Open AccessAn Evaluation Graph of Hourly Rainfall Estimation in Malang
Author(s) -
Richard Mahendra Putra,
Andang Kurniawan,
Imron Ade Rangga,
Muhammad Ryan,
Endarwin,
Ahmad Luthfi
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/303/1/012031
Subject(s) - estimator , quantile , computer science , estimation , statistic , python (programming language) , satellite , statistics , data mining , mathematics , operating system , management , engineering , economics , aerospace engineering
Satellite-based rainfall estimation is evolving rapidly. Most studies use data, which is spatially fine, but poorly regarding time. On the other hand, availability of verification data is also quite rare. This study used Hillman Form B report that was corrected by ME-48 from Malang Climatological Station. 2009-2016 IR1 satellite data were used in hourly temporal resolution (only less than 3% data missing). Four estimation methods were compared: Auto Estimator, CST, mCST, and Quantile Analysis Equation. Data processing was carried out using Python and R statistic as a quality control. The analysis was done by creating a graph that combines False Alarm and Miss Information for each rainfall intensity. Binary transformation was done for enabling information to be plotted. All rainfall estimation methods have a high false alarm (more than 74% at 1 mm) but quite low miss (less than 0.03%). By taking into account its error pattern, satellite data can be used in rainfall observation. The Quantile equation is slightly superior to other methods. This study is relatively inexpensive to be duplicated so it can be used as an evaluation tool for rainfall estimation best practice for Meteorological and Climatological Agency’s network.