High-Resolution Climatic Characterization of Air Temperature

Bulletin of Atmospheric Science and Technology https://doi.org/10.1007/s42865-021-00038-5

RESEARCH ARTICLE

High‑resolution climatic characterization of air temperature in the urban canopy layer

Enea Montoli1 · Giuseppe Frustaci1 · Cristina Lavecchia1 · Samantha Pilati1

Received: 29 April 2021 / Accepted: 27 July 2021 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021

Abstract Urbanized environments are of greater relevance because of the high and still rapidly increasing percentage of the world population living in and around cities and as the pre- ferred location of human activities of every type. For this reason, much attention is paid to the urban climate worldwide. Among the UN 2030 17 Sustainable Development Goals, at least one concerns resilient cities and climate action. The WMO supports these goals pro- moting safe, healthy, and resilient cities by developing specially tailored integrated urban weather, climate, and environmental services. An unavoidable basis for that is an improved observational capability of urban weather and climate, as well as high-resolution modeling. For both the former and the latter, and of primary importance for the latter, urban meteorological surface networks are undoubtedly a very useful basis. Nevertheless, they are often unft for detailed urban climatological studies and they are generally unable to describe the air temperature feld in the urban canopy layer (UCL) with a spatial resolution which is sufcient to satisfy the requirements set by several professional activities and especially for local adaptation measures to climate change. On the other hand, remote sensing data from space ofer a much higher spatial resolution of the surface characteristics, although the frequency is still relatively lower. A useful climatological variable from space is, for instance, the land surface temperature (LST), one of the WMO Essential Climate Vari- ables (ECV). So often used to describe the Surface Urban Heat Islands (S-UHI), LST has no simple correlation with UCL air temperature, which is the most crucial variable for planning and management purposes in cities. In this work, after a review of correlation and interpolation methods and some experimentation, the cokriging methodology to obtain surface air temperature is proposed. The implemented methodology uses high quality but under-sampled in situ measurements of air temperature at the top of UCL, obtained by using a dedicated urban network, and satellite-derived LST. The satellite data used are taken at medium (1 × 1 km2) resolution from Copernicus Sentinel 3 and at high resolution (30 × 30 m2) from NASA-USGS Landsat 8. This fully exportable cokriging-based methodology, which also provides a quantitative measure of the related uncertainties, was tested and used to obtain medium to high spatial resolution air temperature maps of Milan (Italy) and the larger, much populated, but also partly rural, surrounding area of about 6000 km 2. Instantaneous as well as long period mean felds of fne spatially resolved air temperature obtained by this method for selected weather types and diferent Urban Heat Island confgurations represent an important knowledge improvement for the climatology of the urban

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Vol.:(0123456789)1 3 Bulletin of Atmospheric Science and Technology and peri-urban area of Milan. It fnds application not only in more detailed urban climate studies but also in monitoring the efects of urban activities and for the assessment of adaptation and mitigation measures in the urban environment. Finally, the frst set of interactive maps of medium–high resolution UCL air temperature was produced in the framework of the locally funded ClimaMi project and made freely available to urban authorities and professionals as an improved climatological basis for present and future plans and projects to be developed in the framework of the national and international adaptation and mitigation measures.

Keywords Air temperature · Land surface temperature · Urban canopy layer · Urban heat island · Cokriging

1 Introduction

Conurbations are known to be inhabited by an increasingly larger fraction of the global population: this reached 56.2% at the end of 2020 and could grow up to about 70% by 2050, while in Europe, this level has already been exceeded and is now about 75% (UN, World Urbanization Prospects 20181). The urban environment’s relevance under all aspects of human activities and wellness is thus evident, even if the urbanized land surface is a small fraction of the Earth’s surface. In particular, air temperature at the level where inhabitants live and operate is undoubtedly among the frst variables to be considered. In cities, this corresponds to the temperature in the urban canopy layer (UCL), which is defned as the part of the urban atmosphere between the surface and the envelope of building tops or, better, the mean height of buildings and trees (AMS 20202). This particular sub-layer of the more general atmospheric boundary layer (ABL) is characterized by complex interactions between air and surfaces: they are generally multi-oriented in three dimensions and may have very diferent radiative properties. Furthermore, these interactions are also a function of time due to temporal variations in solar irradiance (daily and seasonally) and anthropogenic activities and can signifcantly change with urbanistic modifcations. Both the structural and physiological characteristics of a city are important in defning the continuously variable heat and radiative fuxes, which determine the temperature of the air in the canopy: it is also evident that each urbanized environment has its properties, often much diversifed as a function of regional climates, urbanistic designs, lifestyle of inhabitants, and other cultural aspects. Under this respect, each city or even each borough needs to be studied independently, and only a few rules can be asserted in a general way: for instance, the existence of the Urban Heat Island (UHI) phenomenon, which is quite related to the dimension and population of the city (Zhou et al. 2017). Nevertheless, also the UHI climatology is strongly site-dependent (Oke et al. 2017). Detailed and accurate observations are an essential part of the UCL knowledge, and much has been done during specifc measurement campaigns as BUBBLE (Rotach et al. 2005) or in testbeds as in Birmingham with BUCL (Chapman et al. 2015). The acquired data can then be used to validate meteorological models specifcally developed to describe the urban atmosphere. But, surprisingly, less is done for the urban environment from a

1 https://population.un.org/wup/ 2 https://glossary.ametsoc.org/wiki/ 1 3 Bulletin of Atmospheric Science and Technology climatologically point of view, even if the urban climate is subject to modifcations following urbanistic evolution as well as regional climate change (Milesi and Churkina 2020). Climatological knowledge implies continuous and accurate monitoring, which up to recent times has not been done systematically and coherently in urban environments (Muller et al. 2013). Furthermore, the need for a better description of the actual (and not only future) climate in cities is more and more requested in the framework of adaptation and mitigation plans developed internationally, to achieve specifc UN Sustainable Development Goals3: 11-Sustainable Cities and Communities, and 13-Climate Action. Therefore, continuous and reliable measurements are an essential contribution to resilience: it is of great relevance their transformation in a form (like suitable indexes) easily accessible and directly usable by technical authorities, professionals, and practitioners, who directly impact projects and realizations that, especially in cities, have sensible conse- quences to local and micro-climates and immediate efects on humans and human activities. In fact, global climate change will afect cities as any other part on the Earth, but due to the large percentage of the urbanized population and the complexity and sensibility of urban interactions, the outcomes are of utmost relevance: variations in the precipitation regimes could enhance inundations, as, for instance, more frequent and intense heat waves could positively interact with UHI, severely afecting human wellness and even life. There- fore, as stated in IPCC Special Report - Global Warming of 1.5°C, the observed global warming is of great concern for cities. Specifc actions started in recent years to cope with its efects in urbanized environments both at global and at the national level: for instance, UN-Habitat’s Cities and Climate Change Initiative4 (CCCI), the C40 Cities Climate Leadership Group5 (C40), the Covenant of Mayors EU6 as part of the EU Adaption Strategy, and several National and Regional Adaptation Plans to Climate Change, which all afect cities as a relevant part. Furthermore, the IPCC has recognized the key role of cities in responding to climate change and has proposed that the seventh assessment cycle include a Special Report on Climate Change and Cities (Masson et al. 2020). Both mitigation and adaptation plans are then required and urgent. Supposing that mitigation could be achieved by energy savings adaptation implies mod- ifcation and planning of existing (and planning of new) buildings and other urban ele- ments in order to achieve more comfortable environmental conditions. In both cases, thermal considerations are an essential part of evaluation, plans, and projects. Nevertheless, the necessary detailed knowledge of thermal properties in the urban environments is often lacking: temperature climatology generally relies on measurement by single urban (historical) observatories or by a relatively small number of monitoring stations, which are sometimes heterogeneous in procedures and purposes and belonging to diferent networks. Furthermore, even a relatively large number of dedicated sensors could not be sufcient to cope with the requirements posed by architects and engineers for their plans and projects, which are in the order of at least some tenth of meters for buildings, places, and parks.

