Water Vapor Lidar Observation and Data Assimilation for a Moist Low-Level Jet Triggering a Mesoscale Convective System

Satoru Yoshida aMeteorological Research Institute, Tsukuba, Japan

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Tetsu Sakai aMeteorological Research Institute, Tsukuba, Japan

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Tomohiro Nagai aMeteorological Research Institute, Tsukuba, Japan

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Yasutaka Ikuta aMeteorological Research Institute, Tsukuba, Japan

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Teruyuki Kato aMeteorological Research Institute, Tsukuba, Japan

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Koichi Shiraishi bFukuoka University, Fukuoka, Japan

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Ryohei Kato cNational Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan

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Hiromu Seko aMeteorological Research Institute, Tsukuba, Japan

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Abstract

We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site.

Significance Statement

Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Satoru Yoshida, syoshida@mri-jma.go.jp

Abstract

We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site.

Significance Statement

Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Satoru Yoshida, syoshida@mri-jma.go.jp
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  • Araki, K., T. Kato, Y. Hirockawa, and W. Mashiko, 2021: Characteristics of atmospheric environments of quasi-stationary convective bands in Kyushu, Japan during the July 2020 heavy rainfall event. SOLA, 17, 815, https://doi.org/10.2151/sola.2021-002.

    • Search Google Scholar
    • Export Citation
  • Bielli, S., M. Grzeschik, E. Richard, C. Flamant, C. Champollion, C. Kiemle, M. Dorninger, and P. Brousseau, 2012: Assimilation of water‐vapour airborne lidar observations: Impact study on the COPS precipitation forecasts. Quart. J. Roy. Meteor. Soc., 138, 16521667, https://doi.org/10.1002/qj.1864.

    • Search Google Scholar
    • Export Citation
  • Carroll, B. J., B. B. Demoz, D. D. Turner, and R. Delgado, 2021: Lidar observations of a mesoscale moisture transport event impacting convection and comparison to rapid refresh model analysis. Mon. Wea. Rev., 149, 463477, https://doi.org/10.1175/MWR-D-20-0151.1.

    • Search Google Scholar
    • Export Citation
  • Chen, B., and Z. Liu, 2016: Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite. J. Geophys. Res. Atmos., 121, 11 44211 462, https://doi.org/10.1002/2016JD024917.

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 33853396, https://doi.org/10.1256/qj.05.108.

    • Search Google Scholar
    • Export Citation
  • Duffourg, F., and V. Ducrocq, 2011: Origin of the moisture feeding the heavy precipitating systems over southeastern France. Nat. Hazards Earth Syst. Sci., 11, 11631178, https://doi.org/10.5194/nhess-11-1163-2011.

    • Search Google Scholar
    • Export Citation
  • Duffourg, F., and Coauthors, 2016: Offshore deep convection initiation and maintenance during the HyMeX IOP 16a heavy precipitation event. Quart. J. Roy. Meteor. Soc., 142, 259274, https://doi.org/10.1002/qj.2725.

    • Search Google Scholar
    • Export Citation
  • Duffourg, F., K.-O. Lee, V. Ducrocq, C. Flamant, P. Chazette, and P. Di Girolamo, 2018: Role of moisture patterns in the backbuilding formation of HyMeX IOP13 heavy precipitation systems. Quart. J. Roy. Meteor. Soc., 144, 291303, https://doi.org/10.1002/qj.3201.

    • Search Google Scholar
    • Export Citation
  • Fourrié, N., M. Nuret, P. Brousseau, and O. Caumont, 2021: Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological Cycle in the Mediterranean Experiment. Nat. Hazards Earth Syst. Sci., 21, 463480, https://doi.org/10.5194/nhess-21-463-2021.

    • Search Google Scholar
    • Export Citation
  • Grzeschik, M., and Coauthors, 2008: Four-dimensional variational data analysis of water vapor Raman lidar data and their impact on mesoscale forecasts. J. Atmos. Oceanic Technol., 25, 14371453, https://doi.org/10.1175/2007JTECHA974.1.

