Urban heat waves are intensifying under climate change, posing growing public health risks, particularly in rapidly urbanizing cities. Green infrastructure is widely promoted as a nature-based solution for heat mitigation, yet its health benefits may vary across urban contexts. This study examines how neighborhood-level green infrastructure modifies heat-related health risks in Tabriz, Iran-a historically cold city experiencing increasing heat stress. The Normalized Difference Vegetation Index (NDVI) was derived from Landsat 8 imagery for 190 neighborhoods and classified into quartiles. Heat waves were defined as two or more consecutive days with mean temperatures at or above the 95th percentile. Emergency department visits for cardiovascular, respiratory, and all-cause conditions (2018-2020) were analyzed using Distributed Lag Non-linear Models with quasi-Poisson regression. Neighborhoods with low-to-moderate greenness (second and third NDVI quartiles) consistently exhibited lower relative risks of heat-related cardiovascular and all-cause visits, while both the lowest and highest NDVI quartiles showed elevated risk estimates. Risk patterns varied by lag period and demographic subgroup, with higher vulnerability observed among males and younger adults in highly vegetated areas, though estimates were imprecise. These findings suggest a non-linear relationship between urban greenness and heat-related health risks. Moderate green infrastructure appears most protective, underscoring the importance of context-sensitive and equitable greening strategies for climate adaptation in heat-vulnerable cities.

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

A World Meteorological Organization team has evaluated 2023's Tropical Cyclone Freddy's duration of 36.0 days (with 10-min average wind-speeds of 30 kt or higher) as the world record for longest tropical cyclone duration.

This study examines the temporal and spatial variability of near-surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010-2022 are analysed on an hourly and 3-hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401-405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1 degrees C to 3.8 degrees C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6 degrees C and 2.6 degrees C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s-1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World. The present study provides an investigation of spatiotemporal dynamics in near-surface air temperature and the canopy layer urban heat island (UHICL) across Kuwait City. Analysis of hourly and 3-hourly observations from 2010 to 2022 revealed monthly and diurnal insights into the UHICL characteristics, including magnitudes, growth/decay patterns, and explanations for warming/cooling rates. image

Dust storms are linked to large scale accidents and fatalities in the Central Sonoran Desert, specifically in the central Gila River Valley of the United States during the North American Monsoon. This study analyzes three events where the underlying topography impacts large summertime dust storms and their movement. All three examples demonstrate the shielding or, conversely, funneling effects associated the underlying terrain. The funneling between isolated mountain complexes, such as White Tank Mountains, the Sierra Estrella, the Sacaton Mountains, the San Tan Mountains, and the South Mountain Complex can combine to create substantially higher dust concentrations. Conversely, dust flow into these isolated mountain complexes can also lead to shielding, or blocking, of the dust such that area behind the topographic features experiences less dust while the windward side of the barriers have accented dust concentrations. These dust storm/topography studies provide a first opportunity to identify topographic influences on central Arizona dust storms. Significance statement The purpose of this study is to identify Topographic elements of the central Sonoran Desert located in the Arizona region of the Southwestern United States that impact the movement of dust storms. Using IDW interpolation to analyze PM10 concentration levels and wind speeds, we find that stand-alone mountain complexes can provide significant dust shielding and funneling that effect the overall trajectory of a dust storm in this region.

Satellite precipitation products (SPPs) with high spatial and temporal resolution are considered as a new source of precipitation data to monitor drought events, particularly for data-sparse areas. However, they should be extensively validated against ground-based data before their utilization. In this study, three (SPPs) including Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Climate Prediction Center morphing technique (CMORPH), and PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now) are examined extensively on multiple spatiotemporal scales in precipitation estimation as well as their utility for drought monitoring across the southwestern Iran over 2001–2021. The Asfezari gridded precipitation data developed from a dense of rainfall gauges were used as the reference dataset. The results suggest that IMERG Final Run Version 7 (IMERG-F hereafter) outperforms the other products in representing spatiotemporal patterns of precipitation followed by PDIR-Now and CMORPH based on the statistical indices including relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE). CMORPH product substantially underestimates precipitation values over the elevated regions. The results also suggest that IMERG-F shows the best performance with ground-based data for drought monitoring particularly for the 6 month SPI time scale. IMERG-F was also superior in tests involving correct, false, and missing drought detection. Ultimately, our results show that satellite-based precipitation products can be quite useful in drought monitoring, particularly in areas like southwestern Iran where droughts are frequent and may become more frequent in years to come.

