2019
DOI: 10.3390/cli7070087
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An Unusual Cold February 2019 in Saskatchewan—A Case Study Using NCEP Reanalysis Datasets

Abstract: In February 2019, central Canada, and especially the province of Saskatchewan, experienced extreme cold weather. It was the coldest February in 82 years and the second coldest in 115 years. In this study, we examine National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 data to understand the atmospheric processes leading to this cold snap. A detailed investigation of surface air temperature, sea level pressure, surface fluxes, and winds revealed a lin… Show more

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“…To fully understand the atmospheric processes leading to the 2017 spring floods in Kazakhstan, the data of the NCEP/NCAR Reanalysis 1 (Kalnay et al, 1996) were used to understand the largescale atmospheric circulation from the surface to upper layers. On the basis of the data from 1948 to present, a state-of-the-art analysis/forecast system was used to perform data assimilation in the NCEP/NCAR Reanalysis 1 project, which has been extensively applied in multiple studies (Basu and Sauchyn, 2019;Romanic et al, 2018). In this study, parameters, including the air temperature, geopotential height, and wind, were used to evaluate the relation between atmospheric circulation and 2017 spring floods.…”
Section: Datasetsmentioning
confidence: 99%
“…To fully understand the atmospheric processes leading to the 2017 spring floods in Kazakhstan, the data of the NCEP/NCAR Reanalysis 1 (Kalnay et al, 1996) were used to understand the largescale atmospheric circulation from the surface to upper layers. On the basis of the data from 1948 to present, a state-of-the-art analysis/forecast system was used to perform data assimilation in the NCEP/NCAR Reanalysis 1 project, which has been extensively applied in multiple studies (Basu and Sauchyn, 2019;Romanic et al, 2018). In this study, parameters, including the air temperature, geopotential height, and wind, were used to evaluate the relation between atmospheric circulation and 2017 spring floods.…”
Section: Datasetsmentioning
confidence: 99%