Open AccessProjection Pursuit Regression (PPR) on Statistical Downscaling Modeling for Daily Rainfall Forecasting
Author(s) -
Rio Pradani Putra,
Dian Anggraeni,
Alfian Futuhul Hadi
Publication year - 2021
Publication title -
indonesian journal of statistics and applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v5i2p326-332
Subject(s) - downscaling , climatology , model output statistics , projection (relational algebra) , meteorology , variable (mathematics) , scale (ratio) , mean squared error , environmental science , regression analysis , general circulation model , climate model , gcm transcription factors , climate change , statistics , weather forecasting , mathematics , precipitation , geography , geology , mathematical analysis , oceanography , cartography , algorithm
Rainfall forecasting has an important role in people's lives. Rainfall forecasting in Indonesia has complex problems because it is located in a tropical climate. Rainfall prediction in Indonesia is difficult due to the complex topography and interactions between the oceans, land and atmosphere. With these conditions, an accurate rainfall forecasting model on a local scale is needed, of course taking into account the information about the global atmospheric circulation obtained from the General Circulation Model (GCM) output. GCM may still be used to provide local or regional scale information by adding Statistical Downscaling (SD) techniques. SD is a regression-based model in determining the functional relationship between the response variable and the predictor variable. Rainfall observations obtained from the Meteorology Climatology and Geophysics Council (BMKG) are a response variable in this study. The predictor variable used in this study is the global climate output from GCM. This research was conducted in a place, namely Kupang City, East Nusa Tenggara because it has low rainfall. The Projection Pursuit Regression (PPR) will be used in this SD method for this study. In PPR modeling, optimization needs to be done and model validation is carried out with the smallest Root Mean Square Error (RMSE) criteria. The expected results must have a pattern between the results of forecasts and observations showing or approaching the observational data. The PPR model is a good model for predicting rainfall because The results of the forecast and observation show that the results of the rainfall forecast are observational data.