Precipitation in Arizona, USA, represents a fundamental resource for the needs of agriculture, people, and the environment. However, due to the risk associated with flash flooding, extreme precipitation also constitutes a natural hazard. Available research on precipitation in Arizona has been limited in scope and focus and there currently exists no comprehensive assessment of statewide, historical, mean, and extreme precipitation. We use daily precipitation records from 43 Global Historical Climate Network daily (GHCNd) weather stations to examine trends in mean and extreme precipitation across Arizona from 1950 to 2020. We use a suite of standardized precipitation indices and explore the statistical significance of historical changes at the annual, monthly, and seasonal scales. Our analysis returns a motley collection of results displaying a great degree of spatial variability and sensitivity to the temporal scale of analysis. Mean total precipitation at the statewide scale underwent a small decreasing trend of 0.15 mm year  1, with considerable interannual variability being a dominant feature. The majority of stations experienced no statistically significant change in extreme precipitation at the annual scale. Monsoon season mean precipitation followed a similar pattern to annual means, with an overall reduction of 0.14 mm year  1 across the state. One fourth of stations, all located at elevations lower than 650 m, recorded decreases in monsoon precipitation intensity. Winter season analysis presents a different picture, displaying a positive statewide trend of 0.11 mm year  1 in mean precipitation. One tenth of stations recorded increases in extreme precipitation intensity and decreases in the number of consecutive dry days. Results of our observational analysis indicate a lack of a clear consensus on the climatological trends of mean/ extreme precipitation in Arizona during the study period. Our findings are in contrast to those from other similarly arid areas across the world and highlight the role of regional differences in modulating the potential hydrometeorological impacts associated with climate change. However, while we do not find widespread, significant, modification in the spectrum of precipitation changes examined, the hydrologic system of the state has been - and will continue to be - impacted by the ongoing temperature increase associated with the buildup of greenhouse gases and continued population growth.

Dust storms are a major cause of central Sonoran Desert weather fatalities. Through back-trajectory analysis of North American Monsoon dust storms in central Sonoran Desert of the United States. This study is specific to central Arizona (USA) from 2009 to 2022 using the HYSPLIT model. Our findings have shown that dust storms originate from southerly or near-southerly regions. The dust storms displaying the highest concentrations of particulates show a preference to originate from the southwest. This coincides with the development of a 500 hPa ridge to the east of the study area. The highest concentration storms' back-trajectories display the lowest heights above the ground. Given their southwestern origin, these storms travel upslope along the seasonally dry river beds of the Gila River and its tributaries. Weaker dust storms originate over a wider area with a shift to a southerly direction. Such origination indicates that weaker dust storms are traveling downslope through the washes and channels of the dry Santa Cruz River. As dust concentrations drop, storm direction drifts east and dust height is suspended higher. This paper highlights the spatial variations in central Arizona dust storms, showing the likeliest paths of the strongest events and assists in identifying aeolian dust origins.

