Influence of Local Water Vapor Analysis Uncertainty on Ensemble Forecasts of Tropical Cyclogenesis Using Hurricane Irma (2017) as a Testbed

Christopher M. Hartman aDepartment of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Christopher M. Hartman in
Current site
Google Scholar
PubMed
Close
,
Falko Judt bNational Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Falko Judt in
Current site
Google Scholar
PubMed
Close
, and
Xingchao Chen aDepartment of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Xingchao Chen in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-9844-7836
Restricted access

Abstract

Tropical cyclone formation is known to require abundant water vapor in the lower to middle troposphere within the incipient disturbance. In this study, we assess the impacts of local water vapor analysis uncertainty on the predictability of the formation of Hurricane Irma (2017). To this end, we reduce the magnitude of the incipient disturbance’s water vapor perturbations obtained from an ensemble-based data assimilation system that constrained moisture by assimilating all-sky infrared and microwave radiances. Five-day ensemble forecasts are initialized two days before genesis using each set of modified analysis perturbations. Growth of convective differences and intensity uncertainty are evaluated for each ensemble forecast. We observe that when initializing an ensemble forecast with only moisture uncertainty within the incipient disturbance, the resulting intensity uncertainty at every lead time exceeds half that of an ensemble containing initial perturbations to all variables throughout the domain. Although ensembles with different initial moisture uncertainty amplitudes reveal a similar pathway to genesis, uncertainty in genesis timing varies substantially across ensembles since moister members exhibit earlier spinup of the low-level vortex. These differences in genesis timing are traced back to the first 6–12 h of integration, when differences in the position and intensity of mesoscale convective systems across ensemble members develop more quickly with greater initial moisture uncertainty. In addition, the rapid growth of intensity uncertainty may be greatly modulated by the diurnal cycle. Ultimately, this study underscores the importance of targeting the incipient disturbance with high spatiotemporal water vapor observations for ingestion into data assimilation systems.

Significance Statement

Hurricanes form from clusters of thunderstorms that organize into a coherent system. One of the key ingredients for the formation process is an abundance of moisture. In this study, we test the sensitivity of hurricane formation to the initial moisture content in the vicinity of the cluster of thunderstorms that would become Hurricane Irma (2017). To do so, we initialize sets of forecasts each having a different variability of initial moisture content within the embryonic disturbance. Our results show that the predictability of hurricane formation is highly dependent on the uncertainty of the moisture content within the initial disturbance. Consequently, more high-quality observations of the moisture within the precursor disturbances to hurricanes are expected to improve forecasts of their formation.

© 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: Xingchao Chen, xzc55@psu.edu

Abstract

Tropical cyclone formation is known to require abundant water vapor in the lower to middle troposphere within the incipient disturbance. In this study, we assess the impacts of local water vapor analysis uncertainty on the predictability of the formation of Hurricane Irma (2017). To this end, we reduce the magnitude of the incipient disturbance’s water vapor perturbations obtained from an ensemble-based data assimilation system that constrained moisture by assimilating all-sky infrared and microwave radiances. Five-day ensemble forecasts are initialized two days before genesis using each set of modified analysis perturbations. Growth of convective differences and intensity uncertainty are evaluated for each ensemble forecast. We observe that when initializing an ensemble forecast with only moisture uncertainty within the incipient disturbance, the resulting intensity uncertainty at every lead time exceeds half that of an ensemble containing initial perturbations to all variables throughout the domain. Although ensembles with different initial moisture uncertainty amplitudes reveal a similar pathway to genesis, uncertainty in genesis timing varies substantially across ensembles since moister members exhibit earlier spinup of the low-level vortex. These differences in genesis timing are traced back to the first 6–12 h of integration, when differences in the position and intensity of mesoscale convective systems across ensemble members develop more quickly with greater initial moisture uncertainty. In addition, the rapid growth of intensity uncertainty may be greatly modulated by the diurnal cycle. Ultimately, this study underscores the importance of targeting the incipient disturbance with high spatiotemporal water vapor observations for ingestion into data assimilation systems.

