Open AccessDetermining best East Java monthly rainfall projection using spatial-based validation
Author(s) -
Rikha R. Mahmudiah,
Andre Kurniawan,
Erwin Eka Syahputra Makmur
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/303/1/012023
Subject(s) - weighting , projection (relational algebra) , climatology , resampling , statistics , quartile , bilinear interpolation , meteorology , mathematics , environmental science , geography , algorithm , medicine , confidence interval , radiology , geology
In production of climate change information, determining the best projection is a very important step. Missed projection can lead to loss of public trust in climate change. In some previous studies, most analyses were conducted on point approach due to the lack of observational data covering large spatial areas. In this study, rainfall data from 197 observation points are used to get correction coefficients by comparing it to monthly rainfall historical model from 1972-2005. Area of study is 110.89°E–116.27° E and 8.78°S–5.04°S. Two RCP scenarios (4.5 and 8.5) under CSIROMK3.6 RegCM4 BMKG SEA-Cordex are compared to get its ensemble weighting factor. Validation parameters used are mean absolute error and quartiles of error. Shifting correction is introduced in this study with some evidence based on visual analysis and high correction coefficients from the unshifted one. It is also shown that bilinear resampling gives better result than nearest neighbour. Ensemble weighting factors are then set 0.4 for RCP 8.5 and 0.6 for RCP 4.5. In next 10 years (2017-2026), dry condition average rainfall condition is expected to happen between May and October.