Paper Info
Title
Climate Model Diagnostic Analyzer
Abstract
The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation-and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowdsourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.
Year
Venue
Keywords
2015
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA
climate data, analytics, model evaluation, online collaborative environment, web services, cloud computing
Field
DocType
Citations 
Data science,Data mining,Data modeling,Climate model,Crowdsourcing,Computer science,Artificial intelligence,Analytics,Semantic search,Web service,Semantics,Machine learning,Cloud computing
Conference
2
PageRank 
References 
Authors
0.65
1
7
Name
Order
Citations
PageRank
Seungwon Lee112732.51
Lei Pan261.11
Chengxing Zhai320.99
Benyang Tang41119.34
Terry Kubar520.65
Jia Zhang611624.54
Wei Wang720.65