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Journal of Meteorological Research

, Volume 29, Issue 1, pp 1–27 | Cite as

Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops

  • Xiaolei Zou (邹晓蕾)Email author
  • Fuzhong Weng (翁富忠)
  • Vijay Tallapragada
  • Lin Lin (林 琳)
  • Banglin Zhang (张邦林)
  • Chenfeng Wu (吴陈锋)
  • Zhengkun Qin (秦正坤)
Article

Abstract

The Advanced Microwave Sounding Unit-A (AMSU-A) onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A, the hyperspectral Atmospheric Infrared Sounder (AIRS) onboard Aqua, the High resolution InfraRed Sounder (HIRS) onboard NOAA-19 and MetOp-A, and the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting (HWRF) system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

Key words

model top data assimilation satellite hurricane 

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References

  1. Aumann, H., M. Chahine, and D. Barron, 2003: Sea surface temperature measurements with AIRS: RTG.SST comparison. SPIE Proc., 5151-30, August 2003.Google Scholar
  2. Bister, M., and K. Emanuel, 1997: The genesis of Hurricane Guillermo: TEXMEX analyses and a modeling study. Mon. Wea. Rev., 125, 2662–2682.CrossRefGoogle Scholar
  3. Bosart, L. F., C. S. Velden, W. E. Bracken, et al., 2002: Environmental influences on the rapid intensification of Hurricane Opal (1995) over the Gulf of Mexico. Mon. Wea. Rev., 128, 322–352.CrossRefGoogle Scholar
  4. Bozeman, M. L., D. Niyogi, S. Gopalakrishnan, et al., 2011: An HWRF-based ensemble assessment of the land surface feedback on the post-landfall intensification of Tropical Storm Fay (2008). Nat. Hazards, 63, 1543–1571, doi: 10.1007/s11069-011-9841-5.CrossRefGoogle Scholar
  5. Bracken, W. E., and L. F. Bosart, 2000: The role of synoptic-scale flow during tropical cyclogenesis over the North Atlantic Ocean. Mon. Wea. Rev., 128, 353–376.CrossRefGoogle Scholar
  6. Carr III, L. E., and R. L. Elsberry, 1990: Observational evidence for predictions of tropical cyclone propagation relative to environmental steering. J. Atmos. Sci., 47, 542–546.CrossRefGoogle Scholar
  7. Carter, C., Q. Liu, W. Yang, et al., 2002: Net heat flux, visible/infrared imager/radiometer suite algorithm theoretical basis document. Available at http://npoesslib.ipo.noaa.gov/u-listcategory-v3.php?35.Google Scholar
  8. Challa, M., and R. L. Pfeffer, 1990: Formation of Atlantic hurricanes from cloud clusters and depressions. J. Atmos. Sci., 47, 909–927.CrossRefGoogle Scholar
  9. Chan, J. C. L., 1995: Tropical cyclone activity in the western North Pacific in relation to the stratospheric quasi-biennial oscillation. Mon. Wea. Rev., 123, 2567–2571.CrossRefGoogle Scholar
  10. Chan, J. C. L., 2005: The physics of tropical cyclone motion. Ann. Rev. Fluid Mech., 37, 99–128.CrossRefGoogle Scholar
  11. Cram, T., J. Persing, M. Montgomery, et al., 2007: A Lagrangian trajectory view on transport and mixing processes between the eye, eyewall, and environment using a high-resolution simulation of Hurricane Bonnie (1998). J. Atmos. Sci., 64, 1835–1856.CrossRefGoogle Scholar
  12. Davis, C., and L. Bosart, 2006: The formation of Hurricane Humberto (2001): The importance of extratropical precursors. Quart. J. Roy. Meteor. Soc., 132, 2055–2085.CrossRefGoogle Scholar
  13. DeMaria, M., J.-J. Baik, and J. Kaplan, 1993: Upperlevel eddy angular momentum flux and tropical cyclone intensity change. J. Atmos. Sci., 50, 1133–1147.CrossRefGoogle Scholar
