The Mediterranean is recognized among the most responsive regions to climate change, with annual temperatures projected to increase by 1–5 °C until 2100. Large cities may experience an additional stress discomfort due to the Urban Heat Island (UHI) effect. In the present study, the WRF-ARW numerical weather prediction model was used to investigate the climate change impact on UHI for two Mediterranean cities, Rome and Thessaloniki. For this purpose, three 5-year time-slice simulations were conducted (2006–2010, 2046–2050, 2096–2100) under the Representative Concentration Pathway (RCP) 8.5 emission scenario, with a spatial resolution of 2 km. In order to comprehensively investigate the urban microclimate, we analyze future simulation data across sections crossing urban/non-urban areas, and after grouping them into three classes depending on the location of the grid cells. The urban areas of both cities present increased average minimum temperature (T_min) in winter/summer compared to other rural areas, with an UHI of ~+1.5–3 °C on average at night/early morning. Considering UHI under future climate change, we found no significant variations (~±0.2 °C). Finally, we found that the numbers of days with T_min ≥ 20 °C will mostly increase in urban coastal areas until 2100, while the largest increase of minimum Discomfort Index (DI_min) is expected in urban low-ground areas.

On 21 January 2009, a maturing low‐pressure weather system approached the UK along with several associated frontal systems. As a part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate‐Clouds project, an observational research flight took place in southern England, sampling the leading warm front of this system. During the flight, a distinctive hydrometeor type was repeatedly observed which has not been widely reported in previous studies. We refer to the hydrometeors as “drizzle‐rimed columnar ice” or “ice lollies” for short due to their characteristic shape. We discuss the processes that led to their formation using in situ and remote sensing data.

The urban heat island (UHI) effect poses a critical environmental and public health challenge, particularly under extreme heat wave conditions in the Mediterranean region. Despite the widespread use of the Weather Research and Forecasting (WRF) model for urban climate simulations, a systematic intercomparison of its urban canopy model (UCM) schemes under Mediterranean heat-wave conditions has not previously been conducted. This study evaluates four WRF configurations over Rome, Italy, during the extreme heat wave of 26–30 June 2022 (daily maximum temperatures 35.6–39.2°C): a reference simulation without urban parameterization (REF), the Single-Layer Urban Canopy Model (SLUCM), the Building Effect Parameterization (BEP), and BEP coupled with the Building Energy Model (BEP+BEM), across three nested domains at 9, 3, and 1 km resolution. Urban parameters were derived from CORINE Land Cover and Copernicus High-Resolution Imperviousness data, and model output was evaluated against 2-m air temperature observations from eight ASTI-Network stations. BEP+BEM provides the most consistent performance across resolutions and urbanization levels, yielding the lowest mean absolute error and the most accurate representation of diurnal temperature variability. SLUCM exhibits severe, scale-dependent performance degradation at convection-permitting resolutions (3–1 km) at highly urbanized stations, with mean biases reaching −2.8°C, primarily due to insufficient representation of nocturnal urban heat storage. The largest performance gains occur from 9 to 3 km through improved reproduction of temporal temperature variability, whereas further refinement to 1 km yields diminishing returns. Finally, updated, land use-based urban surface properties are shown to substantially influence UCM evaluation outcomes and scheme selection.

Designing a weather station network is a demanding, multi-objective optimisation problem and usually constrained to local geographies. In this study, the authors deviate from typical approaches that focus the design of weather station networks on a small or country-wide area and present a method that is applicable on a global scale.Prior art suggests that weather networks should exhibit high density, often at 1-3km or finer resolution, especially when deployed over complex topographies and urban landscapes. High station density is usually required to support research on urban micrometeorology, agricultural applications and to capture intricate meteorological mesoscale phenomena such as convective precipitation and sea breeze. High density is also required due to the persistence of temperature inversions at near-surface layers is significantly influenced by topography, leading to prolonged periods of temperature inversion.In this novel approach, the authors suggest the design of a global weather network distributed over millions of hexagons covering the entire world. The number of weather stations per hexagon is determined by the topology (e.g. maximum elevation difference, aspects, water formations, etc.) and the land use (urban coverage, green areas, etc.) of the covered area.The method is materialized via an open-source software tool (available on GitHub) which utilizes freely available elevation data (Copernicus DEM) and land use data (OpenStreetMap) and is capable of preparing the global weather station network in reasonable computation time (~24 hours on a 16-core CPU).Finally, the authors present their findings, discuss the effect of various hexagon sizes and suggest that the design of a global weather station network is viable and computationally feasible.