Research Article
Determination of General Circulation Model Domain Using LASSO to Improve Rainfall Prediction Accuracy in West Java
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290466, author={Nanda Fadhli and Aji Hamim Wigena and Anik Djuraidah}, title={Determination of General Circulation Model Domain Using LASSO to Improve Rainfall Prediction Accuracy in West Java}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={gcm domain statistical downscaling lasso regression}, doi={10.4108/eai.2-8-2019.2290466} }- Nanda Fadhli
Aji Hamim Wigena
Anik Djuraidah
Year: 2020
Determination of General Circulation Model Domain Using LASSO to Improve Rainfall Prediction Accuracy in West Java
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290466
Abstract
The Statistical downscaling technique has often been used to predict rainfall. This technique needsa domain of general circulation model (GCM) data. The selection of GCM domain is an important factor to improvepredictionaccuracy.The goal of this study is to determine the optimum domain. This study uses GCM data from CFSRv2 with gridresolution "2.5°×2.5°" and local rainfall data in West Java. The GCM domain is determined basedon minimum correlation value of 0.3 between GCM data and local rainfall data. Correlations are calculated for the grid in the four directions of the compass with one grid as the reference that straightly above the local rainfall station. The domains are evaluated using the regression model with L1 (LASSO) regularization. The result showed that the optimum domain was 8×5 grids.
