[1] Abramopoulos, F., 1988: Generalized energy and potential enstrophy conserving finite difference schemes for the shallow water equations. Mon. Wea. Rev., 116, 650–662. doi: 10.1175/1520-0493(1988)116<0650:GEAPEC>2.0.CO;2
[2] Albergel, C., P. De Rosnay, G. Balsamo, et al., 2012: Soil moisture analyses at ECMWF: Evaluation using global ground-based in situ observations. J. Hydrometeor., 13, 1442–1460. doi: 10.1175/JHM-D-11-0107.1
[3] Balsamo, G., A. Beljaars, K. Scipal, et al., 2009: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system. J. Hydrometeor., 10, 623–643. doi: 10.1175/2008JHM1068.1
[4] Beljaars, A. C. M., P. Viterbo, M. J. Miller, et al., 1996: The anomalous rainfall over the United States during July 1993. Sensitivity to land surface parameterization and soil moisture anomalies. Mon. Wea. Rev., 124, 362–383. doi: 10.1175/1520-0493(1996)124<0362:TAROTU>2.0.CO;2
[5] Boussetta, S., G. Balsamo, A. Beljaars, et al., 2013: Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. Int. J. Remote Sens., 34, 3520–3542. doi: 10.1080/01431161.2012.716543
[6] Boussetta, S., G. Balsamo, E. Dutra, et al., 2015: Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction. Remote Sens. Environ., 163, 111–126. doi: 10.1016/j.rse.2015.03.009
[7] Case, J. L., F. J. LaFontaine, J. R. Bell, et al., 2014: A real-time MODIS vegetation product for land surface and numerical weather prediction models. IEEE Trans. Geosci. Remote Sens., 52, 1772–1786. doi: 10.1109/TGRS.2013.2255059
[8] Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585. doi: 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
[9] China Meteorological Administration (CMA), 2013: China Climate Bulletin 2012. China Meteorological Administration, Beijing, 56 pp. (in Chinese)
[10] Crawford, T. M., D. J. Stensrud, F. Mora, et al., 2001: Value of incorporating satellite-derived land cover data in MM5/PLACE for simulating surface temperatures. J. Hydrometeor., 2, 453–468. doi: 10.1175/1525-7541(2001)002<0453:VOISDL>2.0.CO;2
[11] Davis, C., T. Warner, E. Astling, et al., 1999: Development and application of an operational, relocatable, mesogamma-scale weather analysis and forecasting system. Tellus Dyn. Meteor. Oceanogr., 51, 710–727. doi: 10.3402/tellusa.v51i5.14490
[12] Di Giuseppe, F., D. Cesari, and G. Bonafé, 2011: Soil initialization strategy for use in limited-area weather prediction systems. Mon. Wea. Rev., 139, 1844–1860. doi: 10.1175/2011MWR3279.1
[13] Dickinson, R. E., J. A. Berry, G. B. Bonan, et al., 2002: Nitrogen controls on climate model evapotranspiration. J. Climate, 15, 278–295. doi: 10.1175/1520-0442(2002)015<0278:NCOCME>2.0.CO;2
[14] Dy, C. Y., and J. C. H. Fung, 2016: Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model. J. Geophys. Res. Atmos., 121, 8777–8800. doi: 10.1002/2015JD024558
[15] Ek, M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. Atmos., 108, 8851. doi: 10.1029/2002JD003296
[16] Ge, Q. S., X. Z. Zhang, and J. Y. Zheng, 2014: Simulated effects of vegetation increase/decrease on temperature changes from 1982 to 2000 across the Eastern China. Int. J. Climatol., 34, 187–196. doi: 10.1002/joc.3677
[17] Gutman, G., and A. Ignatov, 1998: The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19, 1533–1543. doi: 10.1080/014311698215333
[18] Hallikainen, M. T., F. T. Ulaby, M. C. Dobson, et al., 1985: Microwave dielectric behavior of wet soil—Part 1: Empirical models and experimental observations. IEEE Trans. Geosci. Remote Sens., GE-23, 25–34. doi: 10.1109/TGRS.1985.289497
[19] Han, X. Z., J. Yang, S. H. Tang, et al., 2020: Vegetation products derived from Fengyun-3D medium resolution spectral imager-Ⅱ. J. Meteor. Res., 34, 775–785. doi: 10.1007/s13351-020-0027-5
[20] Huang, J., H. M. Van Den Dool, and K. P. Georgarakos, 1996: Analysis of model-calculated soil moisture over the United States (1931–1993) and applications to long-range temperature forecasts. J. Climate, 9, 1350–1362. doi: 10.1175/1520-0442(1996)009<1350:AOMCSM>2.0.co;2
[21] James, K. A., D. J. Stensrud, and N. Yussouf, 2009: Value of real-time vegetation fraction to forecasts of severe convection in high-resolution models. Wea. Forecasting, 24, 187–210. doi: 10.1175/2008WAF2007097.1
