• Andreas, E. L, , Perrson P. O. G. , , and Hare J. E. , 2008: A bulk turbulent air–sea flux algorithm for high-wind, spray conditions. J. Phys. Oceanogr., 38, 15811596.

    • Search Google Scholar
    • Export Citation
  • Beven, J. L., 2006: Blown away: The 2005 Atlantic hurricane season. Weatherwise, 59 (4), 3244.

  • Cecil, D. J., , and Zipser E. J. , 1999: Relationships between tropical cyclone intensity and satellite-based indicators of inner core convection: 85-GHz ice-scattering signature and lightning. Mon. Wea. Rev., 127, 103123.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and Kaplan J. , 1999: An updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 14, 326337.

    • Search Google Scholar
    • Export Citation
  • Durden, S., , and Tanelli S. , 2009: Application of clutter suppression methods to a geostationary weather radar concept. Prog. Electromagn. Res. Lett., 8, 115124.

    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430.

  • Gao, J., , and Droegemeier K. K. , 2004: A variational technique for dealiasing Doppler radial velocity data. J. Appl. Meteor., 43, 934940.

    • Search Google Scholar
    • Export Citation
  • Im, E., and Coauthors, 2007: Workshop report on Nexrad-In-Space—A geostationary satellite Doppler weather radar for hurricane studies. Preprints, 33rd Conf. on Radar Meteorology, Cairns, QLD, Australia, Amer. Meteor. Soc., 4B.5. [Available online at http://ams.confex.com/ams/pdfpapers/123726.pdf.]

    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., , Knaff J. A. , , Berger H. I. , , Herndon D. C. , , Cram T. A. , , Velden C. S. , , Murnane R. J. , , and Hawkins J. D. , 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, 89101.

    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., , Bender M. A. , , and Ross R. J. , 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , and Marks F. D. , 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part II: The GBVTD-simplex center finding algorithm. Mon. Wea. Rev., 128, 19251936.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , Jou B. J.-D. , , Chang P.-L. , , and Deng S.-M. , 1999: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part I: Interpretation of Doppler velocity patterns and the GBVTD technique. Mon. Wea. Rev., 127, 24192439.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , Jou B. J.-D. , , Chang P. L. , , and Marks F. D. Jr., 2000: Tropical cyclone kinematic structure retrieved from single Doppler radar observations. Part III: Evolution and structure of Typhoon Alex (1987). Mon. Wea. Rev., 128, 39824001.

    • Search Google Scholar
    • Export Citation
  • Lin, J. K. H., , Fang H. , , Im E. , , and Quijano U. O. , 2006: Concept study of a 35-m spherical reflector system for NEXRAD in space application. 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conf., Newport, RI, American Institute of Aeronautics and Astronautics, AIAA 2006-1604. [Available online at https://imageserv5.team-logic.com/mediaLibrary/93/Concept_Study_of_a_35-m_Spherical_Reflector_System_for_NEXRAD_in_Space_Application2.pdf.]

    • Search Google Scholar
    • Export Citation
  • Masunaga, H., , and Kummerow C. D. , 2005: Combined radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm. J. Atmos. Oceanic Technol., 22, 909929.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., , and Velden C. S. , 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298.

    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419.

  • Rogers, R., , Aberson S. , , Black M. , , Cione J. , , Dodge P. , , Gamache J. , , Kaplan J. , , and Powell M. , 2006: The intensity forecasting experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 15231537.

    • Search Google Scholar
    • Export Citation
  • Tripoli, G. J., 1992: A nonhydrostatic mesoscale model designed to simulate scale interaction. Mon. Wea. Rev., 120, 13421359.

  • View in gallery

    Illustration of the spiral scan performed by NIS as well as the pointing angles α and β, which are positive (negative) in the north (south) and east (west) directions relative to the satellite subpoint. (a) Note that the scan illuminates a disk of radius 5300 km (approximately ±23° latitude/longitude). (b) The vertical scan geometry is illustrated. The incidence angle ξ is a function of the pointing angles α and β, and reduces to the sum of α and the local latitude φ, when β = 0.

  • View in gallery

    Hurricane Wilma over the period from 0000 UTC 18 Oct to 0000 UTC 21 Oct. The best-track positions and intensities (black) and those from the UW-NMS 2-km simulation (red) are shown.

