Development of a multi-model ensemble seasonal forecasting system and the establishment of a user interaction forum in Myanmar | |
| Author | Dash, Itesh |
| Call Number | AIT Diss. no.DM-20-02 |
| Subject(s) | Weather forecasting--Myanmar Climatology--Myanmar--Mathematical models Climatology--Myanmar--Data processing |
| Note | A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Disaster Preparedness, Mitigation and Management, School of Environment, Resources and Development-School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | The rural population in Myarunar is heavily dependent on agriculture for their livelihoods. Excessive or deficient seasonal rainfall condition results im poor agriculture production, which in tum aggravates socioccanomio issues in the country.The agriculture sector continuously depends on reliable seasonal rainfall forecasts for planning agricultural operations. A seasonal rainfall forecast customization tool called FOCUS was developed and installed in Myanmar's meteorological department. FOCUS was daveloped using hindcast data from seven global climate models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period 1982 to 2011. The country was divided into six homogenous zones based on the annual rainfall patterns and topographical variations. The climate data were quality checked and then used to derive the extreme climatic conditions in the zones and the general trends of the rainfall and temperature. Results suggested that the dry zone is the most critical of all in terms of the rainfall received annually and especially during the monsoon, which comprises about 90% of total annual rainfall in the zone. The dominance of southwest monsoon was evident in all the zones where the contribution from monsoon rainfall is very high. Though the average rainfall in the zones is consistent, the frequency of extreme rainfall events has increased. Three different multi-model ensemble (MME) techniques, viz; a) Arithmetic Mean (AM- MME), b) Weighted Average (WA-MME), and c) supervised Principal Component Regression (PCR-MME) were developed.This study investigated the role of different multi-model ensemble-based seasonal prediction systems in the generation of a skillful and reliable seasonal forecast for the homogeneous climate zones of the country. The predictability of raw GCMs, bias corrected GCMs, and the MMEs were evaluated using root mean square error, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the Relative Operating Characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested thatMME forecasts have shown improved performance (RMSE=1.29) compared to the raw individual models (ECMWE, which is comparatively better among the selected models) with RMSE 4.4 and bias-corrected RMSE=4.3, over Myanmar. Specifically, WA-MME (CC-0.64) and PCR-MME (CC-0.68) methods have shown significant improvement in the high rainfall (delta) zone and with WA-MME (CC-0.57) and PCR-MME (CC-0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC-0.78) and lower tercile categories (ROC-0.85) for the delta region and less skillful over lower rainfall zones like dry zones with ROC-0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period. The meteorological department in Myanmar, with access to such a system, is expected to provide better support to its stakeholders. Besides, an effective communication platform was proposed for the dissemination of the products. The platform demonstrated an improved understanding of climate information among sectoral users. Users show greater confidence in utilizing the products after the deep interaction with the climate experts, and the process was very effective for the exchange of knowledge and ways to improve the climate forecast information for better decision making. |
| Year | 2020 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development + School of Engineering and Technology |
| Department | Department of Development and Sustainability (DDS) |
| Academic Program/FoS | Disaster Preparedness, Mitigation and Management (DPMM) |
| Chairperson(s) | Nagai, Masahiko;Routray, Jayant Kumar (Co-chairperson) |
| Examination Committee(s) | Dhakal, Shobhakar;Pal, Indrejit |
| Scholarship Donor(s) | Regional Integrated Multi-Hazard Early Warning System (RIMES);AIT Fellowship, |
| Degree | Thesis (Ph.D) - Asian Institute of Technology, 2020 |
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