Storm-Scale Data Assimilation for the Analysis and Prediction of a Tornadic Convective System: The Impact of High-Resolution X-Band Radar Data
Alex Schenkman1,2, Ming Xue1,2, Alan Shapiro1,2, Keith Brewster1 and Jidong Gao1
Center for Analysis and Prediction of Storms1 and School of Meteorology2 University of Oklahoma, Norman Oklahoma 73072
March 4, 2009
Submitted to Monthly Weather Review
Corresponding author address: Alex Schenkman School of Meteorology University of Oklahoma, 120 David L. Boren Blvd, Norman OK 73072 [email protected] Abstract
In this paper, the impact of assimilating data from the experimental IP-1 (Integrated
Project 1) X-band radars of CASA (Center for Collaborative Adaptive Sensing of the
Atmosphere, an NSF Engineering Research Center) is examined. The Advanced Regional
Prediction System 3DVAR (ARPS 3DVAR) system, combined with a complex cloud analysis
package, is used to produce analyses in high-frequency intermittent assimilation cycles, for a
case from the spring 2007 CASA data collection period. The case involves a mesoscale
convective vortex that spawned several tornadoes on 9 May 2007 in southwest Oklahoma and
moved through the CASA domain.
A set of experiments that differ in the type of data used are performed to identify the
impact of radial velocity and reflectivity data from CASA radars. The CASA data are assimilated
in addition to available WSR-88D data and conventional forms of observations including those
from the Oklahoma Mesonet. Experiments are conducted on two (nested) grids, at 2 and 0.4 km
resolutions, respectively. After an one-hour long spinup forecast on the coarse grid, 5-minute-
interval assimilation cycles of 60 and 80 minute long are performed on coarse and fine resolution
grids, respectively.
In experiments that assimilate radar data the model predicts the timing and evolution of
the MCV with good accuracy. On the inner grid, CASA radial velocity data have a significant
impact on the analyzed low-level cold pool strength, structure, and simulated evolution of the
tornadic convective system. Oklahoma Mesonet wind data also improve the analysis of the low-
level cold pool, though not as significantly as CASA radial velocity data. Inner grid forecasts
from experiments that use CASA radial velocity data generate 60-80 minute forecasts of tornado-like vortices that track within 5 km/10 min of the confirmed tornado location/time.
i 1. Introduction
In late 2006, the NSF Engineering Research Center (ERC) for the Collaborative and
Adaptive Sensing of the Atmosphere (CASA, McLaughlin et al. 2005) began the experimental operation of its first Integrated Project (IP1, Brotzge et al. 2007) radar network. The CASA IP1 network consists of four X-band dual-polarization radars located in southwest Oklahoma (OK).
One of the important goals of the CASA project is to develop and test short-wavelength low-cost and low-powered radars in high density networks. These networks are designed to more effectively sample the lower atmosphere than long-range less-dense networks, such as the operational WSR-88D radars. Adaptively sampling the atmosphere based on end-user needs is another key component of CASA (McLaughlin et al. 2009).
The CASA-IP1 network was installed between two WSR-88Ds radars: KTLX at
Oklahoma City and KFDR at Frederick, OK, in the Lawton and Chickasha area. This location was chosen because it is upstream of the Oklahoma City metropolitan area, and neither WSR-
88D covers the lowest kilometer of the atmosphere in this area (Xue et al. 2006). Thus, CASA radars can serve to fill the low-level data gap left by the 88D radars. Even though the CASA radars have a relatively coarse beamwidth (1.8° vs. the 0.89° of WSR-88D), their range gate spacing of 100 m (vs. 250 m for WSR-88D), adaptive sampling capabilities, and the short baseline of the CASA radars allow for much higher spatial and temporal resolution than typically available with WSR-88D radars. The high spatial and temporal resolutions are very important for capturing small-scale features such as tornadoes and tornadic circulations.
Assimilation of CASA radar data into storm-scale numerical weather prediction (NWP) models and assessment of their impact are important areas of research within CASA. In this vein, during the CASA Spring Experiment 2007 (CSET-2007, Brewster et al. 2007), quasi real-time
1 forecasts were performed using the Advanced Regional Prediction System (ARPS, Xue et al.
2000; 2001; 2003) and the ARPS Data Analysis System (ADAS, Brewster 1996). Preliminary results from these experiments showed success when assimilating combined reflectivity from
WSR-88D and CASA radars (Brewster et al. 2007).
It has been suggested (e.g., Lilly 1990; Droegemeier 1997; Xue et al. 2003; Hu et al.
2006a; Hu et al. 2006b) that explicit numerical prediction of severe convective storms has become possible. As computers continue to increase in power, it will be possible to explicitly forecast convective storms or even their internal features operationally (e.g. Xue et al. 2007).
This is the basis of the long-range “warn on forecast” vision of the National Weather Service
(Stensrud et al. 2009).
The assimilation of reflectivity and velocity data from Doppler radars is vital to predicting ongoing convection because radar is the only observation platform with the temporal and spatial resolution sufficient to resolve convective storms. Numerous studies (e.g., Sun and
Crook 1998; Weygandt et al. 2002a,b ; Xue et al. 2003; Xiao et al. 2005; Dawson and Xue 2006;
Hu et al. 2006a; Hu et al. 2006b) have shown reasonable success in simulating and forecasting convection when radar data are assimilated.
A number of methods exist for the assimilation of radar data. Sun et al (1991) and Sun and Crook (1997, 1998) have shown that four-dimensional variation analysis (4DVAR) is an effective way to assimilate radar data because it takes advantage of the high temporal resolution of Doppler radar, since 4DVAR fits data to a numerical model over a time window. 4DVAR assimilation of radar data, however, has so far been limited to relatively simple model configurations, usually only with warm-rain microphysics (Sun 2005). Strong nonlinearity with nonlinear model physics, including ice microphysics, often causes difficulties with the
2 minimization convergence in 4DVAR.
Ensemble Kalman filter (EnKF) is another advanced method for assimilating radar data
(Snyder and Zhang 2003; Zhang et al. 2004; Tong and Xue 2005). By using Monte Carlo sampling technique with ensembles, EnKF determines flow-dependent error correlation statistics
(Evensen 1994) so that unobserved state variables of the atmosphere can be ‘retrieved’ from limited radar observations (Tong and Xue 2005). Caya et al. (2005) showed through simulated- data experiments that EnKF and 4DVAR produce analyses of generally similar quality, with a model containing warm-rain microphysics. While these two methods are more advanced theoretically, they are rather expensive computationally, especially at the convection-resolving resolution.
Feedback from the end users of CASA observations as well as analysis and prediction products are important components of the CASA (McLaughlin et al. 2009). For realtime analysis and forecasting, the three-dimensional (3DVAR) data assimilation method is a computationally more efficient alternative to 4DVAR and EnKF.
The ARPS 3DVAR system, described in Gao et al (2002; 2004b) and Hu et al (2006b) is capable of analyzing radar radial velocity data along with conventional observations. Because the 3DVAR formulation lacks a time dimension, experiments are usually run using intermittent analysis cycles to make better use of data distributed in time (Hu et al. 2006a; 2006b). Hu and
Xue (2007) showed that the assimilation window length and assimilation frequency must be carefully considered. The ARPS 3DVAR cost function also includes a mass continuity weak constraint that serves to link three wind components together, and helps to improve wind analysis.
The ARPS 3DVAR is usually supplemented by a cloud analysis package that analyzes
3 radar reflectivity and satellite observations. The use of a cloud analysis has been shown to result
in a more accurate forecast because “spin up” time is decreased (Xue et al. 2003; Hu et al.
2006a). The ARPS cloud analysis was derived from the Local Analysis and Prediction system
(LAPS, Albers et al. 1996) with significant modifications by Zhang et al. (1998), Zhang (1999),
Brewster (2002), and Hu et al (2006a).
Our study seeks to establish the impact of assimilating CASA radar data into a high-
resolution non-hydrostatic numerical model by using the ARPS 3DVAR (3DVAR for short hereafter) and its cloud analysis system. Specifically, CASA radial velocity and reflectivity data over a period of time are assimilated into the ARPS model to analyze a mesoscale convective vortex (MCV, Johnston 1981; Menard and Fritsch 1989) that developed in the northern part of a large mesoscale convective system (MCS) that was present over much of Oklahoma and Texas on 8-9 May 2007. The MCV was observed by the WSR-88Ds KTLX, KFDR, and KVNX as well as four CASA radars as it moved through southwest and central Oklahoma.
This MCV has been documented by Brewster (2008), Kumjian (2008), and Schenkman
(2008). Brewster et al. (2008) showed success in forecasting the evolution of the MCV was achieved in CASA’s near-realtime forecasts (using 1 km horizontal resolution), even in experiments where no radar data were assimilated. It was shown that the addition of reflectivity data improved the accuracy of the MCV position. The MCV position and direction of translation were further improved with the addition of radial velocity data.
