Affiliations: [a] Departamento de Señales Sistemas y Radiocomunicaciones, E.T.S.I Telecomunicación Madrid, Universidad Politécnica de Madrid, Grupo de Automatización en Señal y Comunicaciones, Madrid, Spain | [b] AEMET, Spanish Meteorological Agency, Valladolid, Spain
Correspondence:
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Corresponding author: Diego Andina, Universidad Politécnica de Madrid, Grupo de Automatización en Señal y Comunicaciones, Departamento de Señales Sistemas y Radiocomunicaciones, E.T.S.I Telecomunicación Madrid, Spain.
Abstract: This paper provides a novel method to design a Rainfall Forecasting System based on Neural Networks and a long-term data registry. The System proposed is based on the observations of expert meteorologists after 40 years of research developing local forecast methodologies to predict rainfall events in the Meteorological Observatory of Valladolid, Spain. The Geostrophic Wind, a theoretical value resulting of the balance between the Pressure Gradient Force and the Coriolis Force, is the key parameter which feeds the aforementioned System. The paper focuses on the Geostrophic Wind calculation. We propose a novel method to estimate its direction and magnitude on a local scale, by processing a set of pressure and temperature observations surrounding a central location. This study continues previous research, providing complementary criteria for numerical weather prediction systems based on time-series forecasting and neural networks.
