Abstract
Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude.
Highlights
The measurement of the space–time variability of rainfall is essential for the progress of hydrologic studies, such as the water balance in a watershed, or the execution of projects and actions related to urban development in the field of hydraulic networks [1,2]
Hurricane events are selected with information available from Geostationary Operational Environmental Satellite (GOES) images and MGS surface data
A regionalization of all the parameters that are obtained in this way is carried out and these are plotted in a Mexico map
Summary
The measurement of the space–time variability of rainfall is essential for the progress of hydrologic studies, such as the water balance in a watershed, or the execution of projects and actions related to urban development in the field of hydraulic networks [1,2]. There is an increasing demand to improve rainfall estimates from satellite systems on a different range of scales in time and space. The trend towards increasingly new applications in the field of hydrometeorology requires precise estimates of rainfall for global or local coverage [4,5,6]. The new meteorological radar systems with improved beam resolution have increased signal-noise sensitivity. One of the most important limits of hydrological prediction is due to the low Forecasting 2020, 2, 5; doi:10.3390/forecast2020005 www.mdpi.com/journal/forecasting
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