Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1175/BAMS-D-19-0143.1 |
| Titel (primär) | The Canadian Surface Prediction Archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally |
| Autor | Mai, J.; Kornelsen, K.C.; Tolson, B.A.; Fortin, V.; Gasset, N.; Bouhemhem, D.; Schäfer, D.; Leahy, M.; Anctil, F.; Coulibaly, P. |
| Quelle | Bulletin of the American Meteorological Society |
| Erscheinungsjahr | 2020 |
| Department | MET |
| Band/Volume | 101 |
| Heft | 3 |
| Seite von | E341 |
| Seite bis | E356 |
| Sprache | englisch |
| Supplements | https://journals.ametsoc.org/doi/suppl/10.1175/BAMS-D-19-0143.1/suppl_file/10.1175_BAMS-D-19-0143.2.pdf |
| Abstract | 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23121 |
| Mai, J., Kornelsen, K.C., Tolson, B.A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F., Coulibaly, P. (2020): The Canadian Surface Prediction Archive (CaSPAr): A platform to enhance environmental modeling in Canada and globally Bull. Amer. Meteorol. Soc. 101 (3), E341 - E356 10.1175/BAMS-D-19-0143.1 |
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