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The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.

Warm-sector heavy rainfall (WSHR) events in China have been investigated for many years. Studies have investigated the synoptic weather conditions during WSHR formation, the categories and general features, the triggering mechanism, and structural features of mesoscale convective systems during these rainfall events. The main results of WSHR studies in recent years are summarized in this paper. However, WSHR caused by micro- to mesoscale systems often occurs abruptly and locally, making both numerical model predictions and objective forecasts difficult. Further research is needed in three areas: (1) The mechanisms controlling WSHR events need to be understood to clarify the specific effects of various factors and indicate the influences of these factors under different synoptic background circulations. This would enable an understanding of the mechanisms of formation, maintenance, and organization of the convections in WSHR events. (2) In addition to South China, WSHR events also occur during the concentrated summer precipitation in the Yangtze River-Huaihe River Valley and North China. A high spatial and temporal resolution dataset should be used to analyze the distribution and environmental conditions, and to further compare the differences and similarities of the triggering and maintenance mechanisms of WSHR events in different regions. (3) More studies of the mechanisms are required, as well as improvements to the model initial conditions and physical processes based on multi-source observations, especially the description of the triggering process and the microphysical parameterization. This will improve the numerical prediction of WSHR events.

An interpretable deep inference forecasting model is designed to improve the forecasting capability of sea surface variables. By incorporating the air‐sea coupling mechanism as a dynamic constraint, the interpretability and forecasting performance of the model are improved. More specifically, our findings underscore the critical role of air‐sea interactions in forecasting sea surface variables, especially sea surface temperature (SST) variations induced by tropical cyclones (TCs). Additionally, Liang‐Kleeman information flow (IF), a causal inference method, is introduced to optimize the selection of predictors. Using satellite remote sensing data, our study demonstrates the model's capability in realizing sea surface multivariate forecasts in the South China Sea (SCS) within 10 days. More importantly, the experimental results prove the applicability of the model in both normal and extreme weather conditions, highlighting its effectiveness in enhancing sea surface variables forecasting. Plain Language Summary There are exchanges of momentum, heat, and mass between the ocean and the atmosphere. The sea surface, as a crucial interface for these exchanges, plays a pivotal role in the earth's climate system. Consequently, accurate prediction of sea surface variables is vital for understanding climate dynamics. Despite the considerable forecasting capabilities demonstrated by intelligent forecasting techniques, they still face issues of poor interpretability and low forecasting skills under extreme conditions compared to numerical models. The main reason is that previous intelligent forecasting models often focus on the evolution of a single variable, or only consider interactions within the ocean, and do not forecast under air‐sea coupling conditions. Such practice leads to incomplete systems and incoordination between air‐sea variables, thus failing to describe the interfacial behaviors of the ocean and atmosphere under extreme conditions such as typhoons. This study constructs an interpretable deep inference forecasting model for sea surface variables within the air‐sea coupling framework, illustrating the importance of considering air‐sea interactions to improve the forecasting performance of sea surface variables. Additionally, the model improves the prediction accuracy of sea surface variables (especially sea surface temperature) under extreme weather conditions. Key Points An interpretable forecasting model is proposed for the evolution of sea surface variables in the South China Sea Air‐sea interactions are incorporated as dynamic constraints to improve the forecasting skill and interpretability of the model The model has good prediction performance for sea surface temperature under extreme conditions

Weather systems in the southern Indian Ocean influence East Antarctic precipitation variability and surface mass balance. However, the long-term variability in synoptic-scale weather systems in this region is not well understood due to short instrumental records that are mostly limited to the satellite era (post-1979). Ice core records from coastal East Antarctica suggest significant decadal variability in snowfall accumulation, indicating that data from the satellite era alone are not enough to characterise climate variability in the high southern latitudes. It is therefore challenging to contextualise recent precipitation trends and extremes in relation to climate change in this area. We use synoptic typing of daily 500 hPa geopotential height anomalies and the Law Dome ice core (East Antarctica) annual snowfall accumulation record to investigate whether the 20th Century Reanalysis (20CR) project can represent the synoptic conditions associated with precipitation variability at Law Dome prior to the satellite era. We identify 12 synoptic types using self-organising maps (SOMs) based on their dominant pressure anomaly patterns over the southern Indian Ocean, with 4 types associated with above-average daily precipitation at Law Dome. Our results show the 20th Century Reanalysis project represents the meridional synoptic conditions associated with precipitation variability at Law Dome more reliably from 1948, aligning with the increased availability and thus assimilation of consistent surface pressure data from weather stations in the southern Indian Ocean from the late 1940s. This extends the time period available to contextualise recent trends and extremes in precipitation and synoptic weather conditions by up to 3 decades beyond the satellite era. Furthermore, we find a linear combination of the annual frequency in select synoptic types explains a significant amount of the variability in Law Dome snowfall accumulation compared to any individual synoptic type alone. These results will help future research on contextualising East Antarctic surface mass balance variability prior to the satellite era, with implications for improved understanding of the largest source of potential sea level rise, and the atmospheric conditions leading to decadal precipitation variability.

