• Aberson, S., 2003: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Wea. Rev., 131 , 16131628.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y-R. Guo, A. J. Bourgeois, and Q. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132 , 897914.

    • Search Google Scholar
    • Export Citation
  • Braun, S. A., M. T. Montgomery, and Z-X. Pu, 2006: High-resolution simulation of Hurricane Bonnie (1998). Part I: The organization of eyewall vertical motion. J. Atmos. Sci., 63 , 1942.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and C. Snyder, 2006: Initializing a hurricane vortex with an ensemble Kalman filter. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., 8A.5.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., J-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120 , 13671387.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136 , 19902005.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 30773107.

    • Search Google Scholar
    • Export Citation
  • Eliassen, A., 1959: On the formation of fronts in the atmosphere. The Atmosphere and Sea in Motion, B. Bolin, Ed., Oxford University Press, 227–287.

    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., 2005: Achievement of USWRP hurricane landfall research goal. Bull. Amer. Meteor. Soc., 86 , 643645.

  • Emanuel, K. A., 1995: The behavior of a simple hurricane model using a convective scheme based on subcloud-layer entropy equilibrium. J. Atmos. Sci., 52 , 39603968.

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., 1997: Evaluation of a fully three-dimensional variational Doppler analysis technique. Preprints, 28th Conf. on Radar Meteorology, Austin, TX, Amer. Meteor. Soc., 422–423.

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., 2005: Final report on JHT project: Real-time dissemination of hurricane wind fields determined from airborne Doppler radar data. NOAA/NHC, 38 pp. [Available online at http://www.nhc.noaa.gov/jht/2003-2005reports/DOPLRgamache_JHTfinalreport.pdf].

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., J. S. Griffin Jr., P. P. Dodge, and N. F. Griffin, 2004: Automatic Doppler analysis of three-dimensional wind fields in hurricane eyewalls. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 5D.4. [Available online at ams.confex.com/ams/pdfpapers/75806.pdf].

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., and Y. Noh, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134 , 23182341.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., J. Dudhia, and S-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132 , 103120.

    • Search Google Scholar
    • Export Citation
  • Houze Jr., R. A., and Coauthors, 2006: The hurricane rainband and intensity change experiment—Observations and modeling of Hurricanes Katrina, Ophelia, and Rita. Bull. Amer. Meteor. Soc., 87 , 15031521.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43 , 170181.

  • Krishnamurti, T. N., S. Pattnaik, L. Stefanova, T. S. V. Vijaya Kumar, B. P. Mackey, A. J. O’Shay, and R. J. Pasch, 2005: The hurricane intensity issue. Mon. Wea. Rev., 133 , 18861912.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41 , 28362848.

    • Search Google Scholar
    • Export Citation
  • Marks, F. D., and R. A. Houze, 1987: Inner core structure of Hurricane Alicia from airborne radar observation. J. Atmos. Sci., 44 , 12961317.

    • Search Google Scholar
    • Export Citation
  • Marks, F. D., P. G. Black, M. T. Montgomery, and R. W. Burpee, 2008: Structure of the eye and eyewall of Hurricane Hugo (1989). Mon. Wea. Rev., 136 , 12371259.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and R. A. Black, 2004: Observations of particle size and phase in tropical cyclones: Implications for mesoscale modeling of microphysical processes. J. Atmos. Sci., 61 , 422439.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., H. Zhang, G. Heymsfield, R. Hood, J. Dudhia, J. B. Halverson, and F. Marks, 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63 , 127150.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 , (D14). 1666316682.

    • Search Google Scholar
    • Export Citation
  • Mohr, C. G., L. J. Miller, R. L. Vaughan, and H. W. Frank, 1986: On the merger of mesoscale data sets into a common Cartesian format for efficient and systematic analysis. J. Atmos. Oceanic Technol., 3 , 143161.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120 , 17471763.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77–78 , 5364.

    • Search Google Scholar
    • Export Citation
  • Ray, P. S., D. P. Jorgensen, and S-L. Wang, 1985: Airborne Doppler radar observation of a convective storm. J. Climate Appl. Meteor., 24 , 687698.

    • Search Google Scholar
    • Export Citation
  • Reasor, P. D., M. T. Montgomery, F. D. Marks Jr., and J. F. Gamache, 2000: Low-wavenumber structure and evolution of the hurricane inner core observed by airborne dual-Doppler radar. Mon. Wea. Rev., 128 , 16531680.

    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., M. L. Black, S. S. Chen, and R. A. Black, 2007: An evaluation of microphysics fields from mesoscale model simulations of tropical cyclones. Part I: Comparisons with observations. J. Atmos. Sci., 64 , 18111834.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20 , 99116.

    • Search Google Scholar
    • Export Citation
  • Wong, M. L. M., and J. C. L. Chan, 2006: Tropical cyclone motion in response to land surface friction. J. Atmos. Sci., 63 , 13241337.

  • Xiao, Q., and J. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135 , 33813404.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y-H. Kuo, J. Sun, W-C. Lee, E. Lim, Y-R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44 , 768788.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y-H. Kuo, J. Sun, W-C. Lee, D. M. Barker, and E. Lim, 2007: An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46 , 1422.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Coauthors, 2008: Doppler radar data assimilation in KMA’s operational forecasting. Bull. Amer. Meteor. Soc., 89 , 3943.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., L. Chen, and X. Zhang, 2009: Evaluations of BDA scheme using the Advanced Research WRF (ARW) model. J. Appl. Meteor. Climatol., 48 , 680689.

    • Search Google Scholar
    • Export Citation
  • Yau, M. K., Y. Liu, D-L. Zhang, and Y. Chen, 2004: A multiscale numerical study of Hurricane Andrew (1992). Part VI: Small-scale inner-core structures and wind streaks. Mon. Wea. Rev., 132 , 14101433.

    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and Y. Jin, 2008: High-resolution radar data assimilation for Hurricane Isabel (2003) at landfall. Bull. Amer. Meteor. Soc., 89 , 13551372.

    • Search Google Scholar
    • Export Citation
  • Zhu, T., D-L. Zhang, and F. Weng, 2004: Numerical simulation of Hurricane Bonnie (1998). Part I: Eyewall evolution and intensity changes. Mon. Wea. Rev., 132 , 225241.

    • Search Google Scholar
    • Export Citation
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    Observed (best track) of (a) 5-day hurricane positions and (b) CSLPs for Hurricanes Jeanne (2004), Katrina (2005), and Rita (2005). The X’s in (a) denote the initialization time with the ADR data. The gray segments of the curves denote the WRF model simulation period in our experiments.

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    Horizontal wind field and radar reflectivity at 2.5 km MSL for Hurricanes (a) Jeanne at ∼1800 UTC 24 Sep 2004, (b) Katrina at ∼1800 UTC 27 Aug 2005, and (c) Rita at ∼1800 UTC 20 Sep 2005. The color scale key on the right shows the reflectivity values (dBZ) and the reference velocity vector is shown on the lower right.

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    SLP (thick solid isolines), and surface (10 m) wind vector and speed (shadings with thin isolines) for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by experiments (a) CTL, (b) GTS, and (c) GRV. The shading scale for surface wind speed is on the lower right. Line AB is used for cross sections in Fig. 5.

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    H*WIND isotach analysis (kt) at 2100 UTC 24 Sep 2004 for Hurricane Jeanne (from the NOAA/AOML/HRD Web site).

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    Cross sections of horizontal wind speed (interval: 5 m s−1) above Hurricane Jeanne (2004) along line AB in Fig. 3c, by experiments (a) GTS and (b) GRV.

