CASA and LEAD: adaptive cyberinfrastructure for real-time multiscale weather forecasting

Abstract

Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t yLEAD establish an interactive closed loop between the forecast analysis and the instruments: The data drives the instruments, which, to make more accurate predictions, refocus in a repeated cycle.The "Hypothetical CASA-LEAD Scenario" sidebar provides an example of the unprecedented capabilities these changes afford.Mesoscale meteorology is the study of smaller-scale weather phenomena such as severe storms, tornadoes, and hurricanes. System-level science in this context involves the responsiveness of the forecast models to the weather at hand as well as conditions on the network at large and the large-scale computational resources on which forecasts rely. This responsiveness can be broken down into four narrowly defined goals:• Dynamic workflow adaptivity. Forecasts execute in the context of a workflow, or task graph. Workflows should be able to dynamically reconfigure in response to new events.• Dynamic resource allocation. The system should be able to dynamically allocate resources, including radars and remote observing technologies, to optiTwo closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of lowpower, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems.

Opportunities and Challenges in Running Scientific Workflows on the Cloud

“…While the emergence of SOA as an architectural paradigm provides many benefits for distributed computing, where service abstraction, loose coupling, discoverability and interoperability are some key advantages specifically for the engineering and development of an SWFMS. As a matter of fact, many disciplines (especially in life science) have adopted service implementation, and the Taverna and LEAD [21] workflow systems deal with service workflows explicitly. There are thousands of services developed and available for the myExperiment project, and the LEAD system has developed a tool to wrap and convert ordinary science applications into services.…”

Resource management in data-intensive clouds: Opportunities and challenges

“…By including provisioned network links as part of a single platform for sensing applications, our vision is complementary to recent efforts, e.g., CASA [23] and LEAD [16], to make both sensors and IT resources more flexible in response to real-time weather phenomena. Our example radar workflows are inspired by our current joint work with CASA at the UMass-Amherst.…”

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

“…Errico & Vukicevic 1992;Park & Droegemeier 2000). If such regions are found, an agent communicates with an adaptive observing system, such as the radars being developed by the US National Science Foundation (NSF) Center for the Collaborative Adaptive Sensing of the Atmosphere (Brotzge et al 2006;Plale et al 2006a), and new targeted observations are collected. The process then repeats or is modified automatically if other specified criteria are met.…”

“…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.…”