A. Karpatne, S. Liess, James H. Faghmous, Vipin Kumar
{"title":"A Guide to Climate Datasets: Summary and Research Challenges","authors":"A. Karpatne, S. Liess, James H. Faghmous, Vipin Kumar","doi":"10.1109/MCSE.2015.130","DOIUrl":null,"url":null,"abstract":"Recent growth in the scale and variety of climate data has provided unprecedented opportunities to big data analytics research for understanding the Earth's climate system. There has been an upsurge of climate datasets in the past few decades that are collected using various modes of acquisition (e.g. local sensor recordings or remote sensing instruments), at different scales of observation (both in space and time), and in diverse data types and formats. Climate datasets however exhibit some unique characteristics (e.g. adherence to physical properties and spatio-temporal constraints) that makes it challenging to use traditional data-centric approaches for climate science applications. In this paper, we present a brief introduction of the different categories of climate datasets that are available from various sources. We further describe some of the major data-centric challenges in analyzing climate data.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":"22 1","pages":"1-1"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IMPACT of Computing in Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCSE.2015.130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Recent growth in the scale and variety of climate data has provided unprecedented opportunities to big data analytics research for understanding the Earth's climate system. There has been an upsurge of climate datasets in the past few decades that are collected using various modes of acquisition (e.g. local sensor recordings or remote sensing instruments), at different scales of observation (both in space and time), and in diverse data types and formats. Climate datasets however exhibit some unique characteristics (e.g. adherence to physical properties and spatio-temporal constraints) that makes it challenging to use traditional data-centric approaches for climate science applications. In this paper, we present a brief introduction of the different categories of climate datasets that are available from various sources. We further describe some of the major data-centric challenges in analyzing climate data.