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Open AccessArticle

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

1
Water Research Center, Centro de Investigaciones del Agua-Queretaro (CIAQ), International Flood Initiative, Latin-American and the Caribbean Region (IFI-LAC), International Hydrological Programme (IHP-UNESCO), Universidad Autonoma de Queretaro, Queretaro 76010, Mexico
2
Facultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla Queretaro 76230, Mexico
*
Author to whom correspondence should be addressed.
Climate 2019, 7(11), 131; https://doi.org/10.3390/cli7110131
Received: 24 September 2019 / Revised: 21 October 2019 / Accepted: 23 October 2019 / Published: 12 November 2019
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods. View Full-Text
Keywords: humidity; dewpoint; rainfall; mixing ratio; forecast rainfall model; Clausius–Clapeyron relation; early warning system (EWS); Mexico humidity; dewpoint; rainfall; mixing ratio; forecast rainfall model; Clausius–Clapeyron relation; early warning system (EWS); Mexico
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Gutierrez-Lopez, A.; Cruz-Paz, I.; Muñoz Mandujano, M. Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint. Climate 2019, 7, 131.

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