ALBERT

Description: A framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Kalman filter (EnSRF) technique for assimilating data from more than one radar network is described. The system is tested by assimilating simulated radial velocity and reflectivity data from a Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and a network of four low-cost radars planned for the Oklahoma test bed by the new National Science Foundation (NSF) Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Such networks are meant to adaptively probe the lower atmosphere that is often missed by the existing WSR-88D radar network, so as to improve the detection of low-level hazardous weather events and to provide more complete data for the initialization of numerical weather prediction models. Different from earlier OSSE work with ensemble Kalman filters, the radar data are sampled on the radar elevation levels and a more realistic forward operator based on the Gaussian power-gain function is used. A stretched vertical grid with high vertical resolution near the ground allows for a better examination of the impact of low-level data. Furthermore, the impacts of storm propagation and higher-volume scan frequencies up to one volume scan per minute on the quality of analysis are examined, using a domain of a sufficient size. The generally good analysis compared to earlier work indicates that the filter can effectively handle the non-uniform-resolution data on the radar elevation levels. The assimilation of additional data from a well-positioned (relative to the storm) CASA radar improves the analysis of a supercell storm system that uses data from one WSR-88D radar alone; and the improvement is most significant at the low levels. When data from a single CASA radar are assimilated and when the radar does not provide full coverage of the storm system, significant errors develop in the analysis that cannot be effectively corrected. The combination of three CASA radars produces analyses of similar quality as the combination of one WSR-88D radar and one well-positioned CASA radar. The most significant effect of storm propagation speed appears to be on the data coverage, which in turn affects the analysis quality. It is generally true that the more observations, the better the analysis. The results of the EnSRF assimilation are not very sensitive to the propagation speed. The quality of analysis can be improved by employing faster volume scans. The sensitivity of the EnSRF analysis to the volume scan interval is however much less than that of traditional velocity and thermodynamic retrieval schemes, suggesting the superiority of the EnSRF method compared to traditional methods. The very frequent update of the model state by the filter, even at 1-min intervals, does not show any negative effect, indicating that the analyzed fields are well balanced.

Description: Using a nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing, the authors employ the scaled lagged average forecasting (SLAF) technique, developed originally for global and synoptic-scale prediction, to generate ensemble forecasts of a tornadic thunderstorm complex that occurred in north-central Texas on 28–29 March 2000. This is the first attempt, to their knowledge, in applying ensemble techniques to a cloud-resolving model using radar and other observations assimilated within nonhorizontally uniform initial conditions and full model physics. The principal goal of this study is to investigate the viability of ensemble forecasting in the context of explicitly resolved deep convective storms, with particular emphasis on the potential value added by fine grid spacing and probabilistic versus deterministic forecasts. Further, the authors focus on the structure and growth of errors as well as the application of suitable quantitative metrics to assess forecast skill for highly intermittent phenomena at fine scales. Because numerous strategies exist for linking multiple nested grids in an ensemble framework with none obviously superior, several are examined, particularly in light of how they impact the structure and growth of perturbations. Not surprisingly, forecast results are sensitive to the strategy chosen, and owing to the rapid growth of errors on the convective scale, the traditional SLAF methodology of age-based scaling is replaced by scaling predicated solely upon error magnitude. This modification improves forecast spread and skill, though the authors believe errors grow more slowly than is desirable. For all three horizontal grid spacings utilized, ensembles show both qualitative and quantitative improvement relative to their respective deterministic control forecasts. Nonetheless, the evolution of convection at 24- and 6-km spacings is vastly different from, and arguably inferior to, that at 3 km because at 24-km spacing, the model cannot explicitly resolve deep convection while at 6 km, the deep convection closure problem is ill posed and clouds are neither implicitly nor explicitly represented (even at 3-km spacing, updrafts and downdrafts only are marginally resolved). Despite their greater spatial fidelity, the 3-km grid spacing experiments are limited in that the ensemble mean reflectivity tends to be much weaker in intensity, and much broader in aerial extent, than that of any single 3-km spacing forecast owing to amplitude reduction and spatial smearing that occur when averaging is applied to spatially intermittent phenomena. The ensemble means of accumulated precipitation, on the other hand, preserve peak intensity quite well. Although a single case study obviously does not provide sufficient information with which to draw general conclusions, the results presented here, as well as those in Part II (which focuses solely on 3-km grid spacing experiments), suggest that even a small ensemble of cloud-resolving forecasts may provide greater skill, and greater practical value, than a single deterministic forecast using either the same or coarser grid spacing.

