Abstract

A popular method for obtaining a wind field that satisfies mass continuity and that is consistent with dual-Doppler radial velocity observations is the iterative wind synthesis in Cartesian coordinates. However, in some instances, the iterative method does not converge. Herein, a stability condition is derived for the iterative technique that depends on the horizontal and vertical grid spacings, and the azimuth and elevation angles of the dual-Doppler observations. Alternately, the condition for stability can be expressed as a relationship between the grid box dimensions and the direction of the coplanar arc passing through the grid point. The results of experiments with simulated radar data are consistent with the predicted stability characteristics.

Abstract

On 8 June 1995 scientists participating in the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) collected airborne Doppler radar data in a storm that produced a family of tornadoes near McLean, Texas. The Electra Doppler Radar (ELDORA) scanned three significant tornadoes during their formative and mature stages; one of the tornadoes was of F4/F5 intensity.

Evidence from pseudo-dual-Doppler analyses of the ELDORA data reveals a process of cyclic tornado formation qualitatively similar to that depicted in previous conceptual models. In particular, the rear-flank gust front appears to play a major role in determining the location of the next vortex in the series. When a tornado forms, a small region (3–5 km wide) of outflow surges ahead of the tornado, producing a local bulge in the gust front. A new vorticity maximum may form near the leading edge of the outflow. In contrast to what is suggested by earlier conceptual models, intersection of the rear-flank gust front with a wind shift along the forward flank does not appear to be a necessary element in the formation of the new vortex.

Previous studies have shown that if low-level outflow from the rear flank of a storm surges well ahead of the midlevel updraft, initiation of new deep, moist convection downshear along the gust front may be necessary for storm survival. In contrast, in the McLean storm, the rear gust front did not move ahead of the location of the midlevel updraft. The persistence of the main updraft may have fostered a rapid cyclic process.

The first and second tornadoes each, in short time, became separated from the main updraft. In contrast, the third large tornado in the family (the fourth overall) remained with the main updraft and persisted for over 1 h. Reasons for the transition of the cyclic phase into the long-lived phase will be discussed in Part II of this paper.

Abstract

On 17 May 1981, an extensive dataset was collected for a supercell thunderstorm that produced an F2 tornado near Arcadia in central Oklahoma. Coordinated dual-Doppler scans of the storm by 10-cm research radars were collected at approximately 5-min intervals from 30 min before the tornado touched down until 15 min after the tornado had dissipated. The Arcadia storm was also well sampled by a 444-m-tall instrumented tower. The low-level inflow, updraft, mesocyclone, and rear precipitation core of the supercell all passed across the tower.

A comparison of the instrumented tower measurements with a dual-Doppler synthesis reveals that the latter qualitatively resolved the low-level flow. However, the magnitudes of the low-level horizontal winds and updraft speed were underestimated. In addition, the vertical shear of the horizontal wind in the lowest kilometer was unresolved in the Doppler winds.

In the storm environment, horizontal vorticity was strong (∼1.5 × 10⁻² s⁻¹) and approximately streamwise over the depth of the instrumented tower. Just upstream (northeast) of the updraft, the magnitude of horizontal vorticity was nearly twice this value and had likely been enhanced by baroclinic generation of horizontal vorticity and/or stretching of horizontal vorticity. Tilting of the resulting horizontal vorticity into the vertical produced the pretornadic low-level mesocyclone. Low-level mesocyclone inflow was primarily from the east, but during the tornadic stage, parcels approaching from the north and west were also drawn into the circulation.

The tornado formed southeast of the mesocyclone center and near the tip of the reflectivity hook echo while low-level mesocyclone vorticity was increasing. Tornadogenesis occurred near the nose of the rear downdraft within a region of horizontal shear between southeasterly inflow into the storm and westerly outflow from the rear downdraft. Pressure retrievals suggest the rear downdraft south of the mesocyclone center was associated with a downward-directed perturbation pressure gradient force. The tornado and the parent storm dissipated as outflow surged eastward ahead of the updraft.