Significance Statement

Hurricanes form from clusters of thunderstorms that organize into a coherent system. One of the key ingredients for the formation process is an abundance of moisture. In this study, we test the sensitivity of hurricane formation to the initial moisture content in the vicinity of the cluster of thunderstorms that would become Hurricane Irma (2017). To do so, we initialize sets of forecasts each having a different variability of initial moisture content within the embryonic disturbance. Our results show that the predictability of hurricane formation is highly dependent on the uncertainty of the moisture content within the initial disturbance. Consequently, more high-quality observations of the moisture within the precursor disturbances to hurricanes are expected to improve forecasts of their formation.

© 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: Xingchao Chen, xzc55@psu.edu
Save
  • Asaadi, A., G. Brunet, and M. K. Yau, 2016: On the dynamics of the formation of the Kelvin cat’s-eye in tropical cyclogenesis. Part II: Numerical simulation. J. Atmos. Sci., 73, 23392359, https://doi.org/10.1175/JAS-D-15-0237.1.

    • Search Google Scholar
    • Export Citation
  • Asaadi, A., G. Brunet, and M. K. Yau, 2017: The importance of critical layer in differentiating developing from nondeveloping easterly waves. J. Atmos. Sci., 74, 409417, https://doi.org/10.1175/JAS-D-16-0085.1.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bell, M. M., and M. T. Montgomery, 2019: Mesoscale processes during the genesis of Hurricane Karl (2010). J. Atmos. Sci., 76, 22352255, https://doi.org/10.1175/JAS-D-18-0161.1.

    • Search Google Scholar
    • Export Citation
  • Berry, G. J., and C. D. Thorncroft, 2012: African easterly wave dynamics in a mesoscale numerical model: The upscale role of convection. J. Atmos. Sci., 69, 12671283, https://doi.org/10.1175/JAS-D-11-099.1.

    • Search Google Scholar
    • Export Citation
  • Brammer, A., and C. D. Thorncroft, 2015: Variability and evolution of African easterly wave structures and their relationship with tropical cyclogenesis over the eastern Atlantic. Mon. Wea. Rev., 143, 49754995, https://doi.org/10.1175/MWR-D-15-0106.1.

    • Search Google Scholar
    • Export Citation
  • Brammer, A., C. D. Thorncroft, and J. P. Dunion, 2018: Observations and predictability of a nondeveloping tropical disturbance over the eastern Atlantic. Mon. Wea. Rev., 146, 30793096, https://doi.org/10.1175/MWR-D-18-0065.1.

    • Search Google Scholar
    • Export Citation
  • Braun, S. A., and Coauthors, 2013: NASA’s Genesis and Rapid Intensification Processes (GRIP) field experiment. Bull. Amer. Meteor. Soc., 94, 345363, https://doi.org/10.1175/BAMS-D-11-00232.1.

    • Search Google Scholar
    • Export Citation
  • Cangialosi, J. P., A. S. Latto, and R. Berg, 2018: Tropical cyclone report: Hurricane Irma (AL112017), 30 August–12 September 2017. NHC Tech. Rep., 111 pp., https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf.

  • Chan, M.-Y., and X. Chen, 2022: Improving the analyses and forecasts of a tropical squall line using upper tropospheric infrared satellite observations. Adv. Atmos. Sci., 39, 733746, https://doi.org/10.1007/s00376-021-0449-8.

    • Search Google Scholar
    • Export Citation
  • Chan, M.-Y., F. Zhang, X. Chen, and L. R. Leung, 2020: Potential impacts of assimilating all-sky satellite infrared radiances on convection-permitting analysis and prediction of tropical convection. Mon. Wea. Rev., 148, 32033224, https://doi.org/10.1175/MWR-D-19-0343.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., and F. Zhang, 2019a: Development of a convection‐permitting air‐sea‐coupled ensemble data assimilation system for tropical cyclone prediction. J. Adv. Model. Earth Syst., 11, 34743496, https://doi.org/10.1029/2019MS001795.

