Advances in Atmospheric Sciences

, Volume 35, Issue 6, pp 628–644 | Cite as

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

  • Junhua Yang
  • Zhenming JiEmail author
  • Deliang Chen
  • Shichang Kang
  • Congshen Fu
  • Keqin Duan
  • Miaogen Shen
Original Paper


The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level (surface-sensitive) channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets. Here, we used an improved land use and leaf area index (LAI) dataset in the WRF-3DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels (e.g., channel 3), the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Key words

WRF-3DVAR land use leaf area index radiance assimilation rainfall simulation 

摘 要

同化卫星辐射数据可提高数值天气模式的预测精度. 但由于地表信息的不准确, 以往研究中通常会剔除对地表较敏感的低层通道中的辐射数据, 从而大大降低了卫星数据的使用率. 本研究以青藏高原东北部疏勒河流域2013年7月份降水模拟为例, 通过更新WRF-3DVAR同化系统中下垫面土地利用类型和叶面积指数数据, 来分析改进的土地覆被对辐射资料同化的影响. 结果表明, 更新土地覆被后, 在对地表敏感的窗区通道中, 模拟的辐射亮温值与实际观测值更为接近, 使更多的卫星数据通过偏差检验并在同化系统中得到应用. 而对于非窗区通道, 土地覆被更新对辐射亮温的模拟影响较小. 同化系统中卫星辐射资料利用率的提高使WRF模式对研究区内降水的模拟得到了一定程度的改进.


WRF-3DVAR 土地利用类型 叶面积指数 辐射同化 降水模拟 


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This study was supported by the National Key Research and Development Program of China (Grant No.2016YFA0602701), the National Natural Science Foundation of China (Grant Nos. 41721091, 41630754, 91644225), and the Open Program (Grant No.SKLCS-OP-2017-02) from the State Key Laboratory of Cryospheric Science, Northwest Institute of Eco- Environment and Resources, Chinese Academy of Sciences.

Supplementary material

376_2017_7120_MOESM1_ESM.pdf (421 kb)
Electronic Supplementary Material to: Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013