3 https://www.un.org/sustainabledevelopment/sustainable-development-goals/ 4 https://unhabitat.org/programme/cities-and-climate-change-initiative 5 https://www.c40.org/ 6 https://www.covenantofmayors.eu/ 1 3 Bulletin of Atmospheric Science and Technology

On the other hand, spaceborne remote sensing is producing abundant quantity of observational data that fnd large use in urban description and monitoring: for thermal aspect, this is accomplished by infrared (IR) radiometers measuring the surface outgoing longwave radiation, from which the land surface temperature (LST; Tomlinson et al. 2011; Hulley et al. 2019) can be derived, and several portals are available for downloading LST data at diferent spatial resolutions and temporal intervals. The limitations of remote sensing from space are the disturbing efect of the atmosphere on radiation, with the presence of clouds as a primary source, and the low frequency of satellite passes for the polar-orbiting or a still too low spatial resolution for the geostationary ones. Modeling is another possibility: it is a useful tool to describe with sufcient details both the building-atmosphere interactions (from the bottom, at meter scale, and up) and the urban boundary layer (from the top, high-resolution atmospheric models at km scale and down), given sufcient knowledge of surface and built-up parameters as already made possible by special portals as WUDAPT (Ching et al. 2018), CORINE7 as part of the Coperni- cus initiative, and more specifcally in our case DUSAF8 for Lombardy. Recent urban modeling development is promising, coupling mesoscale meteorological, and building-resolved models: nonetheless, model validation and initialization relays always on suitable measurements at urban meso- to micro scales (Oke 2004), confrming the strong and unavoidable need for a good and reliable dedicated observational network for monitoring climatological purposes. With the technological development in surface networks and spaceborne sensors, and the improvement in modeling, in the last decades, many studies were published describing the urban atmosphere of cities all over the world (Masson et al. 2020). In the case of Milan, the target city chosen for this work, recent and less recent research covers surface observations (Bacci and Maugeri 1992; Mariani et al. 2016) as well as remote sensing from space (Anniballe et al. 2014) and modeling (Mariani et al. 2016; Falasca and Curci 2018). All these works describe with increasing detail the UHI main characteristics in Milan. However, they always rely on a limited set of surface observations by weather stations of heterogeneous typology, such as historical observatories, peri-urban synoptic weather stations, or air quality monitoring stations. The availability of new, better, and extended observational datasets could signifcantly improve the meteorological and climatic knowledge for the city and surrounding areas. Following this reasoning, the present work is devoted to verify the practicability of temperature information (and derived indexes) in urban and peri-urban environments with a spatial resolution compatible with the urbanistic, architectural, and engineering requirements for plans and projects in an adaptation perspective. This is not obtainable from simple monitoring: it is necessarily a climatic activity and requires enhanced measurement and network management quality. Started in 2019, the activities carried out as part of a local project (ClimaMi9) in cooper- ation with local public authorities and professional associations of architects and engineers led to a detailed identifcation of the needs for correct planning and design in the city of Milan. The answers were given in terms of an updated climatological knowledge resolved at borough scale and specifcally based on a dedicated urban network operated since 2011 with metrological criteria (Curci et al. 2017). Furthermore, clear needs at a fner scale led authors to develop a methodology for efcient integration of surface measurements of air

7 https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 8 https://www.geoportale.regione.lombardia.it/ 9 https://www.progettoclimami.it/ 1 3 Bulletin of Atmospheric Science and Technology

Fig. 1 Geographical distribution of automatic weather stations used. a FOMD CN urban stations in the Milan metropolitan area. b FOMD CN subnet downtown Milan. c Total area considered and position of selected automatic weather stations from the three networks: Climate Network CN (FOMD: 21 AWS), Lombardy Regional Meteorological Service (ARPA Lombardia: 26 AWS), Italian amateur network (Meteonetwork: 14 AWS). d Project area centered on Milan, in the northern geographical part of Italy temperature with LST from spaceborne platforms and produce a mean description of the thermal characteristic in the greater Milan area at medium to high spatial resolution. The higher spatial resolution of LST data is a relevant added value and compensates for most applications the limitation in time resolution due to the satellite pass times. Observational data and selection methodologies are described in Section 2, both for surface measurements and satellite products. The adopted methodology and the related moti- vations are presented and discussed in Section 3, while Section 4 illustrates some results obtained for practical applications in urban adaptation. Finally, in Section 5, results are shortly summarized and further developments envisaged.

2 Observations

Several considerations determined the observational dataset used for this work. First of all, the study area was not limited to the city of Milan but was extended to the surrounding region, which is one of the densest populated in Italy with several other smaller towns all around, and venue of intense industrial as well as agricultural activities. Therefore, this

1 3 Bulletin of Atmospheric Science and Technology area (with the city of Milan almost in the center) ofers a large spectrum of land cover and land use, the advantage to be topographically almost fat (laying in the west-central part of Po valley) and climatologically almost homogenous at the meso-synoptic scale. The corner coordinates are as follows (WGS84): 45.05° and 45.70° N, 8.70° and 9.80° E, and the extension is of 6160 km 2, as depicted in Fig. 1. It is also large enough to avoid boundary errors describing the urbanized environment of the city. The time period taken into consideration is limited by the availability of homogeneous and reliable observations, as will be discussed in the next paragraph, but was chosen as long as possible for a statistically signifcant description of the more recent local climate. The time span covers more than 4 years and fve full summer seasons. Observational data are near-surface air temperature measurements by automatic weather stations (AWS) of meteorological urban- and meso-networks and land surface temperatures obtained by spaceborne sensors. In the next paragraphs, the data sets are described in more detail.

2.1 Near‑surface data

The urban meteorological network in Milan is part of the nationwide urban climate network (CN), owned and managed by the Fondazione Osservatorio Meteorologico Milano Duomo (FOMD). CN has already been described in detail (Borghi et al. 2014; Frustaci et al. 2017; Curci et al. 2018) and also with regard to urban applications (Lavecchia et al. 2018). In short, its main characteristics are as follows:

– homogeneity of sensors and methods (maintenance and validation) – periodical calibration of principal sensors with documented reference to international standards – high operational reliability – specifc design for urban climatology and monitoring (and related applications) – siting and exposure of sensors following WMO recommendations (Oke 2007; WMO 2018) – fully documented and regularly updated station and network metadata – local/micro-scale representativeness for all-weather variables – 10 min mean data storage of main essential climate variables (ECVs)

Measurements are automatically gross-checked both at the local data logger of each AWS and the central data server. Being also used for monitoring purposes and operational applications, a subjective validation is then performed by experienced meteorological personnel, and any inconvenience is reported to the technical group for maintenance and repair. Maintenance and calibration procedures have undergone a further improvement during participation in the MeteoMet Project (Merlone et al. 2017; Lopardo et al. 2014; Curci et al. 2017). The Milan metropolitan area subnet has about 20 AWS, gradually set in operation since 2011 downtown, the main outskirts and in some small towns all around, and permitting a sufciently detailed and accurate representation of the meteorological variables at the top of the urban canopy layer (Curci et al. 2017). A list of CN stations is given in the frst part of Table 1.