[22] Jiang, L., F. N. Kogan, W. Guo, et al., 2010: Real-time weekly global green vegetation fraction derived from advanced very high resolution radiometer-based NOAA operational global vegetation index (GVI) system. J. Geophys. Res. Atmos., 115, D11114. doi: 10.1029/2009JD013204
[23] Kogan, F. N., 1997: Global drought watch from space. Bull. Amer. Meteor. Soc., 78, 621–636. doi: 10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
[24] Koster, R. D., S. P. P. Mahanama, T. J. Yamada, et al., 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., 37, L02402. doi: 10.1029/2009GL041677
[25] Kurkowski, N. P., D. J. Stensrud, and M. E. Baldwin, 2003: Assessment of implementing satellite-derived land cover data in the Eta Model. Wea. Forecasting, 18, 404–416. doi: 10.1175/1520-0434(2003)18<404:AOISDL>2.0.CO;2
[26] Lin, T. S., and F. Y. Cheng, 2016: Impact of soil moisture initialization and soil texture on simulated land–atmosphere interaction in Taiwan. J. Hydrometeor., 17, 1137–1355. doi: 10.1175/JHM-D-15-0024.1
[27] Liu, R. G., and Y. Liu, 2013: Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ., 133, 21–37. doi: 10.1016/j.rse.2013.01.019
[28] Liu, Y., J. B. Wang, J. W. Dong, et al., 2020: Variations of vegetation phenology extracted from remote sensing data over the Tibetan Plateau hinterland during 2000–2014. J. Meteor. Res., 34, 786–797. doi: 10.1007/s13351-020-9211-x
[29] Marshall, C. H., K. C. Crawford, K. E. Mitchell, et al., 2003: The impact of the land surface physics in the operational NCEP Eta Model on simulating the diurnal cycle: Evaluation and testing using Oklahoma Mesonet data. Wea. Forecasting, 18, 748–768. doi: 10.1175/1520-0434(2003)018<0748:TIOTLS>2.0.CO;2
[30] Massey, J. D., W. J. Steenburgh, S. W. Hoch, et al., 2014: Sensitivity of near-surface temperature forecasts to soil properties over a sparsely vegetated dryland region. J. Appl. Meteor. Climatol., 53, 1976–1995. doi: 10.1175/JAMC-D-13-0362.1
[31] Miller, J., M. Barlage, X. B. Zeng, et al., 2006: Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set. Geophys. Res. Lett., 33, L13404. doi: 10.1029/2006GL026636
[32] Mu, X. H., T. Zhao, G. Y. Ruan, et al., 2021: High spatial resolution and high temporal frequency (30-m/15-day) fractional vegetation cover estimation over China using multiple remote sensing datasets: Method development and validation. J. Meteor. Res., 35, 128–147. doi: 10.1007/s13351-021-0017-2
[33] Reeves, H. D., K. L. Elmore, G. S. Manikin, et al., 2011: Assessment of forecasts during persistent valley cold pools in the Bonneville Basin by the North American Mesoscale Model. Wea. Forecasting, 26, 447–467. doi: 10.1175/WAF-D-10-05014.1
[34] Rowell, D. P., and C. Blondin, 1990: The influence of soil wetness distribution on short-range rainfall forecasting in the West African Sahel. Q. J. Roy. Meteor. Soc., 116, 1471–1485. doi: 10.1002/qj.49711649611
[35] Yan, Y., J. P. Tang, G. Liu, et al., 2019: Effects of vegetation fraction variation on regional climate simulation over Eastern China. Global Planet. Change, 175, 173–189. doi: 10.1016/j.gloplacha.2019.02.004
[36] Yin, J. F., X. W. Zhan, Y. F. Zheng, et al., 2016: Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction. Agric. Forest Meteor., 218–219, 171–183. doi: 10.1016/j.agrformet.2015.12.001
[37] Zeng, X. B., R. E. Dickinson, A. Walker, et al., 2000: Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteor., 39, 826–839. doi: 10.1175/1520-0450(2000)039<0826:DAEOGK>2.0.CO;2
[38] Zeng, X. B., P. Rao, R. S. DeFries, et al., 2003: Interannual variability and decadal trend of global fractional vegetation cover from 1982 to 2000. J. Appl. Meteor., 42, 1525–1530. doi: 10.1175/1520-0450(2003)042<1525:IVADTO>2.0.CO;2
[39] Zhang, D. L., Y. H. Lin, P. Zhao, et al., 2013: The Beijing extreme rainfall of 21 July 2012: “Right results” but for wrong reasons. Geophys. Res. Lett., 40, 1426–1431. doi: 10.1002/grl.50304
[40] Zheng, D. H., R. Van Der Velde, Z. B. Su, et al., 2014: Assessment of roughness length schemes implemented within the Noah land surface model for high-altitude regions. J. Hydrometeor., 15, 921–937. doi: 10.1175/JHM-D-13-0102.1