This case study is the first to include a comparison of independent measurements of the wind field in and near the low-level mesocyclone of a supercell. The wind analysis is also complemented by the instrumented tower thermodynamic measurements.

Abstract

An “additive noise” method for initializing ensemble forecasts of convective storms and maintaining ensemble spread during data assimilation is developed and tested for a simplified numerical cloud model (no radiation, terrain, or surface fluxes) and radar observations of the 8 May 2003 Oklahoma City supercell. Every 5 min during a 90-min data-assimilation window, local perturbations in the wind, temperature, and water-vapor fields are added to each ensemble member where the reflectivity observations indicate precipitation. These perturbations are random but have been smoothed so that they have correlation length scales of a few kilometers. An ensemble Kalman filter technique is used to assimilate Doppler velocity observations into the cloud model. The supercell and other nearby cells that develop in the model are qualitatively similar to those that were observed. Relative to previous storm-scale ensemble methods, the additive-noise technique reduces the number of spurious cells and their negative consequences during the data assimilation. The additive-noise method is designed to maintain ensemble spread within convective storms during long periods of data assimilation, and it adapts to changing storm configurations. It would be straightforward to use this method in a mesoscale model with explicit convection and inhomogeneous storm environments.

Abstract

The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF) Model is employed as a simplified cloud model at 2-km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single Weather Surveillance Radar-1988 Doppler (WSR-88D) within the U.S. operational network. Observations are assimilated every 2 min for a duration of 60 min and correction of folded radial velocities occurs within the EnKF. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding. The EnKF performs effectively and with robust results across all the cases. Over the first 18–30 min of assimilation, the rms and domain-averaged prior fits to observations in each case improve significantly from their initial levels, reaching comparable values of 3–6 m s⁻¹ and 7–10 dBZ. Representation of mesoscale uncertainty, albeit in the simplest form of initial sounding perturbations, is a critical part of the assimilation system, as it increases ensemble spread and improves filter performance. In addition, assimilation of “no precipitation” observations (i.e., reflectivity observations with values small enough to indicate the absence of precipitation) serves to suppress spurious convection in ensemble members. At the same time, it is clear that the assimilation is far from optimal, as the ensemble spread is consistently smaller than what would be expected from the innovation statistics and the assumed observation-error variance.

Abstract

The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. This work is the companion to , which focused on the quality of analyses during the 60-min analysis period. Here, the focus is on 30-min ensemble forecasts initialized at the end of that period. As in , the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. Various observation-space and state-space verification metrics, computed both for ensemble means and individual ensemble members, are employed to assess the quality of ensemble forecasts comparatively across cases. While the cases exhibit noticeable differences in predictability, the forecast skill in each case, as measured by various metrics, decays on a time scale of tens of minutes. The ensemble spread also increases rapidly but significant outlier members or clustering among members are not encountered. Forecast quality is seen to be influenced to varying degrees by the respective initial soundings. While radar data assimilation is able to partially mitigate some of the negative effects in some situations, the supercell case, in particular, remains difficult to predict even after 60 min of data assimilation.

Abstract

On 8 June 1995, scientists participating in the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) collected a unique dataset with the Electra Doppler Radar (ELDORA). The ELDORA observations document the sequential life cycles of storm-scale circulations associated with three large tornadoes in a supercell thunderstorm near McLean, Texas. A qualitative description of the evolution of the storm was provided in Part I of this paper.

During the first stage of development of each storm-scale circulation, interaction of the updraft with the environmental low-level horizontal vorticity produced a vorticity column that increased in intensity with height. As the vortex matured, vorticity increased greatly at low levels (i.e., below 2 km AGL) and exceeded that aloft. Each tornadic vortex was located near the rear side of the updraft, where the surrounding low-level horizontal vorticity was modified locally, most likely by weak baroclinity within the storm. Tilting of low-level horizontal vorticity into the vertical, followed by stretching of the vertical vorticity, occurred in the air parcels that entered the rear portion of the main storm updraft from its left (as viewed in the direction of storm motion). Although the region of tilting was near the interface of the main updraft and that portion of the downdraft to the left of the updraft, there is no direct evidence in the observations (above 500 m AGL) of generation of cyclonic vertical vorticity by tilting in the downdraft itself.

