在現行機場的氣象觀測儀器主要仍只提供二維的氣象資訊，但對於大部分航空從業人員來說，在航機起飛或降落的過程中2,000呎到4,000呎高度上的氣象資訊是最為未知且最需關注的範圍。因此為了提供三維的氣象資訊，我們利用一個訊號接收機來接收及分析航機自動廣播系統ADS-B的訊號，並且發展一套系統來擷取ADS-B內航機周圍的風速、風向和溫度等氣象資訊。因此，在我們先前的研究中，我們利用航機自動廣播系統來計算出航機周圍的氣象資訊，像是風速以及風向，並且利用解算出的氣象資訊發展出一套異常天氣偵測系統。
為了增強原先的系統並且提供更詳細的異常天氣資訊，像是天氣系統的發展潛勢、位置以及異常天氣持續時間，我們首先利用卡爾曼濾波器 (Kalman filter) 來預測航機進場及離場航道上的風場情況，接著，利用解算出來的風場資訊以及ADS-B溫度資訊來進一步的算出航機航路上的理查森數 (Richardson number)，並藉由理查森數來分析及判斷天氣系統的形成機制以及環境穩定度，而此結果也藉由機長報告來驗證及比較傳統氣象演算法與現代導航監視系統的相容性。
當我們通過理查森數來確認危險天氣系統後，下一步則是在異常氣候發生期間藉由主成分分析（PCA）的概念，來分析以及確定此異常天氣系統所受環境參數變化的影響，以及當前環境是否有利於異常天氣系統發展。最後，提出了此種基於ADS-B資訊發展的三維航空氣象異常天氣系統發展的短期預報雛型，藉此可以提供更多的航空氣象資訊，如亂流類型，異常天氣系統的強度以及風切可能發生的位置。根據本論文實驗的結果，希望可以藉由這些警示報告協助航空管制人員和飛行員採取必要預防措施避開有可能危害飛航安全的天氣系統。

At present meteorology observation instruments provide two-dimensional wind speed and wind direction information in airports. However, the weather information which aviation personnel are most concerned about is the weather situation between the layers of 2,000 feet and 4,000 feet when the aircraft approaches or departs an airport. In order to obtain three-dimensional weather observation data, we developed a software-defined radio (SDR) receiver to receive and analyze the automatic dependent surveillance-broadcast (ADS-B) Mode-S Extended Squitter (ES) signals, and developed a method of deriving meteorological information, wind vector and temperature, which are decoded from the ADS-B Mode-S ES signals. In our previous research, we used aircraft ADS-B Mode-S ES data to estimate wind speed and wind direction around the aircraft, and developed a three-dimensional aviation unusual weather detection system.

In order to enhance this system and offer hazardous weather information in more detail, such as the potential, location and continuity of a weather system, in this work we develop a wind profile now-casting system for approaching and departing aircraft on the flight path using the Kalman filter algorithm. Consequently, the wind profiles are utilized to estimate the Richardson number profile to indicate the dynamic stability and formation of turbulence. The results of the Richardson number are also verified by pilot report cases, to compare the availability between the traditional meteorology algorithm and current surveillance information.

As we confirm the hazardous weather using the Richardson number, the next step is to do the principal components analysis (PCA) during the unusual weather period to identify parameters the variations of which may indicate more correlations among unusual weather cases. Therefore, based on the results of PCA, we can then now-cast whether the environment is favorable for the development of turbulence or not. Finally, a three-dimensional aviation unusual weather now-casting system prototype is presented, based on the ADS-B data that could provide aviation weather information, such as the turbulence type, intensity of weather system and wind shear location. The experimental results show that these warning reports can help air traffic controllers and pilots to take necessary actions to either change their original flight plans or avoid the hazardous weather when approaching an airport.

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