• Bishop, C. M., 2006: Mixture models and EM. Pattern Recognition and Machine Learning, Springer-Verlag, 423–455.

  • Chen, T., and E. Martin, 2009: Bayesian linear regression and variable selection for spectroscopic calibration. Anal. Chim. Acta, 631, 1321, doi:10.1016/j.aca.2008.10.014.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., E. Källén, A. J. Simmons, and L. Haimberger, 2011: Comments on “Reanalyses suitable for characterizing long-term trends.” Bull. Amer. Meteor. Soc., 92, 6570, doi:10.1175/2010BAMS3070.1.

    • Search Google Scholar
    • Export Citation
  • Dessler, A. E., Z. Zhang, and P. Yang, 2008: Water-vapor climate feedback inferred from climate fluctuations, 2003–2008. Geophys. Res. Lett., 35, L20704, doi:10.1029/2008GL035333.

    • Search Google Scholar
    • Export Citation
  • Filiberti, M. A., L. Eymard, and B. Urban, 1994: Assimilation of satellite precipitable water in a meteorological forecast model. Mon. Wea. Rev., 122, 486506, doi:10.1175/1520-0493(1994)122<0486:AOSPWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gao, B.-C., and Y. J. Kaufman, 2003: Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels. J. Geophys. Res., 108, 4389, doi:10.1029/2002JD003023.

    • Search Google Scholar
    • Export Citation
  • Gao, B.-C., P. Yang, G. Guo, S. K. Park, W. J. Wiscombe, and B. Chen, 2003: Measurements of water vapor and high clouds over the Tibetan Plateau with the Terra MODIS instrument. IEEE Trans. Geosci. Remote Sens., 41, 895900, doi:10.1109/TGRS.2003.810704.

    • Search Google Scholar
    • Export Citation
  • Gao, S., F. Ping, and X. Li, 2006: Tropical heat/water vapor quasi-equilibrium and cycle as simulated in a 2D cloud-resolving model. Atmos. Res., 79, 1529, doi:10.1016/j.atmosres.2005.04.002.

    • Search Google Scholar
    • Export Citation
  • Gregory, P., 2005: Linear model fitting (Gaussian errors). Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, 243–284.

  • Kuo, Y.-H., Y.-R. Guo, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Wea. Rev., 121, 12151238, doi:10.1175/1520-0493(1993)121<1215:AOPWMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lu, N., J. Qin, K. Yang, Y. Gao, X. Xu, and T. Koike, 2011: On the use of GPS measurements for Moderate Resolution Imaging Spectrometer precipitable water vapor evaluation over southern Tibet. J. Geophys. Res., 116, D23117, doi:10.1029/2011JD016160.

    • Search Google Scholar
    • Export Citation
  • Lu, N., J. Qin, Y. Gao, K. Yang, K. E. Trenberth, M. Gehne, and Y. Zhu, 2015: Trends and variability in atmospheric precipitable water over the Tibetan Plateau for 2000–2010. Int. J. Climatol., doi:10.1002/joc.4064, in press.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., O. Peters, J. W. Lin, K. Hales, and C. E. Holloway, 2008: Rethinking convective quasi-equilibrium: Observational constraints for stochastic convective schemes in climate models. Philos Trans. Roy. Soc., 366A, 25792602, doi:10.1098/rsta.2008.0056.

    • Search Google Scholar
    • Export Citation
  • Qin, J., K. Yang, N. Lu, Y. Chen, L. Zhao, and M. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sens. Environ., 138, 19, doi:10.1016/j.rse.2013.07.003.

    • Search Google Scholar
    • Export Citation
  • Reitan, C. H., 1963: Surface dew point and water vapor aloft. J. Appl. Meteor., 2, 776779, doi:10.1175/1520-0450(1963)002<0776:SDPAWV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seko, H., T. Miyoshi, Y. Shoji, and K. Saito, 2011: Data assimilation experiments of precipitable water vapour using the LETKF system: Intense rainfall event over Japan 28 July 2008. Tellus, 63A, 402414, doi:10.1111/j.1600-0870.2010.00508.x.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets. J. Geophys. Res., 115, D01110, doi:10.1029/2009JD012442.

