Bibliographie complète
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Type de ressource
Auteurs/contributeurs
- Mariani, Zen (Auteur)
- Huang, Laura (Auteur)
- Crawford, Robert (Auteur)
- Blanchet, Jean-Pierre (Auteur)
- Hicks-Jalali, Shannon (Auteur)
- Mekis, Eva (Auteur)
- Pelletier, Ludovick (Auteur)
- Rodriguez, Peter (Auteur)
- Strawbridge, Kevin (Auteur)
Titre
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Résumé
Abstract. The changing Arctic climate is creating increased
economic, transportation, and recreational activities requiring reliable and
relevant weather information. However, the Canadian Arctic is sparsely
observed, and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to
characterize the Arctic atmosphere for operational forecasting and to
support process studies, satellite calibration/validation, search and rescue
operations (which are increasing in the region), high-impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model
verification and evaluation. To address this need, Environment and Climate
Change Canada commissioned two supersites, one in Iqaluit (63.74∘ N, 68.51∘ W) in September 2015 and the other in Whitehorse (60.71∘ N,
135.07∘ W) in November 2017 as part of the Canadian Arctic Weather
Science (CAWS) project. The primary goals of CAWS are to provide enhanced
meteorological observations in the Canadian Arctic for HIW nowcasting
(short-range forecast) and NWP model verification, evaluation, and process
studies and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in
provincial/territorial capitals and are economic hubs for the region; they also act as transportation gateways to the north and are in the path of
several common Arctic storm tracks. The supersites are located at or next to
major airports and existing Meteorological Service of Canada ground-based
weather stations that provide standard meteorological surface observations
and upper-air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The
suite of in situ and remote sensing instruments at each site is completely automated (no on-site operator) and operates continuously in all weather conditions, providing near-real-time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have
similar instruments, including mobile Doppler weather radars, multiple
vertically profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded
configurations, present weather sensors, fog monitoring devices, radiation
flux sensors, and other meteorological instruments. Details on the two
supersites, the suites of instruments deployed, the data collection methods, and example case studies of HIW events are discussed. CAWS data are publicly accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844d-38fc697049e9, Mariani et
al., 2022a, and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78, Mariani et
al., 2022b); this dataset is being used to improve our understanding of
synoptic and fine-scale meteorological processes in the Arctic and
sub-Arctic, including HIW detection and prediction and NWP verification,
assimilation, and processes.
Publication
Earth System Science Data
Volume
14
Numéro
11
Pages
4995-5017
Date
2022-11-11
Abrév. de revue
Earth Syst. Sci. Data
Langue
en
ISSN
1866-3516
Consulté le
12/08/2024 18:15
Catalogue de bibl.
DOI.org (Crossref)
Autorisations
Référence
Mariani, Z., Huang, L., Crawford, R., Blanchet, J.-P., Hicks-Jalali, S., Mekis, E., Pelletier, L., Rodriguez, P., & Strawbridge, K. (2022). Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites. Earth System Science Data, 14(11), 4995–5017. https://doi.org/10.5194/essd-14-4995-2022
Sujet
Auteur·e·s
Lien vers cette notice