• Baker, A., H. Sodemann, J. Baldini, S. Breitenbach, K. Johnson, J. Van Hunen, and P. Zhang, 2015: Seasonality of westerly moisture transport in the East Asian summer monsoon and its implications for interpreting precipitation δ18O. J. Geophys. Res. Atmos., 120, 58505862, https://doi.org/10.1002/2014JD022919.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bohlinger, P., A. Sorteberg, and H. Sodemann, 2017: Synoptic conditions and moisture sources actuating extreme precipitation in Nepal. J. Geophys. Res. Atmos., 122, 12 65312 671, https://doi.org/10.1002/2017JD027543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., M. E. Peters, and L. E. Back, 2004: Relationships between water vapor path and precipitation over the tropical oceans. J. Climate, 17, 15171528, https://doi.org/10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavazos, T., 1999: Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans. J. Climate, 13, 17181732, https://doi.org/10.1175/1520-0442(2000)013<1718:USOMTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., F. Jiang, Y. Wang, Y. Li, and R. Hu, 2013: Characteristics of the eco-geographical pattern in arid land of Central Asia (in Chinese). Ganhanqu Yanjiu, 30, 385390.

    • Search Google Scholar
    • Export Citation
  • Dai, X., W. Li, and Z. Ma, 2006: Changes of Xinjiang’s water vapor sources in the past ten years (in Chinese). Prog. Nat. Sci., 16, 1651–1656, https://doi.org/10.3321/j.issn:1002-008X.2006.12.018.

    • Crossref
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the date assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dorling, S., T. Davies, and C. Pierce, 1992: Cluster analysis: A technique for estimating the synoptic meteorological controls on air and precipitation chemistry-method and applications. Atmos. Environ., 26A, 25752581, https://doi.org/10.1016/0960-1686(92)90110-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drumond, A., R. Nieto, and L. Gimeno, 2011: Sources of moisture for China and their variations during drier and wetter conditions in 2000–2004: A Lagrangian approach. Climate Res., 50, 215225, https://doi.org/10.3354/cr01043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fremme, A., and H. Sodemann, 2019: The role of land and ocean evaporation on the variability of precipitation in the Yangtze River Valley. Hydrol. Earth Syst. Sci., 23, 25252540, https://doi.org/10.5194/hess-23-2525-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, X., L. Yang, Y. Zhang, and J. Li, 2019: Spatial distribution, temporal variation, and transport characteristics of atmospheric water vapor over Central Asia and the arid region of China. Global Planet. Change, 172, 159178, https://doi.org/10.1016/j.gloplacha.2018.06.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, X., H. Xue, C. Zhao, and D. Lu, 2016: The roles of convective and stratiform precipitation in the observed precipitation trends in Northwest China during 1961–2000. Atmos. Res., 169, 139146, https://doi.org/10.1016/j.atmosres.2015.10.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Q., D. Jiang, X. Lang, and B. Xu, 2018a: Moisture sources of the Chinese Loess Plateau during 1979–2009. Palaeogeogr. Palaeoclimatol. Palaeoecol., 509, 156163, https://doi.org/10.1016/j.palaeo.2016.12.030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Q., D. Jiang, and X. Lang, 2018b: Sources of moisture for different intensities of summer rainfall over the Chinese Loess Plateau during 1979–2009. Int. J. Climatol., 38, e1280e1287, https://doi.org/10.1002/joc.5416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Z., Q. Hu, C. Zhang, X. Chen, and Q. Li, 2016: Evaluation of reanalysis, spatially interpolated and satellite remotely sensed precipitation data set in central Asia. J. Geophys. Res. Atmos., 121, 56485663, https://doi.org/10.1002/2016JD024781.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, W., S. Feng, J. Chen, and F. Chen, 2015: Physical mechanisms of summer precipitation variations in the Tarim Basin in northwestern China. J. Climate, 28, 35793591, https://doi.org/10.1175/JCLI-D-14-00395.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, W., S. Chang, C. Xie, and Z. Zhang, 2017: Moisture sources of extreme summer precipitation events in North Xinjiang and their relationship with atmospheric circulation. Adv. Climate Change Res., 8, 1217, https://doi.org/10.1016/j.accre.2017.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, W., T. Qiu, Z. Yang, D. Lin, J. S. Wright, B. Wang, and X. He, 2018: On the formation mechanism for wintertime extreme precipitation events over the southeastern Tibetan Plateau. J. Geophys. Res. Atmos., 123, 12 69212 714, https://doi.org/10.1029/2018JD028921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mewes, D., and C. Jacobi, 2019: Heat transport pathways into the Arctic and their connections to surface air temperatures. Atmos. Chem. Phys., 19, 39273937, https://doi.org/10.5194/acp-19-3927-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kohonen, T., 1998: The self-organizing map. Neurocomputing, 21, 16, https://doi.org/10.1016/S0925-2312(98)00030-7.