Our study seeks to perform a more detailed analysis and prediction study on the May 8-9,
2007 tornadic MCV case than was captured during the spring 2007 realtime time forecast
(reported in Brewster et al. 2008). A 400 m horizontal-resolution grid, nested in a larger 2 km
resolution grid (Fig. 1a), will be used for both data assimilation and prediction. We hypothesize
4 that higher-resolution forecasts will benefit more from the assimilation of CASA radar data as
they can better take advantage of the high spatial resolution of CASA data. A less significant
impact of CASA data is expected in lower resolution experiments as small-scale features
sampled by CASA will tend to be smoothed out. In a future companion paper, the vortex spin up
processes will be analyzed.
This paper is organized as follows. Section 2 describes the case, model parameters, and
general experiment methodology. Section 3 provides a detailed description of the various experiments and verification technique. Section 4 discusses and verifies the results of assimilation and forecasts on a 2-km grid, while the results from the 400 m nested grid are presented in section 5. Summary and conclusions are given in section 6.
2. Case, model and assimilation system
a. The 8-9 May 2007 MCV case
We apply the ARPS 3DVAR and ARPS prediction model to the MCV of 8-9 May 2007
that produced several weak tornadoes that struck parts of southwest and central Oklahoma on 9
May 2007 (UTC, Fig. 1b). The first tornado caused EF-1 damage in Grady County, near Minco
OK. Another weak tornado produced EF-0 damage near Union City, OK in Canadian County.
The most destructive tornado, also an EF-1, hit El Reno, OK causing an estimated three million
dollars of damage. Two very short-lived EF-1 tornadoes were reported a short time after the El
Reno tornado near Piedmont, OK.
Radial velocity observations from the Terminal Weather Doppler Radar (TDWR,
Turnbull et al. 1989) at Will Rogers World Airport in southwest Oklahoma City reveal a
persistent area of strong shear and convergence associated with the MCV. Figure 2 shows the
radial velocity data from the OKC TDWR at 0446 UTC, 9 May, near the time of the El Reno 5 tornado (Fig. 2). TDWR data suggest that all of the tornadoes that occurred with the MCV
developed initially in this convergent shear zone. These vortices rotated cyclonically around the
northeast portion of the MCV and strengthened as they became co-located with a convective cell
near the center of the MCV.
From the Oklahoma Mesonet temperature data, it can be inferred that the persistent shear
zone was related to the configuration of the low-level cold pool (see Fig. 3 of Schenkman et al.
2008). Specifically, the cold pool surged to the east on the south side of the MCV, while to the
north and east of the MCV the air remained relatively warm. As a result, shear and convergence
was enhanced east of the MCV along the leading-edge of the cold pool. Unfortunately, the
mesonet is too coarse to fully resolve the wind and thermodynamic structure of this shear zone.
b. Grid configuration and model parameters
The assimilation and forecast experiments are conducted on two one-way nested grids
(Fig. 1). The lower resolution (LR) outer grid has a 2 km horizontal grid spacing, is 1000 × 1000
km2 in size and is centered at 34.80N, -98.00W. It covers all of Oklahoma, the northern half of
Texas, southern Kansas and far southeastern Colorado. The nested high-resolution (HR) grid has a 400 m horizontal grid spacing and is 120 × 120 km2 in size. It is centered at 35.25N, -97.80W
and covers a small portion of southwest and central Oklahoma. Lambert conformal map
projection is used. Both grids use a stretched grid in the vertical, with a minimum resolution of
100 m near the surface. Grid stretching is calculated according to a cubic function of height.
There are 43 vertical levels for the outer nest, and 63 vertical levels for the inner nest.
The 0000 UTC National Center for Environmental Prediction (NCEP) North American
Model (NAM) analysis on the 12 km grid was used to provide the initial analysis background for
the outer grid, whose lateral boundary conditions came at 3 hr intervals from the 0000 UTC
6 NAM forecast. The data assimilation cycles of the 400 m grid started at 0100 UTC, using the 2
km forecast valid at the same time as the initial background. Lateral boundary conditions on the
inner grid are obtained from the outer 2 km grid, at 5 minute intervals.
Terrain data for both the inner and outer grid were derived separately from United States
Geographical Survey (USGS) three-second data set. A multi-layer soil model in the ARPS that is
similar to the NOAH land-surface model is used, with 5 vertical soil levels.
As mentioned earlier, ARPS is used as the prediction model in this study. ARPS is a
general-purpose three-dimensional, non-hydrostatic and compressible atmospheric model.
Detailed description of ARPS can be found in Xue et al (2000; 2001; 2003) The common
configurations for both grids include fourth-order advection in both the horizontal and vertical, a
rigid top boundary condition combined with a wave absorbing layer, fourth-order computational
mixing, 1.5-order TKE-based turbulent mixing scheme, Lin et al (1983) three-ice microphysics with the rain intercept parameter set to 8.0 x 105 according to Snook and Xue (2008), surface
fluxes calculated using surface temperature and moisture content predicted by the soil model,
and NASA GSFC long and short wave radiation package. Descriptions on these and other
options can be found in the Xue et al. papers referenced above.
c. Data and 3DVAR analysis parameters
The ARPS 3DVAR is designed to analyze data from many different sources
representing a variety of scales. In this study, we analyze soundings (raobs), wind profilers,
aircraft observations (MDCRS), surface observations (SAO), the Oklahoma Mesonet, WSR-88D and CASA radars through frequent intermittent assimilation cycles. The data are analyzed in three separate analysis passes using different de-correlation scales (Table 1). The de-correlation scale is defined as the radius at which the background error correlation is e-folded (reduced to e-
7 1). In the ARPS 3DVAR, the background error correlation is modeled using recursive filters
(Gao et al. 2004a).
Upper air data from raobs and wind profilers are analyzed in the first analysis pass because these data have the coarsest horizontal resolutions. On the 2 km grid the horizontal de- correlation scale is set to 200 km and for the 400 m grid it is 50 km. Surface observations from
ASOS and the Oklahoma Mesonet are analyzed in the second analysis pass. The de-correlation scales are 75 km and 20 km for the outer and inner grid, respectively. The vertical de-correlation scale is 4 grid points in both first and second pass on both grids.
Radar radial velocity data are used in the third analysis pass. On the outer grid, level-II data are used from six WSR-88D radars: Twin Lakes (KTLX), Vance (KVNX), Dyess (KDYX),
Amarillo (KAMA), Dallas-Fort Worth (KDFW), and Lubbock (KLBB). Level-III reflectivity data are used from KFDR because level-II data from that radar were not available from the
National Climatic Data Center (NCDC). On the inner grid, data from KTLX, KVNX and KFDR are used. In experiments utilizing CASA data, both grids use data from all four CASA radars.
For the high-resolution radar data, the horizontal de-correlation scale is reduced to 4 km for the outer grid and 0.8 km for the inner grid. The vertical scale is set to 2 grid intervals for both grids.
The radar reflectivity data are used through the cloud analysis package as an additional step following the 3DVAR analysis.
Automated preprocessing is performed to remove clutter and anomalous propagation from the radar data. The cleaned up radar data are then mapped from radar coordinates onto model grid points using a local least squares fit. Some details of the radar remapping and preprocessing can be found in Brewster (2005). Within the cloud analysis procedure, the remapped reflectivity data are used to analyze cloud and hydrometeor fields, and to adjust the in-
8 cloud temperature and moisture fields. Additional details on the package can be found in Hu et al.
(2006a). Where reflectivity observations are not available, the background reflectivity value is used in the cloud analysis procedure.
The cloud analysis was originally developed to reduce the spin-up period for forecasts
beginning from a single analysis (Brewster, personal communication). Repeated application of
the original cloud analysis in the high-frequency assimilation cycles of our study led to
unrealistic warming in the middle-troposphere. To eliminate this unrealistic warming, the cloud
analysis was modified so that the cloud water and cloud vapor mixing ratios were only adjusted
once, during the initial application of the analysis. In subsequent analyses, only the rainwater
mixing ratio was adjusted.
The original ARPS radar data preprocessor, 88d2arps, was designed for the so-called “sit
and spin” scan strategy in which a radar covers 360° volumes. CASA radars collect data
dynamically and adaptively, typically in a series of sector scans. Thus in order to apply 88d2arps
to the CASA radar data, we build a pseudo-volume out of a number of sector scans clustered around a certain time, as in Brewster (2007).,
3. Experiment design and verification method
a. 2 km experiments
For our nested assimilation-forecast experiments, a one-hour long ‘spin-up’ forecast is
first performed on the 2 km grid. This forecast starts from a 3DVAR analysis at 0000 UTC 9
May 2007 using all conventional observations and the NAM analysis as the background. This
one-hour forecast serves as the background for the first analysis of the high-frequency, 5-minute
interval, intermittent analysis cycles starting at 0100 UTC 9 May on both grids. These cycles end
9 at 0200 UTC on the 2 km grid, and for the results to be presented here, the 400-m cycles end at
0220 UTC, resulting in an 80 minute long assimilation window (Table 2). The assimilation and forecasts are first performed on the 2 km grid, which provide boundary conditions for the 400 m
grids. Forecasts are launched from the final analysis on each grid and run up to 0500 UTC, 9
May.
