Citation: Musah, A.; Dutra, L.M.M.; Aldosery, A.; Browning, E.; Ambrizzi, T.; Borges, I.V.G.; Tunali,M.; Ba¸ sibüyük, S.; Yenigün, O.; Moreno, G.M.M.; et al. An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande. Data 2022, 7, 106. https://doi.org/10.3390/data7080106 Academic Editors: Vladimir Sreckovic, Milan S. Dimitrijevi´ c and Zoran Mijic Received: 30 June 2022 Accepted: 22 July 2022 Published: 30 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). data Article An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande Anwar Musah 1,2, * , Livia Màrcia Mosso Dutra 3 , Aisha Aldosery 2 , Ella Browning 4 , Tercio Ambrizzi 3 , Iuri Valerio Graciano Borges 3 , Merve Tunali 5 , Selma Ba¸ sibüyük 5 , Orhan Yenigün 5,6 , Giselle Machado Magalhaes Moreno 3 , Ana Clara Gomes da Silva 7 , Wellington Pinheiro dos Santos 7 , Clarisse Lins de Lima 8 , Tiago Massoni 9 , Kate Elizabeth Jones 4 , Luiza Cintra Campos 10 and Patty Kostkova 2 1 UCL Department of Geography, Geospatial Analytics and Computing Group (GSAC), University College London, London WC1E 6BT, UK 2 UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK; a.aldosery@ucl.ac.uk (A.A.); p.kostkova@ucl.ac.uk (P.K.) 3 Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil; livia.dutra@iag.usp.br (L.M.M.D.); tercio.ambrizzi@iag.usp.br (T.A.); iurivalerio@usp.br (I.V.G.B.); gisellemoreno@usp.br (G.M.M.M.) 4 Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London WC1E 6BT, UK; ella.browning.14@ucl.ac.uk (E.B.); kate.e.jones@ucl.ac.uk (K.E.J.) 5 Institute of Environmental Sciences, Bo ˘ gaziçi University, Bebek, Istanbul 34342, Turkey; merve.tunali@boun.edu.tr (M.T.); selmabasibuyuk@gmail.com (S.B.); yeniguno@boun.edu.tr (O.Y.) 6 School of Engineering, European University of Lefke, Lefke 99010, Northern Cyprus, Turkey 7 Department of Biomedical Engineering, Federal University of Pernambuco, Recife-PE 50740-550, Brazil; clara.gomes@ufpe.br (A.C.G.d.S.); wellington.santos@ufpe.br (W.P.d.S.) 8 Polytechnic School of Pernambuco, University of Pernambuco (Poli-UPE), Recife-PE 50720-001, Brazil; cll@ecomp.poli.br 9 Department Systems & Computing, Federal University of Campina Grande, Campina Grande-PB 58429-900, Brazil; massoni@dsc.ufcg.edu.br 10 Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK; l.campos@ucl.ac.uk * Correspondence: a.musah@ucl.ac.uk Abstract: Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction. Dataset: https://figshare.com/s/08449337eb8194848c72 (accessed on 21 July 2022) Dataset License: CC BY 4.0 Data 2022, 7, 106. https://doi.org/10.3390/data7080106 https://www.mdpi.com/journal/data