    • Search Google Scholar
    • Export Citation
  • Chen, X., and F. Zhang, 2019b: Relative roles of preconditioning moistening and global circumnavigating mode on the MJO convective initiation during DYNAMO. Geophys. Res. Lett., 46, 10791087, https://doi.org/10.1029/2018GL080987.

    • Search Google Scholar
    • Export Citation
  • Chen, X., O. M. Pauluis, L. R. Leung, and F. Zhang, 2018a: Multiscale atmospheric overturning of the Indian summer monsoon as seen through isentropic analysis. J. Atmos. Sci., 75, 30113030, https://doi.org/10.1175/JAS-D-18-0068.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., O. M. Pauluis, and F. Zhang, 2018b: Atmospheric overturning across multiple scales of an MJO event during the CINDY/DYNAMO campaign. J. Atmos. Sci., 75, 381399, https://doi.org/10.1175/JAS-D-17-0060.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, Z. Feng, F. Song, and Q. Yang, 2021: Mesoscale convective systems dominate the energetics of the South Asian summer monsoon onset. Geophys. Res. Lett., 48, e2021GL094873, https://doi.org/10.1029/2021GL094873.

    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, Z. Feng, and F. Song, 2022a: Crucial role of mesoscale convective systems in the vertical mass, water, and energy transports of the South Asian summer monsoon. J. Climate, 35, 91108, https://doi.org/10.1175/JCLI-D-21-0124.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, Z. Feng, and Q. Yang, 2022b: Precipitation-moisture coupling over tropical oceans: Sequential roles of shallow, deep, and mesoscale convective systems. Geophys. Res. Lett., 49, e2022GL097836, https://doi.org/10.1029/2022GL097836.

    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, Z. Feng, and Q. Yang, 2023: Diurnal MCSs precede the genesis of Tropical Cyclone Mora (2017): The role of convectively forced gravity waves. J. Atmos. Sci., 80, 14631479, https://doi.org/10.1175/JAS-D-22-0203.1.

    • Search Google Scholar
    • Export Citation
  • Craig, G. C., 1996: Numerical experiments on radiation and tropical cyclones. Quart. J. Roy. Meteor. Soc., 122, 415422, https://doi.org/10.1002/qj.49712253006.

    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., C. A. Reynolds, C. Amerault, and J. Moskaitis, 2012: Adjoint sensitivity and predictability of tropical cyclogenesis. J. Atmos. Sci., 69, 35353557, https://doi.org/10.1175/JAS-D-12-0110.1.

    • Search Google Scholar
    • Export Citation
  • Dunion, J. P., C. D. Thorncroft, and D. S. Nolan, 2019: Tropical cyclone diurnal cycle signals in a hurricane nature run. Mon. Wea. Rev., 147, 363388, https://doi.org/10.1175/MWR-D-18-0130.1.

    • Search Google Scholar
    • Export Citation
  • Dunkerton, T. J., M. T. Montgomery, and Z. Wang, 2009: Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. Atmos. Chem. Phys., 9, 55875646, https://doi.org/10.5194/acp-9-5587-2009.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2018: 100 years of progress in tropical cyclone research. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0016.1.

  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, https://doi.org/10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation
  • Ge, X., Y. Ma, S. Zhou, and T. Li, 2014: Impacts of the diurnal cycle of radiation on tropical cyclone intensification and structure. Adv. Atmos. Sci., 31, 13771385, https://doi.org/10.1007/s00376-014-4060-0.

    • Search Google Scholar
    • Export Citation
  • Gosset, W. S., 1908: The probable error of a mean. Biometrika, 6 (1), 125, https://doi.org/10.2307/2331554.

  • Green, B. W., and F. Zhang, 2013: Impacts of air–sea flux parameterizations on the intensity and structure of tropical cyclones. Mon. Wea. Rev., 141, 23082324, https://doi.org/10.1175/MWR-D-12-00274.1.

