Data Science of the Natural Environment

我们将开发自然环境的数据科学，部署现代机器学习和统计技术，以便在气候变化时做出更明智的决策。虽然数据科学研究的爆炸式增长推动了电子商务和营销、智慧城市、物流和运输、健康和福祉等领域的巨大进步，但这些工具尚未完全部署在人类面临的最紧迫问题之一，即减缓和适应气候变化。该项目汇集了世界领先的统计学家，计算机科学家和环境科学家以及广泛的主要公共和私人利益相关者组织，以实现环境科学数据文化的一步变化。该项目将开发一种新的自然环境数据科学方法，由环境科学的三个代表性重大挑战驱动：预测冰盖融化、建立模型和减轻空气质量差的影响，以及管理土地使用以实现最大的社会效益。在每一个激励挑战中，都已经有了广泛的科学专业知识，在多个尺度上有复杂的过程模型。然而，这种复杂的系统组件的建模通常是让这些组件的天真的整合在一起，和观测数据的校准不足。其结果是预测不佳，不确定性高，因此决策信息不足。随着新形式的环境数据的出现，以及气候变化对自然环境的压力增加，这一差距正成为一个紧迫的问题，我们带来了一个令人印象深刻的团队来解决这个问题。该项目的一个关键主题是集成，开发一套新颖的数据科学工具，以模块化的方式与现有的科学信息流程模型一起工作。通过建立一个跨越数据和环境科学家之间跨学科分工的团队，我们可以确保目前缺乏的方法的必要互操作性。与所有利益相关者环境组织合作，将使该计划的持续共同设计和最终用户科学家的培训成为可能，以确保缩小这一领域的技能差距。由此产生的环境科学数据素养的文化转变将有助于更好的决策，因为气候变化给我们的社会带来了越来越大的压力。

We will develop a data science of the natural environment, deploying modern machine learning and statistical techniques to enable better-informed decision-making as our climate changes. While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change. This project brings together world-leading statisticians, computer scientists and environmental scientists alongside an extensive array of key public and private stakeholder organisations to effect a step change in data culture in the environmental sciences.The project will develop a new approach to data science of the natural environment driven by three representative grand challenges of environmental science: predicting ice sheet melt, modelling and mitigating poor air quality, and managing land use for maximal societal benefit. In each motivational challenge, there is already an extensive scientific expertise, with intricate models of processes at multiple scales. However this sophisticated modelling of system components is usually let down by naive integration of these components together, and inadequate calibration to observed data. The consequence is poor predictions with a high level of uncertainty and hence poorly-informed policy making. As new forms of environmental data become available, and the pressures on our natural environment from climate change increase, this gap is becoming a pressing concern, and we bring an impressive team to bear on the problem.A key theme of the project is integration, developing a suite of novel data science tools which work together in a modular fashion, and with existing scientifically-informed process models. By building a team that spans the inter-disciplinary divisions between data and environmental scientists we can ensure the necessary interoperability of methods that is currently lacking. Working with the full range of stakeholder environmental organisations will enable continual co-design of the programme and training of end-user scientists to ensure a reduction of the skills gap in this area. The resultant culture shift in the data literacy of the environmental sciences will enable better decision-making as climate change places ever greater strains on our society.