• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.615-620
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• 2002

Probability distribution of the sea surface slope is estimated using sun glitter images derived from visible radiometer on Geostationary Meteorological Satellite (GMS) and surface vector winds observed by spaceborne scatterometers. The brightness of the visible images is converted to the probability of wave surfaces which reflect the sunlight toward GMS in grids of 0.25 deg $\times$ 0.25 deg. Slope and azimuth angle required for the reflection of the sun's ray toward GMS are calculated for each grid from the geometry of GMS observation and location of the sun. The GMS images are then collocated with surface wind data observed by three scatterometers. Using the collocated data set of about 30 million points obtained in a period of 4 years from 1995 to 1999, probability distribution function of the surface slope is estimated as a function of wind speed and azimuth angle relative to the wind direction. Results are compared with those of Cox and Munk (1954a, b). Surface slope estimated by the present method shows narrower distribution and much less directivity relative to the wind direction than that reported by Cox and Munk. It is expected that their data were obtained under conditions of growing wind waves. In general, wind waves are not always developing, and slope distribution might differ from the results of Cox and Munk. Most of our data are obtained in the subtropical seas under clear-sky conditions. This difference of the conditions may be the reason for the difference of slope distribution.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.