Climate Data Analysis Tools Research Articles

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

UV-CDAT: Analyzing Climate Datasets from a User's Perspective

The Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) is a new tool for analyzing and visualizing climate data. Here we provide some pointers, background information, and examples to show how the system works.

Using Python for Signal Processing and Visualization

The Python programming language provides a development environment suitable to both computational and visualization tasks. One of Python's key advantages is that it lets developers use packages that extend the language to provide advanced capabilities, such as array and matrix manipulation, image processing, digital signal processing, and visualization. Several popular data exploration and visualization tools have been built in Python, including Visit (www.llnl. gov/visit), Paraview (www.paraview. org), climate data analysis tools (CDAT; www2-pcmdi.llnl.gov/cdat), and VisTrails (www.vistrails .org). In our work, we use VisTrails; however, nearly any Python-enabled application can produce similar results. The neuroscience field often uses bothmultimodal data and computationally complex algorithms to analyze data collected from study participants. Here, we investigate a study in which magnetic resonance imaging (MRI) is combined with electroencephalography (EEG) data to examine working memory.

Climate Data Analysis Tools Facilitate Scientific Investigations

Insightful analysis of geophysical phenomena often depends on the choice of software tools to access, manipulate, and visualize data sets of interest. These data exploration tasks can be efficiently executed in a single computational environment by the freely distributed Climate Data Analysis Tools (CDAT; http://www‐pcmdi.llnl.gov/software‐portal/). Although CDAT has been designed primarily for climate science applications, its capabilities are increasingly being enhanced to address the data needs of other geophysical sciences.The CDAT software is based on the object‐oriented Python computer language, but the software extends existing constructs to achieve capabilities that are relevant to any gridded geophysical data. Depending on the users preference, CDAT can be fully deployed by either a script or graphical user interface.

Data management and analysis for the Earth System Grid

The international climate community is expected to generate hundreds of petabytes of simulation data within the next five to seven years. This data must be accessed and analyzed by thousands of analysts worldwide in order to provide accurate and timely estimates of the likely impact of climate change on physical, biological, and human systems. Climate change is thus not only a scientific challenge of the first order but also a major technological challenge. In order to address this technological challenge, the Earth System Grid Center for Enabling Technologies (ESG-CET) has been established within the U.S. Department of Energy's Scientific Discovery through Advanced Computing (SciDAC)-2 program, with support from the offices of Advanced Scientific Computing Research and Biological and Environmental Research. ESG-CET's mission is to provide climate researchers worldwide with access to the data, information, models, analysis tools, and computational capabilities required to make sense of enormous climate simulation datasets. Its specific goals are to (1) make data more useful to climate researchers by developing Grid technology that enhances data usability; (2) meet specific distributed database, data access, and data movement needs of national and international climate projects; (3) provide a universal and secure web-based data access portal for broad multi-model data collections; and (4) provide a wide-range of Grid-enabled climate data analysis tools and diagnostic methods to international climate centers and U.S. government agencies. Building on the successes of the previous Earth System Grid (ESG) project, which has enabled thousands of researchers to access tens of terabytes of data from a small number of ESG sites, ESG-CET is working to integrate a far larger number of distributed data providers, high-bandwidth wide-area networks, and remote computers in a highly collaborative problem-solving environment.