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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

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 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, 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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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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Jean-Luc Moncet, Gennady Uymin, Pan Liang, and Alan E. Lipton

Abstract

The optimal spectral sampling (OSS) method provides a fast and accurate way to model radiometric observations and their Jacobians (required for inversion problems) as a linear combination of monochromatic quantities. The method is flexible and versatile with respect to the treatment of variable constituents, and the method’s fidelity to reference line-by-line (LBL) calculations is tunable. The focus of this paper is on the modeling of radiances from hyperspectral infrared sounders in both clear and cloudy (scattering) atmospheres for application to retrieval and data assimilation. In earlier articles, the authors presented an approach that performed spectral sampling for each channel sequentially. This approach is particularly robust in terms of preserving fidelity to LBL models and yields ratios of monochromatic calculations per channel of approximately 1:1 for such hyperspectral sensors as the Infrared Atmospheric Sounding Interferometer (IASI) or the Atmospheric Infrared Sounder (AIRS) (when tuned for nominal 0.05-K accuracy). This paper describes the generalization of the OSS concept to minimize the total number of monochromatic points required to model a set of channels across individual spectral bands or across the entire domain of the measurements. Its application to principal components of radiance measurements is addressed. It is found that the optimal solution produced by the OSS method offers computational advantages over existing models based on principal components, but, more importantly, it has superior error characteristics.

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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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Jean-Luc Moncet, Gennady Uymin, Alan E. Lipton, and Hilary E. Snell

Abstract

This paper describes a rapid and accurate technique for the numerical modeling of band transmittances and radiances in media with nonhomogeneous thermodynamic properties (i.e., temperature and pressure), containing a mixture of absorbing gases with variable concentrations. The optimal spectral sampling (OSS) method has been designed specifically for the modeling of radiances measured by sounding radiometers in the infrared and has been extended to the microwave; it is applicable also through the visible and ultraviolet spectrum. OSS is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction models. The novel OSS approach is an extension of the exponential sum fitting of transmittances technique in that channel-average radiative transfer is obtained from a weighted sum of monochromatic calculations. The fact that OSS is fundamentally a monochromatic method provides the ability to accurately treat surface reflectance and spectral variations of the Planck function and surface emissivity within the channel passband, given that the proper training is applied. In addition, the method is readily coupled to multiple scattering calculations, an important factor for treating cloudy radiances. The OSS method is directly applicable to nonpositive instrument line shapes such as unapodized or weakly apodized interferometric measurements. Among the advantages of the OSS method is that its numerical accuracy, with respect to a reference line-by-line model, is selectable, allowing the model to provide whatever balance of accuracy and computational speed is optimal for a particular application. Generally only a few monochromatic points are required to model channel radiances with a brightness temperature accuracy of 0.05 K, and computation of Jacobians in a monochromatic radiative transfer scheme is straightforward. These efficiencies yield execution speeds that compare favorably to those achieved with other existing, less accurate parameterizations.

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