• Aberson, S. D., 2010: 10 years of hurricane synoptic surveillance (1997–2006). Mon. Wea. Rev., 138, 15361549, https://doi.org/10.1175/2009MWR3090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aberson, S. D., 2011: The impact of dropwindsonde data from the THORPEX Pacific area regional campaign and the NOAA hurricane field program on tropical cyclone forecasts in the Global Forecast System. Mon. Wea. Rev., 139, 26892703, https://doi.org/10.1175/2011MWR3634.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aberson, S. D., and J. L. Franklin, 1999: Impact on hurricane track and intensity forecasts of GPS dropwindsonde observations from the first-season flights of the NOAA Gulfstream-IV jet aircraft. Bull. Amer. Meteor. Soc., 80, 421428, https://doi.org/10.1175/1520-0477(1999)080<0421:IOHTAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., and C. F. Mass, 2006: Structure, growth rates, and tangent linear accuracy of adjoint sensitivities with respect to horizontal and vertical resolution. Mon. Wea. Rev., 134, 29712988, https://doi.org/10.1175/MWR3227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andersson, E., A. Hollingsworth, G. Kelly, P. Lönnberg, J. Pailleux, and Z. Zhang, 1991: Global observing system experiments on operational statistical retrievals of satellite sounding data. Mon. Wea. Rev., 119, 18511865, https://doi.org/10.1175/1520-0493(1991)119<1851:GOSEOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., G. Radnóti, S. Healy, and C. Cardinali, 2014: GNSS radio occultation constellation observing system experiments. Mon. Wea. Rev., 142, 555572, https://doi.org/10.1175/MWR-D-13-00130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouttier, F., and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data assimilation system. Quart. J. Roy. Meteor. Soc., 127, 14691488, https://doi.org/10.1002/qj.49712757419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, B. R., and G. J. Hakim, 2015: Sensitivity of intensifying Atlantic hurricanes to vortex structure. Quart. J. Roy. Meteor. Soc., 141, 25382551, https://doi.org/10.1002/qj.2540.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burpee, R. W., J. L. Franklin, S. J. Lord, R. E. Tuleya, and S. D. Aberson, 1996: The impact of Omega dropwindsondes on operational hurricane track forecast models. Bull. Amer. Meteor. Soc., 77, 925934, https://doi.org/10.1175/1520-0477(1996)077<0925:TIOODO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239250, https://doi.org/10.1002/qj.366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cardinali, C., and S. Healy, 2014: Impact of GPS radio occultation measurements in the ECMWF system using adjoint-based diagnostics. Quart. J. Roy. Meteor. Soc., 140, 23152320, https://doi.org/10.1002/qj.2300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charron, M., and et al. , 2012: The stratospheric extension of the Canadian global deterministic medium-range weather forecasting system and its impact on tropospheric forecasts. Mon. Wea. Rev., 140, 19241944, https://doi.org/10.1175/MWR-D-11-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J. H., M. S. Peng, C. A. Reynolds, and C. C. Wu, 2009: Interpretation of tropical cyclone forecast sensitivity from the singular vector perspective. J. Atmos. Sci., 66, 33833400, https://doi.org/10.1175/2009JAS3063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, K.-H., C.-C. Wu, P.-H. Lin, S. D. Aberson, M. Weissmann, F. Harnisch, and T. Nakazawa, 2011: The impact of dropwindsonde observations on typhoon track forecasts in DOTSTAR and T-PARC. Mon. Wea. Rev., 139, 17281743, https://doi.org/10.1175/2010MWR3582.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, K., and Z. Yi, 2016: Adjoint sensitivity study on idealized explosive cyclogenesis. J. Meteor. Res., 30, 547558, https://doi.org/10.1007/s13351-016-5261-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cummings, J. A., and O. M. Smedstad, 2014: Ocean data impact in global HYCOM. J. Atmos. Oceanic Technol., 31, 17711791, https://doi.org/10.1175/JTECH-D-14-00011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343, https://doi.org/10.1256/qj.05.137.

