RT journal article
T1 Forecasting urban temperatures through crowdsourced data from Citizen Weather Stations
A1 Romero Rodríguez, Laura
A1 Guerrero Delgado, MCarmen
A1 Castro Medina, Daniel
A1 Sanchez Ramos, José
A1 Álvarez Domínguez, Servando
A2 Máquinas y Motores Térmicos
K1 Forecasting
K1 Crowdsourcing
K1 Urban temperatures
K1 CWS
K1 Prediction models
AB This work explores the potential of using crowdsourced data from Citizen Weather Stations (CWS) to forecast urban temperatures. Five case studies were selected (Madrid, London, Rome, Paris and Berlin), using data from the manufacturer Netatmo to gather the hourly temperatures of the years 2021 and 2022 for 776 CWS. A quality-control process was implemented to enhance data accuracy, and heat maps were generated. Multiple linear regression models were created for each CWS and month using only reference weather data as input, resulting in the development of accurate models (RMSE lower than 1.5 ◦ C) for an average of 374 CWS. Then, reference weather predictions were used on the 2nd of April 2023 to forecast the hourly temperatures of those CWS for the period from the 3rd to the 9th of April (168 h). The outcomes showed that accurate CWS data and precise prediction models for the reference weather are crucial to improve the accuracy of the forecasts using crowdsourced data. The study demonstrates the potential benefits of using CWS data and simple models to forecast urban temperatures in a cost-efficient way even 168 h ahead, with implications for various sectors such as urban planning, energy consumptions, health impacts, or climate change adaptation strategies.
PB Elsevier
SN 2212-0955
YR 2024
FD 2024-08-31
LK http://hdl.handle.net/10498/35540
UL http://hdl.handle.net/10498/35540
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 22-sep-2026