1 3 Bulletin of Atmospheric Science and Technology

Table 1 List and WGS84 coordinates of AWS: a FOMD urban climate network; b ARPA Lombardy monitoring network; c Association MeteoNetwork. AWS used to defne mean extra-urban temperature for UHI Index are in italics in b, while the frst 8 urban CN AWS used for UHI Index downtown Milan are in bold italics in a a) a) FOMD LonE LatN b) ARPA LonE LatN c) Association LonE LatN CN Lombardy Meteonetwork 1 MILANO 9.229652 45.4799951Osio Sotto 9.61173782 45.6205557 1 Seregno-ovest 9.17903423 45.6538432 Città Studi 2 MILANO 9.163837 45.5025782Rivolta 9.52068901 45.4440905 2 Giussano 9.20547009 45.6907729 Bovisa d'Adda 3 MILANO 9.194909 45.4596413Osnago 9.38855607 45.6776984 3 Sesto San 9.20962215 45.5381437 Centro Giovanni-Parco Nord 4 MILANO San 9.125377 45.4786074Cavenago 9.56265997 45.2692744 Lodi--San 9.52012539 45.2991317 Siro d'Adda Bernardo 5 MILANO Sud 9.200497 45.4312895Sant'Angelo 9.37965968 45.2606673 5 Monza-Via 9.26147461 45.5962635 Lodigiano Sgambati 6 MILANO 9.187711 45.4508266Misinto 9.06639000 45.6613822 6 Mezzana Bigli 8.85000000 45.0700000 Bocconi 7 MILANO 9.211582 45.5101697Castello 8.70057165 45.2468005 7 Treviglio 9.57516861 45.5100362 Bicocca d'Agogna 8 MILANO 9.175951 45.479822 8 Cornale 8.91414460 45.0400766 8 Lodi-Viale 9.49406290 45.3055128 Sarpi Europa 9 Gaggiano 9.033883 45.4061869Pavia 9.16463830 45.1946761 9 Saronno 9.02498300 45.634373 Folperti 10 Magenta 8.883972 45.46630010 Pavia SS 35 9.14666412 45.1806221 10 Lesmo-via Po 9.30796534 45.6558361

11 Rho 9.039521 45.52966411 Voghera 9.01749364 44.9902730 11 Milano-Maxwell 9.24053192 45.4981655

12 Cinisello 9.211591 45.55561912 Busto 8.82387838 45.6263873 12 Carpiano- 9.27087307 45.3384214 Balsamo Arsizio centro Rossini 13 Seregno 9.198415 45.65205613 Somma 8.71269146 45.6495371 13 Monza-Parco 9.28602219 45.6195006 Lombardo 14 Vimercate 9.368517 45.61396114 Arconate 8.84732193 45.5485174 14 Trecate-S. 8.73000000 45.4200000 Maria 15 Melzo 9.423614 45.50459715 Cinisello 9.20560341 45.5426647 parco nord 16 San Donato 9.257977 45.42498516 Corsico 9.09741084 45.4361085 Milanese 17 Lacchiarella 9.137408 45.32140617 Lacchiarella 9.13451726 45.3245174

18 Legnano 8.918795 45.59550618 Motta 8.98856273 45.2819562 Visconti 19 Saronno 9.049397 45.63672719 Rodano 9.35349714 45.4725800

20 Lodi 9.488757 45.30756220 S. 9.48624910 45.1869994 Colombano al Lambro 21 Vigevano 8.859299 45.31803421 Milano 9.18911038 45.4716563 Brera 22 Milano 9.22231514 45.4732257 Juvara 23 Milano 9.25751540 45.4967798 Lambrate 24 Milano 9.19093356 45.4963164 Marche 25 Milano 9.14178627 45.4760634 Zavattari 26 Landriano 9.26715312 45.3205936

Even if dense enough for various applications (from energy consumption management and various climate services), the network can not entirely satisfy requirements by numerous practitioners and local authorities involved in urban planning, park, and building projects, and many other activities to be developed within the framework of climate change resilience plans. Therefore, available data from other stations and networks have been considered to complete, when and where viable, the validated and reliable CN data. In addition, strict criteria were predefned to accept external data in the project enlarged DB, especially similarity or compatibility to CN for typology and installation of sensors, and a sufcient percentage of available and validated data for the time period under consideration. 1 3 Bulletin of Atmospheric Science and Technology

Fig. 2 Correlation examples for air temperature between CN stations taken as reference and nearby (a) an ARPA station (Lacchiarella), with larger spread because of diferent siting and no spikes, and (b) a Mete- oNetwork station (Lodi—San Bernardo), with evident outliers but smaller spread

Using these criteria, useful supplementary stations were selected from ARPA Lom- bardy10 and the Italian amateur Association MeteoNetwork.11 All the used stations are listed in the second and third part of Table 1, while maps describing the study area and the network layout are given in Fig. 1. ARPA Lombardy is the regional environmental agency and archived data are regularly validated. Nevertheless, stations were selected for this work also on the basis of continuity of data and after a comparison with nearby CN data: the same was done for MeteoNetwork. The amateur stations are already subject to acceptability criteria to enter the MeteoNet- work Association, with explicit reference to WMO criteria and metadata publicly available and accessible at the association website. In addition, some members are professional mete- orologists and provided automatic quality procedures for all stations. The stations selected for this work are, in all cases, instrumented with Davis Vantage Pro 2 or Davis Vantage Pro Plus. Data from the 14 selected MeteoNetwork AWS have been controlled at a daily and hourly resolution to eliminate spikes and other doubtful measurements, through a comparison with nearby CN data. Overall, the selection methodology was in substantial accord- ance with published criteria and cautions (Meier et al. 2017; Muller et al. 2015). Typical comparison examples for air temperature are shown in Fig. 2 for unfltered data. In one case (Fig. 2 a), an ARPA station located in the same small town of Lacchiarella, as the CN AWS (440 m apart), presents no outliers. However, the spread is large due to dif- ferent local climatic zones. In another case (Fig. 2 b), a MeteoNetwork station in a peri-urban residential area in the town of Lodi is compared to the CN AWS Lodi, again in a peri-urban/industrial area. In this case, the horizontal distance is larger (2.6 km), but the spread is smaller due to a more similar (partly green) environmental exposure. On the other hand, spikes are frequent but clearly detectable.

