High-resolution Numerical Weather Prediction Model Research Articles

A Proposed Model-Based Methodology for Feature-Specific Prediction for High-Impact Weather

A feature-specific forecasting method for high-impact weather events that takes advantage of high-resolution numerical weather prediction models and spatial forecast verification methodology is proposed. An application of this method to the prediction of a severe convective storm event is given.

Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data

Abstract Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn2 are compared with nighttime profiles of Cn2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification

Abstract The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models. The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used. When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

Non‐Gaussian state space models in decomposition of ice core time series in long and short time‐scales

AbstractStatistical modelling of six time series of geological ice core chemical data from Greenland is discussed. We decompose the total variation into long time‐scale (trend) and short time‐scale variations (fluctuations around the trend), and a pure noise component. Too heavy tails of the short‐term variation makes a standard time‐invariant linear Gaussian model inadequate. We try non‐Gaussian state space models, which can be efficiently approximated by time‐dependent Gaussian models. In essence, these time‐dependent Gaussian models result in a local smoothing, in contrast to the global smoothing provided by the time‐invariant model. To describe the mechanism of this local smoothing, we utilise the concept of a local variance function derived from a heavy‐tailed density. The time‐dependent error variance expresses the uncertainty about the dynamical development of the model state, and it controls the influence of observations on the estimates of the model state components. The great advantage of the derived time‐dependent Gaussian model is that the Kalman filter and the Kalman smoother can be used as efficient computational tools for performing the variation decomposition. One of the main objectives of the study is to investigate how the distributional assumption on the model error component of the short time‐scale variation affects the decomposition. Copyright © 2009 John Wiley & Sons, Ltd.

The Overamplification of Gravity Waves in Numerical Solutions to Flow over Topography

Abstract The tendency of high-resolution numerical weather prediction (NWP) models to overpredict the strength of vertically propagating mountain waves is explored. Discrete analytic mountain-wave solutions are presented for the classical problem of cross-mountain flow in an atmosphere with constant wind speed and stability. Time-dependent linear numerical solutions are also obtained for more realistic atmospheric structures. On one hand, using second-order-accurate finite differences on an Arakawa C grid to model nonhydrostatic flow over what might be supposed to be an adequately resolved 8Δx-wide mountain can lead to an overamplification of the standing mountain wave by 30%–40%. On the other hand, the same finite-difference scheme underestimates the wave amplitude in hydrostatic flow over an 8Δx-wide mountain. Increasing the accuracy of the advection scheme to the fourth order significantly reduces the numerical errors associated with both the hydrostatic and nonhydrostatic discrete solutions. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model is used to generate two 70-member ensemble simulations of a mountain-wave event during the Terrain-Induced Rotor Experiment. It is shown that switching from second-order advection to fourth-order advection leads to as much as a 20 m s−1 decrease in vertical velocity on the lee side of the Sierra Nevada, and that the weaker fourth-order solutions are more consistent with observations.

Simulation and Prediction of Weather Radar Clutter Using a Wave Propagator on High Resolution NWP Data

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Weather radars are essential sensors for observation of precipitation in the troposphere and play a major part in weather forecasting and hydrological modelling. Clutter caused by non-standard wave propagation is a common problem in weather radar applications, and in this paper a method for prediction of this type of weather radar clutter is presented. The method uses a wave propagator to identify areas of potential non-standard propagation. The wave propagator uses a three dimensional refractivity field derived from the geophysical parameters: temperature, humidity, and pressure obtained from a high-resolution Numerical Weather Prediction (NWP) model. The wave propagator is based on the parabolic equation approximation to the electromagnetic wave equation. The parabolic equation is solved using the well-known Fourier split-step method. Finally, the radar clutter prediction technique is used in two cases of superrefraction propagation and the feasibility for detection and removal of clutter echoes while preserving precipitation echoes is illustrated. </para>

