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Keith A. Brewster

Abstract

A scheme to correct phase errors in numerical model forecasts using Doppler radar, radiosonde, profiler, and surface data is demonstrated to improve forecasts in a complex severe thunderstorm situation. The technique is designed to directly address forecast phase errors or initial position errors as part of a data assimilation strategy. In the demonstration the phase error correction is applied near the time of initial cell development and the forecast results are compared to the uncorrected forecast and forecasts made using an analysis created at the time of the observations. Forecasts are verified qualitatively for the position of thunderstorm cells and quantitatively for accumulated precipitation. It is shown that the scheme can successfully correct errors in thunderstorm locations and it has a positive influence on the subsequent forecast. The advantage of the phase correction over the control lasts for about 3 h despite storm dissipation and regeneration, and interactions among multiple storms.

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Keith A. Brewster

Abstract

An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical forecasts at grid resolutions of 2–20 km (meso-γ scale). It is proposed that the process of correcting a numerical forecast field can be simplified if such errors are addressed directly. An objective method of determining the phase error in the forecast by searching for a field of shift vectors that minimizes a squared-error difference from high-resolution observations is described.

Three methods of applying a phase error correction to a forecast model are detailed. The first applies the entire correction at the initial time, the second in discrete steps during an assimilation window, and the third applies the correction continuously through the model's horizontal advection process.

It is shown that the phase correction method is effective in producing an analysis field that agrees with the data yet preserves the structure developed by the model. The three methods of assimilating the correction in the forecast are successful, and a long-term positive effect on the thunderstorm simulation is achieved in the simulations, even as the modeled storms go through a cycle of decline and regeneration.

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Keith A. Brewster

Abstract

An interesting swirl in the cloud base of a severe thunderstorm near Denver, Colorado, is documented with photographs and Doppler radar velocity measurements. The swirl, which produced two funnel clouds, may have been an eddy of a weak midlevel mesocyclone or a result of surface vorticity stretching by the storm's intense updraft.

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Ming Hu, Ming Xue, and Keith Brewster

Abstract

In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model are studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) scheme that contains a mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are examined while Part II focuses on the impact of radial velocity and the mass divergence constraint.

The case studied is that of the 28 March 2000 Fort Worth, Texas, tornado outbreaks. The same case was studied by Xue et al. using the ARPS Data Analysis System (ADAS) and an earlier version of the cloud analysis procedure with WSR-88D level-III data. Since then, several modifications to the cloud analysis procedure, including those to the in-cloud temperature adjustment and the analysis of precipitation species, have been made. They are described in detail with examples.

The assimilation and predictions use a 3-km grid nested inside a 9-km one. The level-II reflectivity data are assimilated, through the cloud analysis, at 10-min intervals in a 1-h period that ends a little over 1 h preceding the first tornado outbreak. Experiments with different settings within the cloud analysis procedure are examined. It is found that the experiment using the improved cloud analysis procedure with reflectivity data can capture the important characteristics of the main tornadic thunderstorm more accurately than the experiment using the early version of cloud analysis. The contributions of different modifications to the above improvements are investigated.

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Keith A. Brewster and Dusan S. Zrnić

Abstract

Doppler radars offer unique data from which it is possible to estimate the turbulent eddy dissipation rates, ε. If the inertial subrange extends to lengths longer than the radar resolution volume size, ε can be obtained from the Doppler spectrum width. Spatial spectra of mean Doppler velocities can also yield ε estimates but only if a significant portion of the analysis length is contained within the inertial subrange. We compare dissipation rate estimates obtained with the two independent measurement techniques. At close range and vertical incidence, agreement between the two independent estimates of ε is within 10%. Furthermore, the slope of the spatial energy densities is very close to −5/3 predicted by Kolmogorov. The energy input is mainly from buoyancy-driven updrafts and the transition wavelength (about 3 km) between the input scale and the inertial subrange is consistent with the updraft-downdraft circulation cell, which is about 10 km. For a more distant storm at a range of 60 km, the filtering of mean velocities by the resolution volume precludes precise estimation of ε from spatial spectra of mean velocities.

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Derek R. Stratman and Keith A. Brewster

Abstract

On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of the storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate that the multimoment microphysics schemes tend to produce better forecasts of tornadic supercells. However, some of the forecasts from the single-moment microphysics schemes perform as well as or better than the forecasts from the multimoment microphysics schemes.

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Ming Hu, Ming Xue, Jidong Gao, and Keith Brewster

Abstract

In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied.

The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes.

When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.

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Jidong Gao, Ming Xue, Keith Brewster, and Kelvin K. Droegemeier

Abstract

In this paper, a new method of dual-Doppler radar wind analysis based on a three-dimensional variational data assimilation (3DVAR) approach is proposed. In it, a cost function, including background term and radial observation term, is minimized through a limited memory, quasi-Newton conjugate-gradient algorithm with the mass continuity equation imposed as a weak constraint. In the method, the background error covariance matrix, though simple in this case, is modeled by a recursive filter. Furthermore, the square root of this matrix is used to precondition the minimization problem.

The current method is applied to Doppler radar observation of a supercell storm, and the analysis results are compared to a conceptual model and previous research. It is shown that the horizontal circulations, both within and around the storms, as well as the strong updraft and the associated downdraft, are well analyzed. Because no explicit integration of the anelastic mass continuity equation is involved, error accumulation associated with such integration is avoided. As a result, the method is less sensitive to the vertical boundary uncertainties.

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Guoqing Ge, Jidong Gao, Keith Brewster, and Ming Xue

Abstract

The radar ray path and beam broadening equations are important for assimilation of radar data into numerical weather prediction (NWP) models. They can be used to determine the physical location of each radar measurement and to properly map the atmospheric state variables from the model grid to the radar measurement space as part of the forward observation operators. Historically, different degrees of approximations have been made with these equations; however, no systematic evaluation of their impact exists, at least in the context of variational data assimilation. This study examines the effects of simplifying ray path and ray broadening calculations on the radar data assimilation in a 3D variational data assimilation (3DVAR) system. Several groups of Observational System Simulation Experiments (OSSEs) are performed to test the impact of these equations to radar data assimilation with an idealized tornadic thunderstorm case. This study shows that the errors caused by simplifications vary with the distance between the analyzed storm and the radar. For single time level wind analysis, as the surface range increases, the impact of beam broadening on analyzed wind field becomes evident and can cause relatively large error for distances beyond 150 km. The impact of the earth’s curvature is more significant, even for distances beyond 60 km, because it places the data at the wrong vertical location. The impact of refractive index gradient is also tested. It is shown that the variations of refractive index gradient have a very small impact on the wind analysis results.

Two time series of 1-h-long data assimilation experiments are further conducted to illustrate the impact of the beam broadening and earth curvature on all retrieved model variables. It is shown that all model variables can be retrieved to some degrees in all data assimilation experiments. Similar to the wind analysis experiments, the impacts of both factors are not obvious when radars are relatively close to the storm. When the radars are far from the storm (especially beyond 150 km), overlooking beam broadening degrades the accuracy of assimilation results slightly, whereas ignoring the earth’s curvature leads to significant errors.

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Alexander D. Schenkman, Ming Xue, Alan Shapiro, Keith Brewster, and Jidong Gao

Abstract

The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8–9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso-γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data.

Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development.

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