    • Search Google Scholar
    • Export Citation
  • Smith, W. L., 1966: Note on the relation between total precipitable water and surface dew point. J. Appl. Meteor., 5, 726727, doi:10.1175/1520-0450(1966)005<0726:NOTRBT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J. Sanford, and G. K. Plattner, 2010: Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 327, 12191223, doi:10.1126/science.1182488.

    • Search Google Scholar
    • Export Citation
  • Starr, D. O., and S. H. Melfi, 1990: The role of water vapor in climate: A strategic research plan for the proposed GEWEX water vapor project (GVap). NASA Rep. NAS 1.55:3120, 50 pp.

  • Thorne, P. W., and R. S. Vose, 2010: Reanalyses suitable for characterizing long-term trends. Bull. Amer. Meteor. Soc., 91, 353361, doi:10.1175/2009BAMS2858.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull. Amer. Meteor. Soc., 90, 311324, doi:10.1175/2008BAMS2634.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, doi:10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, doi:10.1256/qj.04.176.

  • Wang, J., L. Zhang, A. Dai, T. Van Hove, and J. Van Baelen, 2007: A near-global, 8-year, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements. J. Geophys. Res., 112, D11107, doi:10.1029/2006JD007529.

    • Search Google Scholar
    • Export Citation
  • Xu, X., X. Shi, Y. Wang, S. Peng, and X. Shi, 2008: Data analysis and numerical simulation of moisture source and transport associated with summer precipitation in the Yangtze River valley over China. Meteor. Atmos. Phys., 100, 217231, doi:10.1007/s00703-008-0305-8.

    • Search Google Scholar
    • Export Citation
  • Yang, K., T. Koike, H. Fujii, T. Tamura, X. Xu, L. Bian, and M. Zhou, 2004: The daytime evolution of the atmospheric boundary layer and convection over the Tibetan Plateau: Observations and simulations. J. Meteor. Soc. Japan, 82, 17771792, doi:10.2151/jmsj.82.1777.

    • Search Google Scholar
    • Export Citation
  • Yang, K., T. Koike, P. Stackhouse, C. Mikovitz, and S. J. Cox, 2006: An assessment of satellite surface radiation products for highlands with Tibet instrumental data. Geophys. Res. Lett., 33, L22403, doi:10.1029/2006GL027640.

    • Search Google Scholar
    • Export Citation
  • Yang, K., B. Ding, J. Qin, W. Tang, N. Lu, and C. Lin, 2012: Can aerosol loading explain the solar dimming over the Tibetan Plateau? Geophys. Res. Lett., 39, L20710, doi:10.1029/2012GL053733.

    • Search Google Scholar
    • Export Citation
  • Zhai, P., and R. E. Eskridge, 1997: Atmospheric water vapor over China. J. Climate, 10, 26432652, doi:10.1175/1520-0442(1997)010<2643:AWVOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., J. Huang, X. Guan, B. Chen, and L. Zhang, 2013: Long-term trends of precipitable water and precipitation over the Tibetan Plateau derived from satellite and surface measurements. J. Quant. Spectrosc. Radiat. Transfer, 122, 6471, doi:10.1016/j.jqsrt.2012.11.028.

    • Search Google Scholar
    • Export Citation
  • Zhou, T. J., and R. C. Yu, 2005: Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. J. Geophys. Res., 110, D08104, doi:10.1029/2004JD005413.

    • Search Google Scholar
    • Export Citation
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Detecting Long-Term Trends in Precipitable Water over the Tibetan Plateau by Synthesis of Station and MODIS Observations

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  • 1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, and Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, and Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth System, Beijing, China
  • | 4 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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Abstract

Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00303.s1.

Corresponding author address: Ning Lu, No.11A, Datun Road, Chaoyang, Beijing 100101, China. E-mail: ning.robin@gmail.com

Abstract

Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00303.s1.

Corresponding author address: Ning Lu, No.11A, Datun Road, Chaoyang, Beijing 100101, China. E-mail: ning.robin@gmail.com

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