A Polarimetric Radar Forward Operator for Model Evaluation

Abstract A polarimetric radar forward operator has been developed as a tool for the systematic evaluation of microphysical parameterization schemes in high-resolution numerical weather prediction (NWP) models. The application of such a forward operator allows a direct comparison of the model simulations to polarimetric radar observations. While the comparison of observed and synthetic reflectivity gives information on the quality of quantitative precipitation forecasts, the information from the polarimetric quantities allows for a direct evaluation of the capacity of the NWP model to realistically describe the processes involved in the formation and interactions of the hydrometeors and, hence, the performance of the microphysical parameterization scheme. This information is expected to be valuable for detecting systematic model errors and hence improve model physics. This paper summarizes the technical characteristics of the synthetic polarimetric radar (SynPolRad). Different polarimetric radar quantities are computed from model forecasts using a T-matrix scattering code and ice phase hydrometeors are explicitly considered. To do so, the sensitivities of the scattering processes to the microphysical characteristics of different ice hydrometeors are investigated using sensitivity studies. Furthermore, beam propagation effects are considered, including attenuation and beam bending. The performance of SynPolRad and the consistence of the assumptions made in the derivation of the input parameters are illustrated in a case study. The resulting synthetic quantities as well as hydrometeor classification are compared with observations and are shown to be consistent with the model assumptions.

Sensitivity of the LHN scheme to non‐rain echoes

AbstractRadar‐derived quantitative precipitation estimates (QPE) are becoming an increasingly important element in high‐resolution numerical weather prediction (NWP). As such they complement conventional data like surface or upper‐air observations. Unlike the latter, radar data exhibit a highly variable quality, in that they are affected by a number of factors that limit their accuracy in estimating precipitation at the surface. In the context of assimilating radar‐derived QPE in high‐resolution NWP models this poses two salient questions, i.e. how is a specific assimilation scheme affected by errors in the observations, and how can such variable quality be accounted for?This paper addresses the first question and presents a sensitivity study of the Latent Heat Nudging (LHN) scheme in a meso‐gamma scale NWP model to gross errors in the radar data, notably non‐rain echoes. It was found that non‐rain, or clutter, echoes as small as one model pixel can trigger the release of convective instabilities when forced by the LHN scheme, producing model precipitation that is large compared to the original non‐rain signal. Enhancement factors from 3 up to 50 have been found for moderate to high values of CAPE and low to medium intensity clutter signals. Furthermore, the response of the model atmosphere to the forcing was found to be very quick, i.e. on the time scale of convection (less than 10 min for strong forcing to a couple of hours for moderate forcing). Large coherent areas of non‐rain echoes were found to be more likely to initiate such erroneous precipitation enhancement than scattered gridbox‐sized signals. Finally, non‐rain echoes resulting from anomalous propagation of the radar beam, by virtue of the usually very stable conditions with which they are associated, are not very conducive to rainfall amplification. However, a strong spurious vertical circulation, along with undesired mixing, may be induced and adversely impact the local structure of the simulation. Copyright © 2008 Royal Meteorological Society

Fog prediction using a very high resolution numerical weather prediction model forced with a single profile

AbstractFog and low level cloud forecasting is crucial for aviation, land transport and shipping, Current numerical weather prediction (NWP) models can give a broad indication of fog formation and dissipation on scales of a few tens of kilometres, but can provide little detail especially in areas of complex terrain. One‐dimensional models for specific locations taking into account the impact of the surrounding orography on the flow have been developed as a cheap solution, but they are unable to treat complex three dimensional flows. A full NWP model running at a horizontal resolution of around 1 km (or better) may be required to address the three‐dimensional fog forecasting problem in full. Enhancement of the model resolution to such a scale is restrained by available computing power and is not presently feasible for general coverage. However, in the weakly forced situations leading to fog formation, the use of a small, local area (e.g. 50 km × 50 km), high resolution NWP model may be a viable alternative.In this study, the local area NWP model approach is explored, initializing and forcing the model with a time series of single forcing profiles from either a low resolution NWP model, or from a local radiosonde profile. This method of homogeneous single‐profile forcing (SPF) allows the model to be run locally with a small volume of input data. The UK Met Office Unified Model (UM) is set‐up for a 50 km by 50 km horizontal domain, with real orography and a grid spacing of 1 km and 76 vertical levels. The model is initialized and forced with temperature, humidity and wind profiles from a 12 km coarse resolution model. Some typical results from a single‐profile forcing (SPF) model run will be shown and compared with the output from a full NWP model with 1 km resolution and with observational data. © Crown Copyright 2008. Reproduced with the permission of the Controller of HMSO. Published by John Wiley &amp; Sons, Ltd

Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

Abstract A radar simulator for polarimetric radar variables, including reflectivities at horizontal and vertical polarizations, the differential reflectivity, and the specific differential phase, has been developed. This simulator serves as a test bed for developing and testing forward observation operators of polarimetric radar variables that are needed when directly assimilating these variables into storm-scale numerical weather prediction (NWP) models, using either variational or ensemble-based assimilation methods. The simulator takes as input the results of high-resolution NWP model simulations with ice microphysics and produces simulated polarimetric radar data that may also contain simulated errors. It is developed based on calculations of electromagnetic wave propagation and scattering at the S band of wavelength 10.7 cm in a hydrometeor-containing atmosphere. The T-matrix method is used for the scattering calculation of raindrops and the Rayleigh scattering approximation is applied to snow and hail particles. The polarimetric variables are expressed as functions of the hydrometeor mixing ratios as well as their corresponding drop size distribution parameters and densities. The presence of wet snow and wet hail in the melting layer is accounted for by using a new, relatively simple melting model that defines the water fraction in the melting snow or hail. The effect of varying density due to the melting snow or hail is also included. Vertical cross sections and profiles of the polarimetric variables for a simulated mature multicellular squall-line system and a supercell storm show that polarimetric signatures of the bright band in the stratiform region and those associated with deep convection are well captured by the simulator.

Discharge prediction based on multi-model precipitation forecasts

In the field of hydrological prediction for medium-sized watersheds, characterized by complex orography and short response times, forecasts cannot rely only upon observed precipitation: predicted rainfall is in this case an essential input for hydrological models. However, the quality and reliability of deterministic numerical precipitation forecasts driving a hydrological model are often unsatisfactory, because uncertainty in Quantitative Precipitation Forecasts (QPFs) is considerable at the scales of interest for hydrological purposes. The uncertainty inherent in precipitation forecast can be accounted for better estimating the uncertainty associated with the flood forecast, in order to provide a more informative hydrological prediction. The methodology proposed and adopted in this work is based on a hydrological ensemble forecasting approach that uses multiple precipitation scenarios provided by different high-resolution numerical weather prediction models, driving the same hydrological model. In this way, the uncertainty associated with the meteorological forecasts can propagate into the hydrological models and be used in warnings and decision making procedures relying upon a probabilistic approach. In the framework of RISK AWARE, an INTERREG III B EU project, a detailed analysis of two cases of intense precipitation affecting the Reno river basin, a medium-sized catchment in northern Italy, has been performed. One case study has been performed using lateral boundary values derived from analysed fields, the other simulating a real time forecast, i.e., using forecasted boundary conditions. Four different meteorological models (Lokal Modell, RAMS, BOLAM and MOLOCH), operating at different horizontal resolutions, provide QPFs which are used to force the hydrological model. The discharge predictions are obtained by means of the physically based rainfall-runoff model TOPKAPI. The results provide examples of the uncertainties inherent in the QPF and show that the hydrological response of the Reno river basin, as simulated by the TOPKAPI model, is highly sensitive to the correct space-time localization of precipitation, even if the total amount of rainfall is, on average, well forecasted. The system seems able to provide useful information concerning the discharge peaks (amount and timing) for warning purposes.

Turbulent Mixing Processes in Atmospheric Bores and Solitary Waves Deduced from Profiling Systems and Numerical Simulation

