Weather projections for De La Salle University - Manila using statistical downscaling
Statistical downscaling is a method in which small-scale or local-scale weather data can be generated using statistical relationships derived from Global Climate Models (GCMs). It is often used in weather data forecasting as a tool for connecting the global forecasts of the GCMs to the regional / lo...
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| Format: | text |
| Language: | English |
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Animo Repository
2014
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| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/18003 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Statistical downscaling is a method in which small-scale or local-scale weather data can be generated using statistical relationships derived from Global Climate Models (GCMs). It is often used in weather data forecasting as a tool for connecting the global forecasts of the GCMs to the regional / local scale. This paper focused on using statistical downscaling for generating weather projections for De La Salle University - Manila. Results showed that the months of June, July, August and September have the highest number of wet days and the highest average amount of precipitation throughout the year. Results also showed that the simulated data had the same statistical distribution as that of the original data and also statistically had the same mean and variance. Projections made for the simulated data were used as projections for the original data since it was shown that the original and simulated data sets had the same distribution. The projections were then used as insights for future climate scenarios. |
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