  1. Andersson, E., J. Pailleux, J. N. Thépaut, J. R. Eyre, A. P. McNally, G. A. Kelly, and P. Courtier, 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quar. J. Roy. Meteor. Soc., 120(517), 627–653, Scholar
  2. Barker, D. M., W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132(4), 897–914,<0897:ATVDAS>2.0.CO;2.CrossRefGoogle Scholar
  3. Bauer, P., P. Lopez, A. Benedetti, D. Salmond, S. Saarinen, and M. Bonazzola, 2006: Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances in precipitation at ECMWF. II: 4D-Var. Quart. J. Roy. Meteor. Soc., 132(620), 2307–2332, Scholar
  4. Cassardo, C., G. P. Balsamo, C. Cacciamani, D. Cesari, T. Paccagnella, and R. Pelosini, 2002: Impact of soil surface moisture initialization on rainfall in a limited area model: A case study of the 1995 South Ticino flash flood. Hydrological Processes, 16(6), 1301–1317, 1063.CrossRefGoogle Scholar
  5. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surfacehydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129(4), 569–585,<0569:CAALSH>2.0.CO;2.CrossRefGoogle Scholar
  6. Chen, F., Z. Janjić, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterizations in the new land surface scheme of the NCEP mesoscale ETA model. Bound.-Layer Meteor., 85(3), 391–421, Scholar
  7. Chen, F., J. Dudhia, Z. Janjic, and M. Baldwin, 1997: Coupling a land surface model to the NCEP mesoscale Eta model. Preprints, 13th Conf. on Hydrology, Long Beach, CA: American Meteorological Society, 99–100.Google Scholar
  8. Chen, S. H., and W. Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan, 80(1), 99–118, Scholar
  9. Dai, S. P., B. Zhang, H. J. Wang, Y. M. Wang, L. X. Guo, X. M. Wang, and D. Li, 2010: Vegetation cover change and its driving factors over northwest China. Arid Land Geography, 33(4), 636–643, (in Chinese)Google Scholar
  10. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46(20), 3077–3107,<3077: NSOCOD>2.0.CO;2.CrossRefGoogle Scholar
  11. Eyre, J. R., 1989: Inversion of cloudy satellite sounding radiances by nonlinear optimal estimation. II: Application to TOVS data. Quart. J. Roy. Meteor. Soc., 115(489), 1027–1037, Scholar
  12. Eyre, J. R., G. A. Kelly, A. P. McNally, E. Andersson, and A. Persson, 1993: Assimilation of TOVS radiance information through one-dimensional variational analysis. Quart. J. Roy. Meteor. Soc., 119(514), 1427–1463, Scholar
  13. Friedl, M. A., D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf, 2002: Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83(1–2), 287–302, Scholar
  14. Gao, X. J., D. F. Zhang, Z. X. Chen, J. S. Pal, and F. Giorgi, 2007: Land use effects on climate in China as simulated by a regional climate model. Science in China Series D: Earth Sciences, 50(4), 620–628, Scholar
  15. Gao, Y. H., F. Chen, M. Barlage, W. Liu, G. D. Cheng, X. Li, Y. Yu, Y. H. Ran, H. Y. Li, H. C. Peng, and M. G. Ma, 2008: Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, Northwest China. J. Geophys. Res., 113(D20), Scholar
  16. Gao, Y. H., L. H. Xiao, D. L. Chen, F. Chen, J. W. Xu, and Y. Xu, 2017: Quantification of the relative role of land surface processes and large-scale forcing in dynamic downscaling over the Tibetan Plateau. Climate Dyn., 48, 1705–1721, Scholar
  17. Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 38-1–38-4, Scholar
  18. He, W. Y., Z. Q. Liu, and H. B. Chen, 2011: Influence of surface temperature and emissivity on AMSU-A assimilation over land. Acta Meteorologica Sinica, 25(5), 545–557, Scholar
  19. He, Y., H. Yang, T. D. Yao, and J. He, 2012: Numerical simulation of a heavy precipitation in Qinghai-Xizang plateau based on WRF model. Plateau Meteorology, 31(5), 1183–1191. (in Chinese)Google Scholar
  20. Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1999: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181–189, 181.CrossRefGoogle Scholar
  21. Ji, Z. M., and S. C. Kang, 2013: Double nested dynamical downscaling experiments over the Tibetan Plateau and their projection of climate change under RCP scenarios. J. Atmos. Sci., 70(4), 1278–1290, Scholar
  22. Ji, Z. M., S. C. Kang, Q. G. Zhang, Z. Y. Cong, P. F. Chen, and M. Sillanpää, 2016: Investigation of mineral aerosols radiative effects over High Mountain Asia in 1990-2009 using a regional climate model. Atmos. Res., 178–179, 484–496, Scholar
  23. Li, X., M. J. Zeng, Y. Wang, W. L. Wang, H. Y. Wu, and H. X. Mei, 2016: Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radar wind data: Case study of a squall line. Adv. Atmos. Sci., 33(10), 1143–1157, Scholar
  24. Li, Z. H., Z. Q. Gao, W. Gao, R. H. Shi, and C. S. Liu, 2011: Spatio-temporal feature of land use/land cover dynamic changes in China from 1999 to 2009. Transactions of the CSAE, 27(2), 312–322, (in Chinese)Google Scholar
  25. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102(14), 16 663–16 682, Scholar