    • Search Google Scholar
    • Export Citation
  • Hall, N. M. J., G. N. Kiladis, and C. D. Thorncroft, 2006: Three-dimensional structure and dynamics of African easterly waves. Part II: Dynamical modes. J. Atmos. Sci., 63, 22312245, https://doi.org/10.1175/JAS3742.1.

    • Search Google Scholar
    • Export Citation
  • Halperin, D. J., H. E. Fuelberg, R. E. Hart, J. H. Cossuth, P. Sura, and R. J. Pasch, 2013: An evaluation of tropical cyclone genesis forecasts from global numerical models. Wea. Forecasting, 28, 14231445, https://doi.org/10.1175/WAF-D-13-00008.1.

    • Search Google Scholar
    • Export Citation
  • Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: JCSDA Community Radiative Transfer Model (CRTM): version 1. NOAA Tech. Rep. NESDIS-122, 31 pp., https://repository.library.noaa.gov/view/noaa/1157.

  • Han, Y., F. Weng, Q. Liu, and P. van Delst, 2007: A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J. Geophys. Res., 112, D11121, https://doi.org/10.1029/2006JD008208.

    • Search Google Scholar
    • Export Citation
  • Hartman, C. M., X. Chen, and M.-Y. Chan, 2023: Improving tropical cyclogenesis forecasts of Hurricane Irma (2017) through the assimilation of all-sky infrared brightness temperatures. Mon. Wea. Rev., 151, 837853, https://doi.org/10.1175/MWR-D-22-0196.1.

    • Search Google Scholar
    • Export Citation
  • He, J., and Coauthors, 2019: Development and evaluation of an ensemble-based data assimilation system for regional reanalysis over the Tibetan Plateau and surrounding regions. J. Adv. Model. Earth Syst., 11, 25032522, https://doi.org/10.1029/2019MS001665.

    • Search Google Scholar
    • Export Citation
  • Hendricks, E. A., M. T. Montgomery, and C. A. Davis, 2004: The role of “vortical” hot towers in the formation of tropical cyclone Diana (1984). J. Atmos. Sci., 61, 12091232, https://doi.org/10.1175/1520-0469(2004)061<1209:TROVHT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hobgood, J. S., 1986: A possible mechanism for the diurnal oscillations of tropical cyclones. J. Atmos. Sci., 43, 29012922, https://doi.org/10.1175/1520-0469(1986)043<2901:APMFTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation
  • Hopsch, S. B., C. D. Thorncroft, and K. R. Tyle, 2010: Analysis of African easterly wave structures and their role in influencing tropical cyclogenesis. Mon. Wea. Rev., 138, 13991419, https://doi.org/10.1175/2009MWR2760.1.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 32693289, https://doi.org/10.1256/qj.05.135.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Komaromi, W. A., 2013: An investigation of composite dropsonde profiles for developing and nondeveloping tropical waves during the 2010 PREDICT field campaign. J. Atmos. Sci., 70, 542558, https://doi.org/10.1175/JAS-D-12-052.1.

    • Search Google Scholar
    • Export Citation
  • Komaromi, W. A., and S. J. Majumdar, 2015: Ensemble-based error and predictability metrics associated with tropical cyclogenesis. Part II: Wave-relative framework. Mon. Wea. Rev., 143, 16651686, https://doi.org/10.1175/MWR-D-14-00286.1.

    • Search Google Scholar
    • Export Citation
  • Lawton, Q. A., and S. J. Majumdar, 2023: Convectively coupled Kelvin waves and tropical cyclogenesis: Connections through convection and moisture. Mon. Wea. Rev., 151, 16471666, https://doi.org/10.1175/MWR-D-23-0005.1.

    • Search Google Scholar
    • Export Citation
  • Lawton, Q. A., S. J. Majumdar, K. Dotterer, C. Thorncroft, and C. J. Schreck III, 2022: The influence of convectively coupled Kelvin waves on African easterly waves in a wave-following framework. Mon. Wea. Rev., 150, 20552072, https://doi.org/10.1175/MWR-D-21-0321.1.