The diurnal cycle of tropical convection over the Indian region has been analysed in this study for the period of March to June from the thunderstorm reports of ground-based observatories throughout India during 2016–2020. The analyses indicate that during this period of the year, when the land progressively heats up diurnally, under conditions of deficient moisture, synoptic systems interact with the semi-permanent features of the atmosphere over the region to define areas of moisture and wind convergence, which in turn determine the frequency and diurnal cycle of thunderstorm activity. While the easterly and westerly waves are the major synoptic scale weather systems that affect the Indian region during March to May, the atmospheric changes are associated with the onset of the monsoon season in June. Amongst the semi-permanent features, the gradual intensification and migration of the shallow heat low to northwest India during May and June reinforce the western disturbances over this region, thereby intensifying the thunderstorm activity during the afternoon to evening hours over the western Himalayas and northwest Indian region. The role of the low-level anticyclones over the Arabian Sea and the Bay of Bengal is seen in the east–west-oriented moisture gradient across the Indian subcontinent which makes the east Indian subcontinent generally more prone to thunderstorm activity during this season. The east–west-oriented discontinuity line across north India is particularly intense during the morning hours along the foothills of the Himalayas. Its location directs moisture from the Bay of Bengal into the Himalayas causing early initiation of thunderstorm activity over the Himalayas. The discontinuity line moves southwards to the north Indian plains later in the day, although the western end becomes less marked. The north–south-oriented discontinuity line across the Indian subcontinent between the two anticyclones intensifies during the afternoon hours due to land heating and combines with the east–west wind discontinuity to become a T-shaped maximum convergence zone for thunderstorm activity during the afternoon hours over the Indian region, which intensifies as the months progress. With the onset of the southwest monsoon over the south peninsula and east Indian regions in June, the change in wind pattern from an easterly to a southwesterly flow regime over the south peninsula is reflected in an abrupt shift of the afternoon maximum of thunderstorm activity over the inland regions of the south peninsula to an early morning maximum over the southwest peninsular coast. Simultaneously, with the weakening of the anticyclone over the north Bay of Bengal and gradual strengthening of the southerly moisture flow into east and northeast India, there was in-phase increase in thunderstorm activity during the afternoon hours over the plains of east-central India, east India, and adjoining southern parts of northeast India during June. The diurnal pattern of tropical convection significantly affects human lives over the Indian subcontinent.

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.

The rapid growth of cities—along with the increasing connectedness of the world's social, economic, and political systems—has been hypothesized to generate a homogenization of urban form and associated environmental impacts. These hypotheses, however, have rarely been tested. Employing satellite imagery of 150 of the most populous cities in China, India, and the United States, we examine how the area and configuration of built-up land within cities has changed between 1995 and 2015 and assess impacts on the urban heat island effect. We find similar urban form trends across the three countries. The strongest evidence of homogenization is in the connectivity of urban form, while the shape of cities is linked to higher daytime surface urban heat island (SUHI) intensity. In the context of this and other research, we postulate that the identified urban form trends may lead to the homogenization of the biotic and abiotic environment of cities. Homogenization presents an opportunity for cities to learn from each other as they encounter similar ecological outcomes driven, in part, by their increasingly similar urban form.

Public heat alerts are important risk communication tools, but there has been no systematic analysis of how frequently they are issued or how patterns in alert frequency relate to regional climatology or heat-health impacts. We compiled and analyzed all excessive heat warnings and heat advisories (collectively, heat alerts) issued by the U.S. National Weather Service for 2010-19. Heat alert frequency was correlated to climatological indicators derived from reanalysis data aggregated to Weather Forecast Office (WFO) polygons and to estimates of heat-attributable mortality for 134 metropolitan areas. The type of heat alerts used and the frequency with which they were issued were highly variable. Across 77% of the country, heat advisories were the primary product issued. The median location experienced 2.3 heat alert days per year. Regions with the highest frequency (approaching 25 heat alert days per year) included the southern Midwest and Great Plains, as well as the desert Southwest. The 95th-percentile daily maximum heat index was the climatological indicator most strongly correlated with heat alert frequency across all WFOs (r = 0.71). Locations that issued heat alerts more frequently than would be expected based on climatology were primarily located along the Pacific coast; those that issued heat alerts less frequently than expected were in southern Texas and southern Florida, the latter of which includes multiple cities with high rates of heat-attributable mortality. Our results suggest that the public may be receiving mixed signals about the severity of the heat hazard, with some hotter locations particularly underserved by heat risk messaging.

