Comparison of single-site weather generators
A stochastic weather generator uses statistical model to simulate realistic random sequences of atmospheric variables at high speeds, variables which include wind speeds, temperature and precipitation. They are commonly used to produce synthetic weather data, and through the usage of these weather g...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78267 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A stochastic weather generator uses statistical model to simulate realistic random sequences of atmospheric variables at high speeds, variables which include wind speeds, temperature and precipitation. They are commonly used to produce synthetic weather data, and through the usage of these weather generators, we can simulate a time-series of synthetic weather that has statistically similar characteristics to the site where the data has been taken from. This does not equate to a method of weather forecasting, and stochastic weather generators are not predictive tools by design. In this paper, we will be studying and comparing namely two single-site weather generators, WeaGETS and LARS-WG. Using local weather data obtained from the weather station at Changi which includes daily rainfall levels and temperature, we can experimentally make comparisons between the results obtained from both stochastic weather generators. |
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