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    Azimuthally averaged tangential winds (interval: 2 m s−1) for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by experiments (a) GTS and (b) GRV.

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    300-hPa analytical increments of wind vector (maximum vector represents 29.7 m s−1) and temperature (isolines with contour interval of 0.2 K, the negative value dashed) by experiments (a) GTS and (b) GRV, and cross sections of temperature increments (0.2-K interval) across the vortex from west to east in (c) GTS and (d) GRV.

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    Initialization with ADR reflectivity assimilation for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by GVZ experiment (a) surface analysis of SLP (gray isolines with the interval of 2 hPa), 10-m wind (arrows with the maximum vector representing 38.4 m s−1), and recovered composite reflectivity (dBZ) in the analysis (shading with the scales on the right), and cross sections of (b) temperature difference (with 0.1-K interval), (c) water vapor mixing ratio difference (with 0.05 g kg−1 interval), and (d) equivalent potential temperature (θe) difference (with 0.2-K interval) between GVZ and GRV (GVZ–GRV) above the hurricane vortex along the line AB in (a).

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    The 24-h forecasts of SLP (thick solid isolines) and surface (10 m) wind vector and speed (shadings with thin isolines) for Hurricane Jeanne at 1800 UTC 25 Sep 2004 by experiments (a) GTS, (b) GRV, and (c) GVZ. The shading scale for surface wind speed is on the upper right.

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    Vertical cross sections of equivalent potential temperature (solid isolines with interval of 2 K) and horizontal wind speed (shading with the scale on the upper right) at 1800 UTC 25 Sep 2004 (24-h forecast) for experiments (a) GTS, (b) GRV, and (c) GVZ.

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    The column maximum radar reflectivity (dBZ) at 1800 UTC 25 Sep (24-h forecast) for experiments (a) GTS, (b) GRV, and (c) GVZ, and at 0600 UTC 26 September (36-h forecast) for experiments (d) GTS, (e) GRV, and (f) GVZ.

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    The reflectivity (dBZ) image from the Weather Surveillance Radar-1988 Doppler from Melbourne, FL, at 0232 UTC 26 Sep 2004.

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    The 48-h forecasts of Hurricane Jeanne from 1800 UTC 24 Sep through 1800 UTC 26 Sep 2004: (a) tracks and (b) track errors (km) from the four experiments CTL, GTS, GRV, and GVZ.

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    The 48-h forecasts of the intensity for Hurricane Jeanne from 1800 UTC 24 Sep through 1800 UTC 26 Sep 2004: (a) CSLP and (b) MSW speed.

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    Time series of the average mean absolute errors of (a) track, (b) CSLP, and (c) MSW for the three hurricanes by the CTL, GTS, GRV, and GVZ experiments as well as the operational GFDL results with Jeanne initialized at 1800 UTC 24 Sep 2004, Katrina initialized at 1800 UTC 27 Aug 2005, and Rita initialized at 1800 UTC 20 Sep 2005.

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Experiments of Hurricane Initialization with Airborne Doppler Radar Data for the Advanced Research Hurricane WRF (AHW) Model

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  • 1 ESSL/MMM, National Center for Atmospheric Research,* Boulder, Colorado
  • | 2 Northern Gulf Institute, Mississippi State University, Stennis Space Center, Mississippi
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Abstract

Initialization of the hurricane vortex in weather prediction models is vital to intensity forecasts out to at least 48 h. Airborne Doppler radar (ADR) data have sufficiently high horizontal and vertical resolution to resolve the hurricane vortex and its imbedded structures but have not been extensively used in hurricane initialization. Using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation (3DVAR) system, the ADR data are assimilated to recover the hurricane vortex dynamic and thermodynamic structures at the WRF model initial time. The impact of the ADR data on three hurricanes, Jeanne (2004), Katrina (2005) and Rita (2005), are examined during their rapid intensification and subsequent weakening periods before landfall.

With the ADR wind data assimilated, the three-dimensional winds in the hurricane vortex become stronger and the maximum 10-m winds agree better with independent estimates from best-track data than without ADR data assimilation. Through the multivariate incremental structure in WRF 3DVAR analysis, the central sea level pressures (CSLPs) for the three hurricanes are lower in response to the stronger vortex at initialization. The size and inner-core structure of each vortex are adjusted closer to observations of these attributes. Addition of reflectivity data in assimilation produces cloud water and rainwater analyses in the initial vortex. The temperature and moisture are also better represented in the hurricane initialization.

Forty-eight-hour forecasts are conducted to evaluate the impact of ADR data using the Advanced Research Hurricane WRF (AHW), a derivative of the Advanced Research WRF (ARW) model. Assimilation of ADR data improves the hurricane-intensity forecasts. Vortex asymmetries, size, and rainbands are also simulated better. Hurricane initialization with ADR data is quite promising toward reducing intensity forecast errors at modest computational expense.

Corresponding author address: Dr. Qingnong Xiao, National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, P. O. Box 3000, Boulder, CO 80307-3000. Email: hsiao@ucar.edu

Abstract

Initialization of the hurricane vortex in weather prediction models is vital to intensity forecasts out to at least 48 h. Airborne Doppler radar (ADR) data have sufficiently high horizontal and vertical resolution to resolve the hurricane vortex and its imbedded structures but have not been extensively used in hurricane initialization. Using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation (3DVAR) system, the ADR data are assimilated to recover the hurricane vortex dynamic and thermodynamic structures at the WRF model initial time. The impact of the ADR data on three hurricanes, Jeanne (2004), Katrina (2005) and Rita (2005), are examined during their rapid intensification and subsequent weakening periods before landfall.

With the ADR wind data assimilated, the three-dimensional winds in the hurricane vortex become stronger and the maximum 10-m winds agree better with independent estimates from best-track data than without ADR data assimilation. Through the multivariate incremental structure in WRF 3DVAR analysis, the central sea level pressures (CSLPs) for the three hurricanes are lower in response to the stronger vortex at initialization. The size and inner-core structure of each vortex are adjusted closer to observations of these attributes. Addition of reflectivity data in assimilation produces cloud water and rainwater analyses in the initial vortex. The temperature and moisture are also better represented in the hurricane initialization.

Forty-eight-hour forecasts are conducted to evaluate the impact of ADR data using the Advanced Research Hurricane WRF (AHW), a derivative of the Advanced Research WRF (ARW) model. Assimilation of ADR data improves the hurricane-intensity forecasts. Vortex asymmetries, size, and rainbands are also simulated better. Hurricane initialization with ADR data is quite promising toward reducing intensity forecast errors at modest computational expense.

Corresponding author address: Dr. Qingnong Xiao, National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, P. O. Box 3000, Boulder, CO 80307-3000. Email: hsiao@ucar.edu

1. Introduction

Accurate predictions of the track and intensity of landfalling hurricanes are crucial for the protection of life and property in coastal regions. Although progress has been made in track forecasting during the past decade, intensity forecasting remains unsatisfactory (Elsberry 2005). An acknowledged deficiency in all hurricane-forecast systems, including the Advanced Research Hurricane Weather Research and Forecasting (WRF) model (AHW; Davis et al. 2008), is an inaccurate initialization of the inner-core vortex structure. Poor initialization has adverse consequences for hurricane-intensity prediction out to at least 2 days in many forecasts. Conventional observations are too sparse over oceans to resolve hurricane vortex inner structures. Most available satellite wind and temperature data over the hurricane inner-core region are unfortunately contaminated by heavy precipitation and thus produce unreliable data for the region. While cloud and precipitation information provided by satellites (including the current suite of microwave instruments now available) is useful for empirical estimation of intensity, it is difficult to derive the three-dimensional wind and temperature fields from these data needed to adequately initialize a numerical model. Furthermore, the horizontal resolution of many instruments [e.g., the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU)] only partially resolves the core. The usage of satellite data to resolve vortex inner-core structure for improving hurricane-intensity forecasting is therefore limited.

