A cloud-enabled regional climate model evaluation system

Abstract

The climate research community is increasingly interested in utilizing direct, observational measurements to validate model output in an effort to tune those models to better approximate our planet's dynamic climate. The current emphasis on performing these comparisons at regional, as opposed to global, scales presents challenges both scientific and technical, since regional ecosystems are highly heterogeneous and the available data is not readily consumed on a regional basis. If provided with a common approach for efficiently accessing and utilizing the existing observational datasets, climate researchers have the potential to effect lasting societal, economic and political benefits. A key challenge, however, is that model-to-observational comparison requires massive quantities of data and significant computational capabilities. Further complicating matters is the fact that, currently, observational data and model outputs exist in a variety of data formats, utilize varying degrees of specificity and resolution, and reside in disparate, highly heterogeneous data systems. In this paper we present a software architectural approach that leverages the advantages of cloud computing and modern open-source software technologies to address the regional climate modeling problem. Our system, dubbed RCMES, is highly scalable and elastic, allows for both local and distributed management of the satellite observations and generated model outputs, and delivers this information to climate researchers in a way that is easily integrated into existing climate simulations and statistical tools.

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

“…Moreover, as model resolution has increased and models have become more complex, the demands for observations have increased dramatically. Details of the computational, both software and hardware-related, aspects of RCMES are presented by Hart et al (2011) and Crichton et al (2012) and only a brief outline of RCMES is presented below. In addition, most of today's RCMs incorporate and couple a variety of processes within the climate system, and thus require observations of a range of variables.…”

“…This study evaluates three RCM variables-precipitation, surface air temperature, and surface insolation-that are important for surface hydroclimate and widely used in impact assessment studies using the Regional Climate Model Evaluation System (RCMES; Crichton et al 2012;Hart et al 2011;Kim et al 2013b). A brief description of the current and planned capabilities of RCMES is presented in section 2.…”

A canonical model for seasonal climate prediction using Big Data

“…They compared the seasonal climate forecast between different models with observed data. Figure 10 illustrates the RCMES architecture, composed of two main modules: (1) The Regional Climate Model Evaluation Database (RCMED), which performs data ingestion and processing; and (2) The Regional Climate Model Evaluation Toolkit (RCMET), which integrates several tools for analyzing and visualizing information [27].…”

“…Based on historical weather data, Wang Fig. 10 The RCMES architecture [27] Fig. 11 The meteorological data warehouse [28] et al proposed the creation of a tool to support decision making in the face of major climatic variations and natural disasters related to the energy system [31].…”

Practical motion planning for aerial-like virtual agents in Meta!Blast: A full and complex three dimensional virtual environment

“…An idea for implementing motion planning in Meta!Blast and similar computationally complex games and simulations is to use cloud computing. Cloud computing is a new and innovative technology that enables multiple computers in different locations to interact with each other [Aljenaa et al (2011); Hart et al (2011)]. Since Meta!Blast is deployed in the high school classroom, Voronoi graphs and other techniques discussed in Section 1.3 cannot be used due to limited computer resources such as an advanced graphics card.…”