使用CloudSat及ECMWF再分析資料探討南海及海洋大陸地區深對流之環境因子

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姓名

吳宜靜(Yi-Jing Wu) 查詢紙本館藏

畢業系所

遙測科技碩士學位學程

論文名稱

使用CloudSat及ECMWF再分析資料探討南海及海洋大陸地區深對流之環境因子
(Investigate Deep Convections and their Environmental Factors in the MC and SCS by using CloudSat and ECMWF analysis)

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摘要(中)

深對流在地球輻射收支平衡以及水循環扮演重要的角色，南海以及海洋大陸地區(SCS-MC)是世界上最容易發生深對流的地區之一，此區的深對流與多重尺度的天氣/氣候系統緊密的互動，進而影響全球氣候，然而，數值模式中預報SCS-MC的深對流仍然具有挑戰性，因此本研究嘗試利用主動微波觀測衛星CloudSat結合ECMWF再分析資料分析深對流的垂直結構以及基本熱力參數，包含T2m、TPW、SHF、LHF、LTS以及CAPE。
研究結果顯示深對流核心(Deep convective core, DCC)在空間分布上有明顯的日夜變化，日間(~1330 LT) DCC在洋面上以及沿岸地區有較大的出現機率，夜晚(~0130 LT) 則集中於陸地。整體而言。夜間之DCC之次數較日間為頻繁。另一方面，陸地上DCC的垂直結構有較大日夜差異，日間有較強的上衝流，最大回波(約20 dBZ)從6公里延展至14公里高，夜晚時上衝流較弱，因此最大回波延展高度降至12公里。
本研究中分析DCC熱力環境參數的特徵，定義各熱力參數DCC生成的閾值，結果顯示各項參數皆有其特性；T2m 與LTS有特定的好發區間；TPW與DCC發生機率呈正相關；雖然CAPE值則對DCC發生機率較無敏感性，由於SCS與MC的大氣環境普遍因為大氣溼度較高而處於不穩定狀態，因此CAPE值雖非直接因素，但大氣仍需處不穩定條件方利於DCC之生成。在南海地區，除了CAPE外，其他五項熱力因子皆會影響DCC生成的機率。在海洋大陸地區，DCC生成的機率主要與T2m、TPW以及SHF有關。
本研究探討了DCC生成的熱力條件，未來預期結合動力因子以更完整的描述深對流的生成機制，並且探討對流雲生命週期的變化過程，最終反饋予數值參數化方案，作為改進數值天氣預報模式的客觀依據。

摘要(英)

Deep convection has great influence on the earth’s radiation budget and hydrologic cycle. South China Sea- Maritime Continent (SCS-MC) is one of the most convective areas in the world. Deep convection in this area interacts with multiscale weather/climate systems and has influence on global climate. However, it remains a great challenge for models to capture the timing, location, and intensity of deep convection. As a result, the aim of this study is to take the advantage of CloudSat and composite ECMWF reanalysis data to investigate general features of DCC and analyze ingredient elements for deep convection, including TPW, LTS, CAPE, T2m, SHF and LHF.
The results suggest that deep convective cores (DCCs) feature apparent diurnal variation on geospatial distribution. Higher probabilities of DCC are on ocean and coastal region in daytime (~1330 LT), but there are more inland DCC in nighttime (~0130 LT). Generally, the occurrence of DCC in nighttime is higher than daytime. The thermodynamic condition in nighttime is not suitable for DCC development. Therefore, DCC in nighttime might be sustained from the other mechanism. On the other hand, vertical structure of DCC shows difference between daytime and nighttime. In the daytime, maximum echo (~20 dBZ) extends from 6 km to 14 km owing to strong convective updraft. In the nighttime, maximum echo extends from 6 km to 12 km only result from relative stable thermodynamic environment.
The analysis of thermodynamic environmental factors in SCS and MC reveal the relationship to each individual factor. There are specific intervals of T2m and LTS that are favorable to DCC. TPW has positive correlation with DCC probability, while CAPE shows less sensitivity to DCC probability. The environment of SCS and MC are unstable all the time, so the increase of CAPE value has little impact on DCC probability. Over the SCS region, all the factors have sensitivity except CAPE. In the contrast, T2m, TPW and SHF have sensitivity to DCC in the MC region.
In this study, thermodynamic factors for DCC development are investigated. We expect to have a combination and intercomparison for thermodynamic and dynamic factors and further analyze the life cycle of deep convection. We look forward to a better understanding of deep convection mechanism, and the results can provide a positive feedback to improve the simulation of cumulus cloud in NWP models.

關鍵字(中)

★ 深對流
★ 熱力環境特徵
★ CloudSat衛載主動式雷達觀測

關鍵字(英)

★ Deep convective core (DCC)
★ characteristic of thermodynamic environment
★ Space-borne CloudSat active observation

論文目次

摘要 I
Abstract III
Table of Contents V
List of Tables VII
List of Figures VIII
List of Abbreviations XI
1. Introduction 1
1.1. Overview 1
1.1.1. Deep Convection 1
1.1.2. Motivation 3
1.1.3. South China Sea-Maritime Continent (SCS-MC) 4
1.2. Literature Review 7
1.3. Objective 10
2. Data and Methodology 11
2.1. Datasets 11
2.1.1. Satellite Data – Cloudsat 11
2.1.2. Re-Analysis Data Set 13
2.2. Methodology 14
2.2.1. Deep Convective Core (DCC) Identification 14
2.2.2. Thermodynamic Parameter 15
3. Results 18
3.1. General Deep Convection Features 18
3.1.1. DCC Occurrence 19
3.1.2. Vertical Structure of DCC 21
3.2. Deep Convection Sensitivity to Thermodynamic Factors 25
3.2.1. Overall Comparison 25
3.2.2. Diurnal Variation 39
3.2.3. Diurnal Land/Ocean Variation 46
4. Conclusion and Future Work 50
Bibliography 54
Appendix 60

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指導教授

劉千義(Chian-Yi Liu)

審核日期

2019-7-25

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