Abstract
The green vegetation fraction (GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction (NWP) models. However, the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well. In this study, a near real-time (NRT) GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October. Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state, which in turn helps to improve short-range forecasts in arid and semiarid regions of North China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.
Similar content being viewed by others
References
Abramopoulos, F., 1988: Generalized energy and potential enstrophy conserving finite difference schemes for the shallow water equations. Mon. Wea. Rev., 116, 650–662, doi: https://doi.org/10.1175/1520-0493(1988)116<0650:GEAPEC>2.0.CO;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: https://doi.org/10.1175/JHM-D-11-0107.1.
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: https://doi.org/10.1175/2008JHM1068.1.
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: https://doi.org/10.1175/1520-0493(1996)124<0362:TAROTU>2.0.CO;2.
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: https://doi.org/10.1080/01431161.2012.716543.
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: https://doi.org/10.1016/j.rse.2015.03.009.
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: https://doi.org/10.1109/TGRS.2013.2255059.
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: https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.
China Meteorological Administration (CMA), 2013: China Climate Bulletin 2012. China Meteorological Administration, Beijing, 56 pp. (in Chinese)
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: https://doi.org/10.1175/1525-7541(2001)002<0453:VOISDL>2.0.CO;2.
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: https://doi.org/10.3402/tellusa.v51i5.14490.
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: https://doi.org/10.1175/2011MWR3279.1.
Dickinson, R. E., J. A. Berry, G. B. Bonan, et al., 2002: Nitrogen controls on climate model evapotranspiration. J. Climate, 15, 278–295, doi: https://doi.org/10.1175/1520-0442(2002)015<0278:NCOCME>2.0.CO;2.
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: https://doi.org/10.1002/2015JD024558.
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: https://doi.org/10.1029/2002JD003296.
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: https://doi.org/10.1002/joc.3677.
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: https://doi.org/10.1080/014311698215333.
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: https://doi.org/10.1109/TGRS.1985.289497.
Han, X. Z., J. Yang, S. H. Tang, et al., 2020: Vegetation products derived from Fnngyun-3D medium resolution spectral imager- II. J. Meteor. Res., 34, 775–785, doi: https://doi.org/10.1007/s13351-020-0027-5.
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: https://doi.org/10.1175/1520-0442(1996)009<1350:AOMCSM>2.0.co;2.
James, K. A., D. J. Stensrud, and N. Yussouf, 2009: Value of realtime vegetation fraction to forecasts of severe convection in high-resolution models. Wea. Forecasting, 24, 187–210, doi: https://doi.org/10.1175/2008WAF2007097.1.
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: https://doi.org/10.1029/2009JD013204.
Kogan, F. N., 1997: Global drought watch from space. Bull. Amer. Meteor. Soc., 78, 621–636, doi: https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
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: https://doi.org/10.1029/2009GL041677.
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: https://doi.org/10.1175/1520-0434(2003)18<404:AOISDL>2.0.CO;2.
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., 7, 1137–1355, doi: https://doi.org/10.1175/JHM-D-15-0024.1.
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: https://doi.org/10.1016/j.rse.2013.01.019.
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: https://doi.org/10.1007/s13351-020-9211-x.
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: https://doi.org/10.1175/1520-0434(2003)018<0748:TIOTLS>2.0.CO;2.
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: https://doi.org/10.1175/JAMC-D-13-0362.1.
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: https://doi.org/10.1029/2006GL026636.
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: https://doi.org/10.1007/s13351-021-0017-2.
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, 21, 447–467, doi: https://doi.org/10.1175/WAF-D-10-05014.1.
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: https://doi.org/10.1002/qj.49711649611.
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: https://doi.org/10.1016/j.gloplacha.2019.02.004.
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: https://doi.org/10.1016/j.agrformet.2015.12.001.
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: https://doi.org/10.1175/1520-0450(2000)039<0826:DAEOGK>2.0.CO;2.
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: https://doi.org/10.1175/1520-0450(2003)042<1525:IVADTO>2.0.CO;2.
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: https://doi.org/10.1002/grl.50304.
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: https://doi.org/10.1175/JHM-D-13-0102.1.
Acknowledgments
GVF data used in this study are available from the China Meteorological Administration Special Public Welfare Research Fund (GYHY201 106014).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key Research and Development Program of China (2018YFC1506802) and National Natural Science Foundation of China (41705087).
Rights and permissions
About this article
Cite this article
Lu, B., Zhong, J., Wang, W. et al. Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China. J Meteorol Res 35, 505–520 (2021). https://doi.org/10.1007/s13351-021-0163-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-021-0163-6