Virtual Integration of Satellite and In-Situ Observation Networks (VISION)

最近卫星地球观测的扩展提供了一套不断增长的数据，可以帮助我们了解地球系统。来自飞机、船舶和其他平台的现场观测数据的增加也反映了这一点。这些数据已经成熟，可以被各种建模系统利用，比如创建环境的数字双胞胎，以提高我们的理解和决策过程。FAAM机载实验室的bae146飞机是NERCs观测组合的关键部分，最近获得了4900万英镑的资金，将其使用寿命延长至2040年。在这些升级之后，从2026年起，FAAM每年将承担约70个航班。然而，这些飞行是昂贵的，每飞行一小时的全部经济成本为2万英镑（飞行时间本身为4000-5000英镑），排放6.5吨二氧化碳。目前，FAAM占nerc二氧化碳排放量的3.8%，虽然其他缓解策略已经开始实施，但这些策略并不考虑飞行本身。据估计，所有FAAM航班中有20-40%可能是次优的，从这些航班中产生的数据具有较低的科学效益。在这个项目中，我们将开发一个数字孪生体来改进FAAM飞机的战役规划操作，既改进飞行计划以更好地匹配科学目标，又减少实现这些目标所需的飞行次数。因此，这将既减少FAAMs的二氧化碳排放，又提高其科学产出的质量。我们将通过开发一个独立的开源工具包来开发这个数字孪生，该工具包构建在一个已经使用的Python包中，符合国际数据标准，提高模型之间的互操作性。VISION工具包将允许进行各种观测，在该项目中，我们将重点关注FAAM收集的卫星观测和飞行数据。我们将在两个不同的建模系统中演示这个工具包；研究活动和实验预测业务（FORCE）建模基础设施，一个为飞行计划操作设计的预测系统，以及英国地球系统模型（UKESM），以突出其在国际气候建模界的应用，以便更好地为政府和决策者提供信息。虽然我们专注于VISION工具包的这两个应用程序，但它可以应用于大量的国际建模系统，并结合来自各种平台的观察结果。在该项目完成时，除了详细的项目报告外，我们还将为国际社会举办培训活动，以学习如何在他们自己的模型基础设施中实施该工具包。我们的团队汇集了来自国家大气科学中心（NCAS）的大气建模师、观测科学家、软件工程师，以及来自国家地球观测中心（NCEO）和英国地球观测气候信息服务中心（UK EOCIS）的卫星专家，以提供一个新的框架，以减少英国研究飞机的碳足迹，并提供一个工具包，使模型和观测更好地整合。

The recent expansion in satellite Earth observations is providing an ever growing set of data that can help us understand the Earth System. This is mirrored by the increase in in-situ observational data from aircrafts, ships and other platforms. These data are ripe for exploitation by various modelling systems, such as in the creation of digital twins of the environment to improve our understanding and decision making processes.The BAe-146 aircraft of the FAAM Airborne Laboratory is a key part of NERCs observational portfolio, which has recently received £49M of funding to extend its lifetime out to 2040. Following these upgrades, FAAM will undertake around 70 flights per year from 2026 onwards. However, these flights are costly, with each flying hour having a full economic cost of £20,000 (£4000-5000 for the flight time itself) and emitting 6.5 tonnes of CO2. Currently, FAAM accounts for 3.8% of NERCs CO2 emissions, and while other mitigation strategies are already taking place, these do not consider the flights themselves.It is estimated that 20-40% of all FAAM flights may be suboptimal, where the data produced from the flights has a reduced scientific benefit. In this project we will develop a digital twin to improve campaign planning operations for the FAAM aircraft, both to improve the flight plans to better match the scientific aims, and also to reduce the number of flights necessary to achieve those aims. This will therefore both reduce FAAMs CO2 emissions and improve the quality of its scientific output.We will develop this digital twin by developing a standalone open-source toolkit, built within an already-used Python package that complies with international data standards, improving its interoperability between models. The VISION toolkit will allow for a variety of observations, and in this project we will focus on satellite observations and flight data collected by FAAM. We will demonstrate this toolkit within two different modelling systems; the Forecasting Operations for Research Campaigns and Experiments (FORCE) modelling infrastructure, a forecast system designed for flight planning operations, and within the UK Earth System Model (UKESM) to highlight its use to the international climate modelling community to better inform governments and policymakers.While we focus on these two applications of the VISION toolkit, it could be applied to a large number of international modelling systems and incorporate observations from a wide variety of platforms. At the completion of this project, alongside a detailed project report, we will also run a training event for the international community to learn how to implement this toolkit within their own model infrastructure.Our team brings together atmospheric modellers, observational scientists, software engineers from the National Centre for Atmospheric Science (NCAS), and satellite experts from the National Centre for Earth Observation (NCEO) and the UK Earth Observation Climate Information Service (UK EOCIS) to deliver a novel framework to reduce the carbon footprint of the UK research aircraft and to provide a toolkit that will allow for better integration of models and observations.