  14. DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 2076–2087.CrossRefGoogle Scholar
  15. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299.CrossRefGoogle Scholar
  16. Elsberry, R. L., 1995: Tropical cyclone motion. Global Perspectives of Tropical Cyclones, WMO Report No. TCP-38, Elsberry, R. L., Ed., World Meteorological Organization, Geneva, 106–197.Google Scholar
  17. Emanuel, K. A., 1986: An air-sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J Atmos. Sci., 42, 1062–1071.CrossRefGoogle Scholar
  18. Emanuel, K. A., S. Solomon, D. Folini, et al., 2013: Influence of tropical tropoause layer cooling on Atlantic hurricane activity. J. Climate, 26, 2288–2301, doi: 10.1175/JCLI-D-12-0024z.1.CrossRefGoogle Scholar
  19. Gallina, G., and C. Velden, 2002: Environmental vertical wind shear and tropical cyclone intensity change utilizing enhanced satellite derived wind information. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 172–173.Google Scholar
  20. Gopalakrishnan, S., F. Marks, X. Zhang, et al., 2011: The experimental HWRF system: A study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Mon. Wea. Rev., 139, 1762–1784.CrossRefGoogle Scholar
  21. Gopalakrishnan, S., S. Goldenberg, T. Quirino, et al., 2012: Towards improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics. Wea. Forecasting, 27, 647–666.CrossRefGoogle Scholar
  22. Gustafsson, N., X. Y. Huang, X. Yang, et al., 2012: Four-dimensional variational data assimilation for a limited area model. Tellus, 64A, 14985, doi: 0.3402/tellusa.v64i0.14985.Google Scholar
  23. Han, Y, F. Weng, Q. Liu, et al., 2007: A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J. Geophys. Res., 112, D11121, doi: 10.1029/2006JD008208.CrossRefGoogle Scholar
  24. Kleist, D. T., D. F. Parrish, J. C. Derber, et al., 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Wea. Forecasting, 24, 1691–1705.CrossRefGoogle Scholar
  25. Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 2030–2045.CrossRefGoogle Scholar
  26. Leroux, M.-D., M. Plu, D. Barbary, et al., 2013: Dynamical and physical processes leading to tropical cyclone intensification under upper-level trough forcing. J. Atmos. Sci., 70, 2547–2565.CrossRefGoogle Scholar
  27. Liu, Q., F. Weng, and S. J. English, 2011: An improved fast microwave water emissivity model. IEEE Trans. Geosci. Remote Sens., 49, 1238–1250.CrossRefGoogle Scholar
  28. Marin, J., D. Raymond, and G. Raga, 2009: Intensification of tropical cyclones in the GFS model. Atmos. Chem. Phys., 9, 1407–1417.CrossRefGoogle Scholar
  29. McBride, J., and R. Zehr, 1981: Observational analysis of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, 1132–1151.CrossRefGoogle Scholar
  30. McNally, A. P., P. D. Watts, J. A. Smith, et al., 2006: The assimilation of AIRS radiance data at ECMWF. Quart. J. Roy. Meteor. Soc., 132, 935–957.CrossRefGoogle Scholar
  31. Mo, T., 1996: Prelaunch calibration of the Advanced Microwave Sounding Unit-A for NOAA-K. IEEE Trans. Microwave Theory Technol., 44, 1460–1469, doi: 10.1109/22.536029.CrossRefGoogle Scholar
  32. Molinari, J., and D. Vollaro, 2010: Rapid intensification of a sheared tropical storm. Mon. Wea. Rev., 138, 3869–3885.CrossRefGoogle Scholar
  33. Montmerle, T., F. Rabier, and C. Fischer, 2007: Relative impact of polar-orbiting and geostationary satellite radiances in the Aladin/France numerical weather prediction system. Quart. J. R. Meteor. Soc., 133, 655–671.CrossRefGoogle Scholar
