Search Results

You are looking at 1 - 8 of 8 items for :

  • Author or Editor: Alan E. Lipton x
  • Monthly Weather Review x
  • Refine by Access: All Content x
Clear All Modify Search
Alan E. Lipton

Abstract

A retrieval-assimilation method has been developed as a quantitative means to exploit the information in satellite imagery regarding shading of the ground by clouds, as applied to mesoscale weather analysis. Cloud radiative parameters are retrieved from satellite visible image data and used, along with parameters computed by a numerical model, to control the model's computation of downward tadiative fluxes at the ground. These fluxes, in turn, influence the analysis of ground surface temperatures under clouds. The method is part of a satellite-model coupled four-dimensional analysis system that merges information from visible image data in cloudy areas with infrared sounder data in clear areas, where retrievals of surface temperatures and water vapor concentrations are assimilated.

The substantial impact of shading on boundary-layer development and mesoscale circulations was demonstrated in simulations, and the value of assimilating shading retrievals was demonstrated with a case study and with a simulated analysis that included the effects of several potential sources of error. The simulation results imply that assimilation is preferable to ignoring shading, even if the errors in the retrieval-assimilation process happen to compound each other. The case study was performed in the northwestern Texas area, where convective cloud development was influenced by the shading effects of a persistent region of stratiform cloud cover. Analyses that included shading retrieval assimilation had consistently smaller shelter-height temperature errors than analyses without shading retrievals. When clear-area surface temperature retrievals from sounder data were analyzed along with cloudy-area shading retrievals, the contrast in heating between the shaded and clear parts of the domain led to large variations in anallyzed boundary-layer depths and had a modest impact on analyzed wind flow. The analyzed locations of upward vertical motion corresponded roughly to areas of convective cloud development observed in satellite imagery, whereas analyses without shading assimilation lacked substantial vertical motions. Assimilation of water vapor information retrieved from sounder data was beneficial to the representation of water vapor in the analysis.

Full access
Alan E. Lipton and Thomas H. Vonder Haar

Abstract

The development and evaluation of a system for time-continuous mesoscale analysis is presented, with a focus on retrieving water vapor concentrations and ground surface temperatures from VISSR Atmospheric Sounder (VAS) data. The analysis system is distinguished by an intimate coupling of retrieval and numerical modeling processes that avoids some of the problem researchers have encountered when satellite-retrieved parameters have been input to models. The system incorporates virtually all of the temporal, vertical and horizontal structure that can be resolved in VAS soundings while maintaining model-generated gradients. The two primary components of the system are a version of the CSU Regional Atmospheric Modeling System (RAMS) and an algorithm for retrieving meteorological parameters from VAS data.

The analysis system was evaluated by means of simulations, with a domain that consisted of a vertical cross section through a broad mountain slope. The purposes were to determine the accuracy of coupled analysis results under controlled conditions and to compare results of the coupled scheme with those of other analysis schemes. For water vapor analysis, vertical gradients were more accurately resolved with the coupled method than with conventional retrieval from satellite data. The coupled method's incorporation of VAS data from multiple observation times was valuable for making mesoscale horizontal gradients stand out more clearly amid the noise in the water vapor analysis. In addition, the method was relatively robust when confronted with a common problem in analysis of the preconvective atmosphere—contamination of the satellite data by increasing amounts of small convective clouds. Analyses in which surface temperatures were derived from satellite-based retrievals were compared with the alternative of relying on energy balance computations without mesoscale data about soil characteristics. The surface temperatures from the two methods differed by as much as 5 K, giving rise to prominent differences in the induced mesoscale circulations. The energy balance computations were so sensitive to soil characteristics that the satellite retrieval method gave more accurate results even with cloud contamination.

Full access
Alan E. Lipton and Thomas H. Vonder Haar

Abstract

Influences on the mesoscale distribution of summertime convective cloud development in the northeastern Colorado region are described using a new system for time-continuous mesoscale analysis. The analysis system is distinctive in that there is an intimate coupling between integration of a numerical model and retrieval of temperature and water vapor concentrations from VISSR Atmospheric Sounder (VAS) data. We present a case study to compare results of the coupled analysis method with those of related methods, focusing on the roles of variations in ground surface temperatures and water vapor concentrations.

The horizontal and time variations represented in satellite-based (coupled) surface temperature analyses closely corresponded to information from conventional shelter temperature observations, but had much greater detail. In contrast, temperature based on energy balance computations tended to increase too quickly during the morning and were lacking in mesoscale feature. In the water vapor analyses, when the first set of satellite data is less reliable than the later sets, some of the contamination lingers throughout the time-continuous coupled analysis results. However, the coupled method generally appears to be the most valuable method considered in this study because it exploits the major strengths of the numerical model and the satellite data while making it relatively easy to recognize and compensate for any impacts of their weaknesses. In addition, the coupled analysis results illustrated that there can be very large mesoscale gradients in temperatures at the ground surface even on relatively flat terrain. These gradients, in combination with terrain height variations, can play an important role in preconvective water vapor kinematics through their influences on vertical and horizontal winds. The analysis system proved to be valuable for forecasting through the close correspondence between derived stability indices and later convective development in the case we studied.

Full access
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.

Full access
Alan E. Lipton, Donald W. Hillger, and Thomas H. Vonder Haar

Abstract

A method of retrieving the basic vertical structure of water vapor profiles from satellite-observed radiances is presented. The statistical tools of empirical orthogonal function analysis and clustering were used to define classes of vertical structure of water vapor. As a result, any water vapor sounding can be assigned to one of four vertical structure classes. Each class was shown to be identified with certain types of weather features. Multiple regression was used to retrieve approximate total precipitable water by use of brightness temperatures simulated for the Defense Meteorological Satellite Program SSH-2 infrared sounder, resulting in explained variances of about 80%. In addition, discriminant analysis was then applied to retrieve the vertical structure class of each water vapor profile, giving percentages of correct discrimination near 60%. Selection from among the SSH-2 spectral channels was used to optimize both the total water regression and the structure class discrimination. Also, it was shown that separation of soundings by total water content generally improves discrimination skill by a few percent. The results suggest that this retrieval approach should be particularly useful for application to subjective weather forecasting.

Full access
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.

Full access
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.

Full access
Frank H. Ruggiero, Keith D. Sashegyi, Alan E. Lipton, Rangarao V. Madala, and Sethu Raman

Abstract

A satellite–model coupled procedure for assimilating geostationary satellite sounder data was adapted to a mesoscale analysis and forecast system jointly developed by the Naval Research Laboratory and the Air Force Research Laboratory. The coupled procedure involves the use of the model output fields as the first guess for the thermodynamic retrievals. Atmospheric thermodynamic profiles and ground temperatures were retrieved from observed radiances of the VISSR Atmospheric Sounder (VAS) on board the Geostationary Operational Environmental Satellite. The successive corrections objective analysis scheme in the mesoscale analysis and forecast system was modified to consider the horizontal spatial correlation of the satellite data. The procedure was tested using a wintertime case from the 1986 Genesis of Atlantic Lows Experiment project. The retrievals generated by the coupled method were modestly improved relative to independent stand-alone retrievals. Coupled analyses and forecasts of temperature and moisture fields compared favorably to forecasts from a control run without the VAS assimilation.

Full access