    • Search Google Scholar
    • Export Citation
  • Leppert, K. D., II, D. J. Cecil, and W. A. Petersen, 2013a: Relation between tropical easterly waves, convection, and tropical cyclogenesis: A Lagrangian perspective. Mon. Wea. Rev., 141, 26492668, https://doi.org/10.1175/MWR-D-12-00217.1.

    • Search Google Scholar
    • Export Citation
  • Leppert, K. D., II, W. A. Petersen, and D. J. Cecil, 2013b: Electrically active convection in tropical easterly waves and implications for tropical cyclogenesis in the Atlantic and East Pacific. Mon. Wea. Rev., 141, 542556, https://doi.org/10.1175/MWR-D-12-00174.1.

    • Search Google Scholar
    • Export Citation
  • Li, Z., and Z. Pu, 2014: Numerical simulations of the genesis of Typhoon Nuri (2008): Sensitivity to initial conditions and implications for the roles of intense convection and moisture conditions. Wea. Forecasting, 29, 14021424, https://doi.org/10.1175/WAF-D-14-00003.1.

    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., and R. D. Torn, 2014: Probabilistic verification of global and mesoscale ensemble forecasts of tropical cyclogenesis. Wea. Forecasting, 29, 11811198, https://doi.org/10.1175/WAF-D-14-00028.1.

    • Search Google Scholar
    • Export Citation
  • Melhauser, C., and F. Zhang, 2012: Practical and intrinsic predictability of severe and convective weather at the mesoscales. J. Atmos. Sci., 69, 33503371, https://doi.org/10.1175/JAS-D-11-0315.1.

    • Search Google Scholar
    • Export Citation
  • Melhauser, C., and F. Zhang, 2014: Diurnal radiation cycle impact on the pregenesis environment of Hurricane Karl (2010). J. Atmos. Sci., 71, 12411259, https://doi.org/10.1175/JAS-D-13-0116.1.

    • Search Google Scholar
    • Export Citation
  • Minamide, M., and F. Zhang, 2017: Adaptive observation error inflation for assimilating all-sky satellite radiance. Mon. Wea. Rev., 145, 10631081, https://doi.org/10.1175/MWR-D-16-0257.1.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., M. E. Nicholls, T. A. Cram, and A. B. Saunders, 2006: A vortical hot tower route to tropical cyclogenesis. J. Atmos. Sci., 63, 355386, https://doi.org/10.1175/JAS3604.1.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., L. L. Lussier III, R. W. Moore, and Z. Wang, 2010a: The genesis of Typhoon Nuri as observed during the Tropical Cyclone Structure 2008 (TCS-08) field experiment—Part 1: The role of the easterly wave critical layer. Atmos. Chem. Phys., 10, 98799900, https://doi.org/10.5194/acp-10-9879-2010.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., Z. Wang, and T. J. Dunkerton, 2010b: Coarse, intermediate and high resolution numerical simulations of the transition of a tropical wave critical layer to a tropical storm. Atmos. Chem. Phys., 10, 10 80310 827, https://doi.org/10.5194/acp-10-10803-2010.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., and Coauthors, 2012: The Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) experiment: Scientific basis, new analysis tools, and some first results. Bull. Amer. Meteor. Soc., 93, 153172, https://doi.org/10.1175/BAMS-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Núñez Ocasio, K. M., 2021: Tropical cyclogenesis and its relation to interactions between African easterly waves and mesoscale convective systems. Ph.D. dissertation, The Pennsylvania State University, 123 pp., https://etda.libraries.psu.edu/catalog/21775kmn18.

  • Núñez Ocasio, K. M., J. L. Evans, and G. S. Young, 2020: A wave-relative framework analysis of AEW–MCS interactions leading to tropical cyclogenesis. Mon. Wea. Rev., 148, 46574671, https://doi.org/10.1175/MWR-D-20-0152.1.

