Temperature Forecast Using Ridge Regression as Model Output Statistics
| Autor: | Niswatul Qona’ah, Sutikno, Kiki Ferawati, Muhammad Bayu Nirwana |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
geography
geography.geographical_feature_category Scale (ratio) Meteorology Mean squared error Weather forecasting 020207 software engineering 02 engineering and technology General Medicine computer.software_genre Numerical weather prediction Regression Atmosphere Model output statistics Ridge 020204 information systems 0202 electrical engineering electronic engineering information engineering Environmental science computer |
| Zdroj: | Proceeding International Conference on Science and Engineering. 3:383-388 |
| ISSN: | 2598-232X 2597-5250 |
| DOI: | 10.14421/icse.v3.533 |
| Popis: | Over the past few years, BMKG (Meteorological, Climatological and Geophysical Agency) in Indonesia has used numerical weather forecasting techniques, namely Numerical Weather Prediction (NWP). However, the NWP forecast still has a high bias because it is only measured on a global scale and unable to capture the dynamics of atmosphere (Wilks, 2007). Hence, this study implements Ridge Regression as Model Output Statistics (MOS) for temperature forecast. This study uses the maximum temperature (Tmax) and minimum temperature (Tmin) observation at 4 stations in Indonesia as the response variables and NWP as the predictor variable. The results show that the performance of the model based on Root Mean Square Error of Prediction (RMSEP) is considered to be good and intermediate. The RMSEP for Tmax in all stations is intermediate (0.9-1.2), Tmin in all stations is good (0.5-0.8). The prediction result from Ridge Regression is more accurate than the NWP model and able to correct up to 90.49% of the biased NWP for Tmax forecasting. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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