Recent efforts at the Hurricane Research Division (HRD) have demonstrated the feasibility of automating the editing and synthesis process of Doppler radar observations, and starting in 2004 automatic wind fields were generated for several cases (Gamache 2005). In 2005 the real-time transmission of aircraft wind fields to a ground-based station within ∼1 h of data collection was successfully demonstrated. Airborne Doppler radar (ADR) data can capture the hurricane vortex dynamic, thermodynamic and hydrometeor structures (Ray et al. 1985; Marks and Houze 1987; Reasor et al. 2000). With the development of advanced data assimilation, ADR data can improve the specification of the hurricane vortex in the model initial conditions and potentially improve hurricane structure and intensity prediction. Zhao and Jin (2008) indicated that assimilating Doppler radar data is capable of improving the hurricane-intensity and precipitation forecasts at landfall. Houze et al. (2006) also pointed out that the ADR data could be used in modeling to investigate the interactions between rainbands and primary hurricane vortex circulation with respect to intensity changes.

Recently, experimental forecasts using the AHW model have shown some promise in forecasting the intensity of tropical cyclones near landfall (Davis et al. 2008), together with details of the wind field and precipitation structure. However, the initial hurricane vortex is simply interpolated from the analysis of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), or from the analysis of the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane forecast system. These analyses prescribe a synthetic initial vortex, a methodology that has been very successful for track forecasts, but unable to substantially improve the prediction of hurricane intensity (Aberson 2003). We hypothesize that an advanced data assimilation technique together with high-resolution aircraft observations within the inner core can enhance the initial vortex definition and improve subsequent forecast skill for intensity. Simulations based on high-resolution analysis will provide more detailed dynamics and thermodynamics of the vortex structure, eyewall, eye, and inner and spiral rainbands near the eyewall (e.g., Liu et al. 1997; Zhu et al. 2004; Yau et al. 2004; Wong and Chan 2006; Krishnamurti et al. 2005; Braun et al. 2006; Chen and Snyder 2006).

The newly developed WRF three-dimensional variational data assimilation (3DVAR) system (Skamarock et al. 2005) is one such advanced data assimilation algorithm. As the successor of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational data assimilation system (Barker et al. 2004), WRF 3DVAR can produce multivariate analysis that is balanced subject to embedded dynamical and statistical constraints. In this study, we will utilize WRF 3DVAR to initialize hurricanes simulations by assimilating ADR observations from the National Oceanic and Atmospheric Administration (NOAA) P-3 reconnaissance aircraft. Although these simulations were not conducted operationally, forecasts based on 3DVAR initialization using ADR data could be run in real time with current computational capabilities.

Numerical experiments are conducted with three cases (Fig. 1): Jeanne (2004), Katrina (2005), and Rita (2005). For each case, we focus on the rapid intensification and the following weakening stages prior to landfall as shown in Fig. 1b. The next section is an overview of the ADR observations for Hurricanes Jeanne (2004), Katrina (2005), and Rita (2005). Section 3 explains the data assimilation strategy, domain configuration, model setup, and experimental design. Results and discussions are presented in section 4 (for initialization) and 5 (for forecasting). Section 6 summarizes some conclusions of this study with additional discussions.

2. Airborne Doppler radar observations and the associated cases

Doppler winds and reflectivity are used in this study for three cases: Hurricanes Jeanne (2004), Katrina (2005), and Rita (2005). We start with the HRD automatic real-time Doppler wind fields retrieved from tail radar observations of the NOAA P-3s (Gamache et al. 2004). The automatic wind retrieval process involves several passes through the data. The first steps are to eliminate reflections off the sea surface, remove noisy data, and dealias folded Doppler velocities. This is followed by a three-dimensional variational analysis to obtain the wind field using the 3DVAR technique of Gamache (1997). Before assimilating the wind fields into WRF an additional step is performed. Using the P-3 flight track information along with the antenna position information, a time field is generated specifying the time that each xyz grid point was sampled by the radar. The time, wind, and reflectivity fields are then written in the CEDRIC format (Mohr et al. 1986). Using the mean storm motion and the time field, the winds and reflectivity are advected to the assimilation time (typically around 1800 UTC) and remapped using the bilinear interpolation scheme in CEDRIC (Mohr et al. 1986). The final wind analyses are on a Cartesian grid having horizontal and vertical resolutions 2 km and 500 m, respectively. A more detailed discussion of the HRD automatic wind retrieval technique can be found in Gamache (2005).

When assimilating the ADR data into WRF initial conditions, only data from one flight leg near 1800 UTC are used. Figure 2 shows the assimilated wind analysis and radar reflectivity from the flight leg for each hurricane. It typically takes 35–40 min to complete a flight leg. Correction of the data over this time interval carries with it an implicit horizontal resolution limit of at least 5 km or so, given typical storm translation speeds. The expected benefit of the radar information is not on the convective scale, which is expected to exhibit very poor predictability anyway, but on the scale of the vortex, including the major asymmetries. The hurricanes and their related ADR observations are summarized as follows.

a. Hurricane Jeanne (2004)

Jeanne (2004) was a tropical storm with winds less than 35 m s−1 before 17 September. It then gradually strengthened to a hurricane with 42 m s−1 winds by 23 September. Jeanne crossed cooler waters and it decayed to 35 m s−1 by 0000 UTC 24 September. At the time of Fig. 2a (∼1800 UTC 24 September) Jeanne was in the middle of a reintensification period with the winds increasing to 50 m s−1 by 1200 UTC 25 September. Jeanne made landfall on the east coast of Florida at 0400 UTC on 26 September.

b. Hurricane Katrina (2005)

Katrina (2005) was one of the most devastating natural disasters in the history of the United States. Katrina formed around 1200 UTC 24 August and reached hurricane status around 0000 UTC on 26 August. It nearly doubled in size on 27 August and strengthened from a category 3 hurricane to a category 5 in less than 12 h reaching an intensity of 72 m s−1 by 1200 UTC 28 August. Figure 2b shows the wind field/radar reflectivity structure at ∼1800 UTC on 27 August shortly before Katrina entered its rapid intensification stage and about 40 h before landfall.

c. Hurricane Rita (2005)

Rita (2005) was an intense hurricane that reached category 5 strength over the central Gulf of Mexico and had the fourth lowest central pressure on record for the Atlantic. Rita reached hurricane status ∼1200 UTC on 20 September about 150 km east-southeast of Key West, Florida. Once over the warm waters of the Loop Current it rapidly intensified to 75 m s−1 by 0000 UTC 22 September. Rita completed the transition from a tropical storm to a category 5 hurricane in less than 36 h. Figure 2c shows Rita at ∼1800 UTC on 20 September as it was passing 60 km south of Key West and just before it began its rapid intensification.

