Probabilistic weather forecasting using Bayesian Model averaging: the case of Sagcot Regions
| dc.contributor.author | Joel, Emmanuel | |
| dc.date.accessioned | 2024-03-27T10:17:39Z | |
| dc.date.available | 2024-03-27T10:17:39Z | |
| dc.date.issued | 2018 | |
| dc.description | A dissertation submitted in partial/ fulfillment of requirements for award of the Degree of Master of Science in Applied Statistics of Mzumbe University | |
| dc.description.abstract | Over the past decade Tanzania has experienced spontaneous population increase (1.556 mil annual). But the number is estimated to further increase by 2050 to 2.982 mil annual, thus Tanzania is estimated to have population of 137 million people in 2050 (UN, 2015). The fast growing population is mainly depending on rainfed agriculture, which contribute 29 percent of the country GDP and providing employment to 65.5 percent of Tanzanians (Deloitte, 2016). The diversity in climatic and weather activities has posed a challenge in rainfed agriculture especially on when to plant seeds. Therefore, in order to promote agricultural activities, stable and reliable weather information are crucial in order for production to match with population increase. This study explores the challenge facing the Numerical Weather Prediction (NWP) namely WRF-ARW, by creating the system of equation (ensembles) from WRF-ARW resulting from the use of different initial conditions. Ensemble allow for probabilistic forecast to take the form of predictive probability function (PDF). But, raw ensemble forecast system are finite hence they only capture some of the uncertainty of the NWP. Thus, this study used Bayesian Model Averaging (BMA) methods of post processing ensemble forecast to maximize the sharpness of the parameter and calibration. The findings show BMA method successively removes most of under disersion showed by raw ensembles. Thus, calibrated and sharp results of BMA approach resolves a number of the weaknesses of the ensemble forecasts including their under dispersion and the discrepancy between forecasts and observations. Therefore, BMA can be used to attain higher consistency in the probabilistic forecasts of an operational model. | |
| dc.description.sponsorship | Private | |
| dc.identifier.citation | APA | |
| dc.identifier.uri | https://scholar.mzumbe.ac.tz/handle/123456789/512 | |
| dc.language.iso | en | |
| dc.publisher | Mzumbe University | |
| dc.subject | WRF-ARW model | |
| dc.subject | Weather Forecasting | |
| dc.subject | Weather Prediction | |
| dc.subject | Bayesian Model | |
| dc.subject | population | |
| dc.subject | Tanzania | |
| dc.title | Probabilistic weather forecasting using Bayesian Model averaging: the case of Sagcot Regions | |
| dc.type | Thesis |