An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande
Anwar Musah,
Livia Màrcia Mosso Dutra,
Aisha Aldosery,
Ella Browning,
Tercio Ambrizzi,
Iuri Valerio Graciano Borges,
Merve Tunali,
Selma Başibüyük,
Orhan Yenigün,
Giselle Machado Magalhaes Moreno,
Ana Clara Gomes da Silva,
Wellington Pinheiro dos Santos,
Clarisse Lins de Lima,
Tiago Massoni,
Kate Elizabeth Jones,
Luiza Cintra Campos,
Patty Kostkova
Affiliations
Anwar Musah
UCL Department of Geography, Geospatial Analytics and Computing Group (GSAC), University College London, London WC1E 6BT, UK
Livia Màrcia Mosso Dutra
Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil
Aisha Aldosery
UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK
Ella Browning
Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London WC1E 6BT, UK
Tercio Ambrizzi
Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil
Iuri Valerio Graciano Borges
Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil
Merve Tunali
Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey
Selma Başibüyük
Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey
Orhan Yenigün
Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey
Giselle Machado Magalhaes Moreno
Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil
Ana Clara Gomes da Silva
Department of Biomedical Engineering, Federal University of Pernambuco, Recife-PE 50740-550, Brazil
Wellington Pinheiro dos Santos
Department of Biomedical Engineering, Federal University of Pernambuco, Recife-PE 50740-550, Brazil
Clarisse Lins de Lima
Polytechnic School of Pernambuco, University of Pernambuco (Poli-UPE), Recife-PE 50720-001, Brazil
Tiago Massoni
Department Systems & Computing, Federal University of Campina Grande, Campina Grande-PB 58429-900, Brazil
Kate Elizabeth Jones
Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London WC1E 6BT, UK
Luiza Cintra Campos
Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK
Patty Kostkova
UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK
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.