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., C. Amerault, C. A. Reynolds, and P. A. Reinecke, 2014: Initial condition sensitivity and predictability of a severe extratropical cyclone using a moist adjoint. Mon. Wea. Rev., 142, 320342, https://doi.org/10.1175/MWR-D-13-00201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., and K. D. Raeder, 1999: An examination of the accuracy of the linearization of a mesoscale model with moist physics. Quart. J. Roy. Meteor. Soc., 125, 169195, https://doi.org/10.1002/qj.49712555310.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., T. Vukicevic, and K. Raeder, 1993: Examination of the accuracy of a tangent linear model. Tellus, 45A, 462477, https://doi.org/10.3402/tellusa.v45i5.15046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Y. Zhu, 2009: Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models. Tellus, 61A, 179193, https://doi.org/10.1111/j.1600-0870.2008.00388.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., R. H. Langland, S. Pellerin, and R. Todling, 2010: The THORPEX Observation Impact Intercomparison Experiment. Mon. Wea. Rev., 138, 40094025, https://doi.org/10.1175/2010MWR3393.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goerss, J. S., 2009: Impact of satellite observations on the tropical cyclone track forecasts of the Navy Operational Global Atmospheric Prediction System. Mon. Wea. Rev., 137, 4150, https://doi.org/10.1175/2008MWR2601.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goerss, J. S., and R. A. Jeffries, 1994: Assimilation of synthetic tropical cyclone observations into the Navy Operational Global Atmospheric Prediction System. Wea. Forecasting, 9, 557576, https://doi.org/10.1175/1520-0434(1994)009<0557:AOSTCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilton, F., N. C. Atkinson, S. J. English, and J. R. Eyre, 2009: Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments. Quart. J. Roy. Meteor. Soc., 135, 495505, https://doi.org/10.1002/qj.379.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, T. F., and T. E. Rosmond, 1991: The description of the Navy Operational Global Atmospheric Prediction System’s spectral forecast model. Mon. Wea. Rev., 119, 17861815, https://doi.org/10.1175/1520-0493(1991)119<1786:TDOTNO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holdaway, D., R. Errico, R. Gelaro, and J. G. Kim, 2014: Inclusion of linearized moist physics in NASA’s Goddard Earth Observing System data assimilation tools. Mon. Wea. Rev., 142, 414433, https://doi.org/10.1175/MWR-D-13-00193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoover, B. T., 2015: Identifying a barotropic growth mechanism in east Pacific tropical cyclogenesis using adjoint-derived sensitivity gradients. J. Atmos. Sci., 72, 12151234, https://doi.org/10.1175/JAS-D-14-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoover, B. T., and R. H. Langland, 2017: Forecast and observation-impact experiments in the Navy Global Environmental Model with assimilation of ECMWF analysis data in the global domain. J. Meteor. Soc. Japan, 95, 369389, https://doi.org/10.2151/jmsj.2017-023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ishibashi, T., 2010: Optimization of error covariance matrices and estimation of observation data impact in the JMA global 4D-Var system. Research activities in atmospheric and oceanic modelling, CAS/JSC Working Group on Numerical Experimentation Rep. 40, 1.11–1.12.

  • Joo, S., J. Eyre, and R. Marriott, 2013: The impact of MetOp and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev., 141, 33313342, https://doi.org/10.1175/MWR-D-12-00232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, B.-J., H. M. Kim, T. Auligne, X. Zhang, X. Zhang, and X.-Y. Huang, 2013: Adjoint-derived observation impact using WRF in the western North Pacific. Mon. Wea. Rev., 141, 40804097, https://doi.org/10.1175/MWR-D-12-00197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., Y. Ota, T. Miyoshi, and J. Liu, 2012: A simpler formulation of forecast sensitivity to observations: Application to ensemble Kalman filters. Tellus, 64A, 18462, https://doi.org/10.3402/TELLUSA.V64I0.18462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. M., and B. J. Jung, 2009: Singular vector structure and evolution of a recurving tropical cyclone. Mon. Wea. Rev., 137, 505524, https://doi.org/10.1175/2008MWR2643.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. M., S.-M. Kim, and B.-J. Jung, 2011: Real-time adaptive observation guidance using singular vectors for Typhoon Jangmi (200815) in T-PARC 2008. Wea. Forecasting, 26, 634649, https://doi.org/10.1175/WAF-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J., H. M. Kim, and C.-H. Cho, 2014: Influence of CO2 observations on the optimized CO2 flux in an ensemble Kalman filter. Atmos. Chem. Phys., 14, 13 51513 530, https://doi.org/10.5194/acp-14-13515-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, M., H. M. Kim, J. W. Kim, S.-M. Kim, C. S. Velden, and B. T. Hoover, 2017: Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia using an adjoint-based observation impact method. Wea. Forecasting, 32, 579594, https://doi.org/10.1175/WAF-D-16-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S.-M., and H. M. Kim, 2014: Sampling error of observation impact statistics. Tellus, 66A, 25435, https://doi.org/10.3402/tellusa.v66.25435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S.-M., and H. M. Kim, 2018: Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters. Tellus, 70A, 116, https://doi.org/10.1080/16000870.2018.1492839.

    • Search Google Scholar
    • Export Citation
  • Kunii, M., T. Miyoshi, and E. Kalnay, 2012: Estimating the impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., 140, 19751987, https://doi.org/10.1175/MWR-D-11-00205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., 2005: Observation impact during the North Atlantic TReC-2003. Mon. Wea. Rev., 133, 22972309, https://doi.org/10.1175/MWR2978.1.

