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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 童慶斌(Ching-Pin Tung) | |
| dc.contributor.author | Cheng-Jie Lai | en |
| dc.contributor.author | 賴正傑 | zh_TW |
| dc.date.accessioned | 2022-11-23T08:58:36Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-23T08:58:36Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | Allen, M. R., Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 228-232. Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., Ismail, M. (2020). Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Systems and Environment, 4(2), 297-320. Beg, M. F., Miller, M. I., Trouvé, A., Younes, L. (2005). Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International journal of computer vision, 61(2), 139-157. Chen, J., Chen, H., Guo, S. (2018). Multi-site precipitation downscaling using a stochastic weather generator. Climate dynamics, 50(5), 1975-1992. Curry, C. L., van der Kamp, D., Monahan, A. H. (2012). Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed. Climate dynamics, 38(7), 1281-1299. Dupuis, P., Grenander, U., Miller, M. I. (1998). Variational problems on flows of diffeomorphisms for image matching. Quarterly of applied mathematics, 587-600. Glaunès, J., Vaillant, M., Miller, M. I. (2004). Landmark matching via large deformation diffeomorphisms on the sphere. Journal of mathematical imaging and vision, 20(1), 179-200. Grenander, U., Miller, M. I. (1998). Computational anatomy: An emerging discipline. Quarterly of applied mathematics, 56(4), 617-694. Joshi, S. C., Miller, M. I. (2000). Landmark matching via large deformation diffeomorphisms. IEEE transactions on image processing, 9(8), 1357-1370. Kim, J., Chang, J., Baker, N., Wilks, D., Gates, W. (1984). The statistical problem of climate inversion: Determination of the relationship between local and large-scale climate. Monthly weather review, 112(10), 2069-2077. Kirchmeier, M. C., Lorenz, D. J., Vimont, D. J. (2014). Statistical downscaling of daily wind speed variations. Journal of applied meteorology and climatology, 53(3), 660-675. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. Lynch, C., Seth, A., Thibeault, J. (2016). Recent and projected annual cycles of temperature and precipitation in the Northeast United States from CMIP5. Journal of Climate, 29(1), 347-365. Mok, T. C., Chung, A. (2020). Fast symmetric diffeomorphic image registration with convolutional neural networks. Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Orlowsky, B., Seneviratne, S. I. (2013). Elusive drought: uncertainty in observed trends and short-and long-term CMIP5 projections. Hydrology and Earth System Sciences, 17(5), 1765-1781. Pryor, S., Schoof, J. T., Barthelmie, R. (2005). Empirical downscaling of wind speed probability distributions. Journal of Geophysical Research: Atmospheres, 110(D19). Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., . . . Rafaj, P. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic change, 109(1), 33-57. Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water resources research, 17(1), 182-190. van der Kamp, D., Curry, C. L., Monahan, A. H. (2012). Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components. Climate dynamics, 38(7-8), 1301-1311. Von Storch, H., Zorita, E., Cubasch, U. (1993). Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate, 6(6), 1161-1171. Walder, C., Schölkopf, B. (2008). Diffeomorphic dimensionality reduction. Advances in Neural Information Processing Systems, 21, 1713-1720. Wang, S., Herzog, E. D., Kiss, I. Z., Schwartz, W. J., Bloch, G., Sebek, M., . . . Li, J.-S. (2018). Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks. Proceedings of the National Academy of Sciences, 115(37), 9300-9305. Younes, L. (2018). Diffeomorphic Learning. arXiv preprint arXiv:1806.01240. 范丽军, 符淙斌, 陈德亮. (2005). 统计降尺度法对未来区域气候变化情景预估的研究进展. 地球科学进展, 20(3), 320-329. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79348 | - |
| dc.description.abstract | 風速與風向的資訊對於特定的產業與單位非常重要，如港口、機場，在氣候變遷的影響下，由聯合國相關組織發起的第五階段耦合模式對比計畫 (CMIP5) 提供了各種大氣環流模式 (GCMs) 所模擬出的全球氣象資料。由於模式的解析度差，直接使用大尺度 GCM 資料應用於小尺度地區會產生許多誤差，因而發展出許多降尺度模式以提高資料的解析度。 本研究發展一新穎並針對風速與風向資料的統計降尺度模式，並應用於位於基隆港旁的基隆測站。將收集到的日資料依照風速與風向分做數個類別，以旬為單位計算出各類別的機率。本文使用微分同胚 (Diffeomorphism) 的概念類比於特定的小尺度類別機率隨時間變化的過程，此過程透過常微分方程描述並形成一個受大尺度資料及小尺度資料影響的動態系統，並以正交函數來擬合大尺度與小尺度資料之間的函數方程，結合模擬退火演算法 (SA) 訓練出函數中的最佳係數。訓練的目標函式中除了擬合的誤差之外，也包含了微分同胚中所關注的正則項 (Regularization term)。 利用本文的降尺度模式可以產製未來的風玫瑰圖 (Wind rose)，研究結果顯示，在 RCP 8.5 的情境下，基隆測站的風速有減弱的情形，而盛行的風向：東北風與東風發生的頻率減少。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T08:58:36Z (GMT). No. of bitstreams: 1 U0001-2610202117052400.pdf: 4457353 bytes, checksum: d096e424e6558053fea804476ece791a (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS目錄 iv LIST OF FIGURES vi LIST OF TABLES viii 第1章 前言 1 1.1 研究動機 1 1.2 研究目的 2 1.3 論文架構 3 第2章 文獻回顧 4 2.1 風速與風向之統計降尺度 4 2.2 微分同胚 (Diffeomorphism) 9 第3章 資料與研究方法 12 3.1 資料來源與資料處理 12 3.1.1 小尺度觀測資料 12 3.1.2 GCM 模擬資料 13 3.1.3 資料處理 15 3.2 降尺度模式 18 3.2.1 微分同胚 (Diffeomorphism) 18 3.2.2 動態系統 23 3.2.3 模擬退火演算法 28 第4章 結果與討論 33 4.1 模式訓練與驗證結果 34 4.1.1 訓練階段 34 4.1.2 驗證階段 41 4.2 模式測試結果 50 4.3 方法與預報的價值與限制 62 4.3.1 方法的價值與限制 62 4.3.2 預報的價值與限制 63 第5章 結論與建議 65 5.1 結論 65 5.2 建議 66 Reference 68 | |
| dc.language.iso | zh-TW | |
| dc.title | 微分同胚於風速與風向降尺度分析 | zh_TW |
| dc.title | Diffeomorphic Analysis of Wind Speed and Wind Direction Downscaling | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 胡明哲(Ming-Che Hu) | |
| dc.contributor.oralexamcommittee | 闕蓓德(Hsin-Tsai Liu),李明旭(Chih-Yang Tseng) | |
| dc.subject.keyword | 統計降尺度,微分同胚,風速,風向,風玫瑰圖, | zh_TW |
| dc.subject.keyword | Statistical downscaling,Diffeomorphism,Wind speed,Wind direction,Wind rose, | en |
| dc.relation.page | 70 | |
| dc.identifier.doi | 10.6342/NTU202104253 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-27 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| Appears in Collections: | 生物環境系統工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| U0001-2610202117052400.pdf | 4.35 MB | Adobe PDF | View/Open |
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