Verification of analysed and forecasted winter precipitation in complex terrain

Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. Winter precipitation from two NWP models (GEM-LAM and GEM15) and from a precipitation analysis syste...

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Bibliographic Details
Published in:The Cryosphere
Main Authors: Schirmer, M., Jamieson, B.
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2015
Subjects:
Online Access:https://doi.org/10.5194/tc-9-587-2015
https://noa.gwlb.de/receive/cop_mods_00017070
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00017025/tc-9-587-2015.pdf
https://tc.copernicus.org/articles/9/587/2015/tc-9-587-2015.pdf
Description
Summary:Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. Winter precipitation from two NWP models (GEM-LAM and GEM15) and from a precipitation analysis system (CaPA) was verified at approximately 100 stations in the mountains of western Canada and the north-western US. Ultrasonic snow depth sensors and snow pillows were used to observe daily precipitation amounts. For the first time, a detailed objective validation scheme was performed highlighting many aspects of forecast quality. Overall, the models underestimated precipitation amounts, although low precipitation categories were overestimated. The finer resolution model GEM-LAM performed best in all analysed aspects of model performance, while the precipitation analysis system performed worst. An analysis of the economic value of large precipitation categories showed that only mitigation measures with low cost–loss ratios (i.e. measures that can be performed often) will benefit from these NWP models. This means that measures with large associated costs relative to anticipated losses when the measure is not performed should not or not primarily depend on forecasted precipitation.