The commercial application of small Uncrewed Aircraft Systems (UAS), which have much different weather sensitivities than traditional aircraft, has shown exponential growth in recent years, leading to the need for new weather information tailored to meet their still emerging needs. At the same time, plans for developing Urban Air Mobility (UAM) has brought new challenges for the analysis and prediction of aviation weather in the urban landscapes. Similarly, other forms of aviation that conduct flight outside of traditional airport settings such as helicopters and hot air balloons still face challenges in obtaining weather information for their flights. Numerous gaps in low-altitude weather sensing and multiple barriers to weather forecasting tailored for UAS/UAM operations must be overcome. Moreover, weather models, traditional sensing paradigms, and fusion systems cannot support the requirements of these latency-sensitive operations. The concept of "Digital Twins" is gaining popularity among the aviation weather community. A weather digital twin refers to a virtual replica of current and future weather, which enables testing different scenarios and assessing the outcomes of different decisions. By leveraging big data, it is possible to simulate and predict the behavior of a UAS/UAM in different weather conditions.