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Johannes Verlinde

Abstract

An airborne Doppler radar analysis of a waterspout parent storm is presented. An 8-min window centered around the time of the visual observation of the waterspout is presented. The waterspout was associated with a small, intense cloud that developed ahead of a squall line. It was observed by radar from a distance of 3 km, with a cross-beam resolution of approximately 70 m. One radar scan cut through the vortex, revealing the structure. The high-resolution dual-Doppler analysis of the Electra Doppler radar was used to investigate the velocity and vorticity structure of the parent storm. These observations were consistent with a low-level vorticity source. No cloud-scale vorticity was observed until the time of the visual observation of the storm.

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Natasha L. Miles and Johannes Verlinde

Abstract

Linearly organized convection and associated horizontal roll vortices occasionally occur in atmospheric conditions in which theory predicts only cellular organization. One possible contributor to the occurrence of rolls in such conditions is nonlinear interactions between different scales of motion. In the winter of 1997/98, the Lake-Induced Convection Experiment (Lake-ICE) was conducted in part to investigate scale interactions in linearly organized convection. As discussed in of this series, transient linear organization was observed during a wintertime lake-effect event during Lake-ICE. In Part II two-part nonlinear scale interactions and their possible role in the occurrence of linear organization in an unfavorable environment are investigated. Turbulence-scale vertical velocity variance peaks were consistently observed during roll strengthening and decay, suggesting a link between the scales. Composites of the nonlinear interaction terms in the roll-scale vertical turbulent kinetic energy (TKE) budget revealed that nonlinear interactions between the roll and turbulence scales were large compared to the observed change in roll-scale TKE, but do not coincide in time.

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Johannes Verlinde and William R. Cotton

Abstract

Observations collected during the Oklahoma–Kansas PRE-STORM experiment are used to document the evolution and structure of a mesoscale vortex couplet that developed in the mesoscale convective system that occurred on 16–17 June 1985. The evolution of the circulations was captured by dual-Doppler radar observations for 1.4 hours. This allowed an evaluation of the various terms of the vertical vorticity equation, which give insight into the mechanisms that are important in the generation of the circulations. The primary mechanism responsible for the formation of the observed vortices was the interaction of the larger-scale flow with low level momentum transported to higher levels by multiple convective updrafts. As a consequence vertical shear of the horizontal wind was important to initial vorticity production. The vorticity generated in this manner was subsequently increased in strength due to middle level convergence. When the convection weakened and dissipated, the primary source of vorticity was removed, and because this was an unbalanced circulation on a scale less than the Rossby radius of deformation, the vortex broke up and spun down. Comparisons are made with other documented cases, and differences and similarities are pointed out. It is hypothesized that this circulation is a common kind in precipitating mesoscale systems, which has hitherto largely been undetected because its size is too large to be easily observed in a Doppler radar network set up to study thunderstorms, yet too small to be detected by standard sounding networks or most research sounding networks.

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Rodney L. Grady and Johannes Verlinde

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A nonsevere squall line that developed on 21 June 1993 along the northern foothills of the Colorado Rocky Mountains is analyzed using a series of triple-Doppler analyses. This squall line developed in a relatively dry environment characterized by weak low-level but strong upper-level vertical shear of the horizontal winds. The drier thermodynamic profile, as characterized by a higher lifting condensation level, and weak low-level shear resulted in convection forming not along, but rather 7–10 km behind the leading edge of the shallow cold pool. The strong upper-level shear established a predominantly leading anvil. This led to a suppression zone immediately ahead of the leading line, which in turn resulted in a discrete mode of propagation of the squall line. Three different cycles were observed: each cycle had a distinct line of convective cells that initiate, intensify, and then decay. In each case the new cells developed 20–40 km out ahead of the decaying line of the previous cycle.

Many studies have identified low-level shear as being critical to squall line development. Results from this study indicate that there may be a more extensive set of environmental conditions that will lead to long-lived midlatitude squall lines. In particular, the strong upper-level shear played an important role in the characteristics of this storm.

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Jacob M. Petre and Johannes Verlinde
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Johannes Verlinde and William R. Cotton

Abstract

Rapid advances in the quality and quantity of atmospheric observations have placed a demand for the development of techniques to assimilate these data sources into numerical forecasting models. Four-dimensional variational assimilation is a promising technique that has been applied to atmospheric and oceanic dynamical models, and to the retrieval of three-dimensional wind fields from single-Doppler radar observations.

This study investigates the feasibility of using space–time variational assimilation for a complex discontinuous numerical model including cloud physics. Two test models were developed: a one-dimensional and a two-dimensional liquid physics kinematic microphysical model. These models were used in identical-twin experiments, with observations taken intermittently. Small random errors were introduced into the observations. The retrieval runs were initialized with a large perturbation of the observation run initial conditions.

The models were able to retrieve the original initial conditions to a satisfactory degree when observations of all the model prognostic variables were used. Greater overdetermination of the degrees of freedom (the initial condition being retrieved) resulted in greater improvement of the errors in the observations of the initial conditions but at a rapid increase in computational cost. Experiments where only some of the prognostic variables were observed also improved the initial conditions, but at a greater cost. To substantially improve the first guess of the field not observed, some spot observations are needed.

The proper scaling of the variables was found to be important for the rate of convergence. This study suggests that scaling factors related to the error variance of the observations give good convergence rates.

