2013
DOI: 10.2151/sola.2013-033
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Comparison of Snow Water Equivalent Estimated in Central Japan by High-Resolution Simulations Using Different Land-Surface Models

Abstract: We estimated the snow water equivalent (SWE) of snowpack in central Japan from September 2006 to August 2008 by using a 3.3 km-mesh regional climate model with two land-surface models: Noah land-surface model (Noah LSM), and Noah land-surface model with multiparameterization options (Noah MP). The model validation for temporal variations of SWE at the Tohkamachi station and the comparison of modeled maximum SWE with estimated that from observed maximum snow depth at ten sites showed that Noah MP could simulate… Show more

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Cited by 9 publications
(5 citation statements)
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“…Indeed, NoahMP performs better than Noah in its standard configuration and for this reason it is more difficult to further improve model results. A better performance of Noah_MP with respect to Noah was also found by Chen et al (2014) and Kuribayashi et al (2013), who assessed the ability of both models in simulating the snowpack evolution in time. The bottom panels of Fig.…”
Section: Discussionsupporting
confidence: 53%
“…Indeed, NoahMP performs better than Noah in its standard configuration and for this reason it is more difficult to further improve model results. A better performance of Noah_MP with respect to Noah was also found by Chen et al (2014) and Kuribayashi et al (2013), who assessed the ability of both models in simulating the snowpack evolution in time. The bottom panels of Fig.…”
Section: Discussionsupporting
confidence: 53%
“…ICAR reduces the spread of the daily precipitation errors of ERA5, as shown in Fig. 4 (standard deviation of 11.5 mm in ERA5 compared with the 8.4 mm of ICAR), even though the ERA5 errors are already surprisingly low considering the spatial resolution and the fact that precipitation is challenging to simulate by numerical models, especially over complex terrain (Legates, 2014). This validation provides a range of uncertainty estimates to help generate the probability density functions for the perturbations of the ensemble.…”
Section: Atmospheric Simulation Resultsmentioning
confidence: 94%
“…Therefore, it is essential to accurately estimate the snow accumulation and ablation processes in land surface models [Liu et al, 2013], where the snow state variables include snow water equivalent (SWE), snow depth (SD), snow cover fraction (SCF), snow albedo, and snow density. However, snow processes have been described in land surface models with simplified parameterization schemes which have resulted in biased snow state estimates (e.g., SWE, SD, and SCF) [Che et al, 2014;Kuribayashi et al, 2013;Livneh et al, 2010;Roesch, 2006]. Many researchers have been motivated to improve the simulation of snow processes by assimilating satellite observations (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) SCF) into land surface models.…”
Section: Introductionmentioning
confidence: 99%