Abstract Families of solitary waves (“solitons”) associated with two atmospheric bores on the same day were observed by an unprecedented number of ground-based and airborne profiling systems during the International H2O Project (IHOP). In addition, a very high-resolution numerical weather prediction model initialized with real data was used with success to simulate one of the bores and the evolving soliton. The predicted wave amplitude, phase speed, wavelength, and structure compared well to these extraordinarily detailed observations. The observations suggest that during the active phase (when turbulent mixing was active, which was prior to bore collapse), the bores and waves vigorously mixed dry air from above a nocturnal boundary layer down to the surface. Refractivity computed from near-surface radar observations showed pronounced decreases due to sudden drying during the passage of the bores in this phase, but refractivity increases appeared during the period of bore collapse. During both phases, the bores wafted aerosol-laden moist air up to the middle troposphere and weakened the capping inversion, thus reducing inhibition to deep convection development. The model results indicate that the refractivity decreases near the surface were due to drying caused by downward turbulent mixing of air by the wave circulations. Turbulent kinetic energy was generated immediately behind the bore head, then advected rearward and downward by the solitary waves. During the dissipation stage, the lifting by the bore head produced adiabatic cooling aloft and distributed the very moist air near the surface upward through the bore depth, but without any drying due to the absence of vigorous mixing. Thus, this study shows that the moist thermodynamic effects caused by atmospheric bores and solitons strongly depend upon the life cycle of these phenomena.

Atmospheric Predictability at Synoptic Versus Cloud-Resolving Scales

The limited atmospheric predictability has been addressed by the development of ensemble prediction systems (EPS) that are now routinely applied for medium-range synoptic-scale numerical weather prediction (NWP). With the increase of computational power, interest is growing in the design of high-resolution (cloud resolving) NWP models and their associated short-range EPS. This development raises a series of fundamental questions, especially concerning the type of error growth and the validity of the tangent-linear approximation. To address these issues, a comparison between perturbed medium-range (10 day) synoptic-scale integrations (taken from the operational ECMWF EPS with a horizontal resolution of about 80 km) and short-range (1 day) high-resolution simulations (based on the Lokal Modell of the Consortium for Small-Scale Modeling with a grid spacing of 2.2 km) is conducted. The differences between the two systems are interpreted in a nondimensional sense and illustrated with the help of the Lorenz att...

Revisiting the latent heat nudging scheme for the rainfall assimilation of a simulated convective storm

Next-generation, operational, high-resolution numerical weather prediction models require economical assimilation schemes for radar data. In the present study we evaluate and characterise the latent heat nudging (LHN) rainfall assimilation scheme within a meso-γ scale NWP model in the framework of identical twin simulations of an idealised supercell storm. Consideration is given to the model’s dynamical response to the forcing as well as to the sensitivity of the LHN scheme to uncertainty in the observations and the environment.

Assimilating Surface Weather Observations from Complex Terrain into a High-Resolution Numerical Weather Prediction Model

Abstract An anisotropic surface analysis method based on the mother–daughter (MD) approach has been developed to spread valley station observations to grid points in circuitous steep valleys. In this paper, the MD approach is further refined to allow spreading the mountain-top observations to grid points near neighboring high ridges across valleys. Starting with a 3D first guess from a high-resolution mesoscale model forecast, surface weather observations are assimilated into the boundary layer, and pseudo-upper-air data (interpolated from the coarser-resolution analyses from major operational centers) are assimilated into the free atmosphere. Incremental analysis updating is then used to incorporate the final analysis increments (the difference between the final analysis and the first guess) into a high-resolution numerical weather prediction model. The MD approaches (including one with shoreline refinement) are compared with other objective analysis methods using case examples and daily mesoscale real-time forecast runs during November and December 2004. This study further confirms that the MD approaches outperform the other methods, and that the shoreline refinement achieves better analysis quality than the basic MD approach. The improvement of mountain-top refinement over the basic MD approach increases with the percentage of mountain-top stations, which is usually low. Higher skill in predicting near-surface potential temperature is found when surface information is spread upward throughout the boundary layer instead of at only the bottom model level. The results show improved near-surface forecasts of temperature and humidity that are directly assimilated into the model, but poorer forecasts of near-surface winds and precipitation, which are not assimilated into the model.

