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T. Cherubini and S. Businger

Abstract

This paper discusses the derivation of the refractive index structure function. It shows that the traditional formulation, which is based on the hydrostatic assumption, leads to increasing errors with height when compared with a formulation that is based on the potential temperature. The paper corrects a long-standing problem of extrapolating the traditional boundary layer approximation beyond its region of validity (i.e., to the upper troposphere and lower stratosphere). The new derivation may have applications in observational work to measure and seeing and in numerical modeling efforts. A preliminary analysis of the influence of the new formulation in numerical modeling of seeing suggests that impact on seeing will be small in general, because the largest contribution to seeing generally comes from the lower troposphere. However, an accurate profile is needed because other astroclimatic parameters, such as the isoplanatic angle, can suffer from the lack of accuracy at high altitude. This work may also have application in radar meteorology, since clear-air radar sensitivity depends on accurate estimation of .

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T. Cherubini, S. Businger, and R. Lyman

Abstract

An optical turbulence algorithm has been running operationally since April 2005 at the Mauna Kea Weather Center. The algorithm makes use of information on turbulence kinetic energy provided by a planetary boundary layer scheme available in the Pennsylvania State University–NCAR Mesoscale Model and estimates the turbulent fluctuations of the atmospheric refractive index and seeing over the summit area of Mauna Kea. To investigate the potential and limitations of the optical turbulence algorithm, one year of observed seeing data from four observatories is compared with the model forecast seeing and a statistical analysis is carried out. Sensitivity tests regarding the accuracy of the underlying numerical weather forecasts and the model’s eddy diffusivity scheme are performed. Results from a simple calibration of the optical turbulence algorithm are presented.

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T. Cherubini, S. Businger, C. Velden, and R. Ogasawara

Abstract

Tropospheric motions can be inferred from geostationary satellites by tracking clouds and water vapor in sequential imagery. These atmospheric motion vectors (AMV) have been operationally assimilated into global models for the past three decades, with positive forecast impacts. This paper presents results from a study to assess the impact of AMV derived from Geostationary Operational Environmental Satellite (GOES) imagery on mesoscale forecasts over the conventional data-poor central North Pacific region. These AMV are derived using the latest automated processing methodologies by the University of Wisconsin—Cooperative Institute for Meteorological Satellite Studies (CIMSS). For a test case, a poorly forecast subtropical cyclone (kona low) that occurred over Hawaii on 23–27 February 1997 was chosen. The Local Analysis and Prediction System (LAPS) was used to assimilate GOES-9 AMV data and to produce fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) initial conditions. The satellite wind assimilation is carried out on the 27-km-resolution domain covering the central Pacific area. The MM5 was run with three two-way nested domains (27, 9, and 3 km), with the innermost domain moving with the kona low. The AMV data are found to influence the cyclone’s development, improving the prediction of the cyclone’s central pressure and the track of the low’s center. Since September 2003, GOES-10 AMV data have been routinely accessed from CIMSS in real time and assimilated into the University of Hawaii (UH) LAPS, providing high-resolution initial conditions for twice-daily runs of MM5 at the Mauna Kea Weather Center collocated at the UH. It is found that the direct assimilation of AMV data into LAPS has a positive impact on the forecast accuracy of the UH LAPS/MM5 operational forecasting system when validated with observations in Hawaii. The implications of the results are discussed.

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T. Cherubini, S. Businger, R. Lyman, and M. Chun

Abstract

Atmospheric turbulence is a primary concern for astronomers. Turbulence causes amplitude and phase fluctuations in electromagnetic waves propagating through the atmosphere, constraining the maximum telescope resolution and resulting in telescope image degradation. Astronomical parameters that quantify these effects are generically referred to as seeing. Adaptive optics (AO) is used to reduce image degradation associated with optical turbulence. However, to optimize AO, knowledge of the vertical profile of turbulence and overall (integrated) seeing is needed. In this paper, an optical turbulence algorithm is described that makes use of the information on turbulence kinetic energy provided by a planetary boundary layer scheme available in the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Optical turbulence data collected on Mauna Kea during the 2002 site monitoring campaign are used to validate the algorithm, which has been implemented in operational runs of MM5 at the Mauna Kea Weather Center.

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