  • Lennard, C., and G. Hegerl, 2015: Relating changes in synoptic circulation to the surface rainfall response using self-organising maps. Climate Dyn., 44, 861879, https://doi.org/10.1007/s00382-014-2169-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, W., K. Wang, S. Fu, and H. Jiang, 2008: The interrelationship between regional westerly index and the water vapor budget in Northwest China (in Chinese). Bingchuan Dongtu, 30, 2834.

    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., B. R. Lintner, and A. Sweeney, 2017: Characterizing large-scale meteorological patterns and associated temperature and precipitation extremes over the Northwestern United States using self-organizing maps. J. Climate, 30, 28292847, https://doi.org/10.1175/JCLI-D-16-0670.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moody, J. L., and J. N. Galloway, 1988: Quantifying the relationship between atmospheric transport and the chemical composition of precipitation on Bermuda. Tellus, 40B, 463479, https://doi.org/10.3402/tellusb.v40i5.16014.

    • Search Google Scholar
    • Export Citation
  • Newell, R. E., N. E. Newell, Y. Zhu, and C. Scott, 1992: Tropospheric rivers? A pilot study. Geophys. Res. Lett., 19, 24012404, https://doi.org/10.1029/92GL02916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nieto, R., and L. Gimeno, 2019: A database of optimal integration times for Lagrangian studies of atmospheric moisture sources and sinks. Sci. Data, 6, 59, https://doi.org/10.1038/s41597-019-0068-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, D., and T. Zhou, 2017: Why was the arid and semiarid Northwest China getting wetter in the recent decades? J. Geophys. Res. Atmos., 122, 90609075, https://doi.org/10.1002/2016JD026424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, G., and Coauthors, 2005: Climate changes of China’s mainland over the past half century (in Chinese). Acta Meteor. Sin., 63, 942956.

    • Search Google Scholar
    • Export Citation
  • Reusch, D. B., R. B. Alley, and B. C. Hewitson, 2005: Relative performance of self-organizing maps and principal component analysis in pattern extraction from synthetic climatological data. Polar Geogr., 29, 188212, https://doi.org/10.1080/789610199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151058, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheridan, S. C., and C. C. Lee, 2011: The self-organizing map in synoptic climatological research. Prog. Phys. Geogr., 35, 109119, https://doi.org/10.1177/0309133310397582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, Y., Y. Shen, E. Kang, D. Li, Y. Ding, G. Zhang, and R. Hu, 2007: Recent and future climate change in northwest China. Climatic Change, 80, 379393, https://doi.org/10.1007/s10584-006-9121-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skific, N., J. A. Francis, and J. J. Cassano, 2009: Attribution of projected changes in atmospheric moisture transport in the Arctic: A self-organizing map perspective. J. Climate, 22, 41354153, https://doi.org/10.1175/2009JCLI2645.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sodemann, H., and E. Zubler, 2010: Seasonal and inter-annual variability of the moisture sources for Alpine precipitation during 1995–2002. Int. J. Climatol., 30, 947961, https://doi.org/10.1002/joc.1932.

    • Search Google Scholar
    • Export Citation
  • Sodemann, H., C. Schwierz, and H. Wernli, 2008: Interannual variability of Greenland winter precipitation sources: Lagrangian moisture diagnostic and North Atlantic Oscillation influence. J. Geophys. Res. Atmos., 113, D03107, https://doi.org/10.1029/2007JD008503.

    • Search Google Scholar
    • Export Citation
  • Song, S., L. Li, X. Chen, and J. Bai, 2015: The dominant role of heavy precipitation in precipitation change despite opposite trends in west and east of northern China. Int. J. Climatol., 35, 43294336, https://doi.org/10.1002/joc.4290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stohl, A., and P. James, 2004: A Lagrangian analysis of the atmospheric branch of the global water cycle. Part I: Method description, validation, and demonstration for the August 2002 flooding in Central Europe. J. Hydrometeor., 5, 656678, https://doi.org/10.1175/1525-7541(2004)005<0656:ALAOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stohl, A., and P. James, 2005: A Lagrangian analysis of the atmospheric branch of the global water cycle. Part II: Moisture transports between Earth’s ocean basins and river catchments. J. Hydrometeor., 6, 961984, https://doi.org/10.1175/JHM470.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stohl, A., C. Forster, A. Frank, P. Seibert, and G. Wotawa, 2005: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys., 5, 24612474, https://doi.org/10.5194/acp-5-2461-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, B., and H. Wang, 2014: Moisture sources of semiarid grassland in China using the Lagrangian particle model FLEXPART. J. Climate, 27, 24572474, https://doi.org/10.1175/JCLI-D-13-00517.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viste, E., and A. Sorteberg, 2013: Moisture transport into the Ethiopian Highlands. Int. J. Climatol., 33, 249263, https://doi.org/10.1002/joc.3409.