3. Assimilation strategy and experimental design

a. WRF 3DVAR and its assimilation of airborne Doppler radar data

The configuration of WRF 3DVAR is based on the incremental formulation of Courtier et al. (1994), producing a multivariate incremental analysis in model space. The minimization is performed in preconditioned control variable space. The preconditioned control variables are designed based on the characteristics of the ARW model. They are streamfunction, unbalanced velocity potential, unbalanced temperature, pseudo-relative humidity, and unbalanced surface pressure. The unbalanced control variables are constructed in WRF 3DVAR relative to the geostrophic and hydrostatic relations (Barker et al. 2004; Skamarock et al. 2005). A key to 3DVAR is the background error covariance matrix. In this study, the background errors (BEs) were generated using the 30-day 12- and 24-h forecast around the Hurricane Jeanne case. We separately generated the BE for 12- and 4-km resolution domain. Empirical orthogonal functions (EOFs) are applied for the vertical component background error covariance matrix 𝗕. Recursive filtering is performed for the horizontal component of 𝗕. Eigenvectors/eigenvalues of the vertical component are estimated using the National Meteorological Center (NMC) method (Parrish and Derber 1992). Estimates of the recursive filter’s characteristic length scales depend on the variable and its vertical mode. Regression coefficients to calculate the total part of variables from unbalanced variables are also estimated via the NMC method.

The Doppler radar data assimilations in WRF 3DVAR were described in Xiao et al. (2005, 2007) and Xiao and Sun (2007). The capability has been tested in operational forecasting in Korea (Xiao et al. 2008). Assimilating ADR data for hurricane initialization is a new application of WRF 3DVAR. To extract the most useful information from the data, high-resolution assimilation is necessary. In this study, the ADR data assimilation occurs on a domain of 4-km horizontal grid spacing, nested within an outer domain with 12-km spacing. Only conventional Global Telecommunications System (GTS) data are assimilated on the outer domain. The observations, time–space corrected following the vortex, are assumed to be simultaneous. For the airborne Doppler winds, the data from NOAA/HRD are wind components. Assimilation of the winds is straightforward, similar to the assimilation of conventional sounding winds but with a prespecified error (2 m s−1). For the ADR reflectivity data, we follow the same procedure as in Xiao et al. (2007), in that a warm-rain microphysics scheme is used to bridge the relationship among rainwater, cloud water, moisture, and temperature. When rainwater information (from reflectivity) enters into the minimization iteration procedure, the forward microphysical process and its backward adjoint distribute this information to the increments of other variables (under the constraint of the warm-rain microphysics scheme). The warm-rain microphysics is used in WRF 3DVAR because it captures the major hydrometeor process and is relatively easy for the tangent linear and adjoint development. However, it misses the important roles the ice particles play in the development of a hurricane. There have been many studies that show the impact of ice-phased precipitation on hurricane development (Lord et al. 1984; McFarquhar and Black 2004; McFarquhar et al. 2006; Marks et al. 2008; Rogers et al. 2007). We will discuss the drawbacks of using warm-rain microphysics scheme for hurricane initialization in the last section. The observational error for the ADR wind and reflectivity are empirically set as 2.0 m s−1 and 2 dBZ, respectively.

b. AHW model and domain configurations

The numerical model used in this study is the AHW, a derivative of ARW version 21 (Skamarock et al. 2005). It is a compressible, three-dimensional, nonhydrostatic model using terrain-following coordinates and its governing equations are written in flux form. The Runge–Kutta third-order time scheme is employed and fifth- and third-order advection schemes are chosen for the horizontal and vertical directions, respectively.

For the study of Hurricane Jeanne, a spatially fixed, inner domain of 4-km grid spacing was nested interactively within a 12-km outer domain for all simulations. The grid dimensions were 400 × 301 × 35 for domain 1 and 502 × 451 × 35 for domain 2 in the east–west, north–south, and vertical directions, respectively. The following parameterizations were activated for both domains: WRF Single-Moment 3 classes (WSM-3) microphysics scheme (Dudhia 1989); the new Kain–Fritch cumulus parameterization (Kain 2004), which includes deep and shallow convection (only on the outer domain); the Yonsei University (YSU) boundary layer parameterization, which accounts for local and nonlocal mixing (Hong and Noh 2006); the Dudhia shortwave parameterization (Dudhia 1989); and the Rapid Radiative Transfer Model (RRTM) longwave parameterization (Mlawer et al. 1997).

For Hurricanes Katrina and Rita, two moving nests were nested interactively within the outer domain. The innermost nest (domain 3), with 1.33-km grid spacing, was centered within the nest of 4-km grid spacing (domain 2), and the outer domain (domain 1) had a 12-km grid spacing as before. The dimensions were 460 × 351 × 35 for domain 1, 202 × 202 × 35 for domain 2, and 241 × 241 × 35 for domain 3, where domain 2 and 3 are moving nested following the hurricane tracks. The location of the nests was determined by the minimum geopotential height at 500 hPa and was repositioned every 15 min. The physics parameterizations are the same as for the simulations of Hurricane Jeanne, except the microphysics was changed to the WRF Single-Moment 5 classes (WSM-5) scheme (Hong et al. 2004).

The model was integrated for 48 h with a time step of 60, 20, and 6.7 s for domains 1, 2, and 3, respectively.

c. Experimental design

The NCEP/GFS analysis with a spatial resolution of 1° × 1° was used to produce the first guess for all data assimilation. Four sets of experiments for each case were conducted (Table 1): the control run (CTL), which used the NCEP/GFS analysis as the initial condition; the second experiment (GTS), which assimilated only the conventional GTS data in all domains; the third run (GRV), which assimilated GTS in the outer domain (12 km) and the GTS plus airborne radar wind data on domains 2 and 3; and the fourth experiment (GVZ), which used the same strategy as GRV except both the ADR wind and reflectivity data were assimilated on domains 2 and 3.

4. Initialization results with ADR data

We take Hurricane Jeanne (2004) as an example to illustrate the impact of the ADR data assimilation on the vortex structure in the initial conditions. The analysis impact of ADR data assimilation on the other hurricane cases is similar, so it is omitted in the discussion.

a. ADR wind assimilation

Figure 3 shows the sea level pressure (SLP) and 10-m winds with and without ADR wind assimilation for Hurricane Jeanne (2004) at 1800 UTC 24 September (the initialization time of the forecast). The cyclonic circulation is strengthened with a maximum surface wind (MSW) speed increase to 38 m s−1 (Fig. 3c), compared with 23.4 m s−1 in GTS (Fig. 3b) and 23.7 m s−1 in CTRL (Fig. 3a). The observed MSW speed is 44 m s−1. Its central sea level pressure (CSLP) is decreased in experiment GRV after assimilating ADR wind data. Along with the intensity enhancement in wind speeds, the CSLP is correspondingly decreased about 5 hPa through the multivariate constraint in WRF 3DVAR. Figure 4 shows the H*WIND analysis at 2100 UTC 24 September, the nearest in time to 1800 UTC. H*WIND combines data from reconnaissance aircraft, dropsondes, satellite-derived winds, in situ observations, and stepped-frequency microwave radiometer retrievals (Powell et al. 1998; Uhlhorn and Black 2003), and produces a gridded storm-centered 10-m, 1-min, marine exposure sustained wind field. The GRV wind speed distribution in Fig. 3c is much closer to the H*WIND than those in Figs. 3a,b. There are two maximum wind centers in both northwest and southeast quadrants of the vortex in Fig. 3c, due to the data coverage in WRF 3DVAR assimilation. The wind distribution in the assimilated one flight leg data (Fig. 2a) presents the major wind innovations in WRF 3DVAR assimilation are in the northwest and southeast quadrants.