  34. NWP SAF, 2011: Annex to AAPP Scientific Documentation: Pre-Processing of ATMS and CrIS, Document NWPSAF-MO-UD-027. Available at http://research.metoffice.gov.uk/research/interproj/nwpsaf/aapp/index.html.Google Scholar
  35. Pagano, T. S., H. H. Aumann, S. E. Broberg, et al., 2002: On-board calibration techniques and test results for the Atmospheric Infrared Sounder (AIRS). Proceedings of SPIE on Earth Observing Systems VII, 7 July 2002, Seattle, USA.Google Scholar
  36. Pattanayak, S., U. C. Mohanty, and S. G. Gopalakrishnan, 2011: Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system. Nat. Hazards, 63, 1413–1437, doi: 10.1007/s11069-011-9863-z.CrossRefGoogle Scholar
  37. Pfeffer, R. L., and M. Challa, 1981: A numerical study of the role of eddy fluxes of momentum in the development of Atlantic hurricanes. J. Atmos. Sci., 38, 2393–2398.CrossRefGoogle Scholar
  38. Powell, M., 1990: Boundary layer structure and dynamics in outer hurricane rainbands. Part II: Downdraft modification and mixed layer recovery. Mon. Wea. Rev., 118, 918–938.CrossRefGoogle Scholar
  39. Purser, R. J., W.-S. Wu, D. F. Parrish, et al., 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524–1535.CrossRefGoogle Scholar
  40. Purser, R. J., W.-S. Wu, D. F. Parrish, et al., 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536–1548.CrossRefGoogle Scholar
  41. Qin, Z., X. Zou, and F. Weng, 2013, Evaluating added benefits of assimilating GOES imager radiance data in GSI for coastal QPFs. Mon. Wea. Rev., 141, 75–92.CrossRefGoogle Scholar
  42. Ramsay, H. A., 2013: The effects of imposed stratospheric cooling on the maximum intensity of tropical cyclones in axisymmetric radiative-convective equilibrium. J. Climate, 26, 9977–9985, doi: http://dx.doi.org/10.1175/JCLI-D-13-00195.1.CrossRefGoogle Scholar
  43. Riemer, M., M. Montgomery, and M. Nicholls, 2010: A new paradigm for intensity modification of tropical cyclones: Thermodynamic impact of vertical wind shear on the inflow layer. Atmos. Chem. Phys., 10, 3163–3188.CrossRefGoogle Scholar
  44. Riemer, M., and M. Montgomery, 2011: Simple kinematic models for the environmental interaction of tropical cyclones in vertical wind shear. Atmos. Chem. Phys., 11, 9395–9414.CrossRefGoogle Scholar
  45. Simpson, R., and R. Riehl, 1958: Mid-tropospheric ventilation as a constraint on hurricane development and maintenance. Preprints, Tech. Conf. on Hurricanes, Miami Beach, FL, Amer. Meteor. Soc., D4-1–D4-10.Google Scholar
  46. Stengel, M., P. Undén, M. Lindskog, et al., 2009: Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Quart. J. R. Meteor. Soc., 135, 2100–2109.CrossRefGoogle Scholar
  47. Tang, B., and K. Emanuel, 2010: Midlevel ventilation’s constraint on tropical cyclone intensity. J. Atmos. Sci., 67, 1817–1830.CrossRefGoogle Scholar
  48. Untch, A., and A. Simmons, 1999: Increased stratospheric resolution in the ECMWF forecasting system. Proceedings of SODA Workshop on Chemical Data Assimilation. ECMWF Newsletter, 82, 2–8.Google Scholar
  49. Velden, C. S., and L. M. Leslie, 1991: The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea. Forecasting, 6, 244–253.CrossRefGoogle Scholar
  50. Wang Bin, R. L. Elsberry, Wang Yuqing, et al., 1998: Dynamics in tropical cyclone motion: A review. Chinese J. Atmos. Sci., 22, 416–434.Google Scholar
  51. Weng, F., B. Yan, and N. Grody, 2001: A microwave land emissivity model. J. Geophys. Res., 106, 20115–20123.CrossRefGoogle Scholar