We use a combination of a regional climate model (RCM) and a global climate model (GCM) to explore potential changes to the complex precipitation regime in the semi-arid and arid state of Arizona in the American Southwest. The RCM output for the contemporary period (2000-2009) compares well with a gridded precipitation dataset with respect to seasonality, amount, intensity, and diurnal patterns. Output from the GCM forced by the continued buildup of greenhouse gases was dynamically downscaled by the RCM for the period 2090-2099. Results indicate an increase in winter precipitation of 1-2 mm day(-1) in the mountainous areas of the state with somewhat smaller increases for the summer and fall seasons; negligible changes were projected for spring precipitation. Extreme precipitation is projected to increase across much of the state in winter (10-30 mm day(-1)) and to a lesser extent in spring. Our results indicate an increase in the 99th percentile of winter season precipitation. However, we note there is substantial seasonal dependency: statewide-averaged winter season precipitation is projected to undergo greater frequency of wet than dry extreme events, whereas the statewide-averaged summer, fall, and spring seasons, broadly demonstrate an equal likelihood of increased and decreased extreme precipitation. While the RCM captured the unusual observed night-time maximum in summer rainfall in the centre of the state, our results do not indicate any change in the diurnal character or sub-diurnal duration (6-18 hr) of precipitation over the next 100 years.

A new amalgamation of weather stations in and around Joshua Tree National Park in southeastern California has allowed for objective climate analysis regionalization at a much finer scale than past studies. First, it sets a baseline for many regions within the park’s boundaries that were not subject to direct observations. Second, these new observations are key to understanding shifting microclimate regimes in a desert ecosystem prone to the effects of climate change. Principal component analysis was used to regionalize the climate network based on monthly temperature and precipitation climate observations and standardized anomalies. Both the observation values and standardized climate anomalies identified regional boundaries. In general, these boundaries align with traditional ideas and past studies of the Mojave and Sonoran Deserts based on elevation (specifically the 1000-m contour) for the National Park Service. Standardized anomaly values identified a boundary based on seasonal precipitation, whereas observation values identified a boundary based on elevation. The boundary line within the park is similar for both data approaches, with the boundary running along the higher western one-third of the park. Conversely, the two methods differ significantly in the Coachella Valley, where low elevations and low precipitation meet winter-dominated seasonal precipitation. This study highlights the importance and opportunity of field observations to create climatological and ecological regionalization, and it also constructs a baseline to monitor and manage shifting desert regions in the future. Significance Statement This study identifies a high-resolution climate boundary zone in Joshua Tree National Park between the Sonoran and Mojave Deserts. The new transition zone presents the seasonal and elevational temperature and precipitation components of the two deserts, connecting with the unique ecology of the deserts. This finding highlights just one study opportunity of new field observation networks in arid or topographically diverse regions. It also provides a baseline for climate change as a resource for environmental management groups to better understand and preserve our natural spaces.

Satellite remote-sensing products with high spatial and temporal resolution are viable sources of precipitation information, especially for data-sparse and remote regions. The aim of this study is to examine the performance of four precipitation products including Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Tropical Rainfall Measuring Mission (TRMM 3B43), Climate Hazards Centre InfraRed Precipitation with Station data (CHIRPS), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) in estimating precipitation and capturing meteorological droughts over Iran for the time span from 2001 to 2019. For this aim, a ground-based gridded precipitation dataset was constructed over the country using a dense network of quality-controlled rain gauges as a reference dataset. Different statistical metrics including the correlation coefficient (CC), the bias, the relative bias, and the root mean square error (RMSE) were applied to evaluate the performance of the products. The results suggest that GPM IMERG and TRMM 3B43 outperform CHIRPS and ERA5 in capturing the spatial distribution of precipitation and meteorological drought events across the country. The estimates of precipitation from the four products are seasonally influenced, with the least accurate precipitation estimates during summer season over the southern shores of the Caspian Sea. The GPM IMERG and TRMM 3B43, with higher CC and lower RMSE, show better performance in detecting drought events at both short and long time scales while the CHIRPS demonstrates the least accuracy. Spatially, all of the products show the best performance in identifying drought events over western and southwestern regions.