  • View in gallery

    Evolution of the (a)–(c) simulated NIS Ka-band reflectivity and (d)–(f) Doppler radial velocity at 3 km above the sea surface at 12-h intervals spanning the period from 1200 UTC 19 Oct to 1200 UTC 20 Oct. Also shown are the (g)–(i) GBVTD retrieved horizontal wind and (j)–(l) model-simulated horizontal wind for this same level and sequence of times.

  • View in gallery

    West–east vertical cross section of the evolving horizontal wind. The wind fields as (a)–(c) computed directly from the 2-km simulation and (d)–(f) retrieved from the NIS observations using GBVTD are shown.

  • View in gallery

    (a) Time series comparison of the MW at the lowest model level (black; i.e., 125 m above the sea surface), and that computed from the GBVTD retrieval (red) are shown, as is (b) a similar comparison for the RMW (solid lines) and R64 (dashed lines).

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Geostationary Doppler Radar and Tropical Cyclone Surveillance

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  • 1 University of Wisconsin—Madison, Madison, Wisconsin
  • | 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 3 University of Wisconsin—Madison, Madison, Wisconsin
  • | 4 The Center for Research on the Changing Earth System, Clarksville, Maryland
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Abstract

The potential usefulness of spaceborne Doppler radar as a tropical cyclone observing tool is assessed by conducting a high-resolution simulation of an intense hurricane and generating synthetic observations of reflectivity and radial velocity. The ground-based velocity track display (GBVTD) technique is used to process the radial velocity observations and generate retrievals of meteorologically relevant metrics such as the maximum wind (MW), radius of maximum wind (RMW), and radius of 64-kt wind (R64). Results indicate that the performance of the retrieved metrics compares favorably with the current state-of-the-art satellite methods for intensity estimation and somewhat better than current methods for structure (i.e., wind radii).

Corresponding author address: William E. Lewis, Space Science and Engineering Center/Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, WI 53706. E-mail: welewis@wisc.edu

Abstract

The potential usefulness of spaceborne Doppler radar as a tropical cyclone observing tool is assessed by conducting a high-resolution simulation of an intense hurricane and generating synthetic observations of reflectivity and radial velocity. The ground-based velocity track display (GBVTD) technique is used to process the radial velocity observations and generate retrievals of meteorologically relevant metrics such as the maximum wind (MW), radius of maximum wind (RMW), and radius of 64-kt wind (R64). Results indicate that the performance of the retrieved metrics compares favorably with the current state-of-the-art satellite methods for intensity estimation and somewhat better than current methods for structure (i.e., wind radii).

Corresponding author address: William E. Lewis, Space Science and Engineering Center/Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, WI 53706. E-mail: welewis@wisc.edu

1. Introduction

Tropical cyclones (TCs) are among the most devastating natural phenomena on earth, capable of spreading destruction and loss of life across wide geographical areas (e.g., Beven 2006). While the difficulties surrounding TC forecasting are well known (Rappaport et al. 2009), the more fundamental issue of observing the cyclone is itself problematic. This is important because, aside from providing clues to forecasters on short-term intensity change (Cecil and Zipser 1999), particulars of storm structure serve as the basis for both statistical and dynamical forecast model initialization.

Currently, only reconnaissance aircraft equipped with Doppler radar are capable of providing such information at time and space scales that are definitive. As demonstrated during summer 2010 field campaigns such as the Intensity Forecasting Experiment (IFEX; Rogers et al. 2006), Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT; http://met.nps.edu/~mtmontgo/predict.html), and Genesis and Rapid Intensification Processes (GRIP; http://grip.nsstc.nasa.gov), coordinated deployment of several manned and unmanned aerial vehicles was necessary to capture genesis, intensification, and decay. Such data are expected to improve our understanding of these processes, but the transition of this knowledge to an operational framework would require an even more extensive and prolonged deployment of aerial vehicles to guarantee observation of many, and often simultaneous, disturbances. Low-earth orbiting (LEO) satellites with passive microwave imagers [e.g., Special Sensor Microwave Imager (SSM/I), Advanced Microwave Sounding Unit (AMSU), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)] can provide observations of the precipitation field, but they lack the ability to sample the wind field directly and are hampered by narrow swath widths and rather infrequent sampling of individual TCs, sometimes requiring more than a day to resample a system. Geostationary (GEO) satellites, on the other hand, provide superior temporal sampling (with a half-disk of ~30 min and an interest area of ~15 min), but they too are incapable of directly sampling the wind field, necessitating the use of indirect, pattern recognition methods of intensity estimation (Dvorak 1975). What is required is a geosynchronous platform with both wind and precipitation observing capabilities well inside the storm. To that end, a geostationary Ka-band Doppler radar instrument known as Next Generation Weather Radar (NEXRAD) In Space (NIS; Im et al. 2007) has been proposed.