To show how this technique can be used when observations are general functions of the prognostic variables of the model (e.g., reflectivity or liquid water path), a form is derived that shows that this can be accomplished. This is considered to be an advantage of this technique over other assimilation techniques, since it is particularly suitable to remote-sensing systems where only integral parameters or derivatives of model prognostic variables are observed.

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Natasha L. Miles and Johannes Verlinde

Abstract

The cold-air outbreak of 13–14 January 1998 during the Lake-Induced Convection Experiment was characterized by large positive buoyancy flux and moderate wind shear. Although theory predicts only cellular organization in these conditions, transient linear organization was observed. Time series of vertical velocity obtained with the Pennsylvania State University 94-GHz vertically pointing cloud radar, which is sensitive to cloud droplets and ice crystals, were used to document the changes in organization that occurred during this wintertime lake-effect event. The cloud radar was deployed on the downwind shore of southern Lake Michigan and measured high-temporal-resolution vertical velocity data at several in-cloud heights. The duration of the event was 18 h, encompassing three cycles of linear organization switching to cellular organization.

In Part I of this two-part series the authors document the transient nature of the linearly organized convection and evaluate the role of atmospheric conditions in the mode switching between linear and cellular organization. Within the limits of the available measurements, no correlation was found with mean or low-level shear, surface fluxes, or stability parameters. The mode switching in this case does not appear to be controlled by the atmospheric indicators typically associated with linearly organized convection, suggesting that other factors must have played an important role.

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Bing Wu, Johannes Verlinde, and Juanzhen Sun

Abstract

A four-dimensional variational data assimilation system consisting of a three-dimensional time-dependent cloud model with both liquid and ice phase microphysics parameterization was used to assimilate radar data into a cloud model. Data of a severe thunderstorm observed during the Cooperative Huntsville Meteorological Experiment project were assimilated and results compared to a conventional analysis. The analysis system was able to retrieve all the prominent features of the storm, but differed in some of the details. However, the consistency of this retrieval dataset lent credence to the results.

It was found that the algorithm was very sensitive to several coefficients in the microphysical and turbulence parameterizations. Simulations proved to be unable to reproduce the evolution of the observed storm even with parameterization coefficients set at values that produce reasonable storm evolutions. This result has implications for short-range forecasting of convective events. Such forecasts require initial fields that currently can only be derived from observations such as used in this study. The problems with assimilating radar observations point to additional work to design parameterizations that allow models to more accurately simulate actual observed storms.

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David M. Babb, Johannes Verlinde, and Bert W. Rust

Abstract

Remote sensing instruments have the ability to collect data over extensive temporal periods and spatial regions. A common thread between all these sensors is the need to relate the measured quantity to a meaningful observation of a system property. If the relationship between each measurement and the set of atmospheric quantities that influence that measurement is known, the problem can be reduced to a set of linear equations. Solving for the unknown atmospheric quantities then becomes a linear algebra problem, where the solution vector is equal to the inverse of the kernel matrix multiplied by the set of independent measurements. However, in most remote sensing applications, inversion of the kernel matrix is unstable, resulting in the amplification of measurement and computational uncertainties. Techniques to circumvent this error amplification have focused on methods of constraining the solution. In this paper, the authors adapt an existing technique to do such an inversion. Noise reduction is accomplished by the addition of double-sided inequality constraints for each unknown variable. The advantage of such a technique is the ability to individually adjust the solution space of each individual unknown, depending on a priori knowledge.

The inversion algorithm is applied to the problem of retrieving radar Doppler spectra, which have been artificially broadened by turbulent air motions. First, to test the algorithm, radar Doppler spectra were simulated using known drop size and vertical air motion distributions. The simulated spectra were used as input to the retrieval algorithm, and the results were compared to the initial quiet-air spectrum. Results indicate that accurate retrievals can be performed despite the addition of moderate amounts of noise to the simulated spectra. Then, to demonstrate the practical retrieval of quiet-air Doppler spectra, the algorithm was used to process radar observations collected from continental stratocumulus. From these retrievals, a two-dimensional map of the large-scale vertical motions within the cloud was constructed as well as a profile of vertical velocity variance. In addition, a drop size distribution was also derived from an updraft region of the cloud.

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Scott E. Giangrande, David M. Babb, and Johannes Verlinde

Abstract

Spectral processing algorithms employed in millimeter-wave profiling radars typically obtain good signal-to-noise ratios from weakly scattering clouds by incoherently averaging many spectra. Radar operating characteristics dictate sampling times on the order of a few seconds. Presented here are analyses showing that changes in the vertical wind during the sampling period can be a major contributor to the measured spectrum width. Such broadened spectra violate the assumptions made in spectral inversion techniques, and may lead to incorrect interpretations of the turbulent and microphysical characteristics of the radar volume. Moreover, it is shown that there are several factors involved in determining the measured spectral shape: the averaging time window and horizontal advection velocity of the cloud, as well as horizontal inhomogeneities in cloud vertical velocity and microphysical fields. Current processing algorithms do not allow for distinction between these effects, leading to potential for large errors in retrievals. In this paper a simple technique is presented to remove this effect for monomodal spectra. A side product of this algorithm is high temporal resolution estimates of the volume-mean vertical wind.

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