10 https://www.arpalombardia.it/ 11 https://www.meteonetwork.it 1 3 Bulletin of Atmospheric Science and Technology

Fig. 3 Examples of LST images over Milan at (a) 103 × 103 m2 spatial resolution from Sentinel 3A (6th July 2019, 09:11 UTC), well suited to describe the urban meso-scale aspect of the UHI; (b) 30 × 30 m2 from Landsat 8 (17th July 2019, at 10:10 UTC), best suited to describe local and micro-scale urban details. Temperature is given in Kelvin and the corners latitude and longitude are as follows: 45° 18′ ÷ 45° 36′ N; 9° 00′ ÷ 9° 30′ E

As anticipated, the measurement time span was defned as large as possible for a statistically signifcant description of the more recent climate, but compatible with data availability from urban CN and supplementary stations. It was set from 1st July 2015 to 31st August 2019, covering more than four full years and fve summer seasons. In every case, data are on an hourly basis, for a total of 36,552 hourly ECV data records: more specifcally, 8664 in winter and 10,320 in summer months, because of a special interest for applications in the extreme seasons (10 min mean values were also available and used for COK, as explained in Sections 3.4 and 4).

2.2 Remote‑sensed data

While the extended network described in the previous paragraph is a consistent improvement relative to the normal (synoptic or meso-synoptic) operational ones, it is still spatially insufcient for the resolution requirements for urban applications, as depicted in Section 1. Remote sensing in the electromagnetic spectrum is a direct way to obtain much higher spatial resolution: for temperature, space-borne infrared (IR) sensors are nowadays largely available. The inconvenience of this tool is the temporal resolution (limited to the time of satellite passes over a specifc location) and the presence of clouds, in diferent but always disturbing forms, which absorb and difuse longwave radiation. A satellite product of immediate interest is LST, derived from one or more IR channels, with auxiliary information from other (optical) ones, acquired from the same but often from diferent platforms. Based on data characteristics and easiness of access, the choice adopted here is limited to LST products released from 2 diferent sources: ESA Sentinel 312 (Copernicus Open Access Hub13) and NASA Landsat 814 (USGS EarthExplorer15). In the frst case, LST is derived mainly from the sea and land surface temperature radiometer (SLSTR, operating in the IR) and partly with the Ocean and Land Colour

12 https://sentinel.esa.int/web/sentinel/missions/sentinel-3 13 https://scihub.copernicus.eu/ 14 https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-overview 15 https://earthexplorer.usgs.gov/ 1 3 Bulletin of Atmospheric Science and Technology

Instrument (OLCI, operating in the visible part of the electromagnetic spectrum). To infer LST, the Split-Window method is used (Sobrino et al. 2016), producing a medium spatial resolution (1000 m); in the second one, LST is derived with the Mono-Window method (Ermida et al. 2020; Gorelick et al. 2017) at a high resolution (30 m) using raw data from the thermal infrared sensor (TIRS) and also from the operational land imager (OLCI) channels. In both cases, auxiliary information from other platforms and models are used to deal with surface diferential characteristics (albedo, vegetation fraction) and atmospheric infuence on IR radiation (water vapor content, aerosols). Examples of LST images for two situations treated in this work are given in Fig. 3: at the lower resolution, it is already possible to obtain a clear description of the meso-scale thermal structure of the UHI, while the higher one allows analysis at the building (or building complex) scale. Data selection was made following specifc criteria to optimize results for applications. In particular:

– all the available but totally cloud-free images covering the study area in order to exclude any noise in the data elaboration – both Sentinel 3A and 3B, when applicable, and all Landsat 8 LST products

A further selection was introduced for specifc weather situations of interest in temperature-sensitive urban applications, as explained in the next paragraph. In total, the satellite dataset examined is composed of the following:

– 462 Sentinel 3A and 3B, – 42 Landsat 8

raster LST subsets for the whole study area and time period (as defned at the end of the previous paragraph), while about one thousand were examined for accurate selection.

2.3 Selection of weather situations

The main interest in investigating the UCL is the UHI: therefore, the first selection is made on situations with a well-developed UHI effect. This is generally evaluated on an index defined as the temperature difference urban-rural: in our case, the index is computed for CN AWS relative to a set of extra-urban stations, considered representative of the regional rural background, generally supposed to be isotropic. In fact, in the Milan case (Frustaci et al. 2019a), considering the topographical homogeneity of the plane region (central-west part of the Po valley) around the city, the isot- ropy assumption appears to be sufficiently justified. The rural reference is then sim- ply computed as the mean temperature of 5 accurately selected extra-urban stations (all from the ARPA Lombardy network: Arconate, Landriano, Motta Visconti, Parco Nord, and Rivolta d’Adda, as underlined in Table 1, b), and all located in a radius of less than 30 km from Milan city center.16 Relating on the recent Milan climatology (Frustaci et al. 2019b), the UHI Index has a yearly mean of about 4.5°C. It can be occasionally as high as 10°C: therefore, only situations were selected with a UHI Index greater or equal to 3°C on at least one of the 8 CN

16 https://iris.arpalombardia.it/gisINM/common/webgis_central.php 1 3 Bulletin of Atmospheric Science and Technology stations downtown Milan (the frst 8 in the left columns of Table 1): this choice is obvi- ously site-specifc and strongly dependent on the urbanistic structure of each city. Meso-synoptic weather could mask the UHI phenomenon; to efectively select real UHI situations, further meteorological flters were applied:

– pressure situations, with mean daily pressure greater than the climatological mean; – stable anticyclonic weather circulations over the region according to a dated but regional-specifc weather type defnition (Borghi and Giuliacci 1979; Alessio et al. 1989); – wind speed lower than 1.3 m/s, the fgure was chosen again considering the regional and local climatology, characterized by frequent wind calms and very weak thermal breezes the most of the year.

Moreover, this meteorological fltering is separately made for winter (DJF) and summer months (JJA) according to main diferentiate application interests and for the morning and evening hours (around 10 and 21 UTC, respectively), due to the satellite pass times over the area. Finally, all selected episodes were classifed into 6 diferent types, depending on the urban CN station where the maximum of the UHI Index is found: weak mesoscale circulations and other efect could, in fact, elongate or displace the UHI dome in one direction or the other, producing well-diferentiated aspect of the UHI itself. This classifcation is given in Table 2, together with occurrences and relative frequencies observed in the project time span. Because 3 stations (MI Bocconi, MI Centro, MI Sarpi) are near each other in the most central part of the city, they were considered together in defning the most symmetric and well-centered UHI form. In the summer morning, UHI is normally at a minimum in Milan with a clear urban cool island efect as in other compact cities (Yang et al 2017; Gonçalves et al. 2018), and, therefore, no episode was found with UHI Index > 3°C. Table 2 shows that the centered symmetric UHI is the most frequent confguration in Milan, in agreement with the climatological prevalence of low winds and calms in the area. The second most frequent is characterized by an UHI maximum shifted toward the east both in winter and summer, caused by the westerly zonal circulation in correspondence with low-pressure systems NE of the Alps. Finally, the third one, observed mainly in the summer evenings, is the NW shift likely caused by post-meridian thermal lows over the NW Alps (a frequent regional situation in summer afternoons and early evenings, producing orographic convection not necessarily bound with frontal systems). While UHI is a typical urban phenomenon, heat waves (HW) are synoptically determined and almost independent from surface characteristics. However, due to their relevance in cities for several applications and inhabitants’ wellness, especially if combined with a well-developed UHI, they were also considered. Heatwaves are here defned (other defnitions are sometimes used) as a period of time of at least 2 consecutive days with daily temperature extremes over the 95th percentile of the local summer climatology (WMO 2015): relating to the climate normal (CLINO) 1961–1990, the Milan fgures are 23.2 and 33.1°C. Thus, UHI efect is often present, and HW episodes have been classifed in relation to the UHI Index maximum in 3 classes depending on the position of maximum, and for summer mornings (12 episodes) and evenings (8 episodes), as given in Fig. 4 (in this case, we omitted fltering for UHI Index > 3 °C). Incidentally, we note that values for the 95th percentile of minimum and maximum temperatures will change with changing climate: for CLINO 1971–2000, we fnd 23.4°C and 1 3 Bulletin of Atmospheric Science and Technology Frequency (%) Frequency 2.3 58.4 19.7 17.6 0.6 1.4 100 Number of episodes Number 8 202 68 61 2 5 346 Summer evenings Frequency (%) Frequency 0.5 79.4 0.0 18.7 1.4 0.0 100 3 °C), depending on the position of the the CN station where 3 °C. Time is referred to satellite passes, which occur around 10 (mornings) occur around satellite passes, which to is referred 3 °C. Time > Number of episodes Number 1 166 0 39 3 0 209 Winter evenings Winter Frequency (%) Frequency 17.8 45.6 3.3 15.6 15.6 2.2 100 Number of episodes Number 16 41 3 14 14 2 90 Winter morningsWinter City zone NE Center NW E W S T Daily and seasonal occurrences and relative frequencies of the classes of the 6 diferent Milan UHI (> frequencies and seasonal occurrencesDaily and relative and 21 UTC (evenings) and 21 UTC 2 Table Milan downtown CN AWS maximum UHI Index is observed. Note that in the summer mornings, Note is observed. there is no episode of UHI maximum UHI Index MI Bicocca MI Bocconi, Centro, SarpiMI Bocconi, Centro, MI Bovisa MI Città Studi MI San Siro MI Sud Totals Season/time