To demonstrate the capability of NIS as a TC-observing tool, a numerical simulation of the Atlantic basin’s Hurricane Wilma (2005) is conducted in order to produce synthetic observations of reflectivity and radial wind in accord with the NIS instrument specifications. The ground-based velocity track display (GBVTD) technique is used to retrieve meteorologically useful TC metrics from the raw NIS data, and the results are compared to current state-of-the-art, satellite-based TC surveillance methods.

Section 2 provides a brief description of the NIS instrument. Section 3 gives a summary of the experiment design, including the chosen case and the simulation details. Section 4 describes the methodology for simulating the types of observations that NIS is anticipated to provide. Section 5 demonstrates the value of these observations in monitoring the evolution of a TC, and section 6 provides discussion and motivation for further work.

2. NEXRAD in space

The NIS radar is anticipated to operate at 35 GHz, with a minimum detectable signal of 5 dBZ and a Doppler precision of 0.3 m s−1. To achieve the desired horizontal and vertical resolutions (~13 km and 300 m, respectively) from geosynchronous orbit, a spherical antenna with an effective aperture of 28 m is required. An innovative spiraling dual feed is employed to cover a disc on the earth’s surface with a diameter of 5300 km, which results in coverage to approximately 23°N/S of the equator (Fig. 1a). If NIS were to be deployed at 80°W, for example, this would permit the observation of the entire Caribbean Sea and much of the main development region (MDR) in the eastern Pacific (one might also imagine a deployment in the central tropical Atlantic, say at 40°W, which would allow the Atlantic MDR to be observed in its entirety). The time required to complete a full spiral scan is 1 h (shorter times are possible for coverage of an area of interest smaller than the full disc), thus providing surveillance of the tropics (or some subsection thereof) at a temporal resolution similar to current GEO satellites.

Fig. 1.
Fig. 1.

Illustration of the spiral scan performed by NIS as well as the pointing angles α and β, which are positive (negative) in the north (south) and east (west) directions relative to the satellite subpoint. (a) Note that the scan illuminates a disk of radius 5300 km (approximately ±23° latitude/longitude). (b) The vertical scan geometry is illustrated. The incidence angle ξ is a function of the pointing angles α and β, and reduces to the sum of α and the local latitude φ, when β = 0.

Citation: Journal of Atmospheric and Oceanic Technology 28, 10; 10.1175/JTECH-D-11-00060.1

Readers interested in a more detailed treatment of NIS operating characteristics are referred to the papers by Lin et al. (2006) and Im et al. (2007).

3. Hurricane Wilma

The University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) (Tripoli 1992) is used to produce a 72-h simulation of Wilma, beginning from the Geophysical Fluid Dynamics Laboratory (GFDL) analysis at 0000 UTC 18 October. The simulation employs a triply nested grid configuration with 32-, 8-, and 2-km horizontal grid spacing on the outer grid and respective nests, 31 vertical levels with grid spacing of 250 m from the lowest layer to 1 km at the top, as well as a novel parameterization for surface fluxes modulated by sea spray in high-wind conditions (Andreas et al. 2008).

The track and intensity of Wilma, both in the best-track dataset (black) and the UW-NMS simulation (red), are shown in Figs. 2a,b, respectively. The simulated track agrees well with observations through 24 h or so, but exhibits a westward bias thereafter, resulting in landfall on the Yucatan Peninsula near 1800 UTC 20 October. The intensity evolution is somewhat better captured, with the simulated minimum in pressure (888 hPa) occurring 6 h after the observed minimum. Figures 3a–c show the TC structure by way of simulated Ka-band reflectivity at 3 km over the period from 1200 UTC 19 October to 1200 UTC 20 October. This clearly shows secondary eyewall formation (SEF) and a subsequent eyewall replacement cycle (ERC), as was observed in nature, though again the timing of the event is some 6 h late.

Fig. 2.
Fig. 2.

Hurricane Wilma over the period from 0000 UTC 18 Oct to 0000 UTC 21 Oct. The best-track positions and intensities (black) and those from the UW-NMS 2-km simulation (red) are shown.

Citation: Journal of Atmospheric and Oceanic Technology 28, 10; 10.1175/JTECH-D-11-00060.1

Fig. 3.
Fig. 3.