    • Search Google Scholar
    • Export Citation
  • Núñez Ocasio, K. M., A. Brammer, J. L. Evans, G. S. Young, and Z. L. Moon, 2021: Favorable monsoon environment over eastern Africa for subsequent tropical cyclogenesis of African easterly waves. J. Atmos. Sci., 78, 29112925, https://doi.org/10.1175/JAS-D-20-0339.1.

    • Search Google Scholar
    • Export Citation
  • Ou, T., D. Chen, X. Chen, C. Lin, K. Yang, H.-W. Lai, and F. Zhang, 2020: Simulation of summer precipitation diurnal cycles over the Tibetan Plateau at the gray-zone grid spacing for cumulus parameterization. Climate Dyn., 54, 35253539, https://doi.org/10.1007/s00382-020-05181-x.

    • Search Google Scholar
    • Export Citation
  • Peng, M. S., B. Fu, T. Li, and D. E. Stevens, 2012: Developing versus nondeveloping disturbances for tropical cyclone formation. Part I: North Atlantic. Mon. Wea. Rev., 140, 10471066, https://doi.org/10.1175/2011MWR3617.1.

    • Search Google Scholar
    • Export Citation
  • Poterjoy, J., and F. Zhang, 2014a: Predictability and genesis of Hurricane Karl (2010) examined through the EnKF assimilation of field observations collected during PREDICT. J. Atmos. Sci., 71, 12601275, https://doi.org/10.1175/JAS-D-13-0291.1.

    • Search Google Scholar
    • Export Citation
  • Poterjoy, J., and F. Zhang, 2014b: Intercomparison and coupling of ensemble and four-dimensional variational data assimilation methods for the analysis and forecasting of Hurricane Karl (2010). Mon. Wea. Rev., 142, 33473364, https://doi.org/10.1175/MWR-D-13-00394.1.

    • Search Google Scholar
    • Export Citation
  • Rajasree, V. P. M., A. P. Kesarkar, J. N. Bhate, V. Singh, U. Umakanth, and T. H. Varma, 2016a: A comparative study on the genesis of north Indian Ocean tropical cyclone Madi (2013) and Atlantic Ocean tropical cyclone Florence (2006). J. Geophys. Res. Atmos., 121, 13 82613 858, https://doi.org/10.1002/2016JD025412.

    • Search Google Scholar
    • Export Citation
  • Rajasree, V. P. M., A. P. Kesarkar, J. N. Bhate, U. Umakanth, V. Singh, and T. Harish Varma, 2016b: Appraisal of recent theories to understand cyclogenesis pathways of tropical cyclone Madi (2013). J. Geophys. Res. Atmos., 121, 89498982, https://doi.org/10.1002/2016JD025188.

    • Search Google Scholar
    • Export Citation
  • Ruppert, J. H., Jr., A. A. Wing, X. Tang, and E. L. Duran, 2020: The critical role of cloud–infrared radiation feedback in tropical cyclone development. Proc. Natl. Acad. Sci. USA, 117, 27 88427 892, https://doi.org/10.1073/pnas.2013584117.

    • Search Google Scholar
    • Export Citation
  • Russell, J. O. H., and A. Aiyyer, 2020: The potential vorticity structure and dynamics of African easterly waves. J. Atmos. Sci., 77, 871890, https://doi.org/10.1175/JAS-D-19-0019.1.

    • Search Google Scholar
    • Export Citation
  • Russell, J. O. H., A. Aiyyer, and J. Dylan White, 2020: African easterly wave dynamics in convection-permitting simulations: Rotational stratiform instability as a conceptual model. J. Adv. Model. Earth Syst., 12, e2019MS001706, https://doi.org/10.1029/2019MS001706.

    • Search Google Scholar
    • Export Citation
  • Schmid, J., 2000: The SEVIRI instrument. Proc. 2000 EUMETSAT Meteorological Satellite Data User’s Conf., Bologna, Italy, EUMETSAT, 13–32, https://www-cdn.eumetsat.int/files/2020-04/pdf_ten_msg_seviri_instrument.pdf.

