Sensitivity of land surface schemes to snow cover in the WRF model

doi:10.11755/j.issn.1006-7639-2025-06-0953

Abstract

Abstract:

The complex influence of snow cover on surface energy processes constitutes a critical source of uncertainty in wintertime numerical simulations over complex terrain and therefore warrants further investigation. Comparative simulation experiments were conducted for a snow-covered period (18-26 February) and a snow-free period (11-19 January) in 2014 over the Lanzhou New Area using the Weather Research and Forecasting (WRF) model version 4.3. Four land surface models (LSMs), SLAB, Pleim-Xiu, RUC, and NoahMP were systematically evaluated against observations from four meteorological towers to reveal the impact of snow cover on simulation accuracy and scheme sensitivity. Satisfactory performance was achieved during the snow-free period: correlation coefficients (R) of air temperature ranged from 0.80 to 0.97, with normalized centered root mean square errors (NCRMSE) of 0.27-0.60. The R of wind speed ranged from 0.46 to 0.82, and the absolute bias was generally below 0.5 m·s^-1, successfully reproducing slope wind circulation. Conversely, simulation accuracy declined significantly during the snow-covered period. R of air temperature for half of the LSMs decreased below 0.80, cold biases exceeded 5.00 ℃, and NCRMSE increased to 0.38-0.79. Wind speed NCRMSE increased to 0.77-2.52, while wind direction frequency errors doubled. Taylor diagram analysis demonstrated that snow cover enhanced the sensitivity to LSMs, indicated by increased dispersion in normalized standard deviation among the schemes. NoahMP exhibited the superior performance with the lowest cold bias under snow-covered conditions (R≈0.9; NCRMSE<0.5), emphasizing the significance of accurate snow process representation for improving winter meteorological simulation in complex terrain.

Key words: Lanzhou New Area, WRF simulation, snow cover, land surface scheme, near-surface meteorological fields

摘要：

积雪对地表能量过程的复杂影响是冬季复杂地形区数值模拟的关键不确定源，亟待深入研究。利用WRF v4.3模式，针对兰州新区2014年有雪期（2月18—26日）与无雪期（1月11—19日）开展模拟对比试验，基于4座测风塔观测数据，系统评估了SLAB、Pleim-Xiu、RUC和NoahMP 4种陆面方案对近地面气象要素的模拟性能，揭示了积雪对模拟精度的影响及其对陆面方案的敏感性。结果表明，无雪期模拟效果良好：气温模拟相关系数（R）为0.80~0.97，归一化中心均方根误差（Normalized Centered Root Mean Square Errors，NCRMSE）为0.27~0.60；风速模拟R为0.46~0.82，绝对偏差普遍低于0.5 m·s^-1，且能较好地再现坡风环流特征。而在积雪期，模拟精度显著下降：约半数方案气温R低于0.80，最大冷偏差超过5.00 ℃，NCRMSE升至0.38~0.79；风速NCRMSE增至0.77~2.52，风向频率误差可达无雪期的2倍。泰勒图分析进一步表明，积雪增强了模拟结果对陆面方案的敏感性，有雪期各方案归一化标准差的离散性显著大于无雪期。在4种方案中，NoahMP在积雪期表现最优，其气温R稳定在0.9左右，冷偏差最小，且NCRMSE多低于0.5。准确表征积雪过程对提升冬季复杂地形区的气象模拟能力具有重要意义。

关键词: 兰州新区, WRF模式, 积雪效应, 陆面过程参数化方案, 近地面气象场

CLC Number:

P435

ZHOU Yanyan, WANG Ying, WEI Ruirui, ZHAO Tianyi. Sensitivity of land surface schemes to snow cover in the WRF model[J]. Journal of Arid Meteorology, 2025, 43(6): 953-966.

周燕艳, 王颖, 魏瑞瑞, 赵天一. WRF模式的陆面过程方案对积雪的敏感性分析[J]. 干旱气象, 2025, 43(6): 953-966.

Figures/Tables 10

Fig.1 WRF four-domain nested grid configuration (a) and terrain elevation (the color shaded, Unit: m) and locations of four meteorological towers (black dots) in the innermost D04 (b)

Fig.2 The 500 hPa geopotential height (blue contour lines, Unit: dagpm) and temperature (red contour lines, Unit: ℃) at 08:00 on 19 February 2014 during snow-covered period (a) and 08:00 on 13 January 2014 during snow-free period (b) (The red star represents the location of Lanzhou New Area)

Fig.3 MODIS true-color images showing the D04 region with snow cover (a) at 08:00 on 19 February 2014 and without snow cover at 08:00 on 13 January 2014 (b)

Fig.4 Scatter plot of T2 and T70 from various land surface schemes against observations during snow-covered period （* indicates significance at the 95% confidence level， the underlined numbers represent the optimal values. The same as below）

Fig.5 Diurnal variations of WS10 and WS70 observed and simulated by four land surface schemes at four meteorological towers during snow-covered period

Fig.6 Wind roses diagrams of the 10-m and 70-m observations from each meteorological tower， together with four land surface schemes during snow-covered period

Fig.7 Scatter plot of T2 and T70 from various land surface schemes against observations during snow-free period

