Valeria Garbero

Urban Heat Island (UHI) is a phenomenon where urban areas experience significantly higher temperatures than their rural surroundings. This effect is primarily due to heat retention in buildings and roads, reduced evapotranspiration, and heat generated by vehicles, industrial activities, and air conditioning. Accurate UHI modelling is crucial for understanding its impacts and helping urban planners and policymakers in developing effective strategies to mitigate heat stress in cities. Advanced urban canopy schemes within atmospheric models play a key role, as they aim to realistically represent the complex interactions between urban surfaces and the atmosphere. The TERRA_URB (TU) model, developed within the COSMO Consortium, parametrizes the effects of buildings and streets on energy and moisture exchanges between the surface and atmosphere. Additionally, it accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU requires the definition of parameters describing the geometrical and thermal urban features, such as street aspect ratio, building surface fraction, building height, impervious surface area, anthropogenic heat flux. These parameters were derived from the Local Climate Zone (LCZ) classification, using both global and local dataset. This study evaluated the performance of the TU scheme, implemented within the ICON model, by simulating past heat waves over Turin at 500 m resolution in hindcast mode. The ability of the TU scheme to reproduce UHI effects was assessed by comparing ICON simulations, with and without TU, against observational data. These included ground-level measurements from the Arpa Piemonte meteo-hydrological network and vertical temperature profiles from radiometers located in both the city center and surrounding areas. Furthermore, a denser crowdsourced air temperature network, NetAtmo, was evaluated to assess the potential of the citizen science data for improving urban climate studies. The results demonstrated that the TU scheme significantly improved the representation of the UHI effect.

Heat waves are becoming more frequent and intense due to climate change, posing significant threats to urban environments. The Urban Heat Island (UHI) and Urban Dry Island (UDI) effects exacerbate these impacts by increasing local temperatures and decreasing moisture compared to surrounding rural areas. This study focuses on the metropolitan area of Turin, Italy, leveraging observational data to characterise the UHI and UDI phenomena and identify urban areas most at risk from extreme heat events.20 years of hourly data coming from the ground network of ARPA Piemonte are analysed to map thermal and moisture patterns within the city. The analysis reveals intra-urban areas where the combination of population density, urban morphology, and temperature or moist anomalies heightens vulnerability to heat waves.This work aims to support climate change adaptation strategies by providing essential information for risk assessment and urban planning. Identifying heat-vulnerable areas enables the design of targeted interventions, such as creating green infrastructure, urban cooling strategies, and implementing heat alert systems. The results underscore the importance of observational data in developing climate services tailored to urban environments, where localised phenomena like UHI and UDI require precise monitoring and targeted solutions.The findings provide valuable insights for policymakers and urban planners to enhance climate resilience in urban areas. The proposed approach serves as a replicable model for other cities facing similar challenges, fostering proactive adaptation to the rising hazard of extreme heat.This research is conducted within the framework of RETURN (Multi-Risk Science for Resilient Communities under a Changing Climate) project, funded under the Italian PNRR (Piano Nazionale Ripresa e Resilienza - National Recovery and Resilience Plan), as part of the Spoke 8 DS - Science Underpinning Climate Services for Risk Mitigation and Adaptation.

A new urban canopy scheme for the ICON atmospheric model is presented. Increasing the resolution of atmospheric models for numerical weather prediction (NWP) or climate simulations allows, among others, for a more realistic description of the processes at the land surface. Here, one field of growing interest are the processes in urban areas. Beside their relevance for the meteorological modelling, there is a general trend in most countries that the number of people living in towns is significantly increasing. During the recent years, an urban canopy parameterization was developed for the multi-layer land surface scheme TERRA of the Consortium for Small-scale Modeling (COSMO) mesoscale atmospheric model. This parameterisation, TERRA_URB, originally developed for the climate version of COSMO and then ported to the NWP version, was shown to be able to reproduce the key urban meteorological features for different European cities. In the framework of the transition of the COSMO Consortium to the ICON model, TERRA_URB needs to be implemented in ICON. Furthermore, an updated set of urban canopy parameters needs to be provided, for describing the urban characteristics down to a mesh size of 1 km, and below. For these purposes, the COSMO Consortium organises the dedicated Priority Project CITTA’. First results are presented for TERRA_URB in the ICON limited-area model ICON-LAM for different cities of interest of the CITTA’ partners. The preliminary results indicate already that urban features like the urban heat island effect are well represented. This is in agreement with the experiences with TERRA_URB in the COSMO model, both the climate as well as the NWP version.

Since Howard (1833) first suggested that air temperatures recorded in urban areas are higher than in the surrounding countryside, there have been hundreds of studies of the Urban Heat Island (UHI) phenomenon, which is due to the different thermal properties between urbanized and natural lands, anthropogenic heat emissions, human-induced pollution and limited wind blowing among buildings. The impervious land cover type and the presence of sheltering constructions trap heat during the day and release it during the night,  resulting in higher night-time temperatures. The UHI intensity is commonly computed as the difference between an urban and a rural measurement site and, therefore, the definition of station pairs is a crucial task for its evaluation. To this end, we propose a powerful method capable of highlighting the thermal pattern typical of each weather station: the Mean Temperature Difference (MTD) method. Principal Component Analysis (PCA) is adopted to cluster similar thermal behaviours, allowing an objective classification of the stations. The strength and novelty of this data-based approach, which employs hourly temperature measurements, lays in the fact that any preliminary assumption about the landscape characterizing each station (i.e. urban or rural) is not needed, making it less arbitrary and more objective than other methods. The application of the proposed method to the metropolitan area of Turin (Italy) shows that the joint use of MTD with PCA yields reliable and easily interpretable results, also in an area with complex morphology (orographic and hydrographic heterogeneity, different land uses, etc.). Once the best urban and rural pairs have been identified, the characteristics of the Urban Heat Island are shown, highlighting its seasonal and daily properties.

