Plateau Meteorology

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2020 , Vol. 39 >Issue 6: 1195 - 1206

DOI: https://doi.org/10.7522/j.issn.1000-0534.2020.00052

Evaluations and Analysis of Applicability of the Different Parameterization Schemes and Reanalysis Data in the Typical Alpine Lake Areas

  • Xianyu YANG ,
  • Yaqiong Lü ,
  • Jun WEN ,
  • Yaoming MA ,
  • Anning HUANG ,
  • Hui TIAN ,
  • Shaobo ZHANG
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  • College of Atmospheric Sciences/ Plateau Atmosphere and Environment Key Laboratory of Sichuan Province，Chengdu University of Information Technology，Chengdu，610225，Sichuan，China;Northwest Institute of Eco-Environment andＲesources Chinese Academy of Sciences，Lanzhou 730000，Gansu，China;Institute of Mountain Hazards and Environment，Chinese Academy of Sciences，Chengdu，610041，Sichuan，China;Key Laboratory of Tibetan Environment Change and Land Surface Process．Institute of Tibetan Plateau Research Chinese Academy of Sciences，Beijing 100101，China;University of Chinese Academy of Sciences，Beijing 100049，China;CAS Center for Excellence in Tibetan Plateau Earth Sciences，Beijing 100101，China;School of Atmospheric Sciences，Nanjing University，Nanjing 210023，Jiangsu，China

Received date: 2019-11-14

  Online published: 2020-12-28

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Abstract

To explore the simulation performance including latent heat flux， heat flux and other land-atmospheric interaction parameters of the boundary layer parameterization schemes， the land surface parameterization schemes， and the reanalysis data in the study area of the Lake Ngoring and the Lake Nam Co， 11 WRF experiments were carried out from 21 to 30 June 2013， then compared the simulation results with the observation data.The results showed that the WRF model with different parameterization schemes is potential in simulation the average daily variation characteristics of sensible heat flux， the latent heat flux， and 2 m temperature， however， the WRF model overestimate the maximum value of them.In the Lake Ngoring area， the experiment Case8 （RUC+FNL+YSU） showed the best simulation for latent heat flux on grassland， which RMSE was 27.16 W·m-2.The experiment Case10 （CLM+YSU+ NCEP-2） showed the best simulation for sensible heat flux and 2 m temperature on grassland， which RMSE were 29.01 W·m-2， 1.41 ℃， respectively.The experiment Case5 （CLM+FNL+BL） showed the best simulation for 2 m temperature above lake.Overall， the Case10 performed best in simulation the heat flux on grassland and 2 m temperature， which generalized RMSE was 1.70.In the Lake Nam Co area， the experiment Case6 （SLAB+FNL+YSU） showed the best simulation for latent heat flux on grassland， which RMSE was 16.11 W·m-2.The experiment Case8 showed the best simulation for sensible heat flux， which RMSE was 42.93 W·m-2.The experiment Case7 （NOAH+FNL+YSU） showed the best simulation for 2 m temperature above grassland， which RMSE was 0.69 ℃.Overall， the Case 1 （CLM+YSU+FNL） performed best in simulation the heat flux and 2 m temperature on grassland， which generalized RMSE was 1.0.

Key words： Lake Gyaring; Lake Ngoring; Nam co Lake; WRF model; numerical simulation

Cite this article

Xianyu YANG , Yaqiong Lü , Jun WEN , Yaoming MA , Anning HUANG , Hui TIAN , Shaobo ZHANG . Evaluations and Analysis of Applicability of the Different Parameterization Schemes and Reanalysis Data in the Typical Alpine Lake Areas[J]. Plateau Meteorology, 2020 , 39(6) : 1195 -1206 . DOI: 10.7522/j.issn.1000-0534.2020.00052

