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WRF4SG: A Scientific Gateway for climate experiment workflows

Conferencia: EGU General Assembly 2013
Año: 2013
Tipo de contribución: Oral
Archivo PDF: 2013_blanco_EGU.pdf
Poster: 2013_blanco_EGU_Presentation.pdf

The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely
used by the weather and climate communities. As opposite to other application-oriented models, WRF provides
a flexible and computationally-efficient framework which allows solving a variety of problems for different
time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a
research tool in modeling physics, dynamics, and data assimilation by the research community.
Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among
the one of the most cutting-edge applications. These workflows are complex due to both large storage and the
huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called
WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve
WRF users needs (see [1] and [2]).
WRF4SG provides services for different use cases that describe the different interactions between WRF
users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE
uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be
carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common
experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the
cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge
amount of computing resources in order to generate useful simulations:
* Resource configuration and user authentication: The first step is to authenticate on users’ Grid resources
by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the
experiment.
* Data assimilation: In order to assimilate the data sources, the user has to select them browsing through
LFC Portlet.
* Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment
(i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind,
precipitation, ...), the start and end date simulation and the requirements of the experiment.
* Monitor workflow: In order to monitor the experiment the user will receive notification messages based on
events and also the gateway will display the progress of the experiment.
* Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using
LFC Portlet.
The objectives of WRF4SG can be described by considering two goals. The first goal is to show how
WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And
the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using
heterogeneous computing resources such as HPC and Grid.
[1] Kacsuk, P.: P-GRADE portal family for grid infrastructures. Concurrency and Computation: Practice
and Experience. 23, 235-245 (2011).
[2] http://www.meteo.unican.es/software/wrf4g

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