  52. Weng, F., and Q. Liu, 2003: Satellite data assimilation in numerical weather prediction models. Part I: Forward radiative transfer and Jacobian modeling in cloudy atmospheres. J. Atmos. Sci., 60, 2633–2646.CrossRefGoogle Scholar
  53. Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 3799–3807.CrossRefGoogle Scholar
  54. Weng, F., T. Zhu, and B. Yan, 2007: Satellite data assimilation in numerical weather prediction models. Part II: Uses of rain-affected radiances from microwave observations for hurricane vortex analysis. J. Atmos. Sci., 64, 3910–3925.CrossRefGoogle Scholar
  55. Weng, F., X. Zou, X. Wang, et al., 2012: Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. J. Geophys. Res., 117, D19112, doi: 10.1029/2012JD018144.Google Scholar
  56. Weng, F., H. Yang, and X. Zou, 2013: On convertibility from antenna to sensor brightness temperature for ATMS. IEEE Trans. Geo. Remote Sen., 10, 771–775.CrossRefGoogle Scholar
  57. Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Threedimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905–2916.CrossRefGoogle Scholar
  58. Wu, X., and W. L. Smith, 1997: Emissivity of rough sea surface for 8–13 m: Modeling and verification. Appl. Opt., 36, 2609–2619.CrossRefGoogle Scholar
  59. Wu, Y., and X. Zou, 2008: Numerical test of a simple approach for using TOMS total ozone data in hurricane environment. Quart. J. R. Meteor. Soc., 134, 1397–1408.CrossRefGoogle Scholar
  60. Yang, H., and X. Zou, 2013: Optimal ATMS remapping algorithm for climate research. IEEE Trans. Geo. Remote Sensing, 52, 7290–7296.CrossRefGoogle Scholar
  61. Yan, B., F. Weng, and K. Okamoto, 2004: Improved estimation of snow emissivity from 5 to 200 GHz. Proceedings of 8th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications, 24–27 February 2004, Rome, Italy.Google Scholar
  62. Yeh, K.-S., X. Zhang, S. Gopalakrishnan, et al., 2012: The AOML/ESRL hurricane research system: Performance in the 2008 hurricane season. Nat. Hazards, 63, 1439–1449, doi: 10.1007/s11069-011-9787-7.CrossRefGoogle Scholar
  63. Zehr, R., 1992: Tropical Cyclogenesis in the Western North Pacific. NOAA Tech. Rep. NESDIS 61, 181 pp.Google Scholar
  64. Zhang, X., T. S. Quirino, K.-S. Yeh, et al., 2011: HWRFx: Improving hurricane forecast with high-resolution modeling. Comput. Sci. Eng., 13, 13–21.CrossRefGoogle Scholar
  65. Zou, X., F. Weng, B. L. Zhang, et al., 2013: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J. Geophys. Res., 118, 11558–11576.Google Scholar
  66. Zou, X., L. Lin, and F. Weng, 2013: Absolute calibration of ATMS upper level temperature sounding channels using GPS RO observations. IEEE Trans. Geosci. Remote Sens., 52, 1397–1406.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xiaolei Zou (邹晓蕾)
    • 1
    Email author
  • Fuzhong Weng (翁富忠)
    • 2
  • Vijay Tallapragada
    • 3
  • Lin Lin (林 琳)
    • 4
  • Banglin Zhang (张邦林)
    • 3
  • Chenfeng Wu (吴陈锋)
    • 5
  • Zhengkun Qin (秦正坤)
    • 6
  1. 1.Earth System Science Interdisciplinary CenterUniversity of MarylandMarylandUSA
  2. 2.NOAA Center for Satellite Applications and ResearchCollege ParkUSA
  3. 3.NOAA NCEP Environmental Modeling CenterCollege ParkUSA
  4. 4.I. M. Systems Group, Inc.RockvilleUSA
  5. 5.Xiamen Meteorological BureauXiamenChina
  6. 6.Nanjing University of Information Science & TechnologyNanjingChina

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