    • Search Google Scholar
    • Export Citation
  • Harnisch, F., M. Weissmann, C. Cardinali, and M. Wirth, 2011: Experimental assimilation of DIAL water vapour observations in the ECMWF global model. Quart. J. Roy. Meteor. Soc., 137, 15321546, https://doi.org/10.1002/qj.851.

    • Search Google Scholar
    • Export Citation
  • Hirockawa, Y., T. Kato, H. Tsuguti, and N. Seino, 2020: Identification and classification of heavy rainfall areas and their characteristic features in Japan. J. Meteor. Soc. Japan, 98, 835857, https://doi.org/10.2151/jmsj.2020-043.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., T. Fujita, Y. Ota, and Y. Honda, 2021a: Variational data assimilation system for operational regional models at Japan Meteorological Agency. J. Meteor. Soc. Japan, 99, 15631592, https://doi.org/10.2151/jmsj.2021-076.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., M. Satoh, M. Sawada, H. Kusabiraki, and T. Kubota, 2021b: Improvement of the cloud microphysics scheme of the mesoscale model at the Japan Meteorological Agency using spaceborne radar and microwave imager of the Global Precipitation Measurement as reference. Mon. Wea. Rev., 149, 38033819, https://doi.org/10.1175/MWR-D-21-0066.1.

    • Search Google Scholar
    • Export Citation
  • Ishihara, M., Y. Kato, T. Abo, K. Kobayashi, and Y. Izumikawa, 2006: Characteristics and performance of the operational wind profiler network of the Japan Meteorological Agency. J. Meteor. Soc. Japan, 84, 10851096, https://doi.org/10.2151/jmsj.84.1085.

    • Search Google Scholar
    • Export Citation
  • Japan Meteorological Agency, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Japan Meteorological Agency, accessed 20 April 2023, https://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2019-nwp/index.htm.

  • Jeong, J.-H., D.-I. Lee, and C.-C. Wang, 2016: Impact of the cold pool on mesoscale convective system–produced extreme rainfall over southeastern South Korea: 7 July 2009. Mon. Wea. Rev., 144, 39854006, https://doi.org/10.1175/MWR-D-16-0131.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kamineni, R., T. N. Krishnamurti, R. A. Ferrare, S. Ismail, and E. V. Browell, 2003: Impact of high resolution water vapor cross-sectional data on hurricane forecasting. Geophys. Res. Lett., 30, 1234, https://doi.org/10.1029/2002GL016741.

    • Search Google Scholar
    • Export Citation
  • Kato, T., 2006: Structure of the band-shaped precipitation system inducing the heavy rainfall observed over northern Kyushu, Japan on 29 June 1999. J. Meteor. Soc. Japan, 84, 129153, https://doi.org/10.2151/jmsj.84.129.

    • Search Google Scholar
    • Export Citation
  • Kato, T., 2018: Representative height of the low-level water vapor field for examining the initiation of moist convection leading to heavy rainfall in East Asia. J. Meteor. Soc. Japan, 96, 6983, https://doi.org/10.2151/jmsj.2018-008.

    • Search Google Scholar
    • Export Citation
  • Kato, T., and H. Goda, 2001: Formation and maintenance processes of a stationary band-shaped heavy rainfall observed in Niigata on 4 August 1998. J. Meteor. Soc. Japan, 79, 899924, https://doi.org/10.2151/jmsj.79.899.

    • Search Google Scholar
    • Export Citation
  • Kato, T., and Coauthors, 2003: Reason for the failure of the simulation of heavy rainfall during X-BAIU-01—Importance of a vertical profile of water vapor for numerical simulations. J. Meteor. Soc. Japan, 81, 9931013, https://doi.org/10.2151/jmsj.81.993.

    • Search Google Scholar
    • Export Citation
  • Kawano, T., and R. Kawamura, 2020: Genesis and maintenance processes of a quasi-stationary convective band that produced record-breaking precipitation in northern Kyushu, Japan on 5 July 2017. J. Meteor. Soc. Japan, 98, 673690, https://doi.org/10.2151/jmsj.2020-033.

