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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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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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George D. Modica, Scot T. Heckman, and Roy M. Rasmussen

Abstract

A hydrostatic regional prediction model is modified to permit the existence of both liquid and ice hydrometeors within the same grid volume. The modified model includes an efficient ice-water saturation adjustment and a simple procedure to create or remove cloud water or ice. The objective was to determine whether such a model could provide deterministic forecasts of aircraft icing conditions in the 6–36-h period. The model was used to simulate an orographically forced icing event (the Valentine's Day storm of 12–14 February 1990) that occurred during the 1990 phase of the Winter Icing and Storms Project (WISP-90). Output from a 24-h nested-grid integration of the model was compared to observations taken during WISP-90. The model produced a thin (∼1-2 km deep) supercooled liquid water (SLW) cloud that was in good agreement with observations in terms of initiation, duration, liquid water content, and location. Results of the simulation also suggest that slantwise ascent can be an important component in the production of SLW.

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Xiaolei Zou, Qingnong Xiao, Alan E. Lipton, and George D. Modica

Abstract

The influence of Geostationary Operational Environmental Satellite (GOES) brightness temperature data on the numerical simulations of Hurricane Felix is investigated. Satellite data are included as an augmentation to a bogus data assimilation (BDA) procedure using a mesoscale adjoint modeling system. The assimilation of satellite data modified not only the environmental flow but also the structure of the initial vortex, which is located over a region devoid of satellite data. This modification resulted in a reduction of the 12-h forecast errors verified by radiosonde data. Despite the fact that the forecast using only the bogus surface low at the initial time was very good, track and intensity forecasts beyond 2 days of model integration were shown to be improved further by including satellite data in the initialization procedure. Differences in the prediction of Hurricane Felix with and without satellite data were also found in the prediction of the upper-level jet, the cold temperature trough ahead of the hurricane, the size of the hurricane eye, and the location of the maximum hydrometeor. Although the focus of this study is to assess the effect of the direct use of satellite brightness temperature data on hurricane prediction, it is also noted that the BDA experiment including only the bogus data shows a positive effect of the BDA vortex on the environmental flow during the forecast period, as verified by satellite observations.

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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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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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Frank H. Ruggiero, John Michalakes, Thomas Nehrkorn, George D. Modica, and Xiaolei Zou

Abstract

Updated versions of the Tangent Linear Model (TLM) and adjoint of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) have been developed and are now available to the meteorological community. The previous version of the MM5 TLM and adjoint were designed for single-processor computer architectures, based on version 1 of MM5, and were hand coded, which made it difficult to maintain up-to-date versions of the TLM and the adjoint as MM5 evolved. The new TLM and adjoint are based on version 3 of MM5 and run efficiently on multiple-processor computers. The TLM and adjoint were developed with the aid of the Tangent Linear and Adjoint Model Compiler (TAMC) automatic code generator. While some manual intervention is still necessary, the use of the automatic code generator can significantly speed code development and lower code maintenance costs. The new TLM and adjoint contain most of the physics packages and observation operators that were available in the MM5 version 1 TLM and adjoint. The new adjoint has been combined with the MM5 version 3 nonlinear model and an updated minimization module in a four-dimensional variational data assimilation analysis configuration. Accuracy of the new TLM and adjoint has been verified by individual unit and system tests as well as comparisons with the adjoint from MM5 version 1. Timing tests showed substantial decreases in time to solution when increasing the number of processors devoted to the problem.

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