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A Study of the Mesoscale Wind Circulation in a Land-Sea Breeze Regime

Student Paper—First Place Winner of the Father James B. Macelwane Annual Award in Meteorology

Jeffrey D. Hawkins

The mesoscale wind circulation along the central Oregon coast is investigated by use of surface data gathered during the Coastal Upwelling Experiment 2 (CUE-2). A case with prevailing NNW winds, typical of the general pattern, was studied intensely and was compared with results from a similar study for a SE flow.

Buoy and coastal land station data provide time series and diurnal plots of: 1) the air and sea surface temperature; 2) the horizontal wind components; and 3) the wind speed.

The case with prevailing NNW winds (26–28 July 1973) had the following important characteristics: 1) a well-defined sea breeze event with supporting coastal temperature differential and less significant land breeze; 2) the formation of an eddy south of Cape Lookout; and 3) strong upwelling and sea surface temperatures affected by surface winds. In contrast, the case with SE winds (23–24 August 1973) showed only a noticeable sea breeze event with half the coastal temperature differential of the previous case.

It is strongly suggested that the mesoscale surface winds and the factors that influence them in this region be studied further if sea surface temperature forecasts are to be made.

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James S. Goerss, Christopher S. Velden, and Jeffrey D. Hawkins

Abstract

Experimental wind datasets were derived for two time periods (13–20 July and 24 August–10 September 1995) from GOES-8 observations processed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW CIMSS). The first dataset was focused on Tropical Storm Chantal, and the second dataset was focused on the multiple-storm environment that included Hurricanes Humberto, Iris, and Luis. Both datasets feature a processing and quality control strategy designed to optimize the quantity and content of geostationary satellite-derived winds in the vicinity of tropical cyclones. Specifically, the winds were extracted from high-density targets obtained from multispectral imagery, which included three water vapor bands (6.7, 7.0, and 7.3 μm), infrared, and visible. The Navy Operational Global Atmospheric Prediction System (NOGAPS) was used as the vehicle to determine the impact of these winds upon tropical cyclone track forecasts. During the 1995 Atlantic hurricane season the NOGAPS forecasts were found to be quite skillful, displaying relative improvement of tropical cyclone position error with respect to CLIPER (climate and persistence) of 20% at 24 h, 35% at 48 h, and 33% at 72 h. The NOGAPS data assimilation system was run with and without the high-density GOES-8 winds for the two aforementioned time periods. The assimilation of these winds resulted in significant improvements in the NOGAPS forecasts for Tropical Storm Chantal and Hurricane Iris and mixed results for Hurricanes Humberto and Luis. Overall, for all four cyclones, the NOGAPS forecasts made with the use of the UW CIMSS winds displayed relative improvement of forecast position error with respect to those made without the use of the UW CIMSS winds of 14% at 24 h, and 12% at both 48 and 72 h.

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Jeffrey D. Hawkins, Thomas F. Lee, Joseph Turk, Charles Sampson, John Kent, and Kim Richardson

Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location.

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Peter G. Black, Russell L. Elsberry, Lynn K. Shay, Ray P. Partridge, and Jeffrey D. Hawkins

Abstract

Three drifting buoys were successfully air-dropped ahead of Hurricane Josephine. This deployment resulted in detailed simultaneous measurements of surface wind speed, surface pressure and subsurface ocean temperature during and subsequent to storm passage. This represents the first time that such a self-consistent data set of surface conditions within a tropical cyclone has been collected. Subsequent NOAA research overflights of the buoys, as part of a hurricane planetary boundary-layer experiment, showed that aircraft wind speeds, extrapolated to the 20 m level, agreed to within ±2 m s−1, pressures agreed to within ±1 mb, and sea-surface temperatures agreed to within ±0.8°C of the buoy values. Ratios of buoy peak 1 min wind (sustained wind) to one-half h mean wind > 1.3 were found to coincide with eyewall and principal rainband features.

Buoy trajectories and subsurface temperature measurements revealed the existence of a series of mesoscale eddies in the subtropical front. Buoy data revealed storm-generated, inertia-gravity-wave motions superposed upon mean current fields, which reached a maximum surface speed > 1.2 m s−1 immediately following storm passage. A maximum mixed-layer-temperature decrease of 1.8°C was observed to the right of the storm path. A temperature increase of 3.5°C at 100 m and subsequent decrease of 4.8°C following storm passage indicated a combination of turbulent mixing, upwelling and horizontal advection processes.

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Steven D. Miller, John M. Forsythe, Philip T. Partain, John M. Haynes, Richard L. Bankert, Manajit Sengupta, Cristian Mitrescu, Jeffrey D. Hawkins, and Thomas H. Vonder Haar

Abstract

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

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Steven D. Miller, Jeffrey D. Hawkins, John Kent, F. Joseph Turk, Thomas F. Lee, Arunas P. Kuciauskas, Kim Richardson, Robert Wade, and Carl Hoffman

Under the auspices of the National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Integrated Program Office (IPO), the Naval Research Laboratory (NRL) has developed “NexSat” (www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html)—a public-access online demonstration over the continental United States (CONUS) of near-real-time environmental products highlighting future applications from the Visible/Infrared Imager/Radiometer Suite (VIIRS). Based on a collection of operational and research-grade satellite observing systems, NexSat products include the detection, enhancement, and where applicable, physical retrieval of deep convection, low clouds, light sources at night, rainfall, snow cover, aircraft contrails, thin cirrus layers, dust storms, and cloud/aerosol properties, all presented in the context of value-added imagery. The purpose of NexSat is threefold: 1) to communicate the advanced capabilities anticipated from VIIRS, 2) to present this information in near–real time for use by forecasters, resource managers, emergency response teams, civic planners, the aviation community, and various government agencies, and 3) to augment the NRL algorithm development multisensor/model-fusion test bed for accelerated transitions to operations during the NPOESS era. This paper presents an overview of NexSat, highlighting selected products from the diverse meteorological phenomenology over the CONUS.

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Jeffrey D. Hawkins, Jeremy E. Solbrig, Steven D. Miller, Melinda Surratt, Thomas F. Lee, Richard L. Bankert, and Kim Richardson

Abstract

Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

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James P. Kossin, John A. Knaff, Howard I. Berger, Derrick C. Herndon, Thomas A. Cram, Christopher S. Velden, Richard J. Murnane, and Jeffrey D. Hawkins

Abstract

New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geostationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene–type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool.

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Elaine M. Prins, Christopher S. Velden, Jeffrey D. Hawkins, F. Joseph Turk, Jaime M. Daniels, Gerald J. Dittberner, Kenneth Holmlund, Robbie E. Hood, Arlene G. Laing, Shaima L. Nasiri, Jeffery J. Puschell, J. Marshall Shepherd, and John V. Zapotocny
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John Kaplan, Christopher M. Rozoff, Mark DeMaria, Charles R. Sampson, James P. Kossin, Christopher S. Velden, Joseph J. Cione, Jason P. Dunion, John A. Knaff, Jun A. Zhang, John F. Dostalek, Jeffrey D. Hawkins, Thomas F. Lee, and Jeremy E. Solbrig

Abstract

New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.

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