  • Schreck, C. J., III, 2015: Kelvin waves and tropical cyclogenesis: A global survey. Mon. Wea. Rev., 143, 39964011, https://doi.org/10.1175/MWR-D-15-0111.1.

    • Search Google Scholar
    • Export Citation
  • Schreck, C. J., III, 2016: Convectively coupled Kelvin waves and tropical cyclogenesis in a semi-Lagrangian framework. Mon. Wea. Rev., 144, 41314139, https://doi.org/10.1175/MWR-D-16-0237.1.

    • Search Google Scholar
    • Export Citation
  • Sieron, S. B., E. E. Clothiaux, F. Zhang, Y. Lu, and J. A. Otkin, 2017: Comparison of using distribution-specific versus effective radius methods for hydrometeor single-scattering properties for all-sky microwave satellite radiance simulations with different microphysics parameterization schemes. J. Geophys. Res. Atmos., 122, 70277046, https://doi.org/10.1002/2017JD026494.

    • Search Google Scholar
    • Export Citation
  • Sieron, S. B., F. Zhang, E. E. Clothiaux, L. N. Zhang, and Y. Lu, 2018: Representing precipitation ice species with both spherical and nonspherical particles for radiative transfer modeling of microphysics-consistent cloud microwave scattering properties. J. Adv. Model. Earth Syst., 10, 10111028, https://doi.org/10.1002/2017MS001226.

    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 34403459, https://doi.org/10.1175/2008JAS2597.1.

    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., and F. Zhang, 2010: Factors affecting the predictability of Hurricane Humberto (2007). J. Atmos. Sci., 67, 17591778, https://doi.org/10.1175/2010JAS3172.1.

    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., S. A. Braun, and C.-L. Shie, 2011: Environmental influences on the strength of tropical Storm Debby (2006). J. Atmos. Sci., 68, 25572581, https://doi.org/10.1175/2011JAS3648.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 125 pp., https://doi.org/10.5065/D68S4MVH.

  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Search Google Scholar
    • Export Citation
  • Smith, R. K., and M. T. Montgomery, 2012: Observations of the convective environment in developing and non-developing tropical disturbances. Quart. J. Roy. Meteor. Soc., 138, 17211739, https://doi.org/10.1002/qj.1910.

    • Search Google Scholar
    • Export Citation
  • Tang, B. H., and Coauthors, 2020: Recent advances in research on tropical cyclogenesis. Trop. Cyclone Res. Rev., 9, 87105, https://doi.org/10.1016/j.tcrr.2020.04.004.

    • Search Google Scholar
    • Export Citation
  • Tang, X., and F. Zhang, 2016: Impacts of the diurnal radiation cycle on the formation, intensity, and structure of Hurricane Edouard (2014). J. Atmos. Sci., 73, 28712892, https://doi.org/10.1175/JAS-D-15-0283.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2010: Ensemble-based sensitivity analysis applied to African easterly waves. Wea. Forecasting, 25, 6178, https://doi.org/10.1175/2009WAF2222255.1.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and D. Cook, 2013: The role of vortex and environment errors in genesis forecasts of Hurricanes Danielle and Karl (2010). Mon. Wea. Rev., 141, 232251, https://doi.org/10.1175/MWR-D-12-00086.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., C. D. Thorncroft, and M. A. Janiga, 2012a: Atlantic tropical cyclogenesis: A three-way interaction between an African easterly wave, diurnally varying convection, and a convectively coupled atmospheric Kelvin wave. Mon. Wea. Rev., 140, 11081124, https://doi.org/10.1175/MWR-D-11-00122.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., C. D. Thorncroft, and C. J. Schreck III, 2012b: Impacts of convectively coupled Kelvin waves on environmental conditions for Atlantic tropical cyclogenesis. Mon. Wea. Rev., 140, 21982214, https://doi.org/10.1175/MWR-D-11-00305.1.