Nowadays, cities are the preferred location for more than half of the human population and the places where major human-perceived climate change impacts occur. In an increasingly urbanized world, it is essential to represent such areas adequately in Numerical Weather Prediction (NWP) models, not only to correctly forecast air temperature, but also the human heat stress and the micro-climate phenomena induced by the cities. Among them, the best known is the Urban Heat Island (UHI) effect, which refers to the significantly higher temperatures experienced by a metropolitan area than its rural surroundings. Currently, the COSMO model employs a zero-order urban description, which is unable to correctly reproduce the UHI effect: cities are simply represented as natural lands with increased surface roughness length and reduced vegetation cover. However, the reproduction of the urban climate features in NWP and regional climate models is possible with the use of the so-called urban canopy models, that are able to parameterize the interaction between the urbanized surface and the overlying atmosphere. In this context, a new bulk parameterization scheme, TERRA_URB (TU), has been developed within the COSMO Consortium. TU offers an intrinsic representation of urban physics: the effect of buildings, streets and other man-made layers on the surface-atmosphere interaction is described by parameterizing the impervious water balance, translating the 3D urban-canopy parameters into bulk parameters with the Semi-empirical Urban canopy parameterization (SURY) and using the externally calculated anthropogenic heat flux as additional heat source. In this work, we present high-resolution simulations with the TU scheme, for different European cities, Turin, Naples and Moscow. An in-depth evaluation and verification of the performances of the recent COSMO version with TU scheme and new implemented physical parameterizations, such the ICON-like surface-layer turbulence scheme and the new formulation of the surface temperature, have been carried out. The validation concerned the 2-meter temperature and was performed for 1- or 2-week selected periods over the 3 European cities characterized by different environment and climate, namely the Moscow megacity in Russia and Turin and Naples in Italy. Even if the three domains are morphologically different, the results follow a common behavior. In particular, the activation of TERRA_URB provides a substantial improvement in capturing the UHI intensity and improving air temperature forecasts in urban areas. Potential benefits in the model performance also arise from a new turbulence scheme and the representation of skin-layer temperature (for vegetation). Our model framework provides promising perspectives for enhancing urban climate modelling, although further investigations are needed.

Numerical models are operationally used for weather forecasting activities to reduce the risks of several hydro-meteorological disasters. The overarching goal of this work is to evaluate the Weather Research and Forecasting (WRF) model predictive capabilities over the Italian national territory in the year 2018, in two specific cloud resolving configurations. The validation is carried out with a fuzzy logic approach, by comparing the precipitation predicted by the WRF model, and the precipitation observed by the national network. The fuzzy logic technique, by considering different intensity thresholds, allows to identify the reliable spatial scales of the forecasts. The same approach is applied to evaluate the performances of COSMO-2I model, a state-of-the-art numerical model configuration used for operational activities. For the entire year, except for summer, the model predictive capabilities are high, with useful forecasts for structures of medium intensities down toO(10 km) length scales. In summer the skills decrease mainly because of localization errors. The work aims to provide a robust evaluation of the forecast performances of another convection permitting operational meteorological models currently available in Italy.

The increase in built surfaces constitutes the main reason for the formation of the Urban Heat Island (UHI), that is a metropolitan area significantly warmer than its surrounding rural areas. The urban heat islands and other urban-induced climate feedbacks may amplify heat stress and urban flooding under climate change and therefore to predict them correctly has become essential. Currently in the COSMO model, cities are represented by natural land surfaces with an increased surface roughness length and a reduced vegetation cover, but this approach is unable to correctly reproduce the UHI effect. By increasing the model resolution, a representation of the main physical processes that characterize the urban local meteorology should be addressed, in order to better forecast temperature, moisture and precipitation in urban environments. Within the COSMO Consortium a bulk parameterization scheme (TERRA_URB or TU) has been developed. It parametrizes the effects of buildings, streets and other man-made impervious surfaces on energy, moist and momentum exchanges between the surface and atmosphere, and additionally accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU implements an impervious water-storage parameterization, and the Semi-empirical Urban canopy parametrization (SURY) that translates 3D urban canopy into bulk parameters. This paper presents evaluation results of the TU scheme in high-resolution simulations with a recent COSMO model version for selected European cities, namely Turin, Naples and Moscow. The key conclusion of the work is that the TU scheme in the COSMO model reasonably reproduces UHI effect and improves air temperature forecasts for all the investigated urban areas, despite each city has very different morphological characteristics. Our results highlight potential benefits of a new turbulence scheme and the representation of skin-layer temperature (for vegetation) in the model performance. Our model framework provides perspectives for enhancing urban climate modelling, although further investigations in improving model parametrizations, calibration and the use of more realistic urban canopy parameters are needed.

The scope of this work is to assess the progresses made in the warning alert system of Piedmont since the 1994 flood. We used the COSMO model at high horizontal resolution forced by ERA5 re-forecast to simulate the November 1994 event, performing also a simple sensitivity test regarding the parameterization of convection. We compared the results with the original forecast and with the available observations, in order to understand how the emission of the alert would have been affected using the current operational system.