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  • Author or Editor: George D. Modica x
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Alan E. Lipton
and
George D. Modica

Abstract

Assimilation of satellite data can enhance the ability of a mesoscale modeling system to produce accurate short-term forecasts of clouds and precipitation, but only if there is a mechanism for the satellite-derived information to propagate coherently from the analysis into the forecast period. In situations where stratiform cloud cover inhibits surface heating, assimilation of visible image data can be beneficial for analyses, but those data present particular challenges for application to numerical forecasts. To address the forecast problem, a method to adjust the humidity field and the radiative parameterization of a model was developed such that satellite retrievals of cloud properties have an impact that extends well into the forecast. The adjustment directs the model’s cloud diagnosis and radiation algorithms to produce results that agree with satellite retrievals valid at the forecast initiation time. Experiments showed a high level of fidelity between a short-term forecast made with this method and coincident analyses produced with satellite data. In comparison with a forecast made using a standard model formulation, the adjusted model produced 1) surface insolation fields that were far more realistic, 2) more accurate shelter-height temperatures, and 3) mesoscale circulation features that were more consistent with observed diurnal convective cloud development.

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Frank H. Ruggiero
,
George D. Modica
, and
Alan E. Lipton

Abstract

An assimilation system that performs continuous assimilation of satellite imager data and intermittent assimilation of hourly surface observations is described. The system was applied to a case study of the southeast United States that was heavily influenced by the shading effect of an area of morning stratiform clouds. The results of analyses produced during the assimilation show improvement in the depiction of the modified surface heating effects beneath the cloudy region as well as in important convective precursors such as mass and moisture convergence and convective available potential energy in the cloudy and adjoining regions. Without assimilation of these data, the numerical model was less able to simulate these thermally forced circulations.

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George D. Modica
,
Samuel Y-K. Yee
, and
Joseph Venuti

Abstract

Results are presented from an analysis of variance as a function of horizontal scale. The normalized difference-field spectra of kinetic energy, temperature, vapor mixing ratio, and cloud-water mixing ratio were computed as a function of wavenumber at several model levels within and just above the planetary boundary layer (PBL). The analysis was performed on simulations from a three-dimensional (3D) hydrodynamic mesoscale model that contained a soil-vegetation canopy model. The analysis was intended to highlight (in terms of wave spectra) the impact of changes in lower-boundary forcing through horizontal variations in soil and plant type. Experiments showed that the use in the model of 1° resolution databases of soil and vegetation type produced higher amounts of variance in the simulated fields at most wavelengths–often by more than 10%–when compared to a simulation that utilized a uniform distribution. Furthermore, the use of databases generated by random specification of soil and vegetation types resulted in yet higher amounts of variance at most wavelengths. The normalized difference-field spectra of energy, temperature, and water vapor mixing ratio generally displayed positive slope (largest values at highest wavenumber) at the lowest model level and tended toward negative slope at higher levels. The magnitudes of the spectra also diminished rapidly with height. The effect of the lateral boundary conditions was generally much greater in terms of the spectral magnitudes than that due to the soil-vegetation databases.

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Alan E. Lipton
,
George D. Modica
,
Scot T. Heckman
, and
Arthur J. Jackson

Abstract

A system for time-continuous mesoscale weather analysis is applied to a study of convective cloud development in central Florida. The analysis system incorporates water vapor concentrations and surface temperatures retrieved from infrared VISSR (Visible–Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) satellite data, with coupling between the retrieval process and time integration of a mesoscale model. Analyses prepared with variations of this coupled system are compared with a control numerical analysis prepared with only conventional meteorological observations and are validated against surface and upper-air data collected for the Convection and Precipitation/Electrification experiment. The coupled analyses assimilate six sets of VAS data over an 8-h period on 19 July 1991 and depict water vapor gradients at far greater horizontal resolution than is available from conventional observations and with an overall accuracy better than the control analysis. The coupled system's ability to assimilate multiple sets of VAS data, with meteorological continuity provided by the model, was important to the accuracy and the breadth of coverage of the water vapor analysis amid changing cloud cover conditions. The surface temperature information provided by the VAS was neither harmful nor very helpful to the mesoscale analysis for this case, owing to the combination of mediocre satellite viewing conditions and the apparent low importance of land surface temperature gradients to the meteorology of the day. Convective stability parameters computed from the coupled analysis data at 1000 local time corresponded closely with patterns of cloud development in the early afternoon.

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