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Fig. 4 Frequencies of UHI Index maxima locations during (a) morning and (b) evening heat wave episodes

Fig. 5 Examples of linear correlations between Sentinel 3 LST data in pixels (1 × 1 km) coincident with AWS positions and T a measured by urban and peri-urban AWS. a Winter morning: 26th December 2016 – 10:20 UTC. b Spring evening: 21st April 2018 – 22:10 UTC

33.6°C, while for CLINO 1981–2010, the numbers are 24.1°C and 34.7°C, and for CLINO 1991–2020, we get 25.0°C and 35.0°C. Therefore, percentages also slightly change. Never- theless, for the present work, we retain the frequencies in Fig. 4

3 Methodology

Methods to obtain LST from spaceborne radiometers account for various efects related to surface properties and radiative characteristics of the atmosphere, but generally, LST values are found in good agreement with in situ measurements of soil temperatures (Gallo et al. 2011; Good et al. 2017). Nevertheless, the relationship between LST and air temperature is not straightforward: at large-scale and medium resolution, good correlations are found in the night with minimum air temperature, at a minor level also during the day with air temperature maxima (Chung et al. 2020), while some authors do not consider viable to obtain air temperatures from satellite data (Xiong and Chen 2017). Moreover, in urban areas, the relationship is further complicated by enhanced surface heterogeneity, which gives rise to relevant and variable horizontal gradients in surface properties and afects the ofset between in situ air temperature measurements and satellite- derived LST (Elmes et al. 2020; Sun et al. 2020). Anyway, for this research and considering the availability of the dedicated and enhanced urban surface network described above, the frst attempt was to investigate this relationship specifcally for the Milan area.

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3.1 Correlations

Experiments were done in a limited number of episodes, using both medium (1000 m) and high resolution (30 m) LST data from Sentinel 3 and Landsat 8. Direct correlation examples between LST values for the pixels corresponding to the positions of AWS and the corresponding air temperature measurements (T a) by the AWS themselves are shown in Fig. 5. Considering the built and rural environment, a bilinear correlation was also examined between LST and (T a, NDVI), where NDVI is the normalized diference vegetation index: LST = m × T + m × NDVI + c; NDVI = NIR−VIS Ta a NDVI NIR+VIS

and where NIR is a thermal channel in the near-infrared and VIS is a channel in the visible part of the electromagnetic spectrum. Results for a sample of situations using Sentinel 3 data are summarized in Table 3. Correla- tion coefcients are in some cases sufciently good, in others unsatisfying: this is especially true for the evening cases, where NDVI values are missing and data were taken from morning passes. Table 3 and Fig. 5 demonstrate that uncertainties in the estimation of Ta using LST are sometimes quite large and unsuited for practical applications: even if results are sometimes encouraging, reliability is not assured and other methodologies have to be taken into consideration to obtain reasonable air temperature felds. In conclusion, considering the non-fully satisfying results obtained with direct linear/ bilinear correlations, other methods have been investigated.

3.2 Inverse distance weighting

Inverse distance weighting (IDW) is the simplest method for spatial interpolation. IDW calcu- lates the value of a variable of interest at an unsampled point by taking a weighted average of the values of the observed variables in a neighborhood (Shepard 1968; Barnhill et al. 1980; Bartier and Keller 1996; Huang et al. 2011). In this method, the weights are decreasing functions of dis- ∗ tance with the inverse power being the most used weighting function. Given Ta the variable of interest at the unsampled point x0 , the IDW interpolation method can be written as follows:

∗ n T x0 = w ∙ T x a i=1 i i

where n is the number of sampled observations at the i locations, and wi are the weights, which may be written as follows: 1 p di wi = ni dp ∑k=1 k,i

where ni is the number of sampled observations at the i locations and p is the chosen power law index.

3.3 Kriging

Ordinary Kriging (OK) is a geostatistical interpolation method that estimates the variable of interest’s value at unsampled points using a collection of observations in a neighborhood

1 3 Bulletin of Atmospheric Science and Technology 20:44 40 1.2 09:23 15.9 39.7 1.2 2018–10-24 0.7 0.6 − 7.6 − 5.7 0.4 0.9 − 4.0 20:59 99 0.9 09:37 26.7 9.2 1.9 2018–09-11 0.8 0.6 − 16.5 18.8 0.3 0.2 − 7.5 21:07 83 1.2 09:45 40.0 17.7 1.7 2018–04-21 0.8 0.03 − 16.5 13.6 0.5 0.4 − 7.9 20:41 1 2.7 09:19 2.0 8.2 1.4 2018–02-27 0.03 − 0.3 6.7 − 1.4 0.3 0.3 − 6.7 / 37 1.2 09:11 4.4 2017–11-19 0.7 0.9 − 3.4 and NDVI for some episodes observed with Sentinel 3. For the evening episodes (PM), episodes with the observed episodes some 3. For Sentinel evening for NDVI and a T / 77 0.8 09:30 28.6 2017–06-17 0.8 0.3 0.3 / 112 0.5 10:03 4.7 2016–12-28 0.9 0.5 − 7.1 / 28 1.2 09:15 0.0 2016–12-26 0.6 0.8 0.5 Ta NDVI Ta NDVI 2 2 R UTC F (sign. if ≳ 40) R Std. Err. (LST) Err. Std. F (sign. if ≳ 40) UTC m m c Std. Err. (LST) Err. Std. m m c Date: Results of bilinear correlation between LST and theand variable independent LST correlationbilinear of Results between PM Episodes, AM NDVI Time AM Episodes & NDVI Time m and c the coefcients the from morning taken and data passes, when possible. F is the were F-Test, unavailable are NDVI 3 Table

1 3 Bulletin of Atmospheric Science and Technology and by evaluating the spatial variance using semi-variograms analysis (Goovaerts 1997). ∗ Given Ta the estimate of the variable of interest at the location x0 , the kriging method estimator may be written as the following linear combination:

∗ n T x0 = λ ∙ T x a i=1 i a i

where λi are the unknown kriging weights and n is the number of sampled values at location xi. The weights are determined using processing and ftting the experimental semi- variogram, built upon the set of observed values at known locations:

1 N(h) 2 1,2(h) = [T x − T x + h ] 2N(h) i=1 i i

where h is the distance between locations, N indicates the number of pairs points, and T is the variable of study, in this work the near-surface air temperature (Gilbert 1987; Isaaks and Srivastava 1989).