Vertical cross sections of horizontal wind along the line AB in Fig. 3c for GRV and GTS experiments are presented in Fig. 5. A strong and clear maximum wind band with a contracted radius in Fig. 5b is produced after assimilating the ADR wind data. Some asymmetry is apparent, and the maximum wind speed is located at around 900 hPa (Fig. 5b). The pattern and the maximum value concentrate in a small scale within the vortex. However, in the GTS data assimilation experiment, the hurricane vortex circulation is very weak and the radius of maximum wind is too large (Figs. 5a). The CTL experiment has a similar structure as GTS, so it is omitted in the discussion. To our knowledge, the vortex feature in Fig. 5b has not been produced in 3DVAR-based hurricane initialization with any other kind of real data before, except for methods that prescribe a synthetic vortex (Xiao et al. 2009). The recovery of the vortex feature is attributed to assimilating the very high horizontal and vertical resolution ADR data in the inner-core region of the storm. The assimilated airborne Doppler radar data extend up to the top of troposphere. Both the data vertical coverage and the WRF 3DVAR structure function are the reasons of the coherent deep vortex structure in Fig. 5b.

The azimuthally mean tangential wind in Fig. 6 shows again the maximum wind located at the lower level, consistent with the cross sections in Fig. 5. The radius of maximum winds with the ADR wind assimilation is much closer to the hurricane vortex message (74 km) than the GTS experiment. The storm with the ADR wind data assimilation is enhanced with a 40 m s−1 maximum azimuth mean of tangential wind in the boundary layer, whereas peak winds are quite weak without ADR data assimilation, about 24 m s−1 in both the GTS and CTL experiments.

WRF 3DVAR also produces temperature increments using ADR data due to the multivariate incremental structure in the system (Fig. 7). At 300 hPa, both temperature and wind vector increments in the vortex region in GTS experiment are small (Fig. 7a) because no GTS observations exist in the vortex at high levels. The small increments for both the wind vector and temperature in the hurricane vortex region are due to the covariance with the hurricane environment. However, once assimilating the ADR wind data in the vortex region, positive temperature increments occur associated with the strong wind vector increments and the maximum positive temperature increment is 1.117°C at 300 hPa in the vortex inner region (Fig. 7b). In the vertical cross sections above the hurricane vortex, GTS shows only slight increments of temperature above 850 hPa (Fig. 7c). On the contrary, GRV produces a vertical incremental structure with the largest positive temperature increments in the high troposphere around 300 hPa. In the middle troposphere around 500 hPa, there is a negative layer of temperature increments. Notice that both GTS and GRV produce a negative layer of temperature increments above the vortex near the surface due to the assimilation of winds from buoys and ships. It is clear that the ADR data assimilation not only improves the three-dimensional inner vortex wind structure, but also contributes to the vortex’s warm-core structure as well. It should be mentioned that, the temperature response to the wind increments is rather small due to the use of more “climatological” error covariance from 1-month forecasts as opposed to more flow-dependant error covariance that would know about the presence of the hurricane. The vortex region is highly imbalanced; the climatological error covariance can only construct a small part of the warm core, a highly unbalanced structure.

b. ADR reflectivity assimilation

Figure 8 shows the ADR reflectivity assimilation results of Hurricane Jeanne at 1800 UTC 24 September 2004 in the GVZ experiment. The CSLP (987 hPa) and 10-m wind analysis in GVZ (Fig. 8a) are similar to the results of GRV (Fig. 3c). However, GVZ produces cloud water and rainwater analyses after assimilating reflectivity data. Compared with the observation (Fig. 2a), the reflectivity information is only partially recovered to the analysis. Because the microphysics involved in the reflectivity assimilation has many “on–off” switches, the linearity assumption used in the WRF 3DVAR for the reflectivity assimilation is compromised. In addition, the warm-rain scheme in WRF 3DVAR does not represent well the microphysical process above the melting level (∼500 hPa) for hurricanes. Nevertheless, GVZ contains some hydrometeor representation in the initialization, compared to no hydrometeors in GRV.

HH Figures 8b–d illustrates the changes occurring in variables not directly assimilated owing to the addition of reflectivity data. These changes arise from the multivariate structure in the analysis. The temperature and water vapor mixing ratio in the mid to lower levels above hurricane vortex are decreased, while in upper levels are increased (Figs. 8b,c). The storm center’s stability ∂θe/∂z increases (Fig. 8d), while at about 120-km radius, the stability between 900 and 500 hPa decreases, with a maximum difference of θe at around 900 hPa. GVZ also produces a warmer upper-level core. In general, ADR reflectivity assimilation increases the atmospheric stability in the vortex center, while decreasing stability outside the eye region.

5. Impacts on the AHW forecasts

a. Hurricane structure

In general, the ADR data assimilation improves the forecast of hurricane structures for all of the three cases during the entire 48-h forecast period. In this section, we take Hurricane Jeanne (2004) as an example to illustrate these improvements by comparing the forecast structures with and without ADR data assimilation.

Figure 9 presents SLP and 10-m wind vectors and wind speed at 24-h from the GTS, GRV, and GVZ experiments. The 24-h distribution of the predicted SLP shows quite different structure in the three experiments. The CSLP is only 986 hPa in GTS (Fig. 9a), 972 hPa in GRV (Fig. 9b), and 965 hPa in GVZ (Fig. 9c). The observed CSLP at the time is 952 hPa (Fig. 1). Although all do not attain the observed SLP minimum, the assimilation of ADR data results in better SLP forecast relative to the experiment of GTS. The predicted wind structures also exhibit significant differences. GRV and GVZ both increase the 10-m MSW speed to 42.4 and 47.6 m s−1, respectively, up from 40.3 m s−1 in GTS. The MSW speed of 47.6 m s−1 in GVZ is very close to the observed (48.8 m s−1). Comparing the wind features among Figs. 9a–c, another obvious difference is that ADR data assimilation experiments (GRV and GVZ) predict structures with smaller radius of MSW than experiment GTS (cf. Figs. 9b,c with Fig. 9a). The forecast in experiment GTS is effectively missing an inner core.

Cross sections of equivalent potential temperature (θe) and horizontal wind speed through Hurricane Jeanne (2004) for GTS, GRV, and GVZ experiments at 1800 UTC 25 September (24-h forecast) are shown in Fig. 10. Consistent with results shown in Fig. 9, the inner core is effectively absent in experiment GTS. The GFS analysis, which is the background state, is too coarse to resolve any inner-core structure; the GTS data cannot enhance the inner core, and apparently the model is unable to contract the inner core within the first 24 h of the forecast. In experiment GRV (Fig. 10b), there is an inner core showing evidence of a frontlike structure in θe with vertical plume of high θe values radially inward from the strongest winds. With the reflectivity also assimilated (Fig. 10c), there arises a more obvious outward slope of the tangential wind contours (and therefore angular momentum surfaces—not shown), and a further, modest increase in intensity. The structures in both Figs. 10b,c are clearly more indicative of an intensifying hurricane near category 3 intensity than is the structure in Fig. 10a (e.g., Eliassen 1959; Emanuel 1995).

Figure 11 shows the composite reflectivity at 24- and 36-h forecasts for the GTS, GRV, and GVZ experiments. Experiment GTS does not produce an eyewall, indicating that the vortex is not well organized (Figs. 11a,d). However, GRV and GVZ produce well-organized hurricane structures in radar reflectivity with compact eyewalls embedded in the vortex. Comparison of the observed reflectivity at landfall (Fig. 12) with these forecasts suggests that both GRV and GVZ produce a realistic distribution of reflectivity, but the heavier rainband over the east coast of Florida and the suggestion of a break in reflectivity on the east side of the eyewall in GVZ matches the observations somewhat better.

b. Hurricane track and intensity verification

Verification of track and intensity for all three hurricanes is discussed in this subsection for experiments CTL, GTS, GRV, and GVZ. ADR data assimilation improves the intensity forecast for all three hurricanes (Jeanne, Katrina, and Rita). It seems that the forecast improvement in track is not as significant as in intensity, but it is noticeable. It is consistent with the idea that the hurricane track is mostly influenced by the environment, instead of the inner structure of the hurricane. In this subsection, verification results of Hurricane Jeanne (2004) are presented in detail, consistent with previous sections. The average forecast errors of track, CSLP, and MSW for all three cases are then shown. The forecast error is calculated at a 6-h interval using the best-track data as truth.