References

[1]Adrian R, O'Reilly C M, Zagarese H, et al, 2009.Lakes as sentinels of climate change［J］.Limnology and Oceanography, 54（6part2）: 2283-2297.DOI: 10.4319/lo.2009.54.6_part_2.2283.
[2]Biermann T, Babel W, Ma W Q, et al, 2014.Turbulent flux observations and modelling over a shallow lake and a wet grassland in the Nam Co basin, Tibetan Plateau［J］.Theoretical and Applied Climatology, 116（1-2）: 301-316.DOI: 10.1007/s00704-013-0953-6.
[3]Blanken P D, Rouse W R, Culf A D, et al, 2000.Eddy covariance measurements of evaporation from Great Slave lake, Northwest Territories, Canada［J］.Water Resources Research, 36（4）: 1069-1077.DOI: 10.1029/1999WR900338.
[4]Jankov I, Gallus W A, Segal M, et al, 2005.The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall［J］.Weather and Forecasting, 20（6）: 1048-1060.DOI: 10.1175/WAF888.1.
[5]Jankov I, Schultz P J, Anderson C J, et al, 2007.The impact of different physical parameterizations and their interactions on cold season QPF in the American River basin［J］.Journal of Hydrometeorology, 8（5）: 1141-1151.DOI: 10.1175/JHM630.1.
[6]Miao J F, Chen D, Wyser K, et al, 2008.Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast: Influence of PBL and LSM parameterizations［J］.Meteorology and Atmospheric Physics, 99（1-2）: 77-103.DOI: 10.1007/s00703-007-0267-2.
[7]Miao J F, Wyser K, Chen D, et al, 2009.Impacts of boundary layer turbulence and land surface process parameterizations on simulated sea breeze characteristics［J］.Annales Geophysicae, 27（6）: 2303-2320.DOI: 10.5194/angeo-27-2303-2009.
[8]Xiu A, Pleim J E, 2001.Development of a land surface model.Part I: Application in a mesoscale meteorological model［J］.Journal of Applied Meteorology, 40（2）: 192-209.DOI: 10.1175/1520-0450（2001）040<0192: DOALSM>2.0.CO； 2.
[9]Yang X, Lu Y, Ma Y, et al, 2015.Summertime Thermally-Induced Circulations over the Lake Nam Co Region of the Tibetan Plateau［J］.Journal of Meteorological Research, 29（2）: 305-314.DOI: 10.1007/s13351-015-4024-z.
[10]曾新民, 吴志皇, 宋帅, 等, 2012.WRF模式不同陆面方案对一次暴雨事件模拟的影响［J］.地球物理学报, 55（1）: 16-28.DOI: 10. 6038/j.issn.0001-5733.2012.01.002.
[11]戴永久, 魏楠, 黄安宁, 等, 2018.通用陆面模式（CoLM）湖泊过程方案与性能评估［J］.科学通报, 63（2）: 3002-3021.DOI: 10. 1360/N972018-00609.
[12]傅抱璞, 1997.我国不同自然条件下的水域气候效应［J］.地理学报, 52（3）: 56-63.
[13]高笃鸣, 李跃清, 蒋兴文, 等, 2016.WRF模式多种边界层参数化方案对四川盆地不同量级降水影响的数值试验［J］.大气科学, 40（2）: 371-389.DOI: 10.3878/j.issn.1006-9895.1503.14323.
[14]胡伟, 马伟强, 马耀明, 等, 2020.GLDAS资料驱动的Noah-MP陆面模式青藏高原地表能量交换模拟性能评估［J］.高原气象, 39（3）: 486-498.DOI: 10.7522/j.issn.1000-0534.2019.00060．
[15]李斐, 邹捍, 周立波, 等, 2017.WRF模式中边界层参数化方案在藏东南复杂下垫面适用性研究［J］.高原气象, 36（2）: 340-357.DOI: 10.7522/j.issn.1000-0534.2016.00084.
[16]李宏毅, 肖子牛, 朱玉祥, 2018.藏东南草地下垫面地气通量交换日变化的数值模拟［J］.高原气象, 37（2）: 443-454.DOI: 10. 7522/j.issn.1000-0534.2017.00052.
[17]李瑞青, 吕世华, 韩博, 等, 2012.青藏高原东部三种再分析资料与地面气温观测资料的对比分析［J］.高原气象, 31（6）: 1488-1502.
[18]李昀英, 叶成志, 钟中, 2010.陆面参数化方案对两例不同类型暴雨可预报性的影响［J］.大气科学, 34（2）: 407-417.
[19]李照国, 吕世华, 奥银焕, 等, 2012.黄河源区生态环境变化对湖泊效应影响的数值模拟［J］.高原气象, 31（6）: 1591-1600.
[20]吕雅琼, 马耀明, 李茂善, 等, 2008.纳木错湖夏季典型大气边界层特征的数值模拟［J］.高原气象, 26（4）: 733-740.
[21]吕雅琼, 杨显玉, 马耀明, 2007.夏季青海湖局地环流及大气边界层特征的数值模拟［J］.高原气象, 26（4）: 686-692.
[22]马晨晨, 余晔, 何建军, 等, 2014.黄土高原地区两种再分析资料的模拟效果分析［J］.高原气象, 33（3）: 698-711.DOI: 10.7522/j.issn.1000-0534.2014.00038
[23]马红云, 郭品文, 宋洁, 2009.耦合不同陆面方案的WRF模式对2007年7月江淮强降水过程的模拟［J］.大气科学, 33（3）: 557-567.
[24]宋兴宇, 文莉娟, 李茂善, 等, 2020.不同湖泊模式对青藏高原典型湖泊适用性对比研究［J］.高原气象, 39（2）: 213-225.DOI: 10.7522/j.issn.1000-0534.2019.00102.
[25]孙菽芬, 2002.陆面过程研究的进展［J］.新疆气象（6）: 1-6.DOI: 10.3969/j.issn.1002-0799.2002.06.001
[26]汪雅, 苗峻峰, 谈哲敏, 2015.陆面过程参数化对宁波地区雷暴过程模拟的影响［J］.大气科学学报, 38（3）: 299-309.DOI: 10.13878/j.cnki.dqkxxb.20130310001.
[27]徐慧燕, 朱业, 刘瑞, 等, 2013.长江下宋游地区不同边界层参数化方案的试验研究［J］.大气科学, 37（1）: 149-159.DOI: 10. 3878/j.issn.1006-9895.2012.12021.
[28]杨显玉, 文军, 2012.扎陵湖和鄂陵湖大气边界层特征的数值模拟［J］.高原气象, 31（4）: 927-934.
[29]赵鸣, 2008.边界层和陆面过程对中国暴雨影响研究的进展［J］.暴雨灾害（2）: 186-190.DOI: 10.3969/j.issn.1004-9045.2008. 02.016.
[30]赵天保, 符淙斌, 2009a.几种再分析地表气温资料在中国区域的适用性评估［J］.高原气象, 28（3）: 594-606.
[31]赵天保, 符淙斌, 2009b.应用探空观测资料评估几类再分析资料在中国区域的适用性［J］.大气科学, 33（3）: 634-648.
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