    • Search Google Scholar
    • Export Citation
  • Kondo, J., 1975: Air-sea bulk transfer coefficients in diabatic conditions. Bound.-Layer Meteor., 9, 91112, https://doi.org/10.1007/BF00232256.

    • Search Google Scholar
    • Export Citation
  • Lee, K.-O., C. Flamant, F. Duffourg, V. Ducrocq, and J.-P. Chaboureau, 2018: Impact of upstream moisture structure on a back-building convective precipitation system in south-eastern France during HyMeX IOP13. Atmos. Chem. Phys., 18, 16 84516 862, https://doi.org/10.5194/acp-18-16845-2018.

    • Search Google Scholar
    • Export Citation
  • Leuenberger, D., A. Haefele, N. Omanovic, M. Fengler, G. Martucci, B. Calpini, O. Fuhrer, and A. Rossa, 2020: Improving high-impact numerical weather prediction with lidar and drone observations. Bull. Amer. Meteor. Soc., 101, E1036E1051, https://doi.org/10.1175/BAMS-D-19-0119.1.

    • Search Google Scholar
    • Export Citation
  • Lin, G., B. Geerts, Z. Wang, C. Grasmick, X. Jing, and J. Yang, 2019: Interactions between a nocturnal MCS and the stable boundary layer as observed by an airborne compact Raman lidar during PECAN. Mon. Wea. Rev., 147, 31693189, https://doi.org/10.1175/MWR-D-18-0388.1.

    • Search Google Scholar
    • Export Citation
  • Liu, H.-Y., J.-F. Gu, Y. Wang, and J. Xu, 2023: What controlled the low-level moisture transport during the extreme precipitation in Henan Province of China in July 2021? Mon. Wea. Rev., 151, 13471365, https://doi.org/10.1175/MWR-D-22-0200.1.

    • Search Google Scholar
    • Export Citation
  • Luo, Y., Y. Gong, and D.-L. Zhang, 2014: Initiation and organizational modes of an extreme-rain-producing mesoscale convective system along a mei-yu front in East China. Mon. Wea. Rev., 142, 203221, https://doi.org/10.1175/MWR-D-13-00111.1.

    • Search Google Scholar
    • Export Citation
  • Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. Wiley-Blackwell, 424 pp.

  • Murakami, M., 1990: Numerical modeling of dynamical and microphysical evolution of an isolated convective cloud. J. Meteor. Soc. Japan, 68, 107128, https://doi.org/10.2151/jmsj1965.68.2_107.

    • Search Google Scholar
    • Export Citation
  • Nagata, K., 2011: Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan Meteorological Agency. RSMC Tokyo—Typhoon Center Tech. Review 13, 37–50, https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text13-2.pdf.

  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Search Google Scholar
    • Export Citation
  • Peters, J. M., E. R. Nielsen, M. D. Parker, S. M. Hitchcock, and R. S. Schumacher, 2017: The Impact of low-level moisture errors on model forecasts of an MCS observed during PECAN. Mon. Wea. Rev., 145, 35993624, https://doi.org/10.1175/MWR-D-16-0296.1.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and Coauthors, 2017: Dropsonde observations of total integrated water vapor transport within North Pacific atmospheric rivers. J. Hydrometeor., 18, 25772596, https://doi.org/10.1175/JHM-D-17-0036.1.

    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, https://doi.org/10.1175/2007MWR2123.1.

    • Search Google Scholar
    • Export Citation
  • Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 12661298, https://doi.org/10.1175/MWR3120.1.

    • Search Google Scholar
    • Export Citation
  • Sakai, T., T. Nagai, T. Matsumura, M. Nakazato, and M. Sasaoka, 2005: Vertical structure of a nonprecipitating cold frontal head as revealed by Raman lidar and wind profiler observations. J. Meteor. Soc. Japan, 83, 293304, https://doi.org/10.2151/jmsj.83.293.