Evolution of the (a)–(c) simulated NIS Ka-band reflectivity and (d)–(f) Doppler radial velocity at 3 km above the sea surface at 12-h intervals spanning the period from 1200 UTC 19 Oct to 1200 UTC 20 Oct. Also shown are the (g)–(i) GBVTD retrieved horizontal wind and (j)–(l) model-simulated horizontal wind for this same level and sequence of times.

Citation: Journal of Atmospheric and Oceanic Technology 28, 10; 10.1175/JTECH-D-11-00060.1

4. Simulated NIS observations and retrieved horizontal wind

To gauge the degree to which NIS could capture evolving TC structure, synthetic observations are created from the model simulation described in section 3. The Satellite Data Simulator Unit (SDSU), a stand-alone package capable of simulating visible, infrared, and microwave observations, is used to simulate the equivalent radar reflectivity factor Ze from the modeled thermodynamic and microphysical fields. The microwave portion of the SDSU follows Masunaga and Kummerow (2005) and includes two-way path-integrated attenuation.

The Doppler radial velocity VR that is observed by NIS is computed from the three velocity components (u, υ, w) and terminal velocity (υT) fields output by the model as
e1
where the arguments θ and γ are given by
e2
e3
The arguments α and β are the pointing angles in the north–south and east–west directions (see Fig. 1a), respectively, assuming the satellite subpoint is set at 80°W. The radius of the earth at the equator is a (6378 km), and d is the range (km) from the radar instrument to the target. The first term in (1) represents VV, the component of VR resulting from the fall speed of the hydrometeor, and the last two terms, collectively, represent that resulting from the horizontal wind VH.

The simulated observations of Ze and VR are first computed on the high-resolution model domain and then degraded to NIS resolution using a Gaussian beam convolution algorithm provided with the SDSU package.

From the standpoint of TC surveillance, it is practical to consider the retrieved horizontal wind field because this field forms the basis for much of the information (maximum sustained wind, and radius of hurricane- and gale-force winds, e.g.) contained in TC advisories. One point of concern with regard to retrieving the horizontal wind is the incidence angle of the radar beam with the local vertical. As this angle ξ approaches zero, the contribution of VH to VR also approaches zero. Therefore, at nadir, there is no possibility of retrieving VH. The incidence angle of the NIS beam with the local vertical can be written as
e4
Figure 1b depicts the geometry when β = 0, in which case ξ is the simply the sum of α and the local latitude. This demonstrates that ξ is always greater than the pointing angles for off-nadir viewing, which guarantees a significant horizontal wind signal for most TC applications. For the case considered here, the maximum pointing angle is only 3.7°, while ξ ranges from 18° to 26.5°.

To retrieve the horizontal wind, the GBVTD technique introduced by Lee et al. (1999) is adapted for use with NIS. Developed for ground-based NEXRAD applications, GBVTD is a natural choice here because the location of the NIS radar, like that of a Weather Surveillance Radar-1988 Doppler (WSR-88D) instrument, is fixed with respect to earth. GBVTD employs a Fourier decomposition of the Doppler radial velocity to reconstruct the horizontal and radial components of the vortex circulation. Its efficacy has been well documented (e.g., Lee and Marks 2000; Lee et al. 2000).

It should be noted that in no part of the observation simulation methodology detailed above are the complications of Doppler velocity aliasing or the contamination of returns by surface clutter considered. Our approach here is notional: we assume that velocity dealiasing has been carried out by a standard method (e.g., Gao and Droegemeier 2004), that a clutter suppression method, such as the one proposed by Durden and Tanelli (2009), has been applied, and that measurement uncertainties resulting from finite sampling are those identified by the nominal configuration.

5. Results

Figure 3 shows snapshots of NIS Ka-band reflectivity (Figs. 3a–c), NIS Doppler radial velocity (Figs. 3d–f), GBVTD-retrieved horizontal wind (Figs. 3g–i), and true (i.e., model) horizontal wind (Figs. 3j–l) at 3 km above the sea surface over a 24-h period (1900/1200–2000/1200 UTC) during which time the simulated TC was developing a secondary eyewall. Despite the coarseness of the NIS field of view with respect to the model grid spacing (13 versus 2 km), and despite observing the storm at a rather extreme angle (~22° from vertical), the GBVTD-retrieved winds are in remarkably good agreement with those from the model simulation. The local wind maximum along the spiral band in the eastern semicircle of the storm is captured quite well, as is the broadening of the wind field with time.