Description: An identical twin methodology is applied to a three-dimensional cloud model to study the dynamics of adjustment in deep convective storms. The principal goal is to diagnose how mass and velocity fields mutually adjust in order to better understand the relative information content (value) of observations, the physical interdependency among variables, and to help in the design of dynamically consistent analyses to ensure smooth startup of numerical prediction models. Using a control simulation (“truth” or “nature” run) of an idealized long-lived bow echo convective system, a series of adjustment experiments is created by resetting, in various combinations, the horizontal and vertical velocity components of the control run to their undisturbed base state values during the mature stage of storm system evolution. The integrations then are continued for comparison against the control. This strategy represents a methodology for studying transient response to an impulsive perturbation in a manner conceptually similar to that used in geostrophic and hydrostatic adjustment. The results indicate that resetting both horizontal velocity components alters the character of the convection and slows considerably the overall storm system evolution. In sharp contrast, when only the vertical velocity component is reset, the model quickly restores both updrafts and downdrafts to nearly their correct (control run) values, producing subsequent storm evolution virtually identical to that of the control run. Other combinations yield results in between these two extremes, with the cross-line velocity component proving to be most important in restoration toward the control run. This behavior is explained by acoustic adjustment of the pressure and velocity fields in direct response to changes in velocity divergence forced by the withdrawal of wind information.

Description: During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in their spatial resolution. The first set of runs used 4-km horizontal grid spacing with 35 vertical levels while the second used 2-km grid spacing and 51 vertical levels. Output from these daily forecasts is analyzed to assess the numerical forecast sensitivity to spatial resolution in the upper end of the convection-allowing range of grid spacing. The focus is on the central United States and the time period 18–30 h after model initialization. The analysis is based on a combination of visual comparison, systematic subjective verification conducted during the Spring Experiment, and objective metrics based largely on the mean diurnal cycle of the simulated reflectivity and precipitation fields. Additional insight is gained by examining the size distributions of the individual reflectivity and precipitation entities, and by comparing forecasts of mesocyclone occurrence in the two sets of forecasts. In general, the 2-km forecasts provide more detailed presentations of convective activity, but there appears to be little, if any, forecast skill on the scales where the added details emerge. On the scales where both model configurations show higher levels of skill—the scale of mesoscale convective features—the numerical forecasts appear to provide comparable utility as guidance for severe weather forecasters. These results suggest that, for the geographical, phenomenological, and temporal parameters of this study, any added value provided by decreasing the grid increment from 4 to 2 km (with commensurate adjustments to the vertical resolution) may not be worth the considerable increases in computational expense.

Description: In Part I, the authors used a full physics, nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing to generate the ensemble forecasts of an observed tornadic thunderstorm complex. The principal goal was to quantify the value added by fine grid spacing, as well as the assimilation of Doppler radar data, in both probabilistic and deterministic frameworks. The present paper focuses exclusively on 3-km horizontal grid spacing ensembles and the associated impacts on the forecast quality of temporal forecast sequencing, the construction of initial perturbations, and data assimilation. As in Part I, the authors employ a modified form of the scaled lagged average forecasting technique and use Stage IV accumulated precipitation estimates for verification. The ensemble mean and spread of accumulated precipitation are found to be similar in structure, mimicking their behavior in global models. Both the assimilation of Doppler radar data and the use of shorter (1–2 versus 3–5 h) forecast lead times improve ensemble precipitation forecasts. However, even at longer lead times and in certain situations without assimilated radar data, the ensembles are able to capture storm-scale features when the associated control forecast in a deterministic framework fails to do so. This indicates the potential value added by ensembles although this single case is not sufficient for drawing general conclusions. The creation of initial perturbations using forecasts of the same grid spacing shows no significant improvement over simply extracting perturbations from forecasts made at coarser spacing and interpolating them to finer grids. However, forecast quality is somewhat dependent upon perturbation amplitude, with smaller scaling values leading to significant underdispersion. Traditional forecast skill scores show somewhat contradictory results for accumulated precipitation, with the equitable threat score most consistent with qualitative performance.

