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A Study of an Algorithm for the Surface Temperature Forecast: From Road Ice Risk to Farmland Application.

Authors :
Del Vecchio, Maria Chiara
Ceppi, Alessandro
Corbari, Chiara
Ravazzani, Giovanni
Mancini, Marco
Spada, Francesco
Maggioni, Enrico
Perotto, Alessandro
Salerno, Raffaele
Source :
Applied Sciences (2076-3417); 7/15/2020, Vol. 10 Issue 14, p4952, 22p
Publication Year :
2020

Abstract

The presence of road ice has always been a key issue during winter months. A reliable forecast system capable of predicting the Land Surface Temperature (LST) and, consequently, its formation is one of the best strategies to operate towards reducing both vehicles accidents and waste of chemical solvents used for prevention which have a significant economic and environmental impact. Hence, the Meteo Expert Centre (MEC) has developed an algorithm for LST forecasts able to issue ice risk warnings as well. This algorithm operationally works every day in real-time and it is here tested, first, on a paved area of the Pedemontana Lombarda motorway and the Milano Linate airport airstrip, and, afterwards, since the LST plays a crucial role in understanding phenomena of energy exchange between soil, vegetation, and atmosphere, its knowledge and prediction becomes relevant also for other purposes such as agricultural management and irrigation system control, further experiments are carried out over two agricultural fields, one in the North and the other in the South of Italy during the SIM (Smart Irrigation Management) project. All LST analyses showed encouraging results with reasonable high values of statistical scores, in both applications on asphalted and different vegetated terrains, demonstrating that the developed algorithm has a high versatility even on completely different types of surfaces, and it can be applied as a valid tool for road ice risk warnings too. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
10
Issue :
14
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
144773983
Full Text :
https://doi.org/10.3390/app10144952