A number of experiments, with variable sets of radar data, were run on the 2 km grid.
All experiments on this grid assimilate conventional observations at the start (0100 UTC) and
end (0200 UTC) times of the assimilation window. The experiments then differ based upon what
(if any) radar data they assimilate. With the exception of one experiment (NORADLR) all
experiments assimilate both radial velocity (Vr) and reflectivity (Z) from the WSR-88D radars listed in section 2b. Additional experiments assimilate CASA Vr (VrLR) only, CASA Z (ZLR) only, and both CASA VR and Z (VrZLR) together with the WSR-88D data. Experiment 88DLR uses radar data from the WSR-88D network only (Table 2). Here LR denotes low resolution.
b. 400m experiments
The 0100 UTC “spin-up” forecast on the outer grid is interpolated onto the 400 m grid to
provide a background for the 0100 UTC 3DVAR analysis on this grid. The assimilation window
runs in 5 minute cycles from 0100 UTC to 0220 UTC for the majority of inner grid experiments
(the one exception being NORADHR which assimilates data at 0100 and 0200 UTC only). An
inner grid control experiment, CNTLHR, assimilates 5 minute data from the Oklahoma Mesonet
in addition to WSR-88D and CASA Vr and Z (Table 2). Experiments 88DHR, VrHR, ZHR, and
VrZHR are analogous to their LR counterparts except their assimilation window ends at 0220
UTC instead of 0200 UTC (these experiments assimilated conventional data, including mesonet data at 0100 and 0200 UTC only). An additional experiment (ZHR5MM) was conducted on the
10 inner grid with the same configuration as ZHR except Oklahoma Mesonet data were assimilated
every 5 minutes. A schematic of the experiment temporal configuration is presented in Fig. 3.
The attributes of all experiments are summarized in Table 2. c. Verification
Experiments results are analyzed via qualitative comparison with one another as well as quantitative verification against observations. The latter is performed against Oklahoma
Mesonet data, surface observations with high spatial and temporal resolution. This verification method differs from that of previous studies of the simulation and prediction of convective storms (Hu 2005; Hu et al. 2006a; Hu et al. 2006b; Hu and Xue 2007) which relied on equitable threat scores (ETS, Schaefer 1990) to objectively analyze the results. These studies suggest that
ETS, being designed for large-scale applications, has limited applicability to convective-scale discrete features. In those studies, ETSs were calculated based on reflectivity; the calculation of model reflectivity, however, has a significant degree of uncertainty, as related to the uncertainties to the microphysical properties of the model state.
To better evaluate the temporal evolution of the convective-scale features, meteograms from six Oklahoma Mesonet sites: Apache, Ninnekah, Chicasha, Minco, Acme and El Reno (see
Fig. 2a), are compared to meteograms extracted from the 3DVAR analyses from radar-utilizing experiments. Specifically, the temperature, wind speed, and wind direction are taken from the lowest data point in model- extracted soundings at each mesonet site location. Root mean square
(rms) errors, and biases are computed. These mesonet sites were chosen because of their proximity to the MCV and the CASA domain. Objective verification focuses on the temperature, wind speed, and wind direction from the model compared to mesonet observations.
The use of the Oklahoma Mesonet to objectively verify the quality of the analysis has
11 limitations. Most importantly, the spatial resolution of the mesonet is far coarser than both the
LR and HR model grids. As a result, some of the details in sub-mesoscale features (e.g., gust
front location) will be unresolved if they fall between mesonet sites. This could also lead to
large RMS error values in cases when the timing of the model is off by only a few minutes/kilometers. Additionally, the mesonet observations are five minute averages, whereas the model output are instantaneous. Thus we expect more variability in the high-resolution
model results than in the mesonet observations. Finally, unlike ETS, the mesonet sites are point
locations, thus the model is verified only at these point locations rather than throughout the
domain. However, since we are verifying the regions with the cold pool and other important
features, they should be indicative of the quality of our analysis and forecasts at times and
locations of greatest meteorological interest
4. Outer 2 km grid results
Though the primary purpose of the 2 km experiments is to provide boundary conditions
for the inner grid, it will also be instructive to assess the adequacy of the 2 km horizontal
resolution simulations of the 9 May 2007 MCV event. As such, the forecast period from the 2
km resolution experiments is described with particular attention given to contrasting various
experiments with each other as well as with the observed evolution of the MCV. Assimilation results are also briefly discussed and compared.
a. Assimilation results
A qualitative comparison of the results of the 2 km assimilations reveals relatively minor
differences in the reflectivity, wind, and temperature fields between the experiments. All experiments feature a MCS over much of southwest Oklahoma and western north Texas that moves to the northeast at about 10 m s-1 throughout the assimilation window. Convection within 12 the northern portion of this MCS strengthens while the convection further south (in Texas)
weakens. By the end of the assimilation window (0200 UTC), a solid line of convection with
high reflectivity is present in Oklahoma, while in Texas most of the convection has dissipated.
The only notable differences between the experiments are found near and in the CASA domain.
In this portion of the 2 km domain, VrLR and VrZLR feature more noise in the low-level wind
fields than 88D and ZLR. This noise is clearly due to the assimilation of the CASA Vr data that
contain more fine-scale structures, with some possibly being from non-meteorological scatterers
east of the MCS. The differences between the experiments that assimilate CASA Vr (VrLR and
VrZLR) and those that do not (88DLR and ZLR) diminish as the MCS moves further into the
CASA domain, supporting our explanation for the origin of the noisy low-level wind fields in
VrZLR and VrLR.
Quantitative comparison indicates that the 2 km experiments are generally similar during
the assimilation window outside of the CASA domain. Within the CASA domain, comparison
with the mesonet yields higher rms error values for VrZLR and VrLR than for 88DLR and ZLR
(Table 3). Meteograms of wind direction from several mesonet sites show that part of the reason
for the higher rms error values in VrZLR and VrLR is likely due to noise in the wind field
throughout the assimilation window (Fig. 4). These meteograms also show that wind direction from VrZLR is similar to VrLR and wind direction in 88DLR is nearly identical to ZLR . These
trends are also present for temperature and wind speed (not shown) and indicate that assimilating
CASA radial velocity data have a substantially larger impact (and this impact appears to be negative during the assimilation window on the 2 km grid) on the analysis than assimilated
CASA reflectivity data. Although this result seemingly contradicts previous studies (e.g. Hu et
al. 2006a,b) the results can be reconciled. Hu et al (2006a,b) showed that assimilation of
13 reflectivity has a large impact when compared to experiments that only assimilated conventional
observation. Our study shows that assimilating CASA reflectivity data in addition to WSR-88D
data has little impact because the influence of the WSR-88D reflectivity data through the cloud
analysis can be felt at the low levels through the fall of hydrometeors. Conversely, mid level
winds may differ significantly compared to low level winds. WSR-88Ds generally cannot
observe the low levels. Because the CASA IP-1 network observes the low levels, assimilation of
CASA Vr data has a large impact on the low level wind analysis.
b. Forecast results
As in to the analyses, most differences between forecasts in the radar-utilizing (hereafter,
RU) experiments are small. Accordingly, we describe the general evolution of the forecast
period from all RU experiments while highlighting any differences between these experiments
and NORADLR as well as any differences amongst the RU experiments. The forecast period for
the RU experiments begins at 0200 UTC with a large MCS over southwest Oklahoma and northwest Texas. That radar data assimilation reduces “spin-up” time is readily apparent at 0200
UTC as there is no MCS present in NORADLR. The strongest convection (based on reflectivity
factor) in RU experiments and only convection in NORADLR is present in southwest Oklahoma
(not shown).
By 0300 UTC, most of the convection in the RU experiments within TX has dissipated
while a weak mesocyclone has developed in the northern portions of the convection in SW OK.
This is similar to the observed behavior of the MCS, as radar reflectivity animations show that
much of the precipitation in Texas dissipated by 0300 UTC along with a discernible cyclonic
rotation developing in the reflectivity pattern in southwest OK (not shown). A larger –scale cyclonic pattern to the wind field around the mesocyclone, suggests the mesocyclone is
14 developing near the center of a broad MCV. Specifically, the mesocyclone appears to be
generated within a shear zone between strong west winds (rear-inflow) behind the gust front, southeasterly flow in advance of the gust front, and an area of enhanced northwest flow moving south immediately to the west of the mesocyclone. Convection gained slightly better
organization in NORADLR by 0300 UTC, but its development is still significantly under-
forecasted when compared to observations and the RU experiments.
