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

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 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...

Meteonetwork: An Open Crowdsourced Weather Data System

“…Another example comes from the FOMD (Fondazione Osservatorio Milano Duomo) where MNW data are used to integrate the urban meteorological network to analyze climate conditions in the Milan metropolitan area [31,32].…”

High-Resolution Gridded Air Temperature Data for the Urban Environment: The Milan Data Set

“…copernicus.eu/), last accessed on 13 December 2021) initiative, based mainly on operational measurements by Sentinels 3A and 3B, and LST data by NOAA (National Oceanic and Atmospheric Administration (https://www.noaa.gov/), last accessed on 13 December 2021) and USGS (United States Geological Survey (https://earthexplorer.usgs.gov/), last accessed on 13 December 2021), based mainly on Landsat 8 sensors. Correlation, kriging, and cokriging interpolation methods were tested using T a and LST data for the Milan area, and their results were compared, as already described in [12]. Correlation between T a and LST was found to be not sufficiently performing from a meteorological/physical point of view for monitoring and climatological purposes, being relatively good in some cases but practically absent in others.…”

“…We also performed a statistical estimation of the interpolation uncertainty: in all the analyzed situations, we obtained a statistical uncertainty well under 2 • C and in most cases under 1 • C. Interpolation uncertainty is comparable with the AWSs' T a uncertainties due to calibration, siting, and exposure of sensors and to maintenance procedures. Specific development and application of this cokriging methodology to the Milan area and the FOMD Network database are detailed in [12]. The results, mainly obtained in the framework of ClimaMi Project (https://www.…”

“…In this paper, we focus on the assessment of urban air temperature fields, achieved by integrating high-quality near-surface measurements with high-resolution remote-sensed LSTs in Milan and the surrounding area. The integration methodology adopted is based on a cokriging technique (CoK) [11] and was described in a previous article [12], where direct correlations between near-surface air temperatures and LST, inverse distance weights, and ordinary kriging have also been considered and discussed. As a consequent continuation of that work, in the present paper the computed gridded fields of air temperature in the UCL and the related uncertainties are compared with other available temperature data sets based on reanalysis downscaling and with some urban modelling results.…”