Surface Temperature Forecasting

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Weather and Forecasting".

Deadline for manuscript submissions: closed (1 June 2023) | Viewed by 5376

Special Issue Editor


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Guest Editor
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza L. da Vinci 32, 20133 Milano, Italy
Interests: hydrology; meteorology; climatology; forecasting models; water resources; environmental monitoring

Special Issue Information

Dear Colleagues,

Surface temperature is a key variable in hydrological, meteorological, climatological, and many other environmental models due to the interactions between soil, vegetation, water, and the atmosphere. Accurate observations constitute a main factor in model forecasting chains. This Special Issue will focus on newly implemented methods for monitoring and predicting surface temperature in different ecosystems: agricultural and vegetated areas, cold and hot climate regimes, and water bodies such as oceans, lakes, rivers, and wetlands. Particular attention will be paid to topics related to the forecasting of surface temperature in urban areas in climate change scenarios in order to design more sustainable and resilient cities.

Dr. Alessandro Ceppi
Guest Editor

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Keywords

  • surface temperature forecasts
  • remote sensing
  • hydro-meteorological models
  • urban heat island
  • climate change
  • water resources

Published Papers (2 papers)

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Research

20 pages, 5657 KiB  
Article
Systematic Assessment of the Effects of Space Averaging and Time Averaging on Weather Forecast Skill
by Ying Li and Samuel N. Stechmann
Forecasting 2022, 4(4), 949-968; https://doi.org/10.3390/forecast4040052 - 24 Nov 2022
Viewed by 1523
Abstract
Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern [...] Read more.
Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern forecasts, and it is therefore not clear how much improvement in forecast performance is produced via averaging. Here we present a direct investigation of averaging effects, based on data from operational numerical weather forecasts. Data is analyzed for precipitation and surface temperature, for lead times of roughly 1 to 7 days, and for time- and space-averaging diameters of 1 to 7 days and 100 to 4500 km, respectively. For different geographic locations, the effects of time- or space-averaging can be different, and while no clear geographical pattern is seen for precipitation, a clear spatial pattern is seen for temperature. For temperature, in general, time averaging is most effective near coastlines, also effective over land, and least effective over oceans. Based on all locations globally, time averaging was less effective than one might expect. To help understand why time averaging may sometimes be minimally effective, a stochastic model is analyzed as a synthetic weather time series, and analytical formulas are presented for the decorrelation time. In effect, while time averaging creates a time series that is visually smoother, it does not necessarily cause a substantial increase in the predictability of the time series. Full article
(This article belongs to the Special Issue Surface Temperature Forecasting)
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24 pages, 4888 KiB  
Article
High-Resolution Gridded Air Temperature Data for the Urban Environment: The Milan Data Set
by Giuseppe Frustaci, Samantha Pilati, Cristina Lavecchia and Enea Marco Montoli
Forecasting 2022, 4(1), 238-261; https://doi.org/10.3390/forecast4010014 - 8 Feb 2022
Cited by 4 | Viewed by 2883
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Surface Temperature Forecasting)
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