The mesocyclone and MCV moves north-northeast and strengthens in the RU
experiments. At 0355 UTC, all RU experiments place the mesocyclone within 10 km of the reported Minco tornado location, with the closest and strongest mesocyclone present in VrLR
and VrZLR (Fig 5). This trend continues through the forecast period, namely, the mesocyclone tracks about 10 km further northwest, closer to the confirmed tornado locations, in VrLR and
VrZLR than in 88DLR and ZLR (Fig 6). NORADLR develops a broad MCV that tracks within
10 km of Minco and El Reno (see Fig.5b and Fig. 6b). Unlike in the RU experiments, there is not a mesocyclone associated with the MCV in NORADLR.
The main reason for the differences in vortex intensity and location amongst the RU
experiments appears to be related to the position and magnitude of the low-level cold pool associated with the MCS. In VrLR and VrZLR, the enhanced northwest flow to the west of the mesocyclone is much more pronounced while the associated cold pool is about 1° C colder, especially early in the forecast period, than that of 88DLR and ZLR (Fig 7). It is likely that these
differences in cold pool position and strength, while they are most apparent early in the forecast period, impacted the mesocyclone development location leading to changes in the overall track
of the mesocyclone. It will be shown in the following section that the cause of these differences
in cold pool strength is likely the result of the CASA radar’s ability to better resolve the low-
15 level wind field associated with this portion of the cold pool.
Meteograms of temperature, wind speed and wind direction extracted from the 2 km forecasts at select mesonet sites show that VrLR is similar to VrZLR, while ZLR is similar to
88DLR. This is the same trend seen in the assimilation window and once again it demonstrates that assimilating CASA radial velocity has a larger impact than CASA reflectivity at the low levels. Consistent with our qualitative analysis, the meteograms and rms error statistics show that in general there were no substantial differences in the accuracy of the 2 km resolution experiments (not shown).
While there were no substantial differences in the accuracy of the experiments, it should
be noted that all experiments have fairly large errors, especially in their wind direction forecasts.
Some of the largest wind direction errors occur in Minco and El Reno after 0400 UTC (Fig 8).
All experiments forecast a wind with a large northerly component at these sites, while the observed wind was generally from the east. The primary reason for this error is likely that the mesocyclone tracked too far to the east in all of the simulations, this lead to northerly surface winds at Minco and El Reno. This demonstrates the difficulty of objective verification and illustrates the importance of combining quantitative and qualitative verifications in assessing model results. The 2 km forecasts are fairly accurate with the timing and formation of the MCV and associated mesocyclone, but because they are slightly too far to the east, the forecast rms errors are large. In essence, while the model forecasts clearly have value, the verification method and the relatively coarse resolution of the verifying observations, in combination with position error of the predicted features, make informative quantitative verification difficult.
16 5. Inner 400 m grid results
a. Assimilation results
While the differences among the 2 km experiments are relatively small, substantially
larger differences are found between the 400 m analyses and forecasts. Within the assimilation
window, the most pronounced differences are present in the strength and position of the cold
pool and gust front after 0200 UTC. Specifically, experiments that assimilate the CASA radial
velocity data (CNTLHR, VrHR, VrZHR ) or high frequency Oklahoma Mesonet data
(ZHR5MM) analyze the gust-front location about 10-20 km further west than experiments that
do not assimilate such data (88DHR and ZHR) (Fig 9). Meteograms of temperature and wind
direction from Acme (Fig 10) reveal that the further west position of the gust front in these experiments is more accurate than the gust front location in 88DHR and ZHR. CNTLHR has the most accurate analysis of the cold pool location, strength, and timing throughout the assimilation window. VrZHR and VrHR are similar to CNTLHR in terms of timing and location, but the analyzed cold pool is too cold in the first hour of the assimilation window. ZHR5MM is more accurate in terms of cold pool strength than VrZHR and VrHR early in the assimilation window, but advances the gust front too far to the east by 0220 UTC. 88DHR and ZHR analyze the cold pool too far to the east with the gust front passing through Acme about 30 minutes too early.
Additionally, these experiments have too large a westerly wind component at Acme throughout the assimilation window.
The primary reason for the variation in the analyzed cold pool location is the assimilation
(or lack thereof) of low-level wind data. 88DHR and ZHR do not resolve the low-level easterly
inflow ahead of the gust front because lowest-tilt radial velocity data from KTLX and KVNX do
not reach the shallow inflow layer. Because the near-surface winds are incorrectly analyzed as
17 southerly in advance of the gust front and southwesterly within the cold pool in 88DHR and
ZHR, the gust front surges to the east and northeast. Additionally, 88DHR and ZHR are very
similar throughout the assimilation window, suggesting the assimilation of CASA reflectivity
data has little to no impact on the analysis. The explanation for the minimal impact of CASA
reflectivity is the same as presented at the end of section 4a.
Unlike reflectivity assimilation, the assimilation of CASA radial velocity data has a
substantial impact on the analysis because the CASA radars sample the low-level flows that are
important to the evolution and dynamics of the convective system, including the low-level inflow
layer. The inclusion of CASA Vr data in the analysis yields near-surface winds with a large
easterly component. This increases the low-level vertical shear ahead of the gust front which
prevents the cold pool from surging too far to the east, yielding a more accurate analysis of the
cold pool position in CNTLHR, VrZHR, and VrHR. Direct observations of thermodynamic variables (temperature and humidity) from the Oklahoma Mesonet lead to improved analysis of the low-level temperature field throughout much of the assimilation window in CNTLHR and
ZHR5MM. However, the spatial resolution of the mesonet is too coarse to resolve the gust front location with as much accuracy as CASA radial velocity data as is indicated by the erroneous
placement of the gust front in ZHR5MM.
b. Forecast results
Differences in the position of the analyzed cold pool between the various experiments
during the assimilation window lead to substantial differences in the evolution of the MCV and
associated tornado-like vortex-genesis in the forecast period. Specifically, accurate analysis of
the position of the gust front and low-level wind field in advance of the gust front appears to be
crucial in the development of low-level tornado-like vortices (to be defined later). As mentioned
18 in the previous section, CNTLHR, VrZHR, VrHR and ZHR5MM have a more accurate analysis
of the cold pool than 88DHR and ZHR. Among these experiments, CNTLHR is most accurate and is fairly representative of the other three experiments that assimilate low-level CASA radial velocity data at high spatial and temporal resolutions. 88DHR and ZHR are very similar to one another. Given these similarities, the following discussion will focus on the description and comparison of the forecasts from CNTLHR and 88DHR. Attention will be drawn to points of difference between the other experiments and CNTLHR or 88DHR.
Because our experiments have a horizontal resolution of 400 m which is still not high
enough to explicitly resolve tornadoes. We refer to a strong low-level vortex as a tornado-like
vortex (TLV). A TLV is defined in this study as a circulation, in the 400 m experiments, having
the largest vorticity values below 2 km, a temporal continuity of at least 5 minutes, and maximum vertical vorticity values of at least 0.05 s-1.
The forecast period of CNTLHR and 88DHR begins at 0220 UTC with a line of
convection in the southwest corner and stratiform rain in the northwest portion of the domain.
As noted earlier, large differences in the low-level cold pool and wind field are apparent, with the gust front about 20 km further east in 88DHR than it is in CNTLHR in the final analysis. By
0300 UTC, these differences in the gust front position have lead to discernible differences in the evolution of the associated convective line (Fig 11). The convective line has taken on a comma
shape in CNTLHR likely indicative of a developing MCV on its northern edge. On the other
hand, the convective line in 88DHR has maintained an unrealistic nearly-linear orientation as the gust front continues to surge to the northeast.
The first TLV in CNTLHR develops in the southeast portion of the comma-head around
0315 UTC. This TLV moves north-northeast passing within 5 km of the confirmed Minco
19 tornado location at 0335 UTC (c.f., Table 1). Similar TLVs form in both VrHR and VrZHR. In
all three of these experiments, the TLVs appear to form as vorticity, generated along the gust
front, is advected underneath a convective updraft near the center of the MCV. TLVs do not
form in 88DHR because the gust front is too far removed from the MCV-related convection.
These results are consistent with the assertion of Snook and Xue (2008) that accurate prediction
of the cold pool and proper alignment of mid-level updraft/mesocyclone often determines the
success of tornado simulation/prediction.
A vortex does pass close to Minco around 0325 UTC in 88DHR but it is far too weak to
be classified a TLV. Fig 12 contrasts CNTLHR with 88DHR during ~20 min lifetime of the
Minco TLV in CNTLHR. The Minco TLV in VrHR and VrZHR is shown in Fig 13. The first
TLV in ZHR5MM forms about 10 km further east than the TLVs in experiments that assimilated
CASA Vr data. This eastward displacement in the forecast is likely the result of poor initial
placement of the gust front. A TLV also forms in ZHR, but like ZHR5MM it is too far to the
north and east. TLVs do not occur in NORADHR as it does not develop the strong MCV
structure seen in all other experiments (not shown).
