Measurement uncertainty in meteorology has been addressed in a number of recent projects. In urban environments, uncertainty is also affected by local effects which are more difficult to deal with than for synoptic stations. In Italy, beginning in 2010, an urban meteorological network (Climate Network®) was designed, set up and managed at national level according to high metrological standards and homogeneity criteria to support energy applications. The availability of such a high-quality operative automatic weather station network represents an opportunity to investigate the effects of station siting and sensor exposure and to estimate the related measurement uncertainty. An extended metadata set was established for the stations in Milan, including siting and exposure details. Statistical analysis on an almost 3-year-long operational period assessed network homogeneity, quality and reliability. Deviations from reference mean values were then evaluated in selected low-gradient local weather situations in order to investigate siting and exposure effects. In this paper the methodology is depicted and preliminary results of its application to air temperature discussed; this allowed the setting of an upper limit of 1 °C for the added measurement uncertainty at the top of the urban canopy layer.

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 preferred 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 promoting 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 unfit for detailed urban climatological studies and they are generally unable to describe the air temperature field in the urban canopy layer (UCL) with a spatial resolution which is sufficient 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 offer 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 Variables (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 km 2 ) resolution from Copernicus Sentinel 3 and at high resolution (30 × 30 m 2 ) 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 fields of fine spatially resolved air temperature obtained by this method for selected weather types and different Urban Heat Island configurations represent an important knowledge improvement for the climatology of the urban Extended author information available on the last page of the article Bulletin of Atmospheric Science and Technology 1 3 and peri-urban area of Milan. It finds application not only in more detailed urban climat...

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Temperature is the most used meteorological variable for a large number of applications in urban resilience planning, but direct measurements using traditional sensors are not affordable at the usually required spatial density. On the other hand, spaceborne remote sensing provides surface temperatures at medium to high spatial resolutions, almost compatible with the needed requirements. However, in this case, limitations are represented by cloud conditions and passing times together with the fact that surface temperature is not directly comparable to air temperature. Various methodologies are possible to take benefits from both measurements and analysis methods, such as direct assimilation in numerical models, multivariate analysis, or statistical interpolation. High-resolution thermal fields in the urban environment are also obtained by numerical modelling. Several codes have been developed to resolve at some level or to parameterize the complex urban boundary layer and are used for research and applications. Downscaling techniques from global or regional models offer another possibility. In the Milan metropolitan area, given the availability of both a high-quality urban meteorological network and spaceborne land surface temperatures, and also modelling and downscaling products, these methods can be directly compared. In this paper, the comparison is performed using: the ClimaMi Project high-quality data set with the accurately selected measurements in the Milan urban canopy layer, interpolated by a cokriging technique with remote-sensed land surface temperatures to enhance spatial resolution; the UrbClim downscaled data from the reanalysis data set ERA5; a set of near-surface temperatures produced by some WRF outputs with the building environment parameterization urban scheme. The comparison with UrbClim and WRF of the cokriging interpolated data set, mainly based on the urban canopy layer measurements and covering several years, is presented and discussed in this article. This comparison emphasizes the primary relevance of surface urban measurements and highlights discrepancies with the urban modelling data sets.

<p>With the growing relevance of urbanized environments in the framework of adaptation and mitigation plans, improvements in monitoring the urban weather, and specially in the knowledge of the urban climatology and its evolution, are urgently needed. A basic difficulty arises from the fact that dedicated surface observational networks with the desired characteristics of measurement quality and continuity are often lacking in cities, while remote sensing data are mainly used for specific aspects, as for instance the Surface Urban Heat Island, while air temperature is more important for applications. After the experience gained, and the methodologies developed in Milan during a locally co-funded project (ClimaMi: https://www.progettoclimami.it/), the possibility was investigated of a medium- to high-resolution urban climatology mainly derived from observed air temperature and precipitation data.</p><p>The urban specialized surface network (by Fondazione Osservatorio Meteorologico Milano Duomo: FOMD), in operation since 2011 and “metrologically” tested during MeteoMet Project (Merlone et al., 2015), was considered as a reliable basis for a new and more detailed analysis of the most recent urban climate in Milan. To complement the necessarily limited number of high quality measurements by this urban Climate Network (CN),  other  automatic weather stations  (as homogenous as possible to CN) were accurately selected from third-party networks, in particular from the regional (ARPA Lombardy) meso-synoptic one, and from a private citizens association (MeteoNetwork): this helped in setting up a database of reliable hourly observational data (and metadata) in urban and peri-urban environments, dense enough for a mesoscale description of the city main statistical characteristics and for an already significative time span of 5 years.</p><p>Nevertheless, resilience plans by local authorities and professionals often require a spatial resolution of the order of tens of meters: to significantly improve the spatial resolution, space-borne sensors are an obvious and nowadays practical possibility. Furthermore, to make the best use of the quality of (under sampled) surface measurements, and of the high spatial resolution offered by remote sensed data, a cokriging-based methodology (Goovaerts, 1999) was developed and tested for air temperature. While direct correlation methods between Land Surface Temperature (LST) and the (more interesting and required) near-surface air temperature are not straightforward and generally unreliable, the encouraging results obtained in reconstructing air temperature fields by cokriging allowed an analysis of the recent climate of the cities and neighborhoods at medium to high spatial resolution for selected weather types of particular relevance in the definition of resilience measures.</p><p>The same methodology is now under test for precipitation measurements by different sensors and networks, and first results will be presented together with the unprecedented climatological description of temperature in the greater Milan, and analysis of micro-scale urban climate variations in consideration of (present and future) climate monitoring and assessment needs.</p>