Imaging gravity waves in lower stratospheric AMSU-A radiances, Part 2: Validation case study

Abstract. Two-dimensional radiance maps from Channel 9 (~60–90 hPa) of the Advanced Microwave Sounding Unit (AMSU-A), acquired over southern Scandinavia on 14 January 2003, show plane-wave-like oscillations with a wavelength λh of ~400–500 km and peak brightness temperature amplitudes of up to 0.9 K. The wave-like pattern is observed in AMSU-A radiances from 8 overpasses of this region by 4 different satellites, revealing a growth in the disturbance amplitude from 00:00 UTC to 12:00 UTC and a change in its horizontal structure between 12:00 UTC and 20:00 UTC. Forecast and hindcast runs for 14 January 2003 using high-resolution global and regional numerical weather prediction (NWP) models generate a lower stratospheric mountain wave over southern Scandinavia with peak 90 hPa temperature amplitudes of ~5–7 K at 12:00 UTC and a similar horizontal wavelength, packet width, phase structure and time evolution to the disturbance observed in AMSU-A radiances. The wave's vertical wavelength is ~12 km. These NWP fields are validated against radiosonde wind and temperature profiles and airborne lidar profiles of temperature and aerosol backscatter ratios acquired from the NASA DC-8 during the second SAGE III Ozone Loss and Validation Experiment (SOLVE II). Both the amplitude and phase of the stratospheric mountain wave in the various NWP fields agree well with localized perturbation features in these suborbital measurements. In particular, we show that this wave formed the type II polar stratospheric clouds measured by the DC-8 lidar. To compare directly with the AMSU-A data, we convert these validated NWP temperature fields into swath-scanned brightness temperatures using three-dimensional Channel 9 weighting functions and the actual AMSU-A scan patterns from each of the 8 overpasses of this region. These NWP-based brightness temperatures contain two-dimensional oscillations due to this resolved stratospheric mountain wave that have an amplitude, wavelength, horizontal structure and time evolution that closely match those observed in the AMSU-A data. These comparisons not only verify gravity wave detection and horizontal imaging capabilities for AMSU-A Channel 9, but provide an absolute validation of the anticipated radiance signals for a given three-dimensional gravity wave, based on the modeling of Eckermann and Wu (2006).

A radar-based verification of precipitation forecast for local convective storms

Local flash flood storms with a rapid hydrological response are a real challenge for quantitative precipitation forecasting (QPF). It is relevant to assess space domains, to which the QPF approaches are applicable. In this paper an attempt is made to evaluate the forecasting capability of a high-resolution numerical weather prediction (NWP) model by means of area-related QPF verification. The results presented concern two local convective events, which occurred in the Czech Republic (CR) on 13 and 15 July 2002 and caused local flash floods. We used the LM COSMO model (Lokall Model of the COSMO consortium) adapted to the horizontal resolution of 2.8 km over a model domain covering the CR. The 18 h forecast of convective precipitation was verified by using radar rainfall totals adjusted to the measured rain gauge data. The grid point-related root mean square error (RMSE) value was calculated over a square around the grid point under the assumption that rainfall values were randomly distributed within the square. The forecast accuracy was characterized by the mean RMSE over the whole verification domain. We attempt to show a dependence of both the RMSE field and the mean RMSE on the square size. The importance of a suitable merger between the radar and rain gauge datasets is demonstrated by a comparison between the verification results obtained with and without the gauge adjustment. The application of verification procedure demonstrates uncertainties in the precipitation forecasts. The model was integrated with initial conditions shifted by 0.5° distances. The four verifications, corresponding to the shifts in the four directions, show differences in the resulting QPF, which depend on the size of verification area and on the direction of the shift.

Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

Partitioning of snowy and rainy precipitation in a case of a north Adriatic frontal passage

Abstract. A case of snow fall in the plains of the Northern Italian region Veneto is presented from a forecasters' perspective. Contrasting forecast guidance came from the ECMWF global model and the limited area model LAMI. The former showed a marked warm-moist Sirocco flow coming from the Adriatic Sea onto the coast at all levels, the latter discerned a distinct cold air flow from the north-east along the foothills of the Alps. The integrated observing network of the Centro Meteorologico di Teolo ARPA Veneto revealed this cold-air structure and helped the forecaster in the choice of the forecast and underpin the snowfall alert to the road authorities. It is argued that this feature is a crucial element for the occurrence of snowfall over the Veneto plains, and that the high-resolution numerical weather prediction model was essential in describing this mesoscale feature. The nature of the north-easterly flow is thought to be a combination of a Bora like flow and a barrier jet induced by flow blocking by the Alps.

On the Vertical Structure of Modeled and Observed Deep Convective Storms: Insights for Precipitation Retrieval and Microphysical Parameterization

Abstract An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.