  • Langland, R. H., and R. M. Errico, 1996: Comments on “Use of an adjoint model for finding triggers for Alpine lee cyclogenesis.” Mon. Wea. Rev., 124, 757760, https://doi.org/10.1175/1520-0493(1996)124<0757:COOAAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189201, https://doi.org/10.3402/tellusa.v56i3.14413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., R. N. Maue, and C. H. Bishop, 2008: Uncertainty in atmospheric temperature analyses. Tellus, 60A, 598603, https://doi.org/10.1111/j.1600-0870.2008.00336.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., C. Velden, P. M. Pauley, and H. Berger, 2009: Impact of satellite-derived rapid-scan wind observations on numerical model forecasts of Hurricane Katrina. Mon. Wea. Rev., 137, 16151622, https://doi.org/10.1175/2008MWR2627.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leslie, L. M., J. F. LeMarshall, R. P. Morison, C. Spinoso, R. J. Purser, N. Pescod, and R. Seecamp, 1998: Improved hurricane track forecasting from the continuous assimilation of high quality satellite wind data. Mon. Wea. Rev., 126, 12481258, https://doi.org/10.1175/1520-0493(1998)126<1248:IHTFFT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J., and E. Kalnay, 2008: Estimating observation impact without adjoint model in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 134, 13271335, https://doi.org/10.1002/qj.280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and R. T. Marriott, 2014: Forecast sensitivity to observations in the Met Office global numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 140, 209224, https://doi.org/10.1002/qj.2122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lupu, C., P. Gauthier, and S. Laroche, 2012: Assessment of the impact of observations on analyses derived from observing system experiments. Mon. Wea. Rev., 140, 245257, https://doi.org/10.1175/MWR-D-10-05010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahfouf, J.-F., 1999: Influence of physical processes on the tangent-linear approximation. Tellus, 51A, 147166, https://doi.org/10.3402/tellusa.v51i2.12312.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., M. J. Brennan, and K. Howard, 2013: The impact of dropwindsonde and supplemental rawinsonde observations on track forecasts for Hurricane Irene (2011). Wea. Forecasting, 28, 13851403, https://doi.org/10.1175/WAF-D-13-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Osuri, K. K., U. C. Mohanty, A. Routray, and M. Mohapatra, 2012: The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the north Indian Ocean. Int. J. Remote Sens., 33, 16271652, https://doi.org/10.1080/01431161.2011.596849.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ota, Y., J. C. Derber, E. Kalnay, and T. Miyoshi, 2013: Ensemble-based observation impact estimates using the NCEP GFS. Tellus, 65A, 20038, https://doi.org/10.3402/TELLUSA.v65I0.20038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics and adaptive observations. J. Atmos. Sci., 55, 633653, https://doi.org/10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, M. S., and C. A. Reynolds, 2006: Sensitivity of tropical cyclone forecasts as revealed by singular vectors. J. Atmos. Sci., 63, 25082528, https://doi.org/10.1175/JAS3777.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., 2016: On the impact and benefits of AMDAR observations in operational forecasting—Part I: A review of the impact of automated aircraft wind and temperature reports. Bull. Amer. Meteor. Soc., 97, 585602, https://doi.org/10.1175/BAMS-D-14-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., L. Cronce, R. Mamrosh, R. Baker, and P. Pauley, 2016: On the impact and future benefits of AMDAR observations in operational forecasting: Part II: Water vapor observations. Bull. Amer. Meteor. Soc., 97, 21172133, https://doi.org/10.1175/BAMS-D-14-00211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reale, O., E. L. McGrath-Spangler, W. McCarty, D. Holdaway, and R. Gelaro, 2018: Impact of adaptively thinned AIRS cloud-cleared radiances on tropical cyclone representation in a global data assimilation and forecast system. Wea. Forecasting, 33, 909931, https://doi.org/10.1175/WAF-D-17-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., M. S. Peng, and J. H. Chen, 2009: Recurving tropical cyclones: Singular vector sensitivity and downstream impacts. Mon. Wea. Rev., 137, 13201337, https://doi.org/10.1175/2008MWR2652.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., R. Langland, P. M. Pauley, and C. S. Velden, 2013: Tropical cyclone data impact studies: Influence of model bias and synthetic observations. Mon. Wea. Rev., 141, 43734394, https://doi.org/10.1175/MWR-D-12-00300.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosmond, T. E., 1997: A technical description of the NRL adjoint modeling system. Naval Research Laboratory Rep. NRL/MR/7532/97/7230, 62 pp., https://doi.org/10.21236/ADA330960.

    • Crossref
    • Export Citation
  • Rosmond, T. E., and L. Xu, 2006: Development of NAVDAS-AR: Non-linear formulation and outer loop tests. Tellus, 58A, 4558, https://doi.org/10.1111/j.1600-0870.2006.00148.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tyndall, D. P., and J. D. Horel, 2013: Impacts of mesonet observations on meteorological surface analyses. Wea. Forecasting, 28, 254269, https://doi.org/10.1175/WAF-D-12-00027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vukićević, T., and K. Raeder, 1995: Use of an adjoint model for finding triggers for Alpine lee cyclogenesis. Mon. Wea. Rev., 123, 800816, https://doi.org/10.1175/1520-0493(1995)123<0800:UOAAMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C.-C., K.-H. Chou, P.-H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa, 2007: The impact of dropwinsonde data on typhoon track forecasts in DOTSTAR. Wea. Forecasting, 22, 11571176, https://doi.org/10.1175/2007WAF2006062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L., T. Rosmond, and R. Daley, 2005: Development of NAVDAS-AR: Formulation and initial tests of the linear problem. Tellus, 57A, 546559, https://doi.org/10.3402/tellusa.v57i4.14710.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zapotocny, T. H., J. A. Jung, J. F. Le Marshall, and R. E. Treadon, 2007: A two-season impact study of satellite and in situ data in the NCEP Global Data Assimilation System. Wea. Forecasting, 22, 887909, https://doi.org/10.1175/WAF1025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zapotocny, T. H., J. A. Jung, J. F. Le Marshall, and R. E. Treadon, 2008: A two-season impact study of four satellite data types and rawinsonde data in the NCEP Global Data Assimilation System. Wea. Forecasting, 23, 80100, https://doi.org/10.1175/2007WAF2007010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., H. Wang, X.-Y. Huang, F. Gao, and N. A. Jacobs, 2015: Using adjoint-based forecast sensitivity method to evaluate TAMDAR data impacts on regional forecasts. Adv. Meteor., 2015, 427616, https://doi.org/10.1155/2015/427616.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Adjoint-Derived Impact of Assimilated Observations on Tropical Cyclone Intensity Forecasts of Hurricane Joaquin (2015) and Hurricane Matthew (2016)

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  • 1 Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin
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Abstract

The adjoint-derived observation impact method is used as a diagnostic to derive the impact of assimilated observations on a metric representing the forecast intensity of a tropical cyclone (TC). Storm-centered composites of observation impact and the model background state are computed across 6-hourly analysis/forecast cycles to compute the composite observation impact throughout the life cycle of Hurricane Joaquin (2015) to evaluate the impact of in situ wind and temperature observations in the upper and lower troposphere, as well as the impact of brightness temperature and precipitable water observations, on intensity forecasts with forecast lengths from 12 to 48 h. The compositing across analysis/forecast cycles allows for the exploration of consistent relationships between the synoptic-scale state of the initial conditions and the impact of observations that are interpreted as flow-dependent interactions between model background bias and correction by assimilated observations on the TC intensity forecast. The track of Hurricane Matthew (2016), with an extended period of time near the coasts of Florida, Georgia, and the Carolinas, allows for a comparison of the impact of aircraft reconnaissance observations with the impact of nearby overland rawinsonde observations available within the same radius of the TC.

This article is included in the Tropical Cyclone Intensity Experiment (TCI) Special Collection.

Corresponding author: Brett T. Hoover, brett.hoover@ssec.wisc.edu

Abstract

The adjoint-derived observation impact method is used as a diagnostic to derive the impact of assimilated observations on a metric representing the forecast intensity of a tropical cyclone (TC). Storm-centered composites of observation impact and the model background state are computed across 6-hourly analysis/forecast cycles to compute the composite observation impact throughout the life cycle of Hurricane Joaquin (2015) to evaluate the impact of in situ wind and temperature observations in the upper and lower troposphere, as well as the impact of brightness temperature and precipitable water observations, on intensity forecasts with forecast lengths from 12 to 48 h. The compositing across analysis/forecast cycles allows for the exploration of consistent relationships between the synoptic-scale state of the initial conditions and the impact of observations that are interpreted as flow-dependent interactions between model background bias and correction by assimilated observations on the TC intensity forecast. The track of Hurricane Matthew (2016), with an extended period of time near the coasts of Florida, Georgia, and the Carolinas, allows for a comparison of the impact of aircraft reconnaissance observations with the impact of nearby overland rawinsonde observations available within the same radius of the TC.

This article is included in the Tropical Cyclone Intensity Experiment (TCI) Special Collection.

Corresponding author: Brett T. Hoover, brett.hoover@ssec.wisc.edu
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