The above trends persist as the forecasts progress in time. CNTLHR and VrZHR develop
additional TLVs in the Minco-El Reno corridor, while subsequent TLVs are too far east in
Z5MMHR and are absent from VrHR, 88DHR and ZHR. The serial manner in which TLVs
develop in CNTLHR and VrZHR suggests that these experiments are predicting a persistent
shear/convergence zone, similar to that observed by the TDWR (section 2). In VrHR, the
shear/convergence zone develops to the west of the MCV convection stifling additional TLV
development. The persistent shear/convergence zone is not present in 88DHR or ZHR. Fig 14 and Table 4 summarize the track and temporal distribution of the TLVs from all of the HR
20 experiments.
It is important not to focus too heavily on the track and timing of any specific TLV
because predictability at the given temporal and spatial scales is likely low. The presence and
general location of TLV development along a correctly placed shear zone is much more
important in the interpretation of the experiment results. TLVs that form in the Minco to El
Reno corridor associated with the persistent shear/convergence zone indicate that model is
resolving the storm and sub-storm scale structures with good accuracy. As such it can be stated
that experiments that assimilate CASA Vr have a more accurate depiction of the storm-scale
environment than experiments that do not assimilate CASA Vr. This is especially true for
88DHR and ZHR, as those experiments have hardly any TLV development.
Quantitative analysis of experiment results agrees fairly well with our qualitative analysis
as RMS error values show that CNTLHR, VrZHR, VrHR and to some extent ZHR5MM
generally outperform 88DHR and ZHR (Table 5). One exception is found at El Reno where
88DHR and ZHR are comparable or slightly more accurate than CNTLHR, VrZHR, VrHR and
Z5MMHR. The results at El Reno are due to small scale variations in wind and temperature
associated with the TLV development in CNTLHR, VrZHR and VrHR (not shown) which are
difficult to match with observations exactly. In other words, because of the small spatial scale of
the TLVs, even small errors in their position or timing can lead to large RMS errors at an individual mesonet site. The low-level fields in 88DHR and ZHR are smoother, with lower
RMS error values even though they fail to forecast the observed serial development of TLVs.
The meteogram from Chickasha (Fig 15a,b) provides an example of the improvement
realized from the assimilation of CASA radial velocity or high frequency Oklahoma Mesonet
data. All experiments have a cold pool passage that is too early in Chickasha. However,
21 CNTLHR, VrZHR, VrHR and ZHR5MM are only 25-40 minutes too early, while 88DHR and
ZHR are about 80 minutes too early. This leads to large wind direction and temperature errors in
the first half of the forecast period for 88DHR and ZHR.
Acme (Fig 15c,d) and Ninnekah (Fig 15e,f) show the same trend seen at Chickasha. The
forecast temperature by CNTLHR, VrHR and VrHR at Acme are especially accurate, as they
pick up on both the cold pool passage and a short upward trend in temperature that took place
around 0300 UTC within the cold pool. At Ninnekah, the wind direction forecast from
CNTLHR is very accurate as it indicates several shifts in the wind direction in a fashion that is
close to the observed wind.
6. Summary and conclusions
In this study, the impact of CASA radar data on the analysis and prediction of a tornadic
convective system has been examined. The ARPS 3DVAR was used to assimilate data for a
MCS and associated MCV that moved through southwest and central Oklahoma on 8-9 May
2007, during the spring data collection period of CASA Oklahoma testbed (IP1) radars. The
MCV spawned several weak tornadoes along a surface shear and convergence boundary
coincident with a gust front, in central Oklahoma.
A set of assimilation and forecast experiments was conducted that differed in the amount and sources of data assimilated. The results on nested 2 km and 400 m horizontal resolution grids were examined and qualitatively compared to each other as well as to observations of the event. Emphasis was given to the high-resolution results. A quantitative verification was
performed against data from the Oklahoma Mesonet at selected sites. Tornado damage tracks
were also compared to forecast tracks of tornado-like vortices from the 400 m grid-spacing high-
resolution experiments. Through the qualitative and quantitative analyses, several important
22 conclusions can be drawn.
The most important result of this study is the large positive impact that CASA radial
velocity data had on the analysis and subsequent prediction of the low-level wind fields and cold
pool. CASA radial velocity data led to significant improvements in the analyzed low-level winds that are ahead of and associated with the cold pool on the high-resolution grid. These improvements continued into the forecast portion of the experiments, manifested in more
accurate predictions of tornado-like vortices when compared to experiments that did not use
CASA radial velocity.
The assimilation of high-frequency (at 5 minute intervals) low-level wind information
from the Oklahoma Mesonet led to similar improvements to those gained from assimilating
CASA radial velocity. This result suggests that accurate high spatial and temporal resolution
low-level wind information (whether it comes from radar, mesonet, or some other observation
platform) is critical to accurately analyzing low level features that are important for predicting
MCVs and TLVs. However, experiments that assimilated CASA radial velocity data were more
accurate in their analysis and forecast of gust front position than those that relied on mesonet
data, suggesting the importance of the much higher spatial resolution of the radar data. In
contrast, the assimilation of CASA reflectivity data via cloud analysis, added little value over
what was provided by a cloud analysis of WSR-88D reflectivity data (which provided good
coverage above 1 km).
The improvement due to CASA radial velocity assimilation has broad implications
beyond the present study. Accurate analysis and prediction of the low-level cold pool and
surface wind field are very important for the accurate prediction of most convective systems, due
to their active dynamic role in storm system development and evolution. Accurate low-level
23 wind observations can also help alleviate common errors in predicted cold pool strength and
position due to errors in microphysics parameterizations (Snook and Xue 2006). This is also very
important because, at present, there exist many uncertainties with microphysics
parameterizations and significant improvement may require use of much more expensive multi-
moment schemes (Dawson et al. 2009).
In addition to demonstrating the positive impact of CASA radar data, the high-degree of
accuracy in the overall forecast of the MCV and associated tornado-like circulations is also very
interesting; it is the first time, to our knowledge, that such small vortices,predicted several hours
in advance can be verified rather closely with observed tornadoes in both timing and location.
All of the low resolution experiments predicted the evolution of the MCV and associated mesocyclone with great accuracy. The high-resolution experiments that used CASA radial velocity data produced fairly accurate predictions of the Minco tornado more than one hour in advance. Quantitative evaluation showed that these experiments had the most accurate analyses of the MCV and cold pool. The fact that the same vortices were produced with many of the model runs with wide variations in data suggests some degree of predictability for tornadoes of this case when the environmental factors leading to the tornado formation are well captured in the initial conditions. The results also indicate that even with reasonably accurate analysis, the degree of predictability is limited for tornado-scale features, as solutions from CNTLHR and
VrHR do diverge sharply after the Minco TLV dissipates. In a future paper, we will perform more detailed analysis on the model results to better understand the dynamic processes leading to the formation of TLVs and to study the predictability of TLVs and the tornadoes themselves.
It is important to point out that the high resolution experiments are likely not
deterministically predicting the Minco tornado itself. More precisely, the experiments are
24 predicting conditions that are favorable for tornadogenesis in the Minco area, and spinning up a
tornado-like vortex under such conditions. The experiments do not have high enough resolution
to resolve a tornado, and it cannot be said with any large degree of certainty that the modeled
TLVs will develop into tornadoes. Even higher resolution nested grid simulations are likely to provide the answer, a course of action we plan to pursue in the future.
While the above conclusions have provided a detailed picture of the impact of assimilated
CASA radar data, a number of questions remain for future work. Most importantly, it should be
determined if the results of this case generally apply in other cases, and for other types of
convective systems such as supercells and severe squall lines. Also, the specifics of the
conclusions may be dependent on the data assimilation methods used. Parallel efforts are
underway to assimilate the same set of data using the ensemble Kalman filter method.
Acknowledgement: This work was primarily supported by NSF grant EEC-0313747, as part of
ERC CASA. Partial support was also provided by ATM-0530814 and ATM-0738370.
Oklahoma Mesonet data are provided courtesy of the Oklahoma Mesonet, a cooperative venture between Oklahoma State University and The University of Oklahoma and supported by the taxpayers of Oklahoma.
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31 List of figures
Fig. 1. Map of the (a) 2 km and (b) 400 m horizontal resolution domains. Black dots in (a)
correspond to the Oklahoma Mesonet Site used for verification. Tornado start and end
location in (b) are indicated by triangles and squares, respectively. Property damage is
indicated in (b).
Fig. 2. Field of radial velocity from the Oklahoma City TDWR at 0446 UTC 9 May 2007. The
black circle indicates an area of shear and convergence. The black square is the location
of the TVS.
Fig. 3. Timeline of the assimilation windows and forecast periods for the various 2 km and 400
m resolution experiments.
Fig. 4. Meteograms of wind direction from the assimilation window (0100-0200 UTC) of the 2
km experiments at Oklahoma Mesonet sites (a) Acme, (b) Chickasha, and (c) Ninnekah.