West–east vertical cross sections of the horizontal wind that cover the same time interval are shown in Fig. 4. The top row shows the observed wind and the bottom row depicts the wind retrieved for the NIS observations using GBVTD. While the retrieved wind magnitude is generally somewhat smaller than that observed, the overall structure is very well preserved.

Fig. 4.
Fig. 4.

West–east vertical cross section of the evolving horizontal wind. The wind fields as (a)–(c) computed directly from the 2-km simulation and (d)–(f) retrieved from the NIS observations using GBVTD are shown.

Citation: Journal of Atmospheric and Oceanic Technology 28, 10; 10.1175/JTECH-D-11-00060.1

From the retrieved horizontal wind it is possible to derive metrics of TC intensity and structure. Among these are maximum wind (MW), which is analogous to the commonly encountered maximum sustained wind, and various wind radii, such the radius of maximum wind (RMW) and the radius of hurricane-force (i.e., 64 kt) winds (R64). Such parameters are used as inputs to statistical intensity forecast packages, such as the Statistical Hurricane Intensity Prediction Scheme (SHIPS; DeMaria and Kaplan 1999), as well as bogus algorithms needed to initialize dynamical forecast models (e.g., Kurihara et al. 1993).

In this case, the model-based values of these quantities were taken as the spot (i.e., grid point) maximum value for MW, and were computed from the azimuthal mean in the case of RMW and R64. The NIS values were computed in a similar manner from the GBVTD retrievals. The results for both are shown in Fig. 5 for the latter 48 h of the simulation. The evolution of MW, as depicted in Fig. 5a, shows that, until about 1800 UTC 19 October, NIS underestimates the MW. Thereafter, the NIS time series tracks the observed maximum wind closely, seldom deviating from the true value by more than about 5 m s−1. This result is explained by noting that the TC underwent an SEF/ERC about this time, and the wind field expanded accordingly (see Figs. 3 and 4). This expansion enabled NIS, with its coarser resolution relative to the model grid, to better sample the eyewall where the maximum wind is located.

Fig. 5.
Fig. 5.

(a) Time series comparison of the MW at the lowest model level (black; i.e., 125 m above the sea surface), and that computed from the GBVTD retrieval (red) are shown, as is (b) a similar comparison for the RMW (solid lines) and R64 (dashed lines).

Citation: Journal of Atmospheric and Oceanic Technology 28, 10; 10.1175/JTECH-D-11-00060.1

The evolution of the RMW and R64 are shown in Fig. 5b. In this case, NIS does an excellent job of tracking the expansion of the RMW from its initial value of ~20 km to a later value of ~45 km after the ERC. R64, on the other hand, is consistently overestimated in the NIS series, though it is generally within about 10 km for the period considered here.

Statistics for the retrieved MW, RMW, and R64 are shown in Table 1. Over the course of the entire 48-h period, the retrieved MW has an RMS error of 7.27 m s−1, which is in line with the RMS errors (~13 hPa) obtained using the advanced Dvorak technique (ADT; Olander and Velden 2007) for a 10-yr sample of Atlantic cases. The ADT provides no estimates of wind radii, though other satellite-based techniques do exist for this purpose (Kossin et al. 2007). The RMS errors associated with the NIS retrievals of RMW and R64 are 4.6 and 10.8 km, respectively, which are significantly smaller than the errors reported by Kossin et al. (MAE values of 23 and 29.7 km, respectively).

Table 1.

Statistics for retrieved maximum wind (at 125 m), radius of maximum wind, and radius of 64-kt wind.

Table 1.

It is interesting to note that if the statistics are computed over the final 24 h (after which the wind field has expanded), then the performance for maximum wind retrieval improves appreciably (RMSE = 4.1 m s−1), while that for the wind radii is essentially unchanged. This suggests that the retrieval accuracy is likely to improve as the RMW approaches values 2–3 times the instrument resolution (13 km).

6. Discussion

These results demonstrate that, for the first time, it would be possible to obtain hourly direct measurements of the TC wind field that compare quite favorably with current methods for estimating maximum wind and wind radii. In addition, the reflectivity and retrieved horizontal wind fields can be used to infer imminent structure changes, allowing corresponding adjustments to short-term TC forecasts. Other applications, both research oriented as well as operational, are possible, including the use of the NIS data as means of improving TC initialization in numerical models via data assimilation.