Here, we evaluated the influence of outdoor environmental conditions (synoptic weather conditions) on human thermal discomfort in the five macro-regions of Pelotas city, located in the southernmost region of Brazil. To do this, meteorological sensors (HOBO MX2301A) were installed outside the residences to measure the air temperature, dew point temperature, and relative humidity between 18 January and 20 August 2019. Two well-established simplified biometeorological indices were examined seasonally: (i) humidex for the summer months and (ii) effective temperature as a function of wind for the autumn and winter months. Our findings showed seasonal differences related to human thermal discomfort and outdoor environmental conditions. The thermal discomfort was highest in the afternoons during the summer months and at night during the winter months. The seasonal variation in human thermal discomfort was highly associated with the meteorological conditions. In summer, the presence of the South Atlantic Subtropical Anticyclone (SASA) contributed to heat stress. The SASA combined with the continent’s low humidity contributed to the perceived sensation of thermal discomfort. In the winter, thermal discomfort was associated with the decrease in air humidity caused by high atmospheric pressure systems, which led to a decrease in both air temperature and air moisture content. Our findings suggest that a better understanding of the complex interplay between outdoor environmental factors and human thermal comfort is needed in order to mitigate the negative effects of thermal discomfort.

Intensive observations were collected in a wide range of synoptic weather conditions to evaluate variability in the intra-urban heat island on the campus of the University of North Carolina at Charlotte between February 2023 and June 2023. An easily reproducible bicycle-based mobile transit route around the university was traversed during 20 afternoon and 20 evening periods. The magnitude of observed temperature range from an individual data collection period is defined as the campus urban heat island intensity, with areas having more anthropogenic modification also having higher temperatures. While other papers have examined the relationship between the city-scale urban heat island intensity and the present weather conditions, this paper aims to disentangle the relationship between present weather conditions and the magnitude of thermal variability across a small intra-urban campus with diverse land use and land cover characteristics. This will contribute to a better understanding of intra-urban heat islands, particularly identifying days where conditions will be highly dangerous in more developed areas, and not in more natural environments. When comparing the standardized mobile-transit observations to the regionally present weather conditions it is evident that clear and calm conditions often enhance both city-scale and campus-scale heat islands, increasing temperature disparities. While the spatial distribution of warm and cool areas across campus remains relatively constant, the campus-scale heat island is significantly modulated by the present weather conditions. Highlights Synoptic scale weather variables that enhance city-scale UHIs also enhance temperature variability within a more local context. Mobile transects prove to be an effective way to gather data about small-scale, canopy level atmospheric conditions. Spatial patterns of small-scale intra-urban temperature vary throughout the diurnal cycle. Substantial spatial variability in campus-scale intra-urban temperatures can occur on days when ambient weather conditions (calm winds, clear sunny skies, and high humidity) present the greatest risk to human health.