Description: Building upon the authors’ previous work that examined the dynamics of numerically simulated cyclic mesocyclogenesis and its dependence upon model physical and computational parameters, this study likewise uses idealized numerical simulations to investigate associated dependencies upon ambient vertical wind shear. Specifically, the authors examine variations in hodograph shape, shear magnitude, and shear distribution, leading to storms with behavior ranging from steady state to varying degrees of aperiodic occluding cyclic mesocyclogenesis. However, the authors also demonstrate that a different mode of nonoccluding cyclic mesocyclogenesis may occur in certain environments. Straight hodographs (unidirectional shear) produce only nonoccluding cyclic mesocyclogenesis. Introducing some curvature by adding a quarter circle of turning at low levels results in steady, nonoccluding, and occluding modes. When a higher degree of curvature is introduced—for example, turning through half and three-quarter circles—the tendency for nonoccluding behavior is diminished. None of the full-circle hodographs exhibited cycling during 4 h of simulation. Overall, within a given storm, the preferred mode of cycling is related principally to hodograph shape and magnitude of the ambient vertical shear.

Description: Severe convective storms are typically simulated using either an idealized, horizontally homogeneous environment (i.e., single sounding) or an inhomogeneous environment constructed using numerous types of observations. Representing opposite ends of the spectrum, the former allows for the study of storm dynamics without the complicating effects of either land surface or atmospheric variability, though arguably at the expense of physical realism, while the latter is especially useful for prediction and data sensitivity studies, though because of its physical completeness, determination of cause can be extremely difficult. In this study, the gap between these two extremes is bridged by specifying horizontal variations in environmental vertical shear in an idealized, controlled manner so that their influence on storm morphology can be readily diagnosed. Simulations are performed using the Advanced Regional Prediction System (ARPS), though with significant modification to accommodate the analytically specified environmental fields. Several steady-state environments are constructed herein that retain a good degree of physical realism while permitting clear interpretation of cause and effect. These experiments are compared to counterpart control simulations in homogeneous environments constructed using single wind profiles from selected locations within the inhomogeneous environment domain. Simulations in which steady-state vertical shear varies spatially are presented for different shear regimes (storm types). A gradient of weak shear across the storm system leads to preferred cell development on the flank with greater shear. In a stronger shear regime (i.e., in the borderline multicell/supercell regime), however, cell development is enhanced on the weaker shear flank while cell organization is enhanced on the strong shear side. When an entire storm system moves from weak to strong shear, changes in cell structure are influenced by local mesoscale forcing associated with the cold pool. In this particular experiment, cells near the leading edge of the cold pool, where gust front convergence occurs along a continuous line, evolve into a bow-echo structure as the shear increases. In contrast, simulated cells that remain relatively isolated on the flank of the cold pool tend to develop supercellular characteristics.

Description: The NOAA NWS announced at the annual meeting of the American Meteorological Society in February 2003 its intent to create an Internet-based pseudo-operational system for delivering Weather Surveillance Radar-1988 Doppler (WSR-88D) Level II data. In April 2004, the NWS deployed the Next-Generation Weather Radar (NEXRAD) level II central collection functionality and set up a framework for distributing these data. The NWS action was the direct result of a successful joint government, university, and private sector development and test effort called the Collaborative Radar Acquisition Field Test (CRAFT) project. Project CRAFT was a multi-institutional effort among the Center for Analysis and Prediction of Storms, the University Corporation for Atmospheric Research, the University of Washington, and the three NOAA organizations, National Severe Storms Laboratory, WSR-88D Radar Operations Center (ROC), and National Climatic Data Center. The principal goal of CRAFT was to demonstrate the real-time compression and Internet-based transmission of level II data from all WSR-88D with the vision of an affordable nationwide operational implementation. The initial test bed of six radars located in and around Oklahoma grew to include 64 WSR-88D nationwide before being adopted by the NWS for national implementation. A description of the technical aspects of the award-winning Project CRAFT is given, including data transmission, reliability, latency, compression, archival, data mining, and newly developed visualization and retrieval tools. In addition, challenges encountered in transferring this research project into operations are discussed, along with examples of uses of the data.