Fig.8 Diurnal variations of WS10 and WS70 observed and simulated by four land surface schemes at four meteorological towers during snow-free period

Fig.9 Wind roses diagrams of the 10-m and 70-m observations from each meteorological tower， together with four meteorological towers during snow-free period

Fig.10 Normalized Taylor diagrams for T2， T70， WS10， and WS70 from four land surface schemes at four meteorological towers during snow-covered and snow-free periods

References 46

[1]	陈海山, 杜新观, 孙悦, 2022. 陆面过程与天气研究[J]. 地学前缘, 29(5):382-400.
[2]	姜琪, 罗斯琼, 李明, 2022. 不同初始场及陆面方案对青藏高原中东部积雪消融过程的模拟研究[J]. 高原气象, 41(2):430-443.
[3]	李文静, 罗斯琼, 郝晓华, 等, 2021. 青藏高原东部不同季节积雪过程对地表能量和土壤水热影响的观测研究[J]. 高原气象, 40(3):455-471.
[4]	刘晓东, 1989. 冰雪变化对大气环流和天气气候的影响[J]. 地球科学进展, 4(6):53-58.
[5]	卢冰, 王薇, 杨扬, 等, 2019. WRF中土壤图及参数表的更新对华北夏季预报的影响研究[J]. 气象学报, 77(6):1028-1 040.
[6]	鲁峻岑, 张小玲, 王安怡, 等, 2023. 冬季局地环流对成都地区大气污染的影响[J]. 高原山地气象研究, 43(4):91-100.
[7]	乔娟, 张强, 张杰, 2008. 非均匀下垫面陆面过程参数化问题研究进展[J]. 干旱气象, 26(1):73-77.
[8]	任余龙, 张铁军, 柳媛普, 等, 2020. 夏季风过渡区下垫面非均匀性对一次暴雨影响的数值模拟[J]. 干旱气象, 38(5):755-763.
[9]	孙昭萱, 张强, 王胜, 2009. 土壤水热耦合模型研究进展[J]. 干旱气象, 27(4):373-380.
[10]	王升第, 曹斌, 郝建盛, 等, 2023. 新疆季节性积雪对地表温度的影响[J]. 冰川冻土, 45(2):435-445.
[11]	温晓培, 吴炜, 李昌义, 等, 2022. 土地利用资料的更新对四川盆地高温天气数值模拟的影响[J]. 干旱气象, 40(5):868-878.
[12]	吴统文, 钱正安, 宋敏红, 2004. CCM3模式中LSM积雪方案的改进研究 (Ⅰ):修改方案介绍及其单点试验[J]. 高原气象, 23(4):444-452.
[13]	张海宏, 肖建设, 陈奇, 等, 2019. 青海省甘德两次降雪过程的微气象特征分析[J]. 气象, 45(8):1093-1 103.
[14]	张强, 王蓉, 岳平, 等, 2017. 复杂条件陆-气相互作用研究领域有关科学问题探讨[J]. 气象学报, 75(1):39-56.
[15]	张亚莉, 王清平, 万瑜, 等, 2024. 乌鲁木齐机场2021年首场强浓雾特征及成因分析[J]. 沙漠与绿洲气象, 18(4):83-90.
[16]	赵采玲, 张铁军, 王玮, 等, 2018. 不同土地利用数据对中国西北地区风速模拟的影响[J]. 干旱气象, 36(3):397-404.
[17]	BENNETT N D, CROKE B F W, GUARISO G, et al, 2013. Characterising performance of environmental models[J]. Environmental Modelling & Software, 40: 1-20.
[18]	CAO X, ZHANG G, CHEN Y L, et al, 2024. Optimization of snow-related processes in Noah-MP land surface model over the mid-latitudes of Asian region[J]. Atmospheric Research, 311: 107711. DOI: 10.1016/j.atmosres.2024.107711.
[19]	CLINE D W, 1997. Effect of seasonality of snow accumulation and melt on snow surface energy exchanges at a continental alpine site[J]. Journal of Applied Meteorology and Climatology, 36(1): 32-51.
[20]	DE FOY B, MOLINA L T, MOLINA M J, 2006. Satellite-derived land surface parameters for mesoscale modelling of the Mexico City basin[J]. Atmospheric Chemistry and Physics, 6(5): 1 315-1 330.
[21]	DI Z H, ZHANG S L, QUAN J P, et al, 2023. Performance of seven land surface schemes in the WRFv4.3 model for simulating precipitation in the record-breaking Meiyu season over the Yangtze-Huaihe River valley in China[J]. GeoHealth, 7(3): e2022GH000757. DOI: 10.1029/2022GH000757.
[22]	DUDHIA J, 1996. A Multi-Layer Soil Temperature Model for MM5[C]// Proceedings of the Sixth PSU/NCAR Mesoscale Model Users’ Workshop. Boulder: National Center for Atmospheric Research: 22-24.
[23]	GILLIAM R C, PLEIM J E, 2010. Performance assessment of new land surface and planetary boundary layer physics in the WRF-ARW[J]. Journal of Applied Meteorology and Climatology, 49(4): 760-774.
[24]	MENG X H, LÜ S H, ZHANG T T, et al, 2009. Numerical simulations of the atmospheric and land conditions over the Jinta oasis in northwestern China with satellite-derived land surface parameters[J]. Journal of Geophysical Research: Atmospheres, 114(D6). DOI: 10.1029/2008JD010360.
[25]	MOTT R, DANIELS M, LEHNING M, 2015. Atmospheric flow development and associated changes in turbulent sensible heat flux over a patchy mountain snow cover[J]. Journal of Hydrometeorology, 16(3): 1 315-1 340.
[26]	MOTT R, SCHLÖGL S, DIRKS L, et al, 2017. Impact of extreme land surface heterogeneity on micrometeorology over spring snow cover[J]. Journal of Hydrometeorology, 18(10): 2 705-2 722.
