Processing Mesoscale Climatology in a Grid Environment

Abstract

Enhancing the quality of weather and climate forecasts are central scientific research objectives worldwide. However, simulations of the atmosphere, usually demand high processing power and large storage resources. In this context, we present the GBRAMS project, that applies grid computing to speed up the generation of a regional model climatology for Brazil. A grid infrastructure was built to perform long-term integrations of a mesoscale numerical model (BRAMS), managing a queue of up to nine independent jobs submitted to three clusters spread over Brazil. Three distinct middlewares, Globus Toolkit, OurGrid and OAR/CIGRI, were compared in their ability to manage these jobs, and results on the usage of each node of the grid are provided. We analyze the impact of the resulted climatology in the accuracy of climate forecast, showing model bias removal which indicates correctness of the generated climatology. Our central contribution are how to use grid computing to speed-up climatology generation and the middleware impact on this enterprise.

Optimizing an MPI weather forecasting model via processor virtualization

“…BRAMS is open source code freely available at http://brams.cptec.inpe.br/, supported and maintained by a modest software team at CPTEC that continuously transforms research contributions into production quality code to be incorporated at future code versions. It is also a platform for computer science research in themes such as grid computing [12], [13].…”

Design of a Grid workflow for a climate application

“…In this work, we consider the climate normal production for a region in Brazil using the BRAMS model [20]. BRAMS means Brazilian Regional Meteorological System and is a model derived from RAMS that includes tropical …”

“…BRAMS [20] is a meteorological model software widely used in Brazil for weather and climate forecasts. As it uses a regional model approach, first a particular region has to be selected for simulation.…”

“…The meteorological forecast model chosen is the BRAMS which includes parameterizations for tropical regions [20]. We profiled this application, identified the key components and organized it as a Grid workflow.…”

Migration path annotation: cross-continental study of migration-flight response to environmental conditions

“…The reanalysis data sets also provide variables that are not typically measured by weather stations, including turbulence kinetic energy (TKE), vertical air movement, and surface heat flux, which may be highly relevant to studies of flight. High spatial resolution regional reanalysis data sets are available across North America (North American Regional Reanalysis [NARR]; Mesinger et al 2006), South America (Souto et al 2007), and Europe (information available online). 6 They provide data at a relatively high spatiotemporal resolution, typically every 1-4 hours, and at a length scale of 10-40 km.…”