Fig 5. (a) Observed reflectivity factor (dBZ) from KTLX at 0355 UTC 9 May 2007. Forecasted
reflectivity field (shaded), horizontal wind vectors, and vorticity contours (black, at an
interval of 10-3 s-1 starting at 4 x 10-3 s-1) at 0355 UTC from (b) NORADLR, (c) VrZLR,
(d) VrLR, (e) ZLR, and (f) 88DLR. The black triangle marks the confirmed location of
the Minco tornado.
Fig 6. As Fig. 5, but at 0445 UTC 9 May 2007. The black triangle represents the confirmed
location of the El Reno tornado.
Fig 7. Forecast at 0205 UTC 9 May 2007 from (a) VrZLR, (b) VrLR, (c) ZLR, and (d) 88DLR.
Perturbation potential temperature is shaded at 1.5 K intervals starting at -0.5 K and
vectors are for horizontal winds.
32 Fig 8. Meteogram of wind direction over the 2 km experiment forecast period (0200 – 0500 UTC)
at (a) Minco and (b) El Reno Oklahoma. (Note the scale is shifted 180°)
Fig 9. The 0220 UTC analyses of low-level cold pool (shaded at 2 K intervals starting at 0 K)
and wind vectors from (a) CNTLHR, (b) VrZHR, (c)VrHR, (d) Z5MMHR, (e) ZHR and
(f) 88DHR. Fields are as Fig. 7.
Fig 10. Meteograms from the assimilation window (0100-0220 UTC) of the 400 m experiments
at Acme, Oklahoma of (a) temperature and (b) wind direction.
Fig 11. Forecast near-surface reflectivity factor (shaded in 10 dBZ increments starting at 20 dBZ)
and wind (vectors) at 0300 UTC 9 May 2007 from (a) CNTLHR and (b) 88DHR.
Fig 12. Forecast near-surface vorticity (shaded in intervals of 1200 x 10 -5 s-1 starting at 2000 x
10 -5 s-1) and wind vectors between 0320 UTC and 0340 UTC from CNTLHR(left column)
and 88DHR(right column). The black triangle indicates the confirmed location of the
Minco tornado.
Fig 13. Similar to Fig. 11, but at 0335 UTC only and from (a) VrZHR and (b) VrHR.
Fig 14. Minco, Union City, and El Reno tornado track overlaid with TLV tracks from all HR
experiments. The x and y values are relative to the southwestern corner of the 400 m
domain.
Fig 15. Meteograms from the 400 m forecast temperature (left column) and wind direction (right
column) at Chickasha(top row), Acme(middle row), and Ninnekah (bottom row),
Oklahoma.
33 Table 1: Properties of the 3 passes of the 3DVAR analyses.
Experiment Horizontal Vertical Pass Data Resolution Decorrelation scale Decorrelation scale 1 RAOBS, Wind 2 km 200 km Profilers 400 m 50 km 4 grid points 2 SAO, Mesonet, 2 km 75 km MDCRS 400 m 20 km 3 Radar Radial 2 km 4 km 2 grid points velocity 400 m 0.8 km
34 Table 1: List of assimilation and forecast experiments
Resolution Experiment Name CASA WSR-88D High-frequency (5 min) Data Data Assimilation Window VrZLR Vr and Z 88DLR None Vr and Z 2 km VrLR Vr 0100-0200 UTC ZLR Z NORADLR None None Assimilation only at NORADHR None None 0100 and 0200 UTC CNTLHR Vr and Z 88DHR None 400 m VrZHR Vr and Z Vr and Z VrHR Vr 0100-0220 UTC ZHR Z ZHR5MM Z
35 Table 3. RMS error and bias statistics from the 2 km experiments at select Oklahoma Mesonet sites.
Experiment RMS T RMS wspd RMS wdir Bias T Bias wspd Bias wdir Acme VrZLR 1.65 7.16 47.09 1.52 4.89 16.45 88DLR 0.59 7.13 26.02 0.29 6.27 20.23 VrLR 1.86 6.71 59.47 1.69 4.52 14.31 ZLR 0.57 7.23 26.53 0.31 6.35 20.81 Apache VrZLR 1.94 5.03 34.17 1.48 2.95 8.69 88DLR 0.50 2.81 35.92 0.36 2.03 22.50 VrLR 1.95 4.99 35.36 1.55 2.68 11.06 ZLR 0.36 2.72 36.79 0.26 1.87 23.36 Chickasha VrZLR 2.09 6.25 46.51 1.81 4.31 18.67 88DLR 1.11 5.59 29.57 0.96 4.24 13.79 VrLR 2.26 5.41 37.60 2.00 3.52 12.55 ZLR 1.11 5.54 27.76 0.97 4.20 14.16 El Reno VrZLR 1.23 3.68 32.67 0.82 3.11 25.27 88DLR 1.20 3.63 31.73 0.79 3.10 24.65 VrLR 1.24 3.64 33.28 0.83 3.06 25.95 ZLR 1.18 3.63 31.72 0.77 3.10 24.51 Minco VrZLR 0.91 5.82 32.25 -0.45 4.46 11.78 88DLR 0.84 4.89 24.36 -0.64 4.07 -7.76 VrLR 0.87 5.22 26.39 -0.34 4.01 15.72 ZLR 0.86 4.82 24.23 -0.65 4.02 -7.98 Ninnekah VrZLR 1.10 3.39 42.07 0.97 1.74 4.58 88DLR 0.55 4.81 32.20 0.10 3.79 23.94 VrLR 1.17 2.99 48.62 0.96 1.37 5.96 ZLR 0.55 4.84 33.05 0.11 3.87 24.45 Average VrZLR 1.49 5.22 39.13 1.03 3.58 14.24 88DLR 0.80 4.81 29.97 0.31 3.92 16.23 VrLR 1.56 4.83 40.12 1.12 3.19 14.26 ZLR 0.77 4.80 30.01 0.30 3.90 16.55
36 Table 4. Summary of tornado-like vortices generated in the 400 m experiments.
Experiment Minco Union City El Reno (Y/N, start-end time) (Y/N, start-end time) (Y/N, start-end time) CNTLHR Y,0315-0338 and Y,0401-0408 and No 0349-0356 UTC 0412-0417 UTC VrZHR Y,0326-0442 UTC No Y,0446-0456 UTC VrHR Y,0326-0342 UTC No No 88DHR No No No ZHR No Y,0357-0414 UTC No Z5MMHR Y, 0337-0347 UTC No Y, 0427-0436 Observed 0354-0358 UTC 0426-0430 UTC 0443-0450 UTC
37 Table 5. RMS error and bias statistics from the 400 m experiments at select Oklahoma Mesonet sites.
RMS RMS Bias Bias Bias Experiment RMS T wspd wdir T wspd wdir Acme CNTLHR 0.8 2.56 67.67 0.06 0.48 39.98 VrZHR 0.62 2.32 79.05 0.00 0.34 52.21 VrHR 0.71 2.73 75.88 -0.07 -0.35 50.55 88DHR 1.79 3.55 85.47 -1.00 -0.06 52.03 ZHR 1.8 3.47 74.68 -1.09 0.23 59.41 ZHR5MM 1.52 2.28 76.64 -0.86 -0.55 50.2 Apache CNTLHR 0.63 4.61 132.68 0.09 2.28 93.26 VrZHR 0.47 4.37 131.49 -0.03 1.89 83.32 VrHR 0.53 4.96 132.36 0 2.34 85.32 88DHR 0.74 3.65 110.66 0.29 1.39 74.7 ZHR 0.86 3.41 115.85 0.35 0.98 81.4 ZHR5MM 0.87 3.09 109.73 0.57 0.51 55.48 Chickasha CNTLHR 1.14 3.94 36.46 -0.65 3.65 6.32 VrZHR 1.61 3.41 58.59 -0.92 2.72 32.38 VrHR 1.3 2.97 52.86 -0.67 2.13 39.79 88DHR 2.52 3.42 85.17 -1.63 0.5 2.88 ZHR 2.28 2.89 87.36 -1.38 1.08 5.16 ZHR5MM 0.89 3.49 50.07 -0.57 2.44 26.52 El Reno CNTLHR 1.78 6.23 36.47 -1.49 5.4 -32.49 VrZHR 1.66 6.03 49.88 -1.28 5.69 -39.3 VrHR 1.54 6.01 27.21 -1.2 5.56 -23.81 88DHR 1.48 5.89 42.51 -1.11 5.55 -34.58 ZHR 1.47 5.16 26.53 -1.13 4.25 -22.21 ZHR5MM 1.39 4.9 37.9 -1.07 4.26 -26.69 Minco CNTLHR 1.99 5.11 65.67 -1.66 -0.99 3.48 VrZHR 2.07 5.94 83.51 -1.77 -0.35 31.77 VrHR 1.87 5.77 59.76 -1.61 0.58 24.87 88DHR 2.51 4.08 61.97 -2.02 -1.02 19 ZHR 2.64 4.41 47.15 -2.22 -1.17 -1.95 ZHR5MM 2.08 5.11 56.37 -1.76 -2.68 4.17 Ninnekah CNTLHR 1.55 2.07 37.01 -1.15 0.94 18.03 VrZHR 1.8 3.16 45.22 -1.47 0.9 37.58 VrHR 1.69 3.35 64.02 -1.32 0.03 27.3 88DHR 2.38 3.06 83.1 -1.87 0.5 66.95 ZHR 2.41 3.08 90.33 -1.92 0.06 66.92
38 ZHR5MM 1.61 1.44 70.68 -1.18 -0.09 14.08 Average CNTLHR 1.32 4.09 62.66 -0.80 1.96 21.43 VrZHR 1.37 4.21 74.62 -0.91 1.87 32.99 VrHR 1.27 4.30 68.68 -0.81 1.72 34.00 88DHR 1.90 3.94 78.15 -1.22 1.14 30.16 ZHR 1.91 3.74 73.65 -1.23 0.91 31.46 ZHR5MM 1.39 3.39 66.90 -0.81 0.65 20.63
39 (a)
(b)
El Reno 0443-0450 UTC EF-1, $3M Union City 0426-0430 UTC EF-0, $100K
Minco 0354-0358 UTC EF-1, $70K
Fig. 1. Map of the (a) 2 km and (b) 400 m horizontal resolution domains. Black dots in (a) correspond to the Oklahoma Mesonet Site used for verification. Tornado start and end location in (b) are indicated by triangles and squares, respectively. Property damage is indicated in (b).