It appears likely that the currently implemented retrieval method will work better for large TCs, with RMW roughly 2–3 times the instrument resolution. This highlights the larger issue that the resolution of the NIS instrument is not sufficient to resolve some structures (pinhole eyes, eyewall mesovortices, full eyewall/band structure), although the increased sensitivity of the Ka band to cloud water may be expected to partially offset this by revealing increased detail of larger-scale structures.

Acknowledgments

This research was supported by National Aeronautics and Space Administration Grant NNG04GA36G. The contributions by Drs. Eastwood Im, Simone Tanelli, and Ziad Haddad were performed at the Jet Propulsion Laboratory, California Institute of Technology under contract with the National Aeronautics and Space Administration. The authors thank two anonymous reviewers whose comments led to significant improvements in the final version of the manuscript.

REFERENCES

  • Andreas, E. L, , Perrson P. O. G. , , and Hare J. E. , 2008: A bulk turbulent air–sea flux algorithm for high-wind, spray conditions. J. Phys. Oceanogr., 38, 15811596.

    • Search Google Scholar
    • Export Citation
  • Beven, J. L., 2006: Blown away: The 2005 Atlantic hurricane season. Weatherwise, 59 (4), 3244.

  • Cecil, D. J., , and Zipser E. J. , 1999: Relationships between tropical cyclone intensity and satellite-based indicators of inner core convection: 85-GHz ice-scattering signature and lightning. Mon. Wea. Rev., 127, 103123.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and Kaplan J. , 1999: An updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 14, 326337.

    • Search Google Scholar
    • Export Citation
  • Durden, S., , and Tanelli S. , 2009: Application of clutter suppression methods to a geostationary weather radar concept. Prog. Electromagn. Res. Lett., 8, 115124.

    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430.

  • Gao, J., , and Droegemeier K. K. , 2004: A variational technique for dealiasing Doppler radial velocity data. J. Appl. Meteor., 43, 934940.

    • Search Google Scholar
    • Export Citation
  • Im, E., and Coauthors, 2007: Workshop report on Nexrad-In-Space—A geostationary satellite Doppler weather radar for hurricane studies. Preprints, 33rd Conf. on Radar Meteorology, Cairns, QLD, Australia, Amer. Meteor. Soc., 4B.5. [Available online at http://ams.confex.com/ams/pdfpapers/123726.pdf.]

    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., , Knaff J. A. , , Berger H. I. , , Herndon D. C. , , Cram T. A. , , Velden C. S. , , Murnane R. J. , , and Hawkins J. D. , 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, 89101.

    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., , Bender M. A. , , and Ross R. J. , 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , and Marks F. D. , 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part II: The GBVTD-simplex center finding algorithm. Mon. Wea. Rev., 128, 19251936.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , Jou B. J.-D. , , Chang P.-L. , , and Deng S.-M. , 1999: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part I: Interpretation of Doppler velocity patterns and the GBVTD technique. Mon. Wea. Rev., 127, 24192439.

    • Search Google Scholar
    • Export Citation
  • Lee, W.-C., , Jou B. J.-D. , , Chang P. L. , , and Marks F. D. Jr., 2000: Tropical cyclone kinematic structure retrieved from single Doppler radar observations. Part III: Evolution and structure of Typhoon Alex (1987). Mon. Wea. Rev., 128, 39824001.

    • Search Google Scholar
    • Export Citation
  • Lin, J. K. H., , Fang H. , , Im E. , , and Quijano U. O. , 2006: Concept study of a 35-m spherical reflector system for NEXRAD in space application. 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conf., Newport, RI, American Institute of Aeronautics and Astronautics, AIAA 2006-1604. [Available online at https://imageserv5.team-logic.com/mediaLibrary/93/Concept_Study_of_a_35-m_Spherical_Reflector_System_for_NEXRAD_in_Space_Application2.pdf.]

    • Search Google Scholar
    • Export Citation
  • Masunaga, H., , and Kummerow C. D. , 2005: Combined radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm. J. Atmos. Oceanic Technol., 22, 909929.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., , and Velden C. S. , 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298.

    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419.

  • Rogers, R., , Aberson S. , , Black M. , , Cione J. , , Dodge P. , , Gamache J. , , Kaplan J. , , and Powell M. , 2006: The intensity forecasting experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 15231537.

    • Search Google Scholar
    • Export Citation
  • Tripoli, G. J., 1992: A nonhydrostatic mesoscale model designed to simulate scale interaction. Mon. Wea. Rev., 120, 13421359.

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