Environment / Weather
For operations, there is a need for flight data gathering and dissemination specific to autonomous system operations, including microweather forecasting and reporting.
The weather in urban environments is more challenging to characterize than weather outside the urban environment.
Urban environment-induced micro climates can cause sharp changes in wind speed and directions at the scales of meters.
Both modeling and measuring current conditions in these microclimates requires higher- density weather and wind measurements than commonly deployed for traditional aviation operations.
To achieve an adequate degree of weather resiliency that enables reliable and cost-effective UML-4 operations, a combination of airframe and airworthiness improvements, smart siting of UAM aerodromes, and a reduction in weather and wind uncertainty (compared to the state -of-the-art in the 2010s) is required.
The weather information system in UML-4 is a combination of policy, reporting on current weather conditions, forecasting future weather conditions, and information distribution and decision-making.
Weather data collection, analysis, prediction, and reporting has been tailored to meet the needs of the UAM operator to operate as safely and efficiently in high density airspace operations.
Arriving at this structure was the result of workacross many stakeholders from across the UAM and weather ecosystems (e.g.,universities’ offering degrees and research focused on aviation and urban meteorology).
Urban environments are challenging because manmade structures can create sudden changes in wind speed and direction both around buildings and as a result of thermal updrafts over dark surfaces, such as parking lots, and thermal downdrafts over cooler surfaces, such as parks.
While urban environments are typically a few degrees warmer than rural locations, they still are subject to the weather of the local region that, along with manmade structures, can make aspects of ensuring adequate coverage a problem that is unique to each city.
Solving this challenge in weather data collection required balancing the need for greater granularity of weather observations, at a microclimate scale, with the cost of taking those observations.
At UML-4, observations are taken using a layered approach with multiple types of sensors and sources. Three of the layers are described here.
There are fixed, specialized weather-sensing infrastructure, weather data being generated by sensors aboard sUAS and UAM aircraft weather data identified by innovative thinkers utilizing sources such as traffic cameras and other cameras, car temperature sensors, and home weather systems.
The fixed-sensing infrastructure is designed with several features not available in the other two.
It is required to have greater redundancy, and it is scalable, so it is able to provide adequate data when aircraft are not flying as frequently (e.g., early morning hours or during unpredictable weather) while still being affordable.
It is also installed to accommodate areas where a finer granularity of data is needed to such as near UAM aerodromes,in high-density routes and around high rises.
Weather data meeting performance standards, collected from sensors described above, is available for all users, including non-UAM users such as local departments of transportation and research entities.
Utilizing performance standards for the data is a shift from the previous paradigm of certifying sensors to ensure that the data produced met specific specifications.
This reduced the cost of sensors and enabled the innovative use of sensors and technologies to collect weather data.
While local data sources across the country have a similar structure based on weather data interface standards, the funding model for the maintenance of this data varies across entities participating in UAM operations.
While one city could have a publicly funded financial model, another could operate on a “credit system” with entities earning credits for contributing data (e.g., aircraft and aerodrome operators) and expending credits for selling products based on data downloaded from the system.
In addition to the data performance standards and data being correlated with its generating sensor, methods have been developed to continually monitor the data to identify potentially malfunctioning sensors or other issues that would impact the data’s accuracy.
At UML-4, new forecasting models have been developed.
These models were possible because of the availability of data to validate the models, access to high-end computing (HEC) capabilities, and the contributions of the National Weather Service (NWS) and academic entities such as the National Center for Atmospheric Research (NCAR).
Like the process to continually assess aircraft capabilities against potential hazards, forecasting models will continually improve as data sensors get better, HEC becomes better and more accessible, and because of research breakthroughs.
Weather information is provided to UAM users as an additional service provided by a PSU, by a SDSP, or downloaded directly from where the local data is stored.
Weather data utilized here is no longer “raw” data, it has been analyzed and likely formatted to best meet the users’ needs.
It is frequently associated with DSTs.
Weather information at UML-4 is categorized to differentiate between required and enhancing.
Required data would be needed to meet weather-related standards.
The kinds of required data would include weather information necessary for the safety of flight (e.g., winds that could exceed aircraft operating capabilities) and hazardous weather information.
Enhancing weather information could be incorporated with DSTs to recommend energy efficient aircraft routing or alerts to commuters of weather impacts that could impact either their trip to or from work. Another example of a DST would be to utilize the impact of weather conditions on sound to plan the route of an aircraft to remain within or below noise ordinances.
Enhanced weather services are typically “fee-for-service” with a portion of the fees utilized to enhance data collection sensors and or DSTs and thus remain competitive with other weather service providers.
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