40 -30
-15
-1
+1
+15
+30
+45
+60
Fig. 2. Field of radial velocity from the Oklahoma City TDWR at 0446 UTC 9 May 2007. The black circle indicates an area of shear and convergence. The black square is the location of the TVS.
41 El Reno EF-1 tornado occurs -- 4:45Z Union City EF-0 tornado occurs -- 4:25Z Minco EF-1 tornado occurs -- 3:54Z
( CNTLHR, 88DHR,VrZHR,VrHR, ZHR, ZHR5MM)
Fine Assimilation Grid: Δx = Δy = 400 m --3DVAR Assimilation every 5 min.
1 hr. spin-up period ( VrZLR, 88DLR, VRLR, ZLR, NORADLR) 0:00 UTC Analysis Coarse Grid: Δx = Δy = 2 km --3DVAR Assimilation every 5 min.
0:00Z 0:30Z 1:00Z 1:30Z 2:00Z 2:30Z 3:00Z 3:30Z 4:00Z 4:30Z 5:00Z
NAM 0:00 Z 00h NAM 0:00 Z 03h
Fig. 3. Timeline of the assimilation windows and forecast periods for the various 2 km and 400 m resolution experiments.
42 Acme OK, Wind Direction
350 (a) Obs VrZHR 300 VrHR ZHR 250 88DHR
200 Deg. 150
100
50
0 0100 0110 0120 0130 0140 0150 0200 Chickasha OK, Wind Direction
350 (b) Obs VrZHR 300 VrHR ZHR 250 88DHR
200 Deg. 150
100
50
0 0100 0110 0120 0130 0140 0150 0200 Ninnekah OK, Wind Direction
350 (c) Obs VrZHR 300 VrHR ZHR 250 88DHR
200 Deg. 150
100
50
0 0100 0110 0120 0130 0140 0150 0200 time (UTC)
Fig. 4. Meteograms of wind direction from the assimilation window (0100-0200 UTC) of the 2 km experiments at Oklahoma Mesonet sites (a) Acme, (b) Chickasha, and (c) Ninnekah.
43 KTLX Observed Z 0355 UTC 9 May 2007 03:55Z NORADHR 0355 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (a) (b) 70.
608.0
60.
576.0 50. (km)
40.
544.0
30.
512.0 20. 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0 (km) (km) 03:55Z VRZLR 0355 UTC 9 May 2007 03:55Z VRLR 0355 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (c) (d) 70. 608.0
60.
576.0 50. (km)
40.
544.0
30.
512.0 20. 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0
5.0 5.0 (km) (km) 03:55Z ZLR 0355 UTC 9 May 2007 03:55Z 88DLR 0355 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (e) (f) 70. 608.0
60.
576.0 50. (km)
40.
544.0
30.
512.0 20. 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0
5.0 5.0 (km) (km) Fig 5. (a) Observed reflectivity factor (dBZ) from KTLX at 0355 UTC 9 May 2007. Forecasted reflectivity field (shaded), horizontal wind vectors, and vorticity contours (black, at an interval of 10-3 s-1 starting at 4 x 10-3 s-1) at 0355 UTC from (b) NORADLR, (c) VrZLR, (d) VrLR, (e) ZLR, and (f) 88DLR. The black triangle marks the confirmed location of the Minco tornado.
44 04:45Z KTLX Observed Z 0445 UTC 9 May 2007 04:45Z NORADLR 0445 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (a) (b) 70.
608.0
60.
576.0 50. (km)
40.
544.0
30.
20. 512.0 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0 (km) (km) 04:45Z VRZLR 0445 UTC 9 May 2007 04:45Z VRLR 0445 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (c) (d) 70.
608.0
60.
576.0 400.0 50. (km)
40.
544.0
30.
20. 512.0 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0
5.0 5.0 (km) (km) 04:45Z ZLR 0445 UTC 9 May 2007 04:45Z 88DLR 0445 UTC 9 May 2007 Z=1.000 KM MSL Z=1.000 KM MSL (e) (f) 70. 608.0
60.
576.0 50.
400.0 (km)
40.
544.0
30.
512.0 20. 480.0 512.0 544.0 576.0 480.0 512.0 544.0 576.0
5.0 5.0 (km) (km)
Fig 6. As Fig. 5, but at 0445 UTC 9 May 2007. The black triangle represents the confirmed location of the El Reno tornado.
45 VrZLR 0205 UTC 9 May 2007 VrLR 0205 UTC 9 May 2007 FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (a) (b) -0.5
512.0 -2.
-3.5 480.0 (km)
-5.
448.0
-6.5
416.0
-8.
416.0 448.0 480.0 512.0 416.0 448.0 480.0 512.0
10.0 10.0 (km) (km) ptprt (K, SHADED) MIN=-7.55 MAX=2.49 ptprt (K, SHADED) MIN=-7.63 MAX=2.60 U-V (m/s, VECTOR) Umin=-13.30 Umax=21.66 Vmin=-15.05 Vmax=19.37 U-V (m/s, VECTOR) Umin=-13.38 Umax=21.50 Vmin=-15.17 Vmax=19.19 ZLR 02:05 UTC Wed 9 May 2007 88DLR 02:05 UTC 9 May 2007 FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (c) (d) -0.5
512.0 -2.
-3.5 480.0 (km)
-5.
448.0
-6.5
416.0
-8.
416.0 448.0 480.0 512.0 416.0 448.0 480.0 512.0
10.0 10.0 (km) (km) ptprt (K, SHADED) MIN=-7.28 MAX=2.01 ptprt (K, SHADED) MIN=-7.30 MAX=1.77 U-V (m/s, VECTOR) Umin=-13.50 Umax=22.68 Vmin=-10.83 Vmax=17.06 U-V (m/s, VECTOR) Umin=-12.76 Umax=21.04 Vmin=-10.92 Vmax=17.59
Fig 7. Forecast at 0205 UTC 9 May 2007 from (a) VrZLR, (b) VrLR, (c) ZLR, and (d) 88DLR. Perturbation potential temperature is shaded at 1.5 K intervals starting at -0.5 K and vectors are for horizontal winds.
46 Minco OK, Wind Direction El Reno OK, Wind Direction
(a) (b) 150 150
100 100
50 50
0 0 Deg.
310 310 Obs Obs 260 VrZHR 260 VrZHR VrHR VrHR ZHR ZHR 210 210 88DHR 88DHR
0230 0320 0410 0500 0230 0320 0410 0500 time (UTC) time (UTC)
Fig 8. Meteogram of wind direction over the 2 km experiment forecast period (0200 – 0500 UTC) at (a) Minco and (b) El Reno Oklahoma. (Note the scale is shifted 180°)
47 CNTLHR 0220 UTC 9 May 2007 VrZHR 0220 UTC 9 May 2007 FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE)
(a) 0.0 (b)(a) 0.0
-4.0 38.4
-4.0
0.0 0.0 -2. -4.0 -4.0
-4.0
-4.0 25.6 -4. -4.0
0.0 (km)
-4.0 -4.0 -6.
0.0 0.0 -4.0 0.0 12.8
-4.0 -4.0 -8.
-4.0 -10. 0.0 0.0 12.8 25.6 38.4 0.0 12.8 25.6 38.4
5.0 5.0 (km) (km) VrHR 0220 UTC 9 May 2007 ZHR5MM 0220 UTC 9 May 2007 FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (c)(a) 0.0 (a)(d)(a) 0.0
38.4 0.0
-4.0 -4.0 -2. -4.0 -4.0
0.0
-4.0 25.6 -4. -4.0
-4.0 -4.0 (km)
-4.0
-4.0 -4.0 -4.0 -6.
