Service-oriented environments for dynamically interacting with mesoscale weather

Abstract

E ach year across the US, mesoscale weather events-flash floods, tornadoes, hail, strong winds, lightning, and localized winter storms-cause hundreds of deaths, routinely disrupt transportation and commerce, and lead to economic losses averaging more than US$13 billion.1 Although mitigating the impacts of such events would yield enormous economic and societal benefits, research leading to that goal is hindered by rigid IT frameworks that can't accommodate the real-time, on-demand, dynamically adaptive needs of mesoscale weather research; its disparate, high-volume data sets and streams; or the tremendous computational demands of its numerical models and data-assimilation systems.In response to the increasingly urgent need for a comprehensive national cyberinfrastructure in mesoscale meteorology-particularly one that can interoperate with those being developed in other relevant disciplines-the US National Science Foundation (NSF) funded a large information technology research (ITR) grant in 2003, known as Linked Environments for Atmospheric Discovery (LEAD). A multidisciplinary effort involving nine institutions and more than 100 scientists, students, and technical staff in meteorology, computer science, social science, and education, LEAD addresses the fundamental research challenges needed to create an integrated, scalable framework for adaptively analyzing and predicting the atmosphere.LEAD's foundation is dynamic workflow orchestration and data management in a Web services framework. These capabilities provide for the use of analysis tools, forecast models, and data repositories,

Transforming the sensing and numerical prediction of high-impact local weather through dynamic adaptation

“…The limitations of today's static environments were highlighted in §1, and Droegemeier et al (2005) showed real examples in which dynamic adaptation can be of considerable practical benefit. A more complete discussion of dynamic adaptation, including key scientific questions, is presented in §4.…”

“…Because individual services can be concatenated in many different ways (see Droegemeier et al 2005), the LEAD framework provides users with an almost endless set of capabilities ranging from simply accessing the data and perhaps visualizing it to running highly complex and linked data ingest, assimilation and forecast processes in real time, and in a manner that adjusts dynamically to inputs as well as outputs. Further details can be found in Droegemeier et al (2005).…”

“…Droegemeier et al 2005). The tangible outcomes (bottom bar in figure 1) include datasets, model output, gridded analyses, animations, static images and a wide variety of relationships and other information that lead to new knowledge, understanding and ideas.…”

“…Droegemeier et al 2005;Plale et al 2006a;Ramakrishnan et al 2007). A multidisciplinary effort, involving nine institutions and more than 100 scientists, students and technical staff, LEAD has created an integrated, scalable, web services framework in which meteorological analysis tools, forecast models and data repositories can operate as dynamically adaptive, on-demand, grid-enabled systems that (i) change configuration rapidly and automatically in response to weather, (ii) respond dynamically to inputs from users, (iii) initiate other processes automatically, and (iv) steer remote observing technologies to optimize the data collection for the problem at hand.…”

Deadline-sensitive workflow orchestration without explicit resource control

“…In addition, these workflow often access shared resources or data and run computations on grid or cloud systems. For example, a weather prediction workflow is triggered by streaming sensor atmospheric data and consists of a number of data-processing steps that use distributed data and resources [8]. This workflow must complete in a timely manner to generate appropriate forecasts and initiate any emergency management measures that might be necessary.…”