  • Wu, J., and X. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin. J. Geophys., 56, 11021111, https://doi.org/10.6038/cjg20130406.

    • Search Google Scholar
    • Export Citation
  • Yang, L., and Y. Zhang, 2017: Summary of current research on Central Asian vortex. Adv. Climate Change Res., 8, 311, https://doi.org/10.1016/j.accre.2017.03.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., X. Li, and G. Zhang, 2011: Some advances and problems in the study of heavy rain in Xinjiang (in Chinese). Climatic Environ. Res., 16, 188198, https://doi.org/10.3878/j.issn.1006-9585.2011.02.08.

    • Search Google Scholar
    • Export Citation
  • Yang, L., Y. Zhang, and H. Qin, 2015: Some advances and problems of Middle-Asia vortex (in Chinese). Desert Oasis Meteor., 9, 18, https://doi.org/10.3969/j.issn.1002-0799.2015.05.001.

    • Search Google Scholar
    • Export Citation
  • Yao, J., Q. Yang, W. Mao, and X. Han, 2018: Analysis of a summer rainstorm water vapor paths in Tianshan Mountains (Xinjiang) based on HYSPLIT4 model (in Chinese). Plateau Meteor., 37, 6877, 10.7522/j.issn.1000-0534.2017.00031.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., N. Li, H. Qin, J. Li, J. Liu, W. Liu, and B. Meili, 2016: The observational analysis and water vapor characteristics of a rainstorm process in Xinjiang (in Chinese). Torrential Rain Disasters, 35, 537545.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., B. Yu, Z. Wang, and L. Jia, 2018: Dynamic mechanism and water vapor transportation characteristics of two extreme rainstorm events in Ili River valley in summer of 2016 (in Chinese). Torrential Rain Disasters, 37, 435444.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y., Z. Xie, and X. Liu, 2019: An analysis of moisture sources of torrential rainfall events over Xinjiang, China. J. Hydrometeor., 20, 21092122, https://doi.org/10.1175/JHM-D-19-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Moisture Sources of Heavy Precipitation in Xinjiang Characterized by Meteorological Patterns

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  • 1 a Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan, China
  • | 2 b Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 3 c Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
  • | 4 d CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
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Abstract

In this study, the Flexible Particle dispersion model (FLEXPART) is applied to determine the moisture source of heavy precipitation in Xinjiang in the wet season (April–September) from 1979 to 2018. This is investigated for different meteorological patterns of heavy precipitation categories based on the self-organizing maps (SOM) method. The SOM results suggest that there are four main meteorological patterns (N1, N2, N3, and N4) for heavy precipitation in Xinjiang. These match the strength and position of geopotential height anomalies at middle-to-high levels over central Asia and indicate the anomalous activities of the central Asia trough and vortex. Further analysis shows that the heavy precipitation is centered at the Tianshan Mountains and the Kunlun Mountains in the N1 and N3 patterns and around the Tianshan Mountains in the N2 and N4 patterns. There are four moisture source regions that contribute to each of the four meteorological patterns for heavy precipitation in Xinjiang, which are listed in descending order of their contribution rates: southern Xinjiang (29%–37%), north-central Asia (19%–27%), northern Xinjiang (14%–19%), and south-central Asia (13%–16%). The contribution of each source to the heavy precipitation in Xinjiang varies with the meteorological pattern. Additionally, the contribution rates of each source region match well with the precipitation-related particle aggregation before heavy precipitation days. These results help us better understand the moisture source of the heavy precipitation in Xinjiang.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dabang Jiang, jiangdb@mail.iap.ac.cn

Abstract

In this study, the Flexible Particle dispersion model (FLEXPART) is applied to determine the moisture source of heavy precipitation in Xinjiang in the wet season (April–September) from 1979 to 2018. This is investigated for different meteorological patterns of heavy precipitation categories based on the self-organizing maps (SOM) method. The SOM results suggest that there are four main meteorological patterns (N1, N2, N3, and N4) for heavy precipitation in Xinjiang. These match the strength and position of geopotential height anomalies at middle-to-high levels over central Asia and indicate the anomalous activities of the central Asia trough and vortex. Further analysis shows that the heavy precipitation is centered at the Tianshan Mountains and the Kunlun Mountains in the N1 and N3 patterns and around the Tianshan Mountains in the N2 and N4 patterns. There are four moisture source regions that contribute to each of the four meteorological patterns for heavy precipitation in Xinjiang, which are listed in descending order of their contribution rates: southern Xinjiang (29%–37%), north-central Asia (19%–27%), northern Xinjiang (14%–19%), and south-central Asia (13%–16%). The contribution of each source to the heavy precipitation in Xinjiang varies with the meteorological pattern. Additionally, the contribution rates of each source region match well with the precipitation-related particle aggregation before heavy precipitation days. These results help us better understand the moisture source of the heavy precipitation in Xinjiang.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dabang Jiang, jiangdb@mail.iap.ac.cn
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