  26. Parrish, D. F., and J. C. Derber, 1992: The national meteorological center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763,<1747:TNMCSS>2.0.CO;2.CrossRefGoogle Scholar
  27. Rakesh, V., R. Singh, and P. C. Joshi, 2009: Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region. Meteor. Atmos. Phys., 105, 133–155, Scholar
  28. Routray, A., U. C. Mohanty, D. Niyogi, S. R. H. Rizvi, and K. K. Osuri, 2010: Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system. Meteor. Atmos. Phys., 106, 107–125, Scholar
  29. Santos-Alamillos, F. J., D. Pozo-Vázquez, J. A. Ruiz-Arias, and J. Tovar-Pescador, 2015: Influence of land-use misrepresentation on the accuracy of WRF wind estimates: Evaluation of GLCC and CORINE land-use maps in southern Spain. Atmo. Res., 157, 17–28, 2015.01.006.CrossRefGoogle Scholar
  30. Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–275,<0570:TCSIAA>2.0.CO;2.CrossRefGoogle Scholar
  31. Shen, Y., M. N. Feng, H. Z. Zhang, and F. Gao, 2010a: Interpolation methods of China daily precipitation data. Journal of Applied Meteorological Science, 21(3), 279–286, (in Chinese)Google Scholar
  32. Shen, Y., A. Y. Xiong, Y. Wang, and P. P. Xie, 2010b: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res., 115(D2), Scholar
  33. Singh, R., C. M. Kishtawal, P. K. Pal, and P. C. Joshi, 2011: Improved tropical cyclone forecasts over north Indian Ocean with direct assimilation of AMSU-A radiances. Meteor. Atmos. Phys., 115, 15–34, Scholar
  34. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. D. Powers, 2005: A description of the Advanced Research WRF version 2. Tech. Rep. NCAR/TN-468+STR, National Center for Atmospheric Research, Boulder, Colorado, 8 pp, Scholar
  35. Sokol, Z., 2009: Effects of an assimilation of radar and satellite data on a very-short range forecast of heavy convective rainfalls. Atmos. Res., 93, 188–206, Scholar
  36. Sokol, Z., 2011: Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution. Atmos. Res., 100(2–3), 201–212, Scholar
  37. Stensrud, D. J., N. Yussouf, D. C. Dowell, and M. C. Coniglio, 2009: Assimilating surface data into a mesoscale model ensemble: Cold pool analyses from spring 2007. Atmos. Res., 93(1–3), 207–220, 10.009.CrossRefGoogle Scholar
  38. Weng, F. Z., Y. Han, P. Van Delst, Q. H. Liu, T. Kleespies, B. H. Yan, and J. Le Marshall, 2005: JCSDA community radiative transfer model (CRTM). Proceedings of the 14th International TOVS Study Conference, Beijing, China, International TOVS Working Group, 122 pp.Google Scholar
  39. Xiong, C. H., L. F. Zhang, J. P. Guan, J. Peng, and B. Zhang, 2013: Analysis and numerical study of a hybrid BGM-3DVAR data assimilation scheme using satellite radiance data for heavy rain forecasts. Journal of Hydrodynamics, Ser. B, 25(3), 430–439, Scholar
  40. Xu, Y. M., Y. Zhang, and L. Bai, 2016: Study on the spatiotemporal variations of vegetation fraction in Zoige based on remote sensing data. Plateau Meteorology, 35(3), 643–650, (in Chinese)Google Scholar
  41. Xu, Y. M., Y. H. Liu, M. Wei, and J. J. Lv, 2007: Land cover classification of the Yangtze River delta using MODIS data. Acta Geographica Sinica, 62(5), 640–648, (in Chinese)Google Scholar
  42. Yang, J. H., and K. Q. Duan, 2016: Effects of initial drivers and land use on WRF modeling for near-surface fields and atmospheric boundary layer over the northeastern Tibetan Plateau. Advances in Meteorology, Article ID 7849249, Scholar
  43. Zhang, J., R. Z. Zhang, and D. M. Zhou, 2012: A study on water resource carrying capacity in the Shule River Basin based on ecological footprints. Acta Prataculturae Sinica, 21(4), 267–274. (in Chinese)Google Scholar
  44. Zheng, W. Z., L. W. He, Z. Wang, X. B. Zeng, J. Meng, M. Ek, K. Mitchell, and J. Derber, 2012: Improvement of daytime land surface skin temperature over arid regions in the NCEP GFS model and its impact on satellite data assimilation. J. Geophys. Res., 117(D6), D06117, Scholar
  45. Zheng, X., N. A. Wang, Z. L. Li, X. N. Zhang, and L. Wang, 2010: Analysis on land use/cover change during 1990-2005 in Shule River Basin. Journal of Desert Research, 30(4), 857–861, (in Chinese)Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Junhua Yang
    • 1
  • Zhenming Ji
    • 2
    Email author
  • Deliang Chen
    • 3
  • Shichang Kang
    • 1
    • 7
  • Congshen Fu
    • 4
  • Keqin Duan
    • 5
  • Miaogen Shen
    • 6
    • 7
  1. 1.State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences (CAS)LanzhouChina
  2. 2.School of Atmospheric SciencesSun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Earth SciencesUniversity of GothenburgGothenburgSweden
  4. 4.Yale School of Forestry and Environmental StudiesYale UniversityNew HavenUSA
  5. 5.College of Tourism and EnvironmentShaanxi Normal UniversityXi’anChina
  6. 6.Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina
  7. 7.CAS Center for Excellence in Tibetan Plateau Earth SciencesBeijingChina

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