Articles | Volume 9, issue 1
https://doi.org/10.5194/esd-9-167-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-9-167-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2018
Research article |
| 26 Feb 2018
| 26 Feb 2018
A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm
Damián Insua-Costa
CORRESPONDING AUTHOR
Non-Linear Physics Group, Universidade de Santiago de Compostela, Galicia, Spain
Gonzalo Miguez-Macho
CORRESPONDING AUTHOR
Non-Linear Physics Group, Universidade de Santiago de Compostela, Galicia, Spain
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Latest update: 21 Nov 2024
Short summary
We present here a newly implemented water vapor tracer tool into the WRF meteorological model (WRF-WVT). A detailed validation shows high accuracy, with an error of much less than 1 % in moisture traceability. As an example application, we show that for the 2014 Great Lake-effect snowstorm, above 30 % of precipitation in the regions immediately downwind originated from lake evaporation, with contributions exceeding 50 % in the areas with highest snowfall accumulations.
We present here a newly implemented water vapor tracer tool into the WRF meteorological model...
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