The evaluation of snow model in NOAH-MP coupled with WRF model during the periods of heavy snow over the northern and western regions of Iran
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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Land surface schemes have considerable significance in the regional climate models. Due to their role in both surface’s energy and water budget, snow processes are among the most important components of the surface schemes. Snow cover fraction, because of extreme temporal and spatial changes and various features, including high albedo coefficient and very low conductivity, plays an important role in the snow models. This research evaluates snow parameterization in the Advanced Weather Research and Forecasting model (WRF) coupled with the NOAH-MP as a land surface scheme, improved NOAH scheme, through the advanced canopy, snow, and runoff modeling. The snow cover fraction of this scheme is estimated through the hyperbolic tangent relationship between snow height, snow density, and snow melt factor. The snowmelt factor in this model is pre-determined as one since its calibration is difficult due to the lack of access to the observational data at weather stations, using satellite images, and lack of images at most of the snowfall time periods because of the cloud coverage in most parts. For this reason, in this research, the snow cover fraction is evaluated with the default snowmelt coefficient of the model. The WRF-NOAHMP model runs in two separate zones, the northern (Ardebil, Gilan, and Mazandaran provinces) and the western (Kurdistan and Hamedan provinces) regions of Iran, through one-way nesting method with the spatial resolution of 15 kilometers and 5 kilometers for mother and inner domains and during the several periods of heavy snow in the winter of 2013 and 2014. The daily Modis images of the Terra Satellite were used to evaluate the snow cover fraction. Based on the digital elevation model and land use maps, the study area is categorized into five areas, including forests, rangelands, low lands, and mountainous regions with high and low slopes.
The WRF-NOAHMP model is successful in predicting the snow cover fraction in most areas, except mountainous areas with high slopes and rangeland areas; however, the model’s best performance is for low lands due to the highest efficiency coefficient (0.64), the smallest Bias error (-2.4), and Mean Absolute Error (9.4). Moreover, the skill level of the model’s performance (using the area under ROC curve) is good in predicting snowfall in most areas, except for the rangeland area. The WRF-NOAHMP is unsuccessful in estimating the snow depth in forests and mountainous areas with high slopes due to the negative efficiency coefficient, while it has the highest efficiency in estimating snow depth in low lands and mountainous areas with a low slope. Evaluation of the simulated minimum temperature by the model indicates the model’s success in estimating the minimum temperature in all studied areas because of the positive efficiency coefficients. The results of this study show the success of the WRF-NOAHMP in the prediction of the minimum temperature in different regions, while it still has a great deal of uncertainty in the parameterization of the snow cover fraction and the snow depth in mountainous areas with complex topography and areas with surface heterogeneity as well as the parameterization of the snow canopy.
Language:
Persian
Published:
Iranian Journal of Geophysics, Volume:11 Issue: 4, 2018
Pages:
146 to 163
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