct two WRF simulations including a baseline simulation with empirically based cloud condensation nuclei (CCN) number concentrations and a sensitivity simulation with our implementation to account for the effect of aerosols on CCN number concentrations. The first simulation underestimates observed precipitation amounts, particularly in northeastern coastal areas of Korea, whereas the latter shows higher precipitation amounts that are in better agreement with the observations. In addition, the sensitivity model with the aerosol effects reproduces the observed weekly variability, particularly for precipitation frequency with a high R at 0.85, showing 20% increase of precipitation events during the weekend than those during weekdays. We find that the aerosol effect results in higher CCN number concentrations during the weekdays and a three-fold increase of the cloud water mixing ratio through enhanced condensation. As a result, the amount of warm rain is generally suppressed because of the low auto-conversion process from cloud water to rain water under high aerosol conditions. The inefficient conversion, however, leads to higher vertical development of clouds in the mid-atmosphere with stronger updrafts in the sensitivity model, which increases by 21% cold-phase hydrometeors including ice, snow, and graupel relative to the baseline model and ultimately results in higher precipitation amounts in summer.

Simulation of radioactive plume gamma dose over a complex terrain using Lagrangian particle dispersion model

FLEXPART-WRF is a versatile model for the simulation of plume dispersion over a complex terrain in a mesoscale region. This study deals with its application to the dispersion of a hypothetical air borne gaseous radioactivity over a topographically complex nuclear site in southeastern France. A computational method for calculating plume gamma dose to the ground level receptor is introduced in FLEXPART using the point kernel method. Comparison with another similar dose computing code SPEEDI is carried out. In SPEEDI the dose is calculated for specific grid sizes, the lowest available being 250 m, whereas in FLEXPART it is grid independent. Spatial distribution of dose by both the models is analyzed. Due to the ability of FLEXPART to utilize the spatio-temporal variability of meteorological variables as input, particularly the height of the PBL, the simulated dose values were higher than SPEEDI estimates. The FLEXPART-WRF in combination with point kernel dose module gives a more realistic picture of plume gamma dose distribution in a complex terrain, a situation likely under accidental release of radioactivity in a mesoscale range.

Cardiorenal interactions in acute decompensated heart failure: contemporary concepts facing emerging controversies

Simultaneous dysfunction of the heart and the kidney represents a distinct spectrum of disease states composed of complex clinical scenarios with adverse outcomes. Worsening renal function (WRF) in the setting of acute decompensated heart failure (ADHF) is one such clinical setup for which the underlying mechanisms are poorly understood. Apparent discrepancies exist between the emerging data on the cardiorenal interactions of patients with ADHF and contemporary concepts such as the low forward flow or the high backward pressure hypotheses. The findings of recent retrospective studies also suggest that apparent "improvement in renal function" might be yet another risk factor for untoward outcomes in this patient population, further challenging our current understanding of the cardiorenal interactions. Besides, these data do not seem to fully support our conventional thinking about other aspects of these interactions such as the independent adverse impact of WRF on the outcomes of patients with ADHF, pointing to congestion as a possibly overlooked factor. In this article, we provide an overview of these emerging controversial issues with the goal of identifying the areas where clinical research could be most helpful, because it is of paramount importance to characterize the pathways leading to WRF in ADHF to develop a mechanistically relevant management strategy. Although the paucity of data coupled with the complexity of this field precludes any firm conclusion, these discussions are meant to prompt clinicians and researchers to revisit a number of long-believed concepts surrounding the cardiorenal interactions in ADHF.

The formation of a large summertime Saharan dust plume: Convective and synoptic-scale analysis

Haboobs are dust storms produced by the spreading of evaporatively cooled air from thunderstorms over dusty surfaces and are a major dust uplift process in the Sahara. In this study observations, reanalysis, and a high-resolution simulation using the Weather Research and Forecasting model are used to analyze the multiscale dynamics which produced a long-lived (over 2 days) Saharan mesoscale convective system (MCS) and an unusually large haboob in June 2010. An upper level trough and wave on the subtropical jet 5 days prior to MCS initiation produce a precipitating tropical cloud plume associated with a disruption of the Saharan heat low and moistening of the central Sahara. The restrengthening Saharan heat low and a Mediterranean cold surge produce a convergent region over the Hoggar and Aïr Mountains, where small convective systems help further increase boundary layer moisture. Emerging from this region the MCS has intermittent triggering of new cells, but later favorable deep layer shear produces a mesoscale convective complex. The unusually large size of the resulting dust plume (over 1000 km long) is linked to the longevity and vigor of the MCS, an enhanced pressure gradient due to lee cyclogenesis near the Atlas Mountains, and shallow precipitating clouds along the northern edge of the cold pool. Dust uplift processes identified are (1) strong winds near the cold pool front, (2) enhanced nocturnal low-level jet within the aged cold pool, and (3) a bore formed by the cold pool front on the nocturnal boundary layer.

Timing of hemoconcentration during treatment of acute decompensated heart failure and subsequent survival: importance of sustained decongestion

OBJECTIVES

This study sought to determine if the timing of hemoconcentration influences associated survival.

BACKGROUND

Indicating a reduction in intravascular volume, hemoconcentration during the treatment of decompensated heart failure has been associated with reduced mortality. However, it is unclear if this survival advantage stems from the improved intravascular volume or if healthier patients are simply more responsive to diuretics. Rapid diuresis early in the hospitalization should similarly identify diuretic responsiveness, but hemoconcentration this early would not indicate euvolemia if extravascular fluid has not yet equilibrated.

METHODS

Consecutive admissions at a single center with a primary discharge diagnosis of heart failure were reviewed (N = 845). Hemoconcentration was defined as an increase in both hemoglobin and hematocrit levels, then further dichotomized into early or late hemoconcentration by using the midway point of the hospitalization.

RESULTS

Hemoconcentration occurred in 422 (49.9%) patients (41.5% early and 58.5% late). Patients with late versus early hemoconcentration had similar baseline characteristics, cumulative in-hospital loop diuretic administered, and worsening of renal function. However, patients with late hemoconcentration versus early hemoconcentration had higher average daily loop diuretic doses (p = 0.001), greater weight loss (p < 0.001), later transition to oral diuretics (p = 0.03), and shorter length of stay (p < 0.001). Late hemoconcentration conferred a significant survival advantage (hazard ratio: 0.74 [95% confidence interval: 0.59 to 0.93]; p = 0.009), whereas early hemoconcentration offered no significant mortality benefit (hazard ratio: 1.0 [95% confidence interval: 0.80 to 1.3]; p = 0.93) over no hemoconcentration.

CONCLUSIONS

Only hemoconcentration occurring late in the hospitalization was associated with improved survival. These results provide further support for the importance of achieving sustained decongestion during treatment of decompensated heart failure.

A comparison of HWRF, ARW and NMM models in Hurricane Katrina (2005) simulation

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina.