SI2-SSI: Lidar Radar Open Software Environment (LROSE)

现代雷达和激光雷达是各种各样的仪器，能够探测分子、气溶胶、鸟类、蝙蝠和昆虫、风、湿度、云和降水。科学家和工程师使用它们对空气质量和污染、危险的生物羽流、云物理、云层范围、气候模型、数值天气预报、道路天气、航空安全、强对流风暴、龙卷风、飓风、洪水以及鸟类、蝙蝠和昆虫的运动模式进行研究。雷达和激光雷达对于保护社会免受高影响天气的影响以及了解大气和生物圈至关重要，但它们是产生大量数据的复杂工具，给研究人员、学生和仪器开发人员带来了许多挑战。该项目将开发一套新的工具，称为激光雷达开放软件环境(LROSE)，以应对这些挑战，并帮助解决用户在研究和教育领域面临的‘大数据’问题。该项目将开辟科学调查的新途径，包括通过数据同化来改进天气预报，并通过为研究人员、学生和教育工作者提供更好的软件工具，帮助最大化NSF在天气和气候研究方面的投资回报。提高NSF研究的有效性将通过提高对许多与公共安全、国防和全球经济相关的不同科学主题的理解，提供显著的科学和社会效益。LROSE项目将开发一个带有一套软件工具的虚拟工具箱，用于各种科学应用。LRose将被打包，以便它可以在本地或云中的虚拟机(VM)上运行，并配备用于那些典型处理步骤的核心算法模块，这些步骤在同行评议的文献中得到了很好的理解和记录。LROSE将使用户社区能够使用核心工具集来开发新的研究模块，以满足最新科学研究的具体需要。通过VM工具箱和核心软件框架，其他开源雷达软件开发商可以向SET提供他们自己的兼容软件工具。通过将开源方法与虚拟机和云计算的最新发展相结合，我们将开发一个既具有高性能又易于在几乎任何硬件上运行的系统，而不需要复杂的编译环境。LROSE项目将建立在现有原型和现有软件元素的基础上，同时促进社区开发新技术和算法，以分发一套记录在案的软件模块，用于执行雷达和激光雷达分析。这些单元将实施参考已发表论文的经认可的科学方法。基础设施和模块将允许研究人员运行标准程序，从而提高分析的效率和重复性，并鼓励研究人员共同开发新的数据分析科学方法。协作开源方法的使用将产生一套可用的算法模块，使科学家能够以新的、创新的方式探索雷达和激光雷达数据。研究人员将受益于用于促进对天气和气候的了解的改进的工具包，从而在促进科学知识和社会效益方面取得积极成果。

Modern radars and lidars are a diverse class of instruments, capable of detecting molecules, aerosols, birds, bats and insects, winds, moisture, clouds, and precipitation. Scientists and engineers use them to perform research into air quality and pollution, dangerous biological plumes, cloud physics, cloud extent, climate models, numerical weather prediction, road weather, aviation safety, severe convective storms, tornadoes, hurricanes, floods, and movement patterns of birds, bats and insects. Radars and lidars are critical for protecting society from high impact weather and understanding the atmosphere and biosphere, but they are complex instruments that produce copious quantities of data that pose many challenges for researchers, students, and instrument developers. This project will develop a new set of tools called the Lidar Radar Open Software Environment (LROSE) to meet these challenges and help address the 'big data' problem faced by users in the research and education communities. This project will open new avenues of scientific investigation, including data assimilation to improve weather forecasts, and help to maximize returns on NSF investments in weather and climate research by providing better software tools to researchers, students, and educators. Improving the effectiveness of NSF research will provide significant scientific and societal benefits through an improved understanding of many diverse scientific topics that are relevant to public safety, national defense, and the global economy.The LROSE project will develop a 'Virtual Toolbox' with a set of software tools needed for a diverse set of scientific applications. LROSE will be packaged so that it can be run on a virtual machine (VM), either locally or in the cloud, and stocked with core algorithm modules for those typical processing steps that are well understood and documented in the peer-reviewed literature. LROSE will enable the user community to use the core toolset to develop new research modules that address the specific needs of the latest scientific research. Through the VM Toolbox and a core software framework, other developers of open-source radar software can then provide their own compatible software tools to the set. By combining the open source approach with recent developments in virtual machines and cloud computing, we will develop a system that is both highly capable and easy to run on virtually any hardware, without the complexity of a compilation environment. The LROSE project will build on existing prototypes and available software elements, while facilitating community development of new techniques and algorithms to distribute a suite of documented software modules for performing radar and lidar analysis. These modules will each implement accredited scientific methods referencing published papers. The infrastructure and modules will allow researchers to run standard procedures, thereby improving the efficiency and reproducibility of the analyses, and encourage researchers to jointly develop new scientific approaches for data analysis. The use of collaborative open source methods will lead to a suite of available algorithmic modules that will allow scientists to explore radar and lidar data in new, innovative ways. Researchers will benefit from the improved toolset for advancing understanding of weather and climate, leading to a positive outcome in the advancement of scientific knowledge and societal benefits.