Figure 13 shows the track forecasts by experiments CTL, GTS, GRV, and GVZ for Jeanne (2004). There is an initial position deviation in CTL (Fig. 13a) with a position error of 40 km (Fig. 13b). The initial position is adjusted closer to the right position in all data assimilation experiments (GTS, GRV, and GVZ). In the subsequent forecast, all data assimilation experiments have less track deviation than CTL, which is biased to the south of the best track (Fig. 13a). GTS follows the best track in the first 18-h forecasts, and then turns to the south of best track until 36 h. With ADR data assimilation, GRV and GVZ improve the track forecast, especially during the first 24-h forecast. However, reflectivity does not add much benefit to the track forecast (Fig. 13b). In general, all experiments predict a slightly slower-moving speed than observed, with the predicted landfall times about 4–5 h later than best track.

The 48-h evolution of Hurricane Jeanne’s (2004) CSLP and MSW from the best track and the four experiments (CTL, GTS, GRV, and GVZ) are shown in Fig. 14. GTS does not improve the CSLP and MSW in the whole 48-h period. Figure 14a shows that ADR data assimilation experiments (GRV and GVZ) significantly improve the CSLP forecast compared with CTL and GTS, although the storm is still not as deep as observed. The CSLP error of GRV (GVZ) is reduced from 37 hPa in CTL to 23 hPa (12 hPa) at 24 h, and from 20 to 8 hPa (<1 hPa) at 48 h. Similarly, the MSW time series also shows the improvement due to ADR data assimilation. At 24 h, GRV (GVZ) predicts MSW of 92 kt (98 kt), compared with the observed MSW of 105 kt. At the same time, experiments CTL and GTS predict 86 and 82 kt, respectively. Overall, the improved intensity forecast for Jeanne offered by assimilating ADR data lasts between 24 and 48 h and is better maintained for CSLP than for maximum wind.

Similar experiments for Hurricanes Katrina and Rita (2005) were also conducted. The airborne Doppler radar data are at 1800 UTC 27 August 2005 for Hurricane Katrina and at 1800 UTC 20 September 2005 for Hurricane Rita. The hurricane forecasts that initialize from the times of data undergo rapid intensifications and follow weakening periods for the two hurricanes. Instead of describing the verifications for each case, the average mean absolute errors for hurricane position, CSLP, and MSW are calculated for the three cases (Fig. 15). The average mean absolute track errors of CTL at 24- and 48-h forecasts are 58 and 125 km, respectively (Fig. 15a). The GTS run improves the track forecast with position errors at 24 and 48 h reduced to 50 and 83 km, respectively. ADR data assimilation further improves the track forecasts, with position errors at 24 and 48 h for GRV (GVZ) reduced to 28 km (32 km) and 90 km (91 km), respectively. These results demonstrate that ADR data assimilation benefits the hurricane track forecast.

Figures 15b,c indicate that the intensity forecast is more significantly improved by ADR data assimilation than track. While GTS data shows no universal benefits for the intensity forecast, the CSLP average mean absolute errors with ADR data initialization are reduced at each forecast lead time within 48 h. The improvement in MSW is maintained for roughly 30 h, echoing the results for Jeanne alone. Significant reduction of MSW mean absolute errors occurs at the initial time. On average, the error reduction is nearly 29 kt (25 kt) by the ADR data initialization in GRV (GVZ). In response to the vortex wind correction, the CSLP is decreased. The average decrease from the three cases is about 7 hPa, which is not as significant as the MSW increase. The results indicate that the current correlation between CSLP and MSW is relatively weak in the WRF 3DVAR system. The increments in vortex dynamical fields obtained by assimilating ADR wind do not result in a correspondingly large pressure response. Because the background error statistics used in this study were based on statistics averaged over an entire month, they are not flow dependent and therefore do not reflect the vortex structure among variables in the background covariance. We anticipate further improvements by using specific error covariance that recognizes the hurricane vortex structure.

Nevertheless, the hurricane-intensity forecasts are improved with ADR data assimilation using the relatively computationally inexpensive 3DVAR approach. The largest error reduction of CSLP and MSW occurs at 24 h. At 48 h, the CSLPs of both GRV and GVZ experiments still show less error than CTL and GTS. However, the MSW errors at 48 h are larger than CTL and GTS experiments. Comparing GVZ with GRV, we notice that adding ADR reflectivity in hurricane initialization (GVZ) further reduces the hurricane-intensity forecast errors from GRV. Assimilating reflectivity has added value for hurricane-intensity forecast (Figs. 15b,c), even though it does not show much benefit in track forecast (Fig. 15a).

In order for the readers to know the current status of WRF 3DVAR hurricane initialization compared with other schemes, such as the GFDL hurricane initialization scheme, we also calculated the average mean absolute errors of the operational GFDL results for the three cases (dashed line with circle in Fig. 15). The experiment using WRF 3DVAR with ADR data (GVZ) produces better track and intensity forecasts than the GFDL results beyond 20 h. Although the short-term (<20 h) hurricane forecasts (track, CSLP, and WSM) show significant improvement from CTL; however, WRF 3DVAR hurricane initialization still needs further development for short-term hurricane forecasts compared with GFDL scheme. Using previous cycling WRF forecast (with the spunup vortex) as the background for 3DVAR hurricane initialization can further improve the short-term hurricane forecast. As we know, variational data assimilation is a weighted combination of the background and observation information. Because the vortex position in the background filed from previous WRF forecast is not necessarily consistent with the observation, some kind of relocation may be necessary. We have started this work and will report the results in the future.

Examining the maximum errors in our experiments for the three cases, we found that the ability to reduce intensity errors by ADR data assimilation decreases as hurricane intensity increases. CTL has the maximum CSLP error of 35 hPa for Jeanne, 48 hPa for Katrina, and 52 hPa for Rita at initial time. The improvement from ADR data initialization is larger for Hurricane Jeanne than for Katrina and Rita. There are two reasons for this behavior. First, only a single set of background error statistics are computed and these represent Jeanne. The background error covariance used in the experiments for Hurricanes Katrina and Rita (2005) are interpolated from the covariance used for the experiments for Hurricane Jeanne (2004). Second, Hurricanes Katrina and Rita (2005) are much stronger than Jeanne. Hurricane Jeanne (2004) is a category 3 hurricane with a strongest intensity of 951 hPa, whereas both Katrina and Rita (2005) are category 5 hurricanes with strongest intensities of 902 and 897 hPa, respectively. Because of the intensity of Katrina and Rita, and in particular the fact that both storms were entering a rapid intensification stage, the use of monthly mean covariance statistics should be a worse approximation to the true statistics than for Jeanne. It is possible that the approach used herein will be most effective for initialization of larger tropical cyclones or systems not undergoing rapid intensification at initialization time. It seems logical that time-dependent and flow-dependent assimilation strategies will be essential for properly initializing small and rapidly intensifying storms.

6. Summary and discussion

The capability of airborne Doppler radar (ADR) data assimilation to improve hurricane initialization using WRF 3DVAR is examined for Hurricanes Jeanne (2004), Katrina (2005), and Rita (2005). The intensification to peak strength and the following weakening periods of all three cases are selected in our experiments. The ADR wind and reflectivity data are available about 24–30 h before these three hurricanes reached their maximum intensity. Four experiments were conducted for each case: a control run using NCEP/GFS analysis, a run with conventional GTS data assimilation, an experiment with ADR wind data assimilation, and an experiment with combined ADR wind and reflectivity data assimilation. The followings are highlights of our findings from these experiments:

  • Simulations using ADR wind data assimilation markedly improve the representation of the hurricane vortex structure both at the initial time and in the forecast out to about 36 h. The ADR wind assimilation makes important contributions to improving hurricane-intensity and structure forecasts. Hurricane track forecasts also benefited from the assimilation of ADR wind data.

