Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint
| Autor: | Alfonso Gutierrez-Lopez, Martin Muñoz Mandujano, Ivonne Cruz-Paz |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
early warning system (EWS)
Atmospheric Science Flood myth Warning system Meteorology rainfall humidity dewpoint Storm mixing ratio forecast rainfall model Dew point Clausius–Clapeyron relation Quantitative precipitation forecast Early warning system Environmental science Hydrometeorology Precipitation Mexico |
| Zdroj: | Climate Volume 7 Issue 11 |
| ISSN: | 2225-1154 |
| DOI: | 10.3390/cli7110131 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
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