    • Search Google Scholar
    • Export Citation
  • Sakai, T., T. Nagai, T. Izumi, S. Yoshida, and Y. Shoji, 2019: Automated compact mobile Raman lidar for water vapor measurement: Instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis. Atmos. Meas. Tech., 12, 313326, https://doi.org/10.5194/amt-12-313-2019.

    • Search Google Scholar
    • Export Citation
  • Sato, K., and Coauthors, 2016: Influence of the Kuroshio on mesoscale convective systems in the Baiu frontal zone over the East China Sea. Mon. Wea. Rev., 144, 10171033, https://doi.org/10.1175/MWR-D-15-0139.1.

    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., 2015: Sensitivity of precipitation accumulation in elevated convective systems to small changes in low-level moisture. J. Atmos. Sci., 72, 25072524, https://doi.org/10.1175/JAS-D-14-0389.1.

    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and R. H. Johnson, 2005: Organization and environmental properties of extreme-rain-producing mesoscale convective systems. Mon. Wea. Rev., 133, 961976, https://doi.org/10.1175/MWR2899.1.

    • Search Google Scholar
    • Export Citation
  • Thundathil, R., T. Schwitalla, A. Behrendt, S. K. Muppa, S. Adam, and V. Wulfmeyer, 2020: Assimilation of lidar water vapour mixing ratio and temperature profiles into a convection-permitting model. J. Meteor. Soc. Japan, 98, 959986, https://doi.org/10.2151/jmsj.2020-049.

    • Search Google Scholar
    • Export Citation
  • Tollerud, E. I., and Coauthors, 2008: Mesoscale moisture transport by the low-level jet during the IHOP field experiment. Mon. Wea. Rev., 136, 37813795, https://doi.org/10.1175/2008MWR2421.1.

    • Search Google Scholar
    • Export Citation
  • Wang, T., K. Wei, and J. Ma, 2021: Atmospheric rivers and mei-yu rainfall in China: A case study of summer 2020. Adv. Atmos. Sci., 38, 21372152, https://doi.org/10.1007/s00376-021-1096-9.

    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, V., H.-S. Bauer, M. Grzeschik, A. Behrendt, F. Vandenberghe, E. V. Browell, S. Ismail, and R. A. Ferrare, 2006: Four-dimensional variational assimilation of water vapor differential absorption lidar data: The first case study within IHOP_2002. Mon. Wea. Rev., 134, 209230, https://doi.org/10.1175/MWR3070.1.

    • Search Google Scholar
    • Export Citation
  • Xu, W., E. J. Zipser, Y.-L. Chen, C. Liu, Y.-C. Liou, W.-C. Lee, and B. J.-D. Jou, 2012: An orography-associated extreme rainfall event during TiMREX: Initiation, storm evolution, and maintenance. Mon. Wea. Rev., 140, 25552574, https://doi.org/10.1175/MWR-D-11-00208.1.

    • Search Google Scholar
    • Export Citation
  • Yoshida, S., S. Yokota, H. Seko, T. Sakai, and T. Nagai, 2020: Observation system simulation experiments of water vapor profiles observed by Raman lidar using LETKF system. SOLA, 16, 4350, https://doi.org/10.2151/sola.2020-008.

    • Search Google Scholar
    • Export Citation
  • Yoshida, S., T. Sakai, T. Nagai, Y. Ikuta, Y. Shoji, H. Seko, and K. Shiraishi, 2022: Lidar observations and data assimilation of low-level moist inflows causing severe local rainfall associated with a mesoscale convective system. Mon. Wea. Rev., 150, 17811798, https://doi.org/10.1175/MWR-D-21-0213.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., Z. Meng, Y. Huang, and D. Wang, 2019: The mechanism and predictability of an elevated convection initiation event in a weak-lifting environment in central-eastern China. Mon. Wea. Rev., 147, 18231841, https://doi.org/10.1175/MWR-D-18-0400.1.

    • Search Google Scholar
    • Export Citation
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