3.4 Cokriging

Cokriging (COK) is a geostatistical method that uses a pair of spatially correlated variables, the primary and the secondary variables (usually more sampled than the primary one), to predict a spatial representation of the primary variable (Matheron 1971; Goovaerts 1999). This method has been applied for diferent meteorological variables such as temperature and precipitation (Ishida and Kawashima 1993; Adhikary et al. 2017) and spatio-temporal interpolation of temperature (Kilibarda et al. 2014). The (weak) correlation between air temperature and LST data justifes using and testing COK in our case. The cokriging estimator is a linear combination of both a primary (T a ) and secondary (LST) variables and can be written as follows:

∗ n m T x0 = λ ∙ T x + ω ∙ LST x a i=1 i a i j=1 j j

where λα and ωα are weights to be determined for each point xα and n, and m are the numbers of sampling points for the primary and secondary variables. To determine the unknown weights, it is necessary to process and model the experimental variograms for each variable and build an experimental cross-variogram, which describes the spatial correlation between the primary and the secondary variables. Then, a cross-variogram model is fitted to the experimental cross-variogram to obtain a cross-variogram function, fundamental in the determination of the λi and ωj weights. In (1) is the experimental cross-variogram, estimated using the covariance, for the two variables of interest in this work: N(h) 1 , (h) = [T x − T ]∙[LST x + h − LST] Ta LST ( ) a i a i (1) N h i=1

where h is the distance between locations and N indicates the number of pairs points.

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Table 4 Mean interpolation errors (M) and root mean squared errors (RMSE) of computed T a obtained in 3 diferent episodes using coincident AWS measurements and LST data from Sentinel 3. IDW, inverse distance weighting; OK, ordinary Kriging; COK, cokriging

Episodes IDW OK COK

27/02/2018 M = 0.47 RMSE = 1.77 M = 0.17 RMSE = 2.02 M = 0.40 RMSE = 2.04 21/04/2018 M = 0.55 RMSE = 2.29 M = 0.11 RMSE = 2.32 M = 0.51 RMSE = 2.35 24/10/2018 M = 0.53 RMSE = 2.13 M = 0.12 RMSE = 2.11 M = 0.52 RMSE = 2.24

3.5 Assessment

To determine which method among IDW, OK, and COK is most suited to the purpose of this work, a cross-validation approach was applied by means of the leave-one-out method (Dubrule 1983). To compare and evaluate the various methods, the root mean squared error was calculated:

N 1 ∗ 2 RMSE = (Ta − T ) N i ai i

Fig. 6 Comparison between (a) interpolated air temperature with inverse distance weighting, (b) interpolated air temperature with ordinary Kriging, (c) interpolated air temperature with Cokriging, (d) land-use of the interpolation domain, from “Uso del suolo in regione Lombardia. I dati Dusaf, 2010” (the red rec- tangle highlights the basin of the Ticino river), and (e) land surface temperature. Latitude and longitude are respectively between 45° 00′ ÷ 46° 00′ N and 8° 20′ ÷ 9° 40′ E 1 3 Bulletin of Atmospheric Science and Technology

Fig. 7 Comparison between (a) interpolated errors for ordinary Kriging and (b) interpolation errors for cokriging. Coordinates as in Fig. 6

where N is the number of all the AWS stations, Ta the observed value at location ∗ i, and Ta the estimated value at location i. The adopted methodology was assessed using this leave‐one‐out cross-validation and compared to other interpolation methods: Table 4 shows results for a sample of episodes. Root mean squared errors (RMSE) are comparable, while mean interpolation errors (M) are better for ordinary Kriging. Therefore, given the results of this comparison, it may be argued that OK should be chosen as the interpolation method. However, the three interpolation methods were also compared in terms of their fnal outputs, especially with regard to their ability to produce realistic air-temperature felds. In Figure 6 is shown the comparison between IDW, OK, and COK for the 27/02/2018 episode. From the comparison of IDW, OK, and COK, it is possible to observe a strong bull’s-eye efect (Goovaerts 1997) in both IDW and OK, particularly strong for the IDW case and worse where the more isolated stations are located. While this statistical artifact is slightly attenuated in OK is almost absent in COK, slant- ing the choice toward COK instead of IDW and OK. Moreover, thanks to the presence of the LST feld as a secondary variable, the COK is the only interpolation method able to include the efect of the land-use on the air temperature feld. In particular, the COK interpolation is the only one showing the efect on the air temperature from the Ticino river area. In addition, COK outperforms OK also in terms of interpolation errors, as shown in Figure 7. Given the comparison results and the more realistic results obtained with the COK interpolation, cokriging was chosen as the most suited to obtain a near-surface-air-temperature feld for the area of study. In the following, the primary variable is the air temperature collected from the AWS described in Section 2.1, while the secondary variable is the LST measured from spaceborne sensors as in Section 2.2. The analysis is divided in the following steps:

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Fig. 8 Fit of the experimental variogram with a spherical model for the air temperature feld as measured from the AWS on the 29th of January, 2017. Here, (h) is the theoretical spherical model, with r the distance, while a and c are the model’s parameters, here representing the range and the sill

Fig. 9 Fit of the experimental cross-variogram with a spherical model as in Fig. 6 for the 29th January, 2017

a) Processing and ftting of the experimental variograms (Fig. 8) for the AWS air temperature and for the LST: the most suitable ft function for both the air temperature and LST is the spherical model (Gilbert 1987; Isaaks and Srivastava 1989). b) Processing and ftting the sample cross-variogram for the two variables (Fig. 9): the cross-variogram estimator is the covariance and the most suitable ft function is again the spherical model. The cross-variograms show a decreasing trend for all the analyzed cases, in line with the observed positive correlation between air temperature and ground temperature. The linear model of coregionalization (LMC) is enforced together with a test for positive defniteness to test the variogram system’s validity (Bourgault and Marcotte 1991). The LMC imposes a set of restrictions for the three semi-variograms passed to the cokriging equations, avoiding failures in the convergence of the cokriging algorithm. c) Estimation of the air temperature feld by cokriging: the product of this phase is the estimate for air temperature feld over the study area and a standard deviations grid, which quantitatively measures the smoothness and the relative goodness of the cokriging estimate (Journel 1986).