An extreme haze‐fog event occurred during October 20–22, 2013, in Harbin, Northeast China, which lasted for nearly 60 h with local visibility as low as 20 m. However, causes of the extreme haze‐fog formation remain unclear. Through the analysis of in situ data and objective weather circulation classification, it is revealed that high pollutant emissions from biomass burning played a very important role in the extreme event. Stable weather conditions under the circulation type 8 (CT8), marked by weak high‐pressure control, strong inversion (6.55°C), shallow boundary layer depth (<300 m), and high relative humidity (>90%), aided in the accumulation of pollutants and hygroscopic aerosol growth. All of these factors collectively contributed to the extreme haze‐fog formation. The insights derived from this study can improve the predictability of extreme haze‐fog events, and indicate that pollution emissions should be tightly controlled in the adverse meteorological circulation type in Northeast China. An extreme haze‐fog event occurred in Harbin, Northeast China, during October 20–22, 2013. It lasted nearly 60 h with local visibility as low as 20 m. Strong inversion (6.55°C), shallow boundary layer (<300 m), high humidity (>90%), and high pollutant emissions from biomass burning collectively contributed to the extreme haze‐fog formation. The insights derived from this study can improve the predictability of extreme haze‐fog events, and indicate that pollution emissions should be tightly controlled in the adverse meteorological circulation type in Northeast China.

Recurrent dust storms represent a significant concern in Australia because of their related hazards and damages since particulate matter (PM) has harmful impacts on the environmental, health and economic sectors. The particulate matter may be released from natural sources and human activities. The major part of natural particulate matter is emitted into the air by wind erosion processes from desert and semi-desert areas at the world scale. A huge dust storm crossed over several areas of New South Wales (NSW), Australia, including the Sydney region on 21–22 November 2018 and decreased the horizontal visibility to less than 1 km for 22 h. This study examined the synoptic weather conditions, and assessed the air quality and identified the source and transport trajectory of the dust storm over Sydney using ground and satellite remote sensing data. PM10 (< 10 μm) concentrations were obtained from selected air quality monitoring sites operated by the Environmental Protection Agency in NSW. The highest hourly concentration of PM10 (578.7 μg/m 3 ) was recorded at Singleton in the Hunter Valley, while concentrations in Sydney ranged from 480 to 385 μg/m 3 , well above the standard air quality level in Australia (50 μg/m 3 per 24 h). The HYSPLIT back trajectories of air parcels suggest that the potential sources of the dust episode originated from the Lake Eyre Basin and northeast South Australia, the Mundi Mundi plains west of Broken Hill, Cobar and the grazing lands and the red sandplains in northwestern NSW. It then travelled towards the east coast. These long-range airflows transported suspended dust particles, raising air quality to hazardous levels (elevated PM10 levels) over most areas of NSW. The results from the HYSPLIT model for dust movement are confirmed by MODIS satellite images. Many areas of NSW experienced this intense dust storm due to northwest wind generated by the low-pressure systems and cold fronts over South Australia and many parts of western NSW as it moved eastward.

The reproducibility of surface wind and tracer transport simulations from high-resolution weather and transport models was studied over complex terrain in wintertime in Japan. The horizontal grid spacing was varied (5-, 3-, and 1-km grids), and radioactive cesium (Cs-137) from the Fukushima nuclear power plant was used as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results were validated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated as a result of small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion and higher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the Fukushima mountainous region was dramatically improved with increasing model resolution. In the range of mesoscale model resolutions (commonly 1–10 km), it was concluded that a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain.

In February 2017, a wildfire occurred in the Port Hills on the southern boundary of Christchurch city in New Zealand. It was one of the country’s most severe fires of the last decade in terms of the scale of evacuation, infrastructure damage, and property loss. On the third day of the fire, fire behavior was unexpectedly active, and two rapid downhill fire-spread events took place, creating a dangerous situation for firefighters. The aim of this paper is to explore the atmospheric processes that influenced the fire behavior at a range of meteorological scales, from the synoptic to meso- and microscales. Meteorological and fire data analyzed include observed data together with model simulations of weather conditions at different scales: 1) the Weather Research and Forecasting (WRF) numerical weather prediction model, which provided the regional context of the fire; and 2) the California Meteorological (CALMET) diagnostic model, which was used to undertake a higher-resolution investigation of atmospheric processes near the fire. Results indicate that the fire was not strongly seasonally influenced. Instead, it appears the fire conditions were the effect of a specific combination of synoptic weather conditions and local meteorological conditions. The first rapid downhill fire-spread event was the result of airflow interaction with the intricate terrain of the Port Hills under stable nocturnal conditions. The second downhill fire-spread event bears similarities to vorticity-driven lateral spread, because the downhill component of the spread happened on a broad fire flank perpendicular to the surface wind direction and characteristic pyrocumulus convection occurred.