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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.

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Alan E. Lipton

Abstract

Geostationary satellite sounder radiances have typically been averaged over several individual fields of view before the radiances are used for retrieving thermodynamic profiles or for assimilation in weather prediction models. The purpose of the averaging is to compensate for data noise. Cloudy fields of view are excluded from averaging. In areas without a sufficient number of clear fields of view, complete profiles are not retrieved. Clouds thus cause gaps in sounder coverage. This note describes an automated method to select a set of averaging areas for a given field of sounder data, such that the gaps in coverage caused by clouds are as small and as few as possible. Test results are shown, indicating that the method can provide substantially better coverage than is obtained with a commonly used method.

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Alan E. Lipton

Abstract

Surface temperature retrieval in mountainous areas is complicated by the high variability of temperatures that can occur within a single satellite field of view. Temperatures depend in part on slope orientation relative to the sun, which can vary radically over very short distances. The surface temperature detected by a satellite is biased toward the temperatures of the sub-field-of-view terrain elements that most directly face the satellite. Numerical simulations were conducted to estimate the effects of satellite viewing geometry on surface temperature retrievals for a section of central Colorado. Surface temperatures were computed using a mesoscale model with a parameterization of subgrid variations in slope and aspect angles.

The simulations indicate that the slope-aspect effect can lead to local surface temperature variations up to 30°C for autumn conditions in the Colorado mountains. For realistic satellite viewing conditions, these variations can give rise to biases in retrieved surface temperatures of about 3°C. Relative biases between retrievals from two satellites with different viewing angles can be over 6°C, which could lead to confusion when merging datasets. The bias computations were limited by the resolution of the available terrain height data (∼90 m). The results suggest that the biases would be significantly larger if the data resolution was fine enough to represent every detail of the real Colorado terrain or if retrievals were made in mountain areas that have a larger proportion of steep slopes than the Colorado Rockies. The computed bias gradients across the Colorado domain were not large enough to significantly alter the forcing of the diurnal upslope-downslope circulations, according to simulations in which surface temperature retrievals with view-dependent biases were assimilated into time-continuous analyses. View-dependent retrieval biases may be relevant to climatological analysts that rely on remotely sensed data, given that bias-induced errors are systematic.

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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.

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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.

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Alan E. Lipton and Donald W. Hillger

Abstract

In retrieval of atmospheric temperature and moisture soundings from satellite infrared radiance measurements the raw data commonly used consist of dense fields of radiances interrupted by data-free gaps. This note reports an objective analysis procedure which was developed to specifically handle data fields of a discontinuous nature. The method is a correlation-weighted interpolation scheme and includes an oval-extension gap filling feature. Test cases demonstrate the ability of the program to fill gaps caused by instrument calibration periods and by data contamination due to clouds. The procedure is shown to produce much better results within a data-free region than does a similar method without the gap filling feature. An application of this method is also shown in a comparison of satellite-derived atmospheric parameters with conventional observations on a point-to-point basis. However, applications of the procedure are not limited to satellite data analysis, but could include analyses of aircraft data and data from ocean buoys.

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Alan E. Lipton and Thomas H. Vonder Haar

Abstract

Principal components have been widely used in regression retrieval of atmospheric parameters, but when applied to water vapor concentrations their use entails special problems. We discuss two of these problem and present results of retrieval experiments designed to alleviate them. The experiments employed High-resolution Infrared Radiation Sounder satellite data in conjunction with radiosonde observations. We found that mixing ratio is a less appropriate parameter for principal component-based retrieval than is a mean-saturation adjusted mixing ratio. Also, retrieval accuracy was vapor by identifying the optimum numbers of eigenvectors to use when transforming the water vapor profiles and the satellite brightness temperature, respectively, into their principal components. In our studies three eigenvectors were optimal for representation of water vapor, implying that HIRS-2 data are capable of retrieving at least third-order vertical resolution in water vapor profiles. In addition, we compared principal component-based retrieval with standard multiple regression and found that a hybrid of the two methods gave the greatest retrieval accuracy.

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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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Alan E. Lipton and Roger A. Pielke

Abstract

Vertical modes were derived for a version of the Colorado State Regional Atmospheric Mesoscale Modeling System. We studied the impact of three options for dealing with the upper boundary of the model. The standard model formulation holds pressure constant at a fixed altitude near the model top, and produces a fastest mode with a speed of about 90 m s−1. An alternative formulation which allows for an external mode, could require recomputation of vertical modes for every surface elevation on the horizontal grid unless the modes are derived in a particular way. These results have beating on the feasibility of applying vertical mode initialization to models with scaled height coordinates.

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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.

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