0.0 -4.0 0.0
12.8 -4.0 -8.
-4.0 0.0
-4.0 -10. 0.0 0.0 12.8 25.6 38.4 0.0 12.8 25.6 38.4
5.0 5.0 (km) (km) ZHR 0220 UTC 9 May 2007 88DHR 0220 UTC 9 May 2007 FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (e) (f) 0.0
38.4
-2.
-4.0 -4.0
-4.0 -4.0 -4. 25.6 -4.0
-4.0
(km) -4.0 -4.0 -4.0
-4.0 -4.0 -4.0 -6.
-4.0 -4.0
12.8
-8.
0.0
-4.0
-4.0
-10. 0.0 0.0 12.8 25.6 38.4 0.0 12.8 25.6 38.4
5.0 5.0 (km) (km)
Fig 9. The 0220 UTC analyses of low-level cold pool (shaded at 2 K intervals starting at 0 K) and wind vectors from (a) CNTLHR, (b) VrZHR, (c)VrHR, (d) Z5MMHR, (e) ZHR and (f) 88DHR. Fields are as Fig. 7.
48 Acme OK, Temperature 24 (a)
22
20 Obs C
o CNTLHR 18 VrZHR VrHR ZHR5MM ZHR 16 88DHR
14 0100 0120 0140 0200 0220 time (UTC) Acme OK, Wind Direction
350 (b) Obs CNTLHR 300 VrZHR VrHR 250 ZHR5MM ZHR 200 88DHR C o 150
100
50
0 0100 0120 0140 0200 0220 time (UTC)
Fig 10. Meteograms from the assimilation window (0100-0220 UTC) of the 400 m experiments at Acme, Oklahoma of (a) temperature and (b) wind direction.
49 03:00Z CNTLHR T=10800.0 s (3:00:00) 03:00Z 88DHR T=10800.0 s (3:00:00) FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (a) (b) 70.
76.8
60.
50. 51.2 (km)
40.
25.6
30.
20. 0.0 0.0 25.6 51.2 76.8 0.0 25.6 51.2 76.8 10.0 10.0 (km) (km)
Fig 11. Forecast near-surface reflectivity factor (shaded in 10 dBZ increments starting at 20 dBZ) and wind (vectors) at 0300 UTC 9 May 2007 from (a) CNTLHR and (b) 88DHR.
50 CNTLHR 88DHR
0320 UTC 9 May 2007 0320 UTC 9 May 2007 (a) (b) 9200.
67.2 8000.
64.0 6800.
60.8 5600. (km)
4400. 57.6
3200. 54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-21.38 Umax=22.79 Vmin=-10.38 Vmax=17.53 U-V (m/s, VECTOR) Umin=-17.70 Umax=10.10 Vmin=-7.86 Vmax=17.15 Vort*10^5 (1/s, SHADED) MIN=-801. MAX=0.542E+04 Vort*10^5 (1/s, SHADED) MIN=-.115E+04 MAX=0.363E+04
0325 UTC 9 May 2007 0325 UTC 9 May 2007 (c) (d) 9200.
67.2 8000.
64.0 6800.
60.8 5600. (km)
4400. 57.6
3200. 54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-22.83 Umax=29.01 Vmin=-13.12 Vmax=24.46 U-V (m/s, VECTOR) Umin=-28.73 Umax=9.44 Vmin=-9.57 Vmax=12.48 Vort*10^5 (1/s, SHADED) MIN=-.141E+04 MAX=0.887E+04 Vort*10^5 (1/s, SHADED) MIN=-.242E+04 MAX=0.338E+04
0325 UTC 9 May 2007 0330 UTC 9 May 2007 (e) (f) 9200.
67.2 8000.
64.0 6800.
60.8 5600. (km)
4400. 57.6
3200. 54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-21.09 Umax=26.09 Vmin=-13.84 Vmax=21.49 U-V (m/s, VECTOR) Umin=-20.52 Umax=6.67 Vmin=-8.48 Vmax=15.46 Vort*10^5 (1/s, SHADED) MIN=-.203E+04 MAX=0.782E+04 Vort*10^5 (1/s, SHADED) MIN=-.229E+04 MAX=0.231E+04
Fig 12. Forecast near-surface vorticity (shaded in intervals of 1200 x 10 -5 s-1 starting at 2000 x 10 -5 s-1) and wind vectors between 0320 UTC and 0340 UTC from CNTLHR(left column) and 88DHR(right column). The black triangle indicates the confirmed location of the Minco tornado.
51 0335 UTC 9 May 2007 0335 UTC 9 May 2007 (g) (h) 9200.
67.2 8000.
64.0 6800.
60.8 5600. (km)
4400. 57.6
3200. 54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-21.48 Umax=24.27 Vmin=-13.77 Vmax=23.19 U-V (m/s, VECTOR) Umin=-14.00 Umax=4.85 Vmin=-6.29 Vmax=10.10 Vort*10^5 (1/s, SHADED) MIN=-.172E+04 MAX=0.576E+04 Vort*10^5 (1/s, SHADED) MIN=-.131E+04 MAX=0.186E+04 0340 UTC 9 May 2007 0340 UTC 9 May 2007 (i) (j) 9200.
67.2 8000.
64.0 6800.
5600. 60.8 (km)
4400. 57.6
3200.
54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-24.42 Umax=16.53 Vmin=-11.23 Vmax=20.66 U-V (m/s, VECTOR) Umin=-13.89 Umax=3.73 Vmin=-4.22 Vmax=8.47 Vort*10^5 (1/s, SHADED) MIN=-.154E+04 MAX=0.357E+04 Vort*10^5 (1/s, SHADED) MIN=-776. MAX=0.178E+04
Fig. 12. (Continued).
52 03:35Z VrZHR T=12900.0 s (3:35:00) 03:35Z VrHR T=12900.0 s (3:35:00) FIRST LEVEL ABOVE GROUND (SURFACE) FIRST LEVEL ABOVE GROUND (SURFACE) (a) (b) 9200.
67.2 8000.
64.0 6800.
60.8 5600. (km)
4400. 57.6
3200. 54.4
2000.
35.2 38.4 41.6 44.8 48.0 51.2 35.2 38.4 41.6 44.8 48.0 51.2
5.0 5.0 (km) (km) U-V (m/s, VECTOR) Umin=-19.16 Umax=21.68 Vmin=-13.37 Vmax=25.25 U-V (m/s, VECTOR) Umin=-29.36 Umax=25.64 Vmin=-13.17 Vmax=26.55 Vort*10^5 (1/s, SHADED) MIN=-.336E+04 MAX=0.652E+04 Vort*10^5 (1/s, SHADED) MIN=-.276E+04 MAX=0.662E+04
Fig 13. Similar to Fig. 11, but at 0335 UTC only and from (a) VrZHR and (b) VrHR.
53 Tornado and TLV tracks
97
92 VrZHR1 El Reno VrZHR2 87 VrHR Yukon ZHR 82 CNTLHR1 CNTLHR2 77 CNTLHR3 Union City CNTLHR4 y (km) 72 Mustang Z5MMHR1 Z5MMHR2 67 Minco Minco Union City Tuttle El Reno 62
57
52 27 37 47 57 67 x (km)
Fig 14. Minco, Union City, and El Reno tornado track overlaid with TLV tracks from all HR experiments. The x and y values are relative to the southwestern corner of the 400 m domain.
54 Chickasha OK, Temperature Chickasha OK, Wind Direction 24 (a) Obs 350 (b) Obs CNTLHR CNTLHR VrZHR 300 VrZHR 22 VrHR VrHR Z5MMHR Z5MMHR 250 ZHR ZHR 88DHR 20 88DHR 200 C o Deg. 18 150
100 16 50
14 0 0230 0320 0410 0500 0230 0320 0420 0500 Acme OK, Temperature Acme OK, Wind Direction
24 350 (c) Obs (d) CNTLHR VrZHR 300 22 VrHR Z5MMHR 250 ZHR 20 88DHR 200 C o Deg. 18 150 Obs CNTLHR 100 VrZHR 16 VrHR 50 Z5MMHR ZHR 88DHR 14 0 0230 0320 0410 0500 0230 0320 0410 0500 Ninnekah OK,time (UTC) Temperature Ninnekah OK, Wind Direction Obs 24 350 (e) Obs (f) CNTLHR CNTLHR VrZHR VrZHR 300 VrHR 22 VrHR Z5MMHR Z5MMHR 250 ZHR ZHR 88DHR 20 88DHR 200 C o Deg. 18 150
100 16 50
14 0 0230 0320 0410 0500 0230 0320 0410 0500 time (UTC) time (UTC)
Fig 15. Meteograms from the 400 m forecast temperature (left column) and wind direction (right column) at Chickasha(top row), Acme(middle row), and Ninnekah (bottom row), Oklahoma.
55