[27]	NIU G Y, YANG Z L, 2007. An observation-based formulation of snow cover fraction and its evaluation over large North American river basins[J]. Journal of Geophysical Research: Atmospheres, 112(D21): 2007JD008674. DOI: 10.1029/2007JD008674.
[28]	NIU G Y, YANG Z L, MITCHELL K E, et al, 2011. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements[J]. Journal of Geophysical Research: Atmospheres, 116(D12): D12109. DOI:10.1029/2010JD015140.
[29]	PARRA R, 2023. Assessment of land surface schemes from the WRF-chem for atmospheric modeling in the Andean Region of Ecuador[J]. Atmosphere, 14(3): 508. DOI:10.3390/atmos14030508.
[30]	POWERS J G, KLEMP J B, SKAMAROCK W C, et al, 2017. The weather research and forecasting model: Overview, system efforts, and future directions[J]. Bulletin of the American Meteorological Society, 98(8): 1 717-1 737.
[31]	ROMÁN-CASCÓN C, LOTHON M, LOHOU F, et al, 2021. Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)[J]. Geoscientific Model Development, 14(6): 3 939-3 967.
[32]	SCHMIDLI J, BÖING S, FUHRER O, 2018. Accuracy of simulated diurnal valley winds in the Swiss Alps: Influence of grid resolution, topography filtering, and land surface datasets[J]. Atmosphere, 9(5): 196. DOI:10.3390/atmos9050196.
[33]	SEGAL M, GARRATT J R, PIELKE R A, et al, 1991. Scaling and numerical model evaluation of snow-cover effects on the generation and modification of daytime mesoscale circulations[J]. Journal of the Atmospheric Sciences, 48(8): 1 024-1 042.
[34]	SHANGGUAN W, DAI Y J, DUAN Q Y, et al, 2014. A global soil data set for earth system modeling[J]. Journal of Advances in Modeling Earth Systems, 6(1): 249-263.
[35]	SKAMAROCK W C, KLEMP J B, DUDHIA J, et al, 2019. A description of the advanced research WRF model version 4[R]. Boulder: National Center for Atmospheric Research, NCAR/TN-556+STR.
[36]	SMIRNOVA T G, BROWN J M, BENJAMIN S G, et al, 2016. Modifications to the rapid update cycle land surface model (RUC LSM) available in the weather research and forecasting (WRF) model[J]. Monthly Weather Review, 144(5): 1 851-1 865.
[37]	STULL R B, 1988. Mean boundary layer characteristics[M]// An introduction to boundary layer meteorology. Dordrecht: Springer Netherlands: 1-27.
[38]	SUN S, SHI C X, LIANG X, et al, 2023. The evaluation of snow depth simulated by different land surface models in China based on station observations[J]. Sustainability, 15(14): 11284. DOI: 10.3390/su151411284.
[39]	TAYLOR K E, 2001. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 106(D7): 7 183-7 192.
[40]	WALSH J E, 1984. Snow cover and atmospheric variability: Changes in the snow covering the earth’s surface affect both daily weather and long-term climate[J]. American Scientist, 72(1): 50-57.
[41]	XIA G, SMIRNOVA T G, TURNER D D, et al, 2023. Sensitivity of near-surface variables in the RUC land surface model in the weather research and forecasting model[R]. Golden, CO: National Renewable Energy Laboratory.
[42]	ZHANG B, XIONG W, MA M Y, et al, 2022. Super-resolution reconstruction of a 3 arc-second global DEM dataset[J]. Science Bulletin, 67(24): 2 526-2 530.
[43]	ZHANG H L, PU Z X, ZHANG X B, 2013. Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain[J]. Weather and Forecasting, 28(3): 893-914.
[44]	ZHANG T J, 2005. Influence of the seasonal snow cover on the ground thermal regime: An overview[J]. Reviews of Geophysics, 43(4): 2004RG000157. DOI:10.1029/2004RG000157.
[45]	ZHANG X, LIU L Y, CHEN X D, et al, 2021. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery[J]. Earth System Science Data, 13(6): 2 753-2 776.
[46]	ZHANG Y, YAN D D, WEN X H, et al, 2020. Comparative analysis of the meteorological elements simulated by different land surface process schemes in the WRF model in the Yellow River source region[J]. Theoretical and Applied Climatology, 139(1): 145-162.