An Evaluation Graph of Hourly Rainfall Estimation in Malang

Putra, Richard Mahendra; Kurniawan, Andang; Rangga, Imron Ade; Ryan, Muhammad; Endarwin,; Luthfi, Ahmad

doi:10.1088/1755-1315/303/1/012031

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…As stated above, there are several methods to estimate rainfall from satellite images [23]. Once the method proposed here is implemented, it is possible to carry out a comparison of the estimated RR values with satellite images and those measured in MGS.…”

Section: Resultsmentioning

confidence: 99%

“…There are several methods to estimate rainfall from satellite images, i.e., convective stratiform technique (CST), modified convective stratiform technique (mCST), auto-estimator (AE), and quantile analysis formed by regression function of sorted rainfall intensity [23]. The formulas that relate the brightness temperature of the cloud-top with the associate temperature are provided by the National Oceanic and Atmospheric Administration (NOAA).…”

Section: Rainfall Inferred From Ir Satellite Datamentioning

confidence: 99%

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Goes-13 IR Images for Rainfall Forecasting in Hurricane Storms

Meza-Ruiz

Gutiérrez-López

2020

Forecasting

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Rainfall Inferred From Ir Satellite Datamentioning

confidence: 99%