For this storm, the cyclic tornadogenesis process was associated with a mismatch between the horizontal motion of successive tornadoes and the horizontal velocity of the main storm-scale updraft and downdraft. Low-level updraft-relative flow seemed to be the most important factor in determining tornado motion.

Abstract

A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and incorporates variability in both initial and boundary conditions and model physical process schemes. The precipitation assimilation method only incorporates information on when and where rainfall is observed. Model temperature and water vapor mixing ratio profiles at each grid point are modified if rainfall is observed but not predicted, or if rainfall is predicted but not observed. These modifications act to either increase or decrease, respectively, the likelihood that precipitation develops at that grid point.

Two cases are examined in which this technique is applied to assimilate precipitation data every 15 min from 1200 to 1800 UTC, while hourly surface observations are also assimilated at the same time using the more sophisticated ensemble Kalman filter approach. Results show that the simple method for assimilating precipitation data helps the model develop precipitation where it is observed, resulting in the precipitation area being reproduced more accurately than in the run without precipitation-data assimilation, while not negatively influencing the positive results from the surface data assimilation. Improvement is also seen in the reliability of precipitation probabilities for a 1 mm h⁻¹ threshold after the assimilation period, indicating that assimilating precipitation data may provide improved forecasts of the mesoscale environment for a few hours.

Abstract

On 26 May 1991, NOAA P-3 airborne Doppler radar data were collected near two tornadic supercells in the southern Plains during the Cooperative Oklahoma Profiler Studies (COPS-91) field program. The 3-cm radar mounted in the tail of the aircraft was operated using the fore–aft scanning technique (FAST). Both storms were sampled just minutes after each had produced a tornado. The COPS-91 storms are the first tornadic supercells to be sampled extensively by airborne Doppler radar using the FAST methodology.

Pseudo-dual-Doppler analyses of a dissipating storm in southwest Kansas show no remaining low-level circulation, even though the storm had just produced a tornado. The analyses of a storm in northwest Oklahoma reveal better-defined features in the wind field near the surface. In contrast to what has been previously observed in post-tornadic supercells, the cyclonic vorticity in both storms was greater aloft than at low levels. The 26 May 1991 storms provide further evidence that supercells often contain multiple updrafts and mesocyclones. Cyclical mesocyclogenesis was occurring in the northwest Oklahoma storm while pseudo-dual-Doppler data were being collected.

Airborne Doppler radar provides the potential for obtaining datasets throughout the lifetime of a storm at close range, where the observational geometry can be controlled to minimize known errors. The lessons learned from COPS-91 were incorporated into the airborne Doppler strategies employed during the subsequent Verificafion of the Origins of Rotation in Tornadoes Experiment (1994–95).

Abstract

The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach is evaluated for the potential to improve short-range forecasting. Two severe weather cases are examined, in which the assimilation is performed over a 6-h period using hourly surface observations followed by an 18-h simulation period. Ensembles are created in three different ways—by using different initial and boundary conditions, by using different model physical process schemes, and by using both different initial and boundary conditions and different model physical process schemes. The ensembles are compared in order to investigate the role of uncertainties in the initial and boundary conditions and physical process schemes in EnKF data assimilation. In the initial condition ensemble, spread is associated largely with the displacement of atmospheric baroclinic systems. In the physics ensemble, spread comes from the differences in model physics, which results in larger spread in temperature and dewpoint temperature than the initial condition ensemble, and smaller spread in the wind fields. The combined initial condition and physics ensemble has properties from both of the previous two ensembles. It provides the largest spread and produces the best simulation for most of the variables, in terms of the rms difference between the ensemble mean and observations. Perhaps most importantly, this combined ensemble provides very good guidance on the mesoscale features important to the severe weather events of the day.