  • The ADR reflectivity data assimilation in WRF 3DVAR system retrieves portion of the three-dimensional rainwater and cloud water fields of hurricane vortex at initialization. The multivariate responses in other variables are also reasonable. The addition of ADR data produces a realistic eyewall and associated strong convection. Rainbands are also favorably reorganized and appear more realistic.

  • Assimilating only GTS conventional data using WRF 3DVAR has a very slight impact on either the hurricane initialization or the forecast. Because the GTS data has already been assimilated in NCEP/GFS analysis, further enhancement in high-resolution WRF grids is no more beneficial than just downscaling from low-resolution GFS analysis. In addition, GTS data are not in the hurricane vortex region, and do not provide much information in vortex initialization.

  • The benefits of ADR data assimilation are somewhat smaller for stronger, rapidly intensifying Hurricanes Katrina and Rita (2005). We attribute this to a lack of flow dependence of error covariance used in the assimilation and to a lack of including time dependence in the assimilation (e.g., what would occur with 4DVAR). However, we still demonstrate considerable improvement with a computationally modest assimilation approach. Essentially, the large volume of data may offset the limitations of the basic 3DVAR approach.

This study demonstrates the potential for improving the hurricane-intensity forecasts using ADR data for model initialization. More than roughly 12 h before landfall, hurricanes are typically not well observed by land-based Doppler radars. Satellite data are less useful within the vortex due to cloud and rainfall contamination, limited spatial resolution, or suboptimal timing of observations (e.g., from polar-orbiting platforms). Assimilating ADR data is feasible and should be considered in forecasting of landfalling hurricanes so as to reduce the loss of life and property in coastal regions.

In terms of WRF 3DVAR for ADR data assimilation, some limitations also exist. First, a specific background error covariance for hurricanes should be developed and used in hurricane initialization. The background error statistics used in this study are from the traditional NMC technique (Parrish and Derber 1992). It is not totally suitable for the correlations in the hurricane vortex. For example, the CSLP response from assimilation of ADR wind is not enough. The correlation of wind and pressure only presents large-scale features. Second, reflectivity assimilation in WRF 3DVAR uses warm-rain process to bridge rainwater with other model variables in the analysis. At high levels above the melting layer, however, ice-phase hydrometeors contribute to the most of reflectivity measurement. In this regard, a sophisticated microphysics that builds relationships among the whole hydrometeors and other dynamical and thermodynamical variables should be developed in WRF 3DVAR for radar reflectivity data assimilation. Finally, observation error statistics for aircraft radar data are only crudely represented at present. In addition, it should also be noted that the ADR data are not simultaneous, but rather are measured over each flight leg during a 35–45-min period. WRF 3DVAR does not take into account the time differences but instead ingests data at one instant in time. 4DVAR should be a future direction for ADR data assimilation in order to better initialize the time dependence of the vortex necessary to accurately capture rapid intensity change as it is occurring.

Acknowledgments

We are grateful to our colleagues: James Done, Wei Wang, Jimy Dudhia, Dale Barker, and Yongsheng Chen for their help in our experiments. We also wish to express our gratitude to the NOAA/Hurricane Research Division of AOML for supplying the hurricane wind and reflectivity data used in this study. The comments on our initial draft of this work by James Done and Jimy Dudhia are greatly acknowledged. This research was funded by the NCAR Opportunity Fund and by NOAA Grants NA05111706, NA060AR4600181, and NA050AR4601145, and through the Northern Gulf Institute by Grant NA060AR4320264. The first two authors were also supported by the National Natural Science Foundation of China (Grant 40828005) for this research.

REFERENCES

  • Aberson, S., 2003: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Wea. Rev., 131 , 16131628.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y-R. Guo, A. J. Bourgeois, and Q. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132 , 897914.

    • Search Google Scholar
    • Export Citation
  • Braun, S. A., M. T. Montgomery, and Z-X. Pu, 2006: High-resolution simulation of Hurricane Bonnie (1998). Part I: The organization of eyewall vertical motion. J. Atmos. Sci., 63 , 1942.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and C. Snyder, 2006: Initializing a hurricane vortex with an ensemble Kalman filter. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., 8A.5.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., J-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120 , 13671387.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136 , 19902005.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 30773107.

    • Search Google Scholar
    • Export Citation
  • Eliassen, A., 1959: On the formation of fronts in the atmosphere. The Atmosphere and Sea in Motion, B. Bolin, Ed., Oxford University Press, 227–287.

    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., 2005: Achievement of USWRP hurricane landfall research goal. Bull. Amer. Meteor. Soc., 86 , 643645.

  • Emanuel, K. A., 1995: The behavior of a simple hurricane model using a convective scheme based on subcloud-layer entropy equilibrium. J. Atmos. Sci., 52 , 39603968.

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., 1997: Evaluation of a fully three-dimensional variational Doppler analysis technique. Preprints, 28th Conf. on Radar Meteorology, Austin, TX, Amer. Meteor. Soc., 422–423.

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., 2005: Final report on JHT project: Real-time dissemination of hurricane wind fields determined from airborne Doppler radar data. NOAA/NHC, 38 pp. [Available online at http://www.nhc.noaa.gov/jht/2003-2005reports/DOPLRgamache_JHTfinalreport.pdf].

    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., J. S. Griffin Jr., P. P. Dodge, and N. F. Griffin, 2004: Automatic Doppler analysis of three-dimensional wind fields in hurricane eyewalls. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 5D.4. [Available online at ams.confex.com/ams/pdfpapers/75806.pdf].

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., and Y. Noh, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134 , 23182341.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., J. Dudhia, and S-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132 , 103120.

    • Search Google Scholar
    • Export Citation
  • Houze Jr., R. A., and Coauthors, 2006: The hurricane rainband and intensity change experiment—Observations and modeling of Hurricanes Katrina, Ophelia, and Rita. Bull. Amer. Meteor. Soc., 87 , 15031521.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43 , 170181.

  • Krishnamurti, T. N., S. Pattnaik, L. Stefanova, T. S. V. Vijaya Kumar, B. P. Mackey, A. J. O’Shay, and R. J. Pasch, 2005: The hurricane intensity issue. Mon. Wea. Rev., 133 , 18861912.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41 , 28362848.

    • Search Google Scholar
    • Export Citation
  • Marks, F. D., and R. A. Houze, 1987: Inner core structure of Hurricane Alicia from airborne radar observation. J. Atmos. Sci., 44 , 12961317.

    • Search Google Scholar
    • Export Citation
  • Marks, F. D., P. G. Black, M. T. Montgomery, and R. W. Burpee, 2008: Structure of the eye and eyewall of Hurricane Hugo (1989). Mon. Wea. Rev., 136 , 12371259.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and R. A. Black, 2004: Observations of particle size and phase in tropical cyclones: Implications for mesoscale modeling of microphysical processes. J. Atmos. Sci., 61 , 422439.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., H. Zhang, G. Heymsfield, R. Hood, J. Dudhia, J. B. Halverson, and F. Marks, 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63 , 127150.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 , (D14). 1666316682.