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In addition to the standard deviations grid, other sources of uncertainties have to be included considering the mixing of rural and urban data on one side (the former measured normally a 2-m height, the latter in the UCL upper part, and mostly on open typical building rooftops) and of surface (or skin) temperatures on the other one. However, two considerations are straightforward in this respect. First of all, the cokriging method provides an interpolating estimation of the primary (under-sampled) variable by means of a (over-sampled) secondary one: this asymmetry allows to interpret the interpolated variable as efective air temperature. Then, with regard to which atmospheric layer the so obtained air temperature should be assigned, it may be argued that in the UCL, air is normally sufciently well mixed to give almost negligible vertical gradients, especially in morning hours (Aliabadi et al. 2019). Therefore, apart from very locally (microscale) dif- ferences (as, for instance, due to insolated pavement or wall efects; mostly to be excluded considering the network metadata), interpolated air temperature can be considered equiva- lent and comparable (within the urbanized areas and at the building envelope height) to the rural (2m) one.

4 Results

The DB described in Section 2.1 consists of more than 36,000 hourly air temperature records collected by surface stations and used for the selection and fltering of weather and UHI confgurations as shown in Tab. 2. Furthermore, a total of more than 100 useful cloud-free satellite images and related LST data sets were downloaded and analyzed at medium or high spatial resolution above the project area, as presented in Section 2.2. As a result, 72 COK interpolated air temperature felds at medium (100 m) resolution were obtained from Sentinel 3, diferently classifed as explained in paragraph 2.3. In addition, other 38 episodes were obtained at high (30 m) resolution from Landsat 8 for more detailed analysis. In both cases, 10 min mean temperature data from the surface AWS were used to assure the best possible time coincidence with the satellite passes. In the following, single episodes will be presented and discussed together with the mean felds obtained at medium resolution, which are intended to directly support resilience urbanistic projects and other applications.

4.1 Episodes

In Fig. 10, the interpolated near-surface air temperature feld is shown over the project area for a summer evening episode (7th July, 2018) with UHI maximum over the city center, one of the most frequent situations. For comparison, timely coincident AWS measurements are shown as well as the original LST feld. The warmer areas are almost coincident with the city and the other much-urbanized neighborhoods, while the benefcial efect of some green areas north- and northeast- wards is visible. In this case, uncertainty maxima are quite large (up to 2°C), but generally lower than 1°C not too far from AWS locations. The less frequent example of an UHI shifted to the NW relative to downtown Milan is shown in Fig. 11. It is interesting to note that this one is also a HW episode and the whole region is hot, while uncertainties remain everywhere under 1°C.

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Fig. 10 Episode of 7th July, 2018, at 22 UTC. Coordinates as in Fig. 3, Milan in center, borough borders are over-impressed for reference. a AWS air temperature measurements. b Original Sentinel 3A LST image. c Cokriging interpolation of AWS temperatures and LST data at 100 m resolution. d Cokriging uncertainty map. Color scales in (b) Kelvin, in (c) and (d) Celsius

Another HW episode is fnally shown in Fig. 12 for the morning of 6th July, 2019: in this summer case, UHI is almost absent (as said before, no case with UHI > 3°C was found in summer mornings) and the warmer areas are clearly outside the city and mainly in the rural background, while again the uncertainty feld is smooth and well below 1°C. In all these 3 episodes, the diferences between original LST from Sentinel 3 and air temperature felds obtained with COK are evident. While LST felds always show a marked surface UHI efect (S-UHI), the Ta felds are much smoother and the warmer areas in the city show temperatures of only about 1 to 3 degrees higher than the cooler neighborhoods. Finally, in Fig. 12, there is a clear example of the morning urban cool island, with the city center and the most urbanized boroughs in the NW at lower temperatures than most rural backgrounds. For more specifc and detailed applications, higher resolution data are needed: they are provided by Landsat 8 LST products. In Fig. 13, an example is given over the whole project area, with direct comparison between Sentinel 3 and Landsat 8 LST, and the cokriging derived T a felds at medium (from Sentinel 3) and high (from Landsat 8 data) resolution. The general aspect of LST is consistent for Sentinel 3 and Landsat 8 images and the cokriging-derived Ta felds. However, the last are much more detailed and allow evaluation of urbanistic variations of less than 100 m in the horizontal dimension, as exemplifed in the case shown in Fig. 14.

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Fig. 11 Episode of 26th June, 2019, at 10 UTC with temperature maxima in the North-West. a, b, c, d as in Fig. 10

The example relates to the establishing of an extraction activity, with annexed industrial constructions, occurred between 2015 and 2016 in a southwestern outskirt of Milan, Ronchetto sul Naviglio, about 5.8 km from the city center. The land-use variation is documented by the regional land use portal (DUSAF 2014 and 2018), managed by the Lom- bardy Environmental Agency (ARPA Lombardia). In the frst case (2014), the small area shown by the arrow (of about 500 × 500 m 2 inside the 1-km circle) was classifed as arable land, while in 2018, the classifcation changed to industrial settlement. The variation is easily detectable on spaceborne visible images. In our case, two Landsat 8 morning LST frames were used, the frst in summer 2013, and the second one in summer 2019. While the general aspect of LST felds, zoomed in an area of about 9 × 9 km2, is quite similar, a new hot spot is visible in the latter in the NW corner of a local aquifer (colder and greener in the images). In the COK-derived Ta felds at 30-m resolution, the diference is barely but still visible as highlighted by the rows in Figs. 14 e and f, despite the inevitable smoothing and the small diferences in mean temperature between the two diferent days. The result encourages the future assessment of the efects of (even relatively small) urbanistic modifcations on the thermal environment. In the same area, a new building was erected in the same time period: more exactly, this is a residential tower whose plant is about 50 × 25 m2. It is located immediately west of Piazza Tirana and right north-north-east of the above extraction site at a distance of about 0.8 km,

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Fig. 12 Episode of 6th July, 2019, at 10 UTC; a, b, c, d as in Fig. 10

mostly surrounded by a green area. In this case, no diference is noticeable between the two LST images and the COK-derived air temperature. It must be concluded that at this stage, signifcant variations in land use or urbanistic modifcations can be easily detected not only from LST high-resolution data, but also from interpolated air temperature, up to the scale of building blocks (about 102 m), while not for single buildings of limited dimensions.

4.2 Mean felds

Urban heat island descriptions are often based only on single, more, or less representative cases. Given the encouraging results obtained on single episodes, and although the number of useful images is limited, we also constructed mean felds for a statistical representation of the city thermal characteristics: this is motivated by the planning and project requirements as discussed in Section 1. In Fig. 15, two examples are given for the most frequent UHI confguration over Milan: it can be noted that there is a clear diference between the summer case, with NW elongated UHI over the city, and the winter one with an eastward shifted UHI maximum. In both examples, mean uncertainties are similar: minimal where several stations are close to each other (as in the city) and larger far away from AWS positions. In any case,

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Fig. 13 Episode of 26th July, 2019, at 10 UTC with Milan UHI maximum shifted to NE. a LST from Sen- tinel 3. b LST from Landsat 8. c Cokriging Ta feld at 100 m from Sentinel 3. d Cokriging Ta feld at 30 m from Landsat 8. Coordinates as in Fig. 3, borough borders are over-impressed for reference, and Milan downtown is centered in the images. Brighter (colder) areas west (larger) and east (smaller) from Milan downtown correspond to two large green parks (Parco Sempione and Giardini Montanelli, respectively). Color bars in Kelvin in a and b, in Celsius in c and d

uncertainty maxima (as computed from the above methodology) are in the mean always less than 2°C, and mostly about 1°C in all the mean felds produced: these fgures are coherent and not as much larger than the estimated long-term measurement uncertainty of the temperature at UCL top by the CN (Curci et al. 2017). Furthermore, the mean maps show evidence of a heat island efect in some smaller cities in the project area (e.g., Lodi, about 30 km south-east from Milan), illustrating the adopted methodology’s replicability even for less densely populated towns. Altogether, 19 mean air temperature felds have been produced, 8 for HW episodes and 11 for the diferent UHI confgurations relative to day time, season, and UHI maxima position. Further 5 relate to extrema. This data set is still not a comprehensive climatological description of the city and the neighborhoods, but it already represents a useful new tool for urbanistic applications based on an updated and much more detailed climatology.

