气候变化领域集成服务门户(CCPortal): The Canadian surface prediction archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally

CCPortal

DOI	10.1175/BAMS-D-19-0143.1
	The Canadian surface prediction archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally
	Mai J.; Kornelsen K.C.; Tolson B.A.; Fortin V.; Gasset N.; Bouhemhem D.; Schäfer D.; Leahy M.; Anctil F.; Coulibaly P.
发表日期	2020
ISSN	00030007
起始页码	E341
结束页码	E356
卷号	101 期号:3
英文摘要	The Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5-50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online (https://caspar-data.ca) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems. ©2020 American Meteorological Society.
语种	英语
scopus关键词	Boundary layers; Calcium compounds; Climate change; Digital storage; Flood control; Sulfur compounds; Weather forecasting; Energy demand forecasting; Environmental model; Numerical weather forecasts; Numerical weather prediction; Numerical weather prediction models; Operational analysis; Publicly accessible; Temporal resolution; Phosphorus compounds
来源期刊	Bulletin of the American Meteorological Society
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177940
作者单位	University of Waterloo, Waterloo, ON, Canada; McMaster University, Hamilton, ON, Canada; Environment and Climate Change Canada, Dorval, QC, Canada; Helmholtz Centre for Environmental Research, Leipzig, Saxony, Germany; Esri Canada, North York, ON, Canada; Université Laval, Quebec City, QC, Canada; Ontario Power Generation, Niagara-on-the-Lake, ON, Canada
推荐引用方式 GB/T 7714	Mai J.,Kornelsen K.C.,Tolson B.A.,et al. The Canadian surface prediction archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally[J],2020,101(3).
APA	Mai J..,Kornelsen K.C..,Tolson B.A..,Fortin V..,Gasset N..,...&Coulibaly P..(2020).The Canadian surface prediction archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally.Bulletin of the American Meteorological Society,101(3).
MLA	Mai J.,et al."The Canadian surface prediction archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally".Bulletin of the American Meteorological Society 101.3(2020).