    • Search Google Scholar
    • Export Citation
  • Mohr, C. G., L. J. Miller, R. L. Vaughan, and H. W. Frank, 1986: On the merger of mesoscale data sets into a common Cartesian format for efficient and systematic analysis. J. Atmos. Oceanic Technol., 3 , 143161.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120 , 17471763.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77–78 , 5364.

    • Search Google Scholar
    • Export Citation
  • Ray, P. S., D. P. Jorgensen, and S-L. Wang, 1985: Airborne Doppler radar observation of a convective storm. J. Climate Appl. Meteor., 24 , 687698.

    • Search Google Scholar
    • Export Citation
  • Reasor, P. D., M. T. Montgomery, F. D. Marks Jr., and J. F. Gamache, 2000: Low-wavenumber structure and evolution of the hurricane inner core observed by airborne dual-Doppler radar. Mon. Wea. Rev., 128 , 16531680.

    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., M. L. Black, S. S. Chen, and R. A. Black, 2007: An evaluation of microphysics fields from mesoscale model simulations of tropical cyclones. Part I: Comparisons with observations. J. Atmos. Sci., 64 , 18111834.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20 , 99116.

    • Search Google Scholar
    • Export Citation
  • Wong, M. L. M., and J. C. L. Chan, 2006: Tropical cyclone motion in response to land surface friction. J. Atmos. Sci., 63 , 13241337.

  • Xiao, Q., and J. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135 , 33813404.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y-H. Kuo, J. Sun, W-C. Lee, E. Lim, Y-R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44 , 768788.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y-H. Kuo, J. Sun, W-C. Lee, D. M. Barker, and E. Lim, 2007: An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46 , 1422.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Coauthors, 2008: Doppler radar data assimilation in KMA’s operational forecasting. Bull. Amer. Meteor. Soc., 89 , 3943.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., L. Chen, and X. Zhang, 2009: Evaluations of BDA scheme using the Advanced Research WRF (ARW) model. J. Appl. Meteor. Climatol., 48 , 680689.

    • Search Google Scholar
    • Export Citation
  • Yau, M. K., Y. Liu, D-L. Zhang, and Y. Chen, 2004: A multiscale numerical study of Hurricane Andrew (1992). Part VI: Small-scale inner-core structures and wind streaks. Mon. Wea. Rev., 132 , 14101433.

    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and Y. Jin, 2008: High-resolution radar data assimilation for Hurricane Isabel (2003) at landfall. Bull. Amer. Meteor. Soc., 89 , 13551372.

    • Search Google Scholar
    • Export Citation
  • Zhu, T., D-L. Zhang, and F. Weng, 2004: Numerical simulation of Hurricane Bonnie (1998). Part I: Eyewall evolution and intensity changes. Mon. Wea. Rev., 132 , 225241.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Observed (best track) of (a) 5-day hurricane positions and (b) CSLPs for Hurricanes Jeanne (2004), Katrina (2005), and Rita (2005). The X’s in (a) denote the initialization time with the ADR data. The gray segments of the curves denote the WRF model simulation period in our experiments.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 2.
Fig. 2.

Horizontal wind field and radar reflectivity at 2.5 km MSL for Hurricanes (a) Jeanne at ∼1800 UTC 24 Sep 2004, (b) Katrina at ∼1800 UTC 27 Aug 2005, and (c) Rita at ∼1800 UTC 20 Sep 2005. The color scale key on the right shows the reflectivity values (dBZ) and the reference velocity vector is shown on the lower right.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 3.
Fig. 3.

SLP (thick solid isolines), and surface (10 m) wind vector and speed (shadings with thin isolines) for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by experiments (a) CTL, (b) GTS, and (c) GRV. The shading scale for surface wind speed is on the lower right. Line AB is used for cross sections in Fig. 5.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 4.
Fig. 4.

H*WIND isotach analysis (kt) at 2100 UTC 24 Sep 2004 for Hurricane Jeanne (from the NOAA/AOML/HRD Web site).

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 5.
Fig. 5.

Cross sections of horizontal wind speed (interval: 5 m s−1) above Hurricane Jeanne (2004) along line AB in Fig. 3c, by experiments (a) GTS and (b) GRV.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 6.
Fig. 6.

Azimuthally averaged tangential winds (interval: 2 m s−1) for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by experiments (a) GTS and (b) GRV.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 7.
Fig. 7.

300-hPa analytical increments of wind vector (maximum vector represents 29.7 m s−1) and temperature (isolines with contour interval of 0.2 K, the negative value dashed) by experiments (a) GTS and (b) GRV, and cross sections of temperature increments (0.2-K interval) across the vortex from west to east in (c) GTS and (d) GRV.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 8.
Fig. 8.

Initialization with ADR reflectivity assimilation for Hurricane Jeanne at 1800 UTC 24 Sep 2004 by GVZ experiment (a) surface analysis of SLP (gray isolines with the interval of 2 hPa), 10-m wind (arrows with the maximum vector representing 38.4 m s−1), and recovered composite reflectivity (dBZ) in the analysis (shading with the scales on the right), and cross sections of (b) temperature difference (with 0.1-K interval), (c) water vapor mixing ratio difference (with 0.05 g kg−1 interval), and (d) equivalent potential temperature (θe) difference (with 0.2-K interval) between GVZ and GRV (GVZ–GRV) above the hurricane vortex along the line AB in (a).

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 9.
Fig. 9.

The 24-h forecasts of SLP (thick solid isolines) and surface (10 m) wind vector and speed (shadings with thin isolines) for Hurricane Jeanne at 1800 UTC 25 Sep 2004 by experiments (a) GTS, (b) GRV, and (c) GVZ. The shading scale for surface wind speed is on the upper right.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 10.
Fig. 10.

Vertical cross sections of equivalent potential temperature (solid isolines with interval of 2 K) and horizontal wind speed (shading with the scale on the upper right) at 1800 UTC 25 Sep 2004 (24-h forecast) for experiments (a) GTS, (b) GRV, and (c) GVZ.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 11.
Fig. 11.

The column maximum radar reflectivity (dBZ) at 1800 UTC 25 Sep (24-h forecast) for experiments (a) GTS, (b) GRV, and (c) GVZ, and at 0600 UTC 26 September (36-h forecast) for experiments (d) GTS, (e) GRV, and (f) GVZ.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 12.
Fig. 12.

The reflectivity (dBZ) image from the Weather Surveillance Radar-1988 Doppler from Melbourne, FL, at 0232 UTC 26 Sep 2004.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 13.
Fig. 13.

The 48-h forecasts of Hurricane Jeanne from 1800 UTC 24 Sep through 1800 UTC 26 Sep 2004: (a) tracks and (b) track errors (km) from the four experiments CTL, GTS, GRV, and GVZ.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 14.
Fig. 14.

The 48-h forecasts of the intensity for Hurricane Jeanne from 1800 UTC 24 Sep through 1800 UTC 26 Sep 2004: (a) CSLP and (b) MSW speed.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Fig. 15.
Fig. 15.

Time series of the average mean absolute errors of (a) track, (b) CSLP, and (c) MSW for the three hurricanes by the CTL, GTS, GRV, and GVZ experiments as well as the operational GFDL results with Jeanne initialized at 1800 UTC 24 Sep 2004, Katrina initialized at 1800 UTC 27 Aug 2005, and Rita initialized at 1800 UTC 20 Sep 2005.

Citation: Monthly Weather Review 137, 9; 10.1175/2009MWR2828.1

Table 1.

Experimental design.

Table 1.

1

The WRF version 2.1 was used for the experiments of Hurricane Jeanne (2004), but version 2.2 was used for the experiments of Hurricanes Katrina (2005) and Rita (2005).

* The National Center for Atmospheric Research is sponsored by the National Science Foundation.

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