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Optimization of Noah and Noah_MP WRF land surface schemes in snow-melting conditions over complex terrain

Optimization of Noah and Noah_MP WRF land surface schemes in snow-melting conditions over complex... AbstractThe paper presents the results of high-resolution simulations performed with the WRF model, coupled with two different land surface schemes, Noah and Noah_MP, with the aim of accurately reproducing winter season meteorological conditions in a typical Alpine valley. Accordingly, model results are compared against data collected during an intensive field campaign performed in the Adige Valley, in the eastern Italian Alps. In particular, the ability of the model in reproducing the time evolution of 2-m temperature and of incoming and outgoing shortwave and longwave radiation is examined. The validation of model results highlights that, in this context, WRF reproduces rather poorly near-surface temperature over snow-covered terrain, with an evident underestimation, during both daytime and nighttime. Furthermore it fails to capture specific atmospheric processes, such as the temporal evolution of the groundbased thermal inversion. The main cause of these errors lies in the miscalculation of the mean grid cell albedo, resulting in an inaccurate estimate of the reflected solar radiation calculated by both Noah and Noah_MP. Therefore, modifications to the initialization, to the land use classification and to both land surface models are performed to improve model results, by intervening in the calculation of the albedo, of the snow cover and of the surface temperature. Qualitative and quantitative analyses show that, after these changes, a significant improvement in the comparability between model results and observations is achieved. In particular, outgoing shortwave radiation is lowered, 2-m temperature maxima increased accordingly, and ground-based thermal inversions are better captured. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Optimization of Noah and Noah_MP WRF land surface schemes in snow-melting conditions over complex terrain

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
DOI
10.1175/MWR-D-16-0408.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThe paper presents the results of high-resolution simulations performed with the WRF model, coupled with two different land surface schemes, Noah and Noah_MP, with the aim of accurately reproducing winter season meteorological conditions in a typical Alpine valley. Accordingly, model results are compared against data collected during an intensive field campaign performed in the Adige Valley, in the eastern Italian Alps. In particular, the ability of the model in reproducing the time evolution of 2-m temperature and of incoming and outgoing shortwave and longwave radiation is examined. The validation of model results highlights that, in this context, WRF reproduces rather poorly near-surface temperature over snow-covered terrain, with an evident underestimation, during both daytime and nighttime. Furthermore it fails to capture specific atmospheric processes, such as the temporal evolution of the groundbased thermal inversion. The main cause of these errors lies in the miscalculation of the mean grid cell albedo, resulting in an inaccurate estimate of the reflected solar radiation calculated by both Noah and Noah_MP. Therefore, modifications to the initialization, to the land use classification and to both land surface models are performed to improve model results, by intervening in the calculation of the albedo, of the snow cover and of the surface temperature. Qualitative and quantitative analyses show that, after these changes, a significant improvement in the comparability between model results and observations is achieved. In particular, outgoing shortwave radiation is lowered, 2-m temperature maxima increased accordingly, and ground-based thermal inversions are better captured.

Journal

Monthly Weather Review – American Meteorological Society

Published: Oct 2, 2017

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