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Fig. 14 Comparison of high-resolution (30 m) Landsat 8 LST images and derived Ta felds for the area of Ronchetto sul Naviglio (south-east Milan outskirt, about 5.7 km from the city center in the upper right corner, latitude 45° 23′ 17″ ÷ 45° 27′ 48″ N, longitude 9° 4′ 14″ ÷ 9° 11′ 33″ E, about 10 × 10 km 2): a Landsat 8 LST 2013–07-16–10:13 UTC. b Landsat 8 LST 2019–07-17, 10:10 UTC. c Land use map, ed. 2014 (ARPA Lombardia DUSAF). d Land use map, ed. 2018 (ARPA Lombardia DUSAF). e COK-derived Ta for 2013– 07-16–10:13 UTC. f COK-derived T a for 2019–07-17, 10:10 UTC. In a and b, LST color scale in Kelvin, in e and f, air temperature color scale in Celsius

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a) Mean Ta Summer UHI, evening NW b) Ta uncertainty Summer UHI, evening NW

c) Mean Ta for Winter UHI, evening E. d) Ta uncertainty Winter UHI, evening E

Fig. 15 Example of mean felds of air temperature over the Milan area and related uncertainties. Colored dots are the AWS from the 3 diferent networks used. Uncertainties are larger far away from AWS locations and minimal where there are several nearby stations, as in downtown Milan. Color scale is unique left for T a and right for T a uncertainties (both in degree Celsius). Mean time is in all cases about 21 UTC; corner coordinates are 45° 05′ ÷ 45° 41′ N, 8° 42′ ÷ 9° 38′ E. a Mean T a Summer UHI, evening NW, b Ta uncertainty Summer UHI, evening NW, c Mean Ta for Winter UHI, evening E, d Ta uncertainty Winter UHI, evening E

4.3 Applications

Considering the encouraging results obtained, the above-adopted methodology was used to set up an experimental portal for direct and unrestricted access by end-users involved in urbanistic and other thermo-climatic-dependent projects and activities. Maps accessible from the portal separately for seasonal means and extremes and typical weather types are inserted in a GIS server, allowing the extraction of pixel data at the 100 × 100 m2 resolution. The GIS also provides supplementary layers related to land use, green areas, adminis- trative borders, aquifers, and station positions. The freely accessible portal, developed in the locally funded project ClimaMi, is also provided with technical and user guidelines describing details and best data use.

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Simultaneously, many courses were ofered to local ofcials, architects, and engineers to present the results and introduce the facility. The portal is intended to promote the use of updated (and strictly observation-based) climatological information about air temperature and derived indexes at medium/high resolution, already in the planning phase of future projects of urbanistic modifcations and adaptation plans to climate change, energy management of individual building and building blocks, green areas management and improvements, and human wellness in general. In operation since the end of 2020, the portal has already been accessed by numerous users, while improvements and extensions are planned by the end of 2021, in particular integrating precipitation data. Replicability of this realization is also possible and even foreseen for other cities on the basis of the positive experience gained in Milan.

5 Conclusions

The development and validation of a methodology for medium to high spatial resolution thermal felds in the urbanized environment of the greater Milan area has been presented and discussed. This methodology, exclusively based on observations in the 2016–2019 period, interpolates frequent and high-quality air temperature measurements by a relatively dense surface network (partly taken at top of the urban canopy layer and partly in the rural background), with the medium to high spatial resolution products of land surface temperatures (LST) obtained by the regular but infrequent overpassing of selected spaceborne platforms as Sentinel 3 and Landsat 8. The interpolation method provides thermal felds of near-surface air temperature at a resolution compatible with that of space sensors while retaining the quality of the lesser sampled air temperature measurements by the surface network. Limitation in time resolution is outperformed by the much higher spatial resolution, which is relevant for several urban applications and planning. Furthermore, based on cokriging, the method also explicitly produces related uncertainty felds, whose mean values are comparable to the documented long-term uncertainties of the surface network high-quality measurements. The results obtained for a number of single episodes at medium resolution encouraged the use of this methodology for a more accurate description of diferent forms of the Milan urban heat island efect, fltering out as much as possible disturbing synoptic phenomena and classifying the diferent UHI confgurations depending on the shifted position of the UHI Index maximum. Furthermore, a set of mean and extreme felds of air temperature was fnally produced, which not only describes details of the Milan UHI (posing the basis of an unprecedented high-resolution urban and peri-urban spatial climatology), but represents also a useful tool for direct applications in local and regional resilience plans and projects by technical authorities as well as by several practitioners. This data set of interpolated air temperature was fnally made accessible on a public portal for end-users and practical applications together with detailed guidelines and intro- ductory courses in the framework of the locally funded project ClimaMi. While already in operation, the portal is susceptible to improvements as well as the adopted methodology. First of all, the (surface and spaceborne) observational database will be progressively extended in time and quantity, using other platforms and data sets while

1 3 Bulletin of Atmospheric Science and Technology maintaining high-quality standards. On the other hand, the same methodology could be implemented for other essential climatological variables, such as precipitation, integrating weather radar data. Finally, this methodology is completely applicable in any other urbanized area, if there is a sufciently long-term and high-quality surface network as in and around the Milan area.

Acknowledgements Part of this research would not have been possible without ESA Copernicus Sentinel 3 and NASA- U.S. Geological Survey Landsat 8 data. We are grateful to Susanna Di Lernia (FOMD) for reviewing the text, and to both the anonymous review- ers for their much-appreciated comments and suggestions.

Author contribution Enea Montoli developed and applied the cokriging methodology, Giuseppe Frustaci selected and provided the satellite data, the UHI classifcation, and edited most of the article. Cristina Lavecchia produced the GIS maps and supervised all the activities in the framework of the ClimaMi Project (co-funded by Fondazione Cariplo), Samantha Pilati organized the AWS DB, selected external stations, and provided the weather classifcation. All authors have read and agreed to the published version of the manuscript.

Declarations

Confict of interest The authors declare no competing interests.

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Authors and Afliations

Enea Montoli1 · Giuseppe Frustaci1 · Cristina Lavecchia1 · Samantha Pilati1

* Giuseppe Frustaci [email protected] http://www.fondazioneomd.it Enea Montoli http://www.fondazioneomd.it Cristina Lavecchia http://www.fondazioneomd.it Samantha Pilati http://www.fondazioneomd.it

1 Fondazione Osservatorio Meteorologico Milano Duomo, Milan, Italy

1 3