    • Search Google Scholar
    • Export Citation
  • Wang, S., A. H. Sobel, F. Zhang, Y. Q. Sun, Y. Yue, and L. Zhou, 2015: Regional simulation of the October and November MJO events observed during the CINDY/DYNAMO field campaign at gray zone resolution. J. Climate, 28, 20972119, https://doi.org/10.1175/JCLI-D-14-00294.1.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., M. T. Montgomery, and T. J. Dunkerton, 2010: Genesis of pre–Hurricane Felix (2007). Part I: The role of the easterly wave critical layer. J. Atmos. Sci., 67, 17111729, https://doi.org/10.1175/2009JAS3420.1.

    • Search Google Scholar
    • Export Citation
  • Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 37993807, https://doi.org/10.1175/2007JAS2112.1.

    • Search Google Scholar
    • Export Citation
  • Weng, Y., and F. Zhang, 2012: Assimilating airborne Doppler radar observations with an ensemble Kalman filter for convection-permitting hurricane initialization and prediction: Katrina (2005). Mon. Wea. Rev., 140, 841859, https://doi.org/10.1175/2011MWR3602.1.

    • Search Google Scholar
    • Export Citation
  • Weng, Y., and F. Zhang, 2016: Advances in convection-permitting tropical cyclone analysis and prediction through EnKF assimilation of reconnaissance aircraft observations. J. Meteor. Soc. Japan, 94, 345358, https://doi.org/10.2151/jmsj.2016-018.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wing, A. A., 2022: Acceleration of tropical cyclone development by cloud-radiative feedbacks. J. Atmos. Sci., 79, 22852305, https://doi.org/10.1175/JAS-D-21-0227.1.

    • Search Google Scholar
    • Export Citation
  • Ying, Y., and F. Zhang, 2017: Practical and intrinsic predictability of multiscale weather and convectively coupled equatorial waves during the active phase of an MJO. J. Atmos. Sci., 74, 37713785, https://doi.org/10.1175/JAS-D-17-0157.1.

    • Search Google Scholar
    • Export Citation
  • Ying, Y., and F. Zhang, 2018: Potentials in improving predictability of multiscale tropical weather systems evaluated through ensemble assimilation of simulated satellite-based observations. J. Atmos. Sci., 75, 16751698, https://doi.org/10.1175/JAS-D-17-0245.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., and J. A. Sippel, 2009: Effects of moist convection on hurricane predictability. J. Atmos. Sci., 66, 19441961, https://doi.org/10.1175/2009JAS2824.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185, https://doi.org/10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., N. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 35793594, https://doi.org/10.1175/JAS4028.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125, https://doi.org/10.1175/2009MWR2645.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. F. Gamache, and F. D. Marks, 2011: Performance of convection‐permitting hurricane initialization and prediction during 2008–2010 with ensemble data assimilation of inner‐core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810, https://doi.org/10.1029/2011GL048469.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., M. Minamide, and E. E. Clothiaux, 2016: Potential impacts of assimilating all-sky infrared satellite radiances from GOES-R on convection-permitting analysis and prediction of tropical cyclones. Geophys. Res. Lett., 43, 29542963, https://doi.org/10.1002/2016GL068468.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., S. Taraphdar, and S. Wang, 2017: The role of global circumnavigating mode in the MJO initiation and propagation. J. Geophys. Res. Atmos., 122, 58375856, https://doi.org/10.1002/2016JD025665.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., M. Minamide, R. G. Nystrom, X. Chen, S.-J. Lin, and L. M. Harris, 2019: Improving Harvey forecasts with next-generation weather satellites: Advanced hurricane analysis and prediction with assimilation of GOES-R all-sky radiances. Bull. Amer. Meteor. Soc., 100, 12171222, https://doi.org/10.1175/BAMS-D-18-0149.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and Coauthors, 2021: Ensemble-based assimilation of satellite all-sky microwave radiances improves intensity and rainfall predictions for Hurricane Harvey (2017). Geophys. Res. Lett., 48, e2021GL096410, https://doi.org/10.1029/2021GL096410.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 359 359 106
Full Text Views 140 140 63
PDF Downloads 166 166 81