Climate change can manifest in many ways, including impacts on the start, end, and duration of the frost-free season. We examined the climatology and variability of the first fall frost day (FFFD), last spring frost day (LSFD), and length of the frost-free season (LFFS) across Iran for the period 1978-2017. Trend analysis revealed that FFFD shifted later by 6.4 d over the study period while LSFD shifted earlier by slightly over 2 wk, and LFFS is now > 3 wk longer than it was only 4 decades ago. Since land-use changes around meteorological stations may affect the temperature measured at these stations (especially the magnitudes of nocturnal cooling rates), atmospheric thickness changes, which reflect temperature changes and are independent of stationbased measurements, were used as a secondary dataset to investigate minimum temperature trends. The analysis revealed a very strong relationship between frost-related indices and atmospheric thickness. Sequential Mann-Kendall statistical analysis revealed abrupt changes in the applied frost-related indices, minimum temperatures, and atmospheric thicknesses. The first abrupt changes in FFFD and LFFS occurred around 1996, which matched the timing of abrupt changes in atmospheric thickness over Iran. Interestingly, seasonal trend analyses of minimum temperature over the Northern Hemisphere using Era5 reanalysis data indicated consistent regional patterns of warming over the last 4 decades. The results suggest that the increase in LFFS is largely driven by regional-scale warming as opposed to local urbanization and/or land-use changes. Our results document an important and ongoing change of potentially considerable interest to agriculturalists in Iran and elsewhere.

In busy waterways, spatial-temporal discretisation, safe distance and collision avoidance timing are three of the core components of ship traffic flow modelling based on cellular automata. However, these components are difficult to determine in ship traffic simulations because the size, operation and manoeuvrability vary between ships. To solve these problems, a novel traffic flow model is proposed. Firstly, a spatial-temporal discretisation method based on the concept of a standard ship is presented. Secondly, the update rules for ships' motion are built by considering safe distance and collision avoidance timing, in which ship operation and manoeuvrability are thoroughly considered. We demonstrate the effectiveness of our model, which is implemented through simulating ship traffic flow in a waterway of the Yangtze River, China. By comparing the results with actual observed ship traffic data, our model shows that the behaviours and the characteristics of ships' motions can be represented very well, which also can be further used to reveal the mechanism that affects the efficiency and safety of ship traffic.

With the rapid spread of COVID-19 related cases globally, national governments took different lockdown approaches to limit the spread of the virus. Among them, the Government of India imposed a complete nationwide lockdown starting on March 25, 2020. This presented a unique opportunity to explore how a complete standstill in regular daily activities might impact the local environment. In this study, we have analyzed the change in the air quality levels stemming from the reduced anthropogenic activities in one of the most polluted cities in the world, the Delhi Metropolitan Region (DMR). We analyzed station-level changes in particulate matter, PM10 and PM2.5, across the DMR between April 2019 and 2020. The results of our study showed widespread reduction in the levels of both pollutants, with substantial spatial variations. The largest decreases in particulate matter were associated with industrial and commercial areas. Highest levels of PM10 and PM2.5 were observed near sunrise with little change in the time of maximum between 2019 and 2020. The results of our study highlight the role of anthropogenic activities on the air quality at the local level.

Topography can have a significant influence on tornado intensity and direction by altering the near-surface inflow. However, past research involving topographic influence on tornadoes has shown significant variety in investigative approaches and conclusions. This study uses Unpiloted Aerial Systems (UAS)-based high-resolution imagery, UAS-based 3D-modeling products, and correlation analyses to examine topographical influences on a portion of the 01 May 2018 Tescott, Kansas EF3 tornado. Two new metrics, Visible Difference Vegetative Index (VDVI) gap and (VDVI) aspect ratio, are introduced to quantify damage severity using UAS-based imagery and elevation information retrieved from a UAS-based digital surface model (DSM). Areas of enhanced scour are seen along the track in areas of local elevation maxima. Correlation analysis shows that damage severity, as measured by VDVI gap and VDVI aspect ratio, are both well correlated with increasing elevation. VDVI gap is only weakly correlated with slope, while VDVI aspect ratio is not correlated with slope. These findings are statistically significant at p < 0.05. As the tornado weakened in intensity, the path became non-linear, traversing between two local elevation maxima. It is hypothesized that fast-moving intense flow formed and weakened as elevation increased over the short spatial distance. This research shows topography and surface conditions are two of many important variables that should be considered when performing tornado-damage site investigations. It also illustrates the importance of UASs in detailed tornado analysis. VDVI gap and VDVI aspect ratio can provide insight into damage severity as a function of topography.