Published January 1, 2022 | Version v1
Journal article Open

An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande

  • 1. Univ Sao Paulo, Inst Astron Geophys & Atmospher Sci IAG, Dept Atmospher Sci, BR-05508010 Sao Paulo, Brazil
  • 2. UCL, UCL Ctr Digital Publ Hlth & Emergencies, London WC1E 6BT, England
  • 3. UCL, Dept Genet Evolut & Environm, Ctr Biodivers & Environm Res, London WC1E 6BT, England
  • 4. Bogazici Univ, Inst Environm Sci, TR-34342 Istanbul, Turkey
  • 5. Univ Fed Pernambuco, Dept Biomed Engn, BR-50740550 Recife, PE, Brazil
  • 6. Univ Pernambuco Poli UPE, Polytechn Sch Pernambuco, BR-50720001 Recife, PE, Brazil
  • 7. Univ Fed Campina Grande, Dept Syst & Comp, BR-58429900 Campina Grande, Paraiba, Brazil
  • 8. UCL, Dept Civil & Environm Geomat Engn, London WC1E 6BT, England

Description

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.

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