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Course title |
Machine Learning in Atmospheric Thermodynamics |
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Semester |
109-1 |
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Designated for |
COLLEGE OF SCIENCE GRADUATE INSTITUTE OF ATMOSPHERIC SCIENCES |
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Instructor |
WU CHIEN-MING |
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Curriculum Number |
AtmSci8049 |
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Curriculum Identity Number |
229 D3130 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Elective |
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Time |
Friday 7,8,9(14:20~17:20) |
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Remarks |
Restriction: within this department (including students taking minor and dual degree program)
The upper limit of the number of students: 5. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
本課程將討論如何利用機器學習了解在大氣重要物理過程中具有高度非線性特徵的對流過程。主要會使用高解析雲解析模式(Cloud-resolving model, CRM)所產生之模擬結果，利用已知的簡化概念模式如mass flux model, high-order closure model 或buoyancy sorting model 來評估這些模型對於對流的掌握程度，再利用機器學習產生新的模型，比較其中對於對流表現的差異 。課程內容以資料建模與分析為主。 |
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Course Objective |
了解如何用機器學習來掌握大氣中高度非線性的過程，以及發展新的簡化物理模型來描述大氣對流過程。 |
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Course Requirement |
須有自行撰寫與修改程式語言基礎（Fortran 與 Python)，建議學生能先修大學部之流體力學、熱力學、動力學、計算機語言、數值分析等課程，並具備執行以下其中一種模式之能力：CESM, VVM, CWBGFS, SPCAM |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
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References |
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Designated reading |
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Grading |
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