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

Giuseppe Frustaci,Enea Marco Montoli,Samantha Pilati,Cristina Lavecchia

doi:10.3390/forecast4010014

Abstract

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.

Highlights

In the current climate change situation, cities represent one of the most relevant environments, mainly because a large percentage of human population lives in urbanized areas: more than 56% already in 2020, with a still growing trend and a projection up to 68%in 2050
We considered the sentative description of the lowest level of the Milan metropolitan area atmosphere
The results show an average systematic underestimation of the UCL temperatures by the weather research and forecasting model (WRF): the mean bias (WRF computed minus FOMD Network measured) is −0.3 ◦ C in 2014 and −2.2 ◦ C in 2015 (Figure 8)

Summary

Introduction

In the current climate change situation, cities represent one of the most relevant environments, mainly because a large percentage of human population lives in urbanized areas: more than 56% already in 2020, with a still growing trend and a projection up to 68%. UHI has several diversified aspects, depending on regional climate, topographic characteristics, urban shape, and urban metabolism. Atmospheric processes in the urban atmosphere are difficult to handle, essentially because they are dominated by turbulence of the more complex boundary layer and by the large gradients of surface characteristics. Both aspects imply the necessity of meteorological high-resolution spatial and temporal observations, land use and land cover details, and specific complex modelling techniques. A general review of all these items is given in [2]

Methods

Results

Discussion

Conclusion