2015-03-25: Moore, OK tornado and damage survey

Wednesday’s non-chase consisted of me looking up from my son’s bowl of mac and cheese, and seeing a tornado out the window. Okay, there was a little more to it than that. But lately, the storms have considerately come to me when I can’t chase. It’s one of the reasons I live in Norman!

I was aware of the SPC’s moderate risk for severe weather, but I had all but dismissed the tornado risk based on the crashing cold front setup. That setup had burned me too many times in the past. I’ve driven hundreds of miles just to watch nascent storms wither as a relentless southward surge of cold air sliced the legs out from beneath them, leaving me low and gas and without much to show for it. Instead, Wednesday’s storms latched onto the front and rode it south and east. We followed coverage on a couple of the local news networks, watching with rapt fascination as a storm that appeared postfrontal and undercut produced a 30-second funnel cloud near El Reno, OK.

Moore, Oklahoma tornado on 2015-03-25

As seen from near my home in southeast Norman.

Fast forward about an hour: the same storm marched onward to Moore, and in helicopter video, power flashes erupted all over the I-35 corridor. My husband, who had been admiring the mammatus under the anvil in our driveway, suddenly charged into the house, shouting, “I can see it!” I grabbed my son and we dashed across the street. To the north, between two houses, a tapered funnel cloud was clearly visible. I didn’t have time to grab my “good” camera, only got a few grainy pictures with my iPhone. This counts as my son’s first (ex-utero) tornado.

As the damage reports filtered in, and the complexity of the situation became clear, I felt inspired to email some people in the know about participating in the damage survey the next day. It may come as a surprise to some that I’ve never actually participated in a tornado damage survey before. I resolved this year that I would assist in at least one in order to learn firsthand, from the experts, how it was done. By the next morning, I had an invitation. Doug Speheger of the Norman NWS-WFO was gracious enough to let me join his team as they surveyed part of the preliminary track. Rick Smith, the WCM for Norman, tasked us with characterizing the event and focusing in directionality of wind damage indicators (DIs). Signals of a mixed-mode (tornado / straight-line wind) event were already evident in video and reports from the day before.

The Moore EOC whiteboard

The Moore EOC whiteboard listing preliminary damage reports as they came in the evening before.

We started the morning at the Moore EOC, where we conferred with EOC staff. Doug divided up the preliminary track among three teams. Instead of dividing the track into equal thirds, our team took a relatively short but more densely-populated segment near the middle, where local officials had tagged a couple of possible EF2/3 candidate structures. The other two teams took the remaining two, more rural sections of the track, with the added task of determining its start and end points.

I was worried that I might not have the training necessary to contribute to the survey, but instead of clipboards and cameras, we went into the field armed with a pair of iPads. The NWS uses a newbie-friendly, menu-driven app called the Damage Assessment Toolkit (DAT). We snapped pictures of damage, characterized the structures, typed in our comments, and uploaded the data points over the network to the NWS. Rick Smith said it was fun tracking our progress, watching as our data points gradually mapped out the track on screens back in the Norman office.

As we initially drove through the neighborhood northwest of I-35 and 4th St. around 9 a.m., I was surprised by how much cleanup had already been done. The streets were already clear and passable. Piles of branches and shingles had already been hauled to curbs, and some of those piles disappeared over the course of the survey. A handful of heavily damaged houses were marked with a bright orange “X,” indicating that the search-and-rescue teams had paid a visit. Workers were actively moving large, heavy debris out of yards and driveways even as we went door to door. We were actually in a race against time to document the “raw” damage before it was cleaned up.

Crumpled KOMA radio tower in Moore, OK

Of the three ex-KOMA (now KOKC) radio towers, this was the only one left standing, and bent over at that.

We entered some data points in “drive-by” mode; for others, we got out and walked gingerly among roof shingles, shards of glass, and boards with protruding nails. An active hum emanated from the entire damage zone. Chainsaws growled, generators purred, and helicopters circled overhead. News crews, police, and city workers were everywhere. Vans filled with ServeMoore volunteers (many of them teenagers, released from class by Moore Public Schools) went from house to house, offering cleanup help. A cherry-red Coca-Cola delivery truck cruised up and down the streets, the driver handing out bottled water. We overheard the neighborhood mail carrier telling the resident of an “orange X” house that she could apply for a free PO box to use until her house was repaired. She later told a reporter that she had “had it” with Moore after suffering tornado damage three times; she would be moving away soon for good.

We documented a shed blown onto its side, a section of roofing lofted two or three blocks (recognizable from the shingle color), damage to the three ex-KOMA radio towers (two fell down, the one left standing folded 1/3 of the way down at its guy point). Even as we went about our duties, we were aware that the residents were grappling with an unexpected and upsetting interruption in their lives. As a home owner, I empathized with the loss of houses and the scattering of neighbors. After almost two decades with the NWS, Doug is well-practiced in performing damage surveys, and he asked his questions with sensitivity. “Were you here when this happened? Did you have enough warning? Was anyone hurt? We’re glad you’re okay.”

Checking the damage

Tiffany Meyer (left) and Doug Speheger examine the bottom of a collapsed wall in a home near NW 2nd & Arnold Sts in Moore.

We found two particularly intense damage examples that might have qualified as EF2 or greater damage but for some mitigating factor. In one case, a home’s roof had been lifted off, but the failure point was evident. A carport, bolted to the roofline, had been lifted from the driveway by the storm’s winds and peeled off the roof as it blew away. In another, the exterior walls of a home had all collapsed, leaving the interior walls standing like an oversized wine bottle divider. That type of damage might have merited an EF3 rating, but closer examination revealed that the anchor bolts were spaced 8-10 feet apart and only driven about 2 in into the concrete walls. In both cases, the rating stayed at EF1.

It was difficult to differentiate the wind and tornado damage in some areas. For the most part, the winds had moved debris from west to east. We found a few small zones (about half a block in size) where the damage was relatively intense, and clearly convergent. My impression was that we were dealing with a wide swath of mostly straight-line wind damage, with a few small tornadic spinups lasting only a few seconds. A few structures would need to be examined in greater detail.

Compiling the damage survey info in the NWS-Norman WFO

Reconvening with Rick Smith (left) in the WFO, it was decided to announce the finding of EF1 damage and continued investigation.

We returned to Norman around 3 p.m. along with one of the two other survey teams, and related our impressions to Rick Smith and the rest of the office. Based on our report, Rick sent out the following tweets “in time for the four-o’clock news”:

Thanks again to the NWS office in Norman for letting me help make the news!

Edit: Thanks to Doug Speheger for correcting the my interpretation of the “orange X” markings.

Update, 3/30/2015: After some spot-checking of a few structures and a follow-up meeting on 30 March 2015, it was decided to upgrade the house at 2nd & Arnold, as well as a few others, to EF2. NWS-Norman released this damage contour map that afternoon:

Note that this map is still preliminary and could change further!

Update, 4/3/2015: Jim LaDue, another damage survey participant with much more experience than I, conveys his detailed thoughts on this event here.

If it looks like a tornado…

I have a new paper in the November issue of Monthly Weather Review entitled, “Near-Surface Vortex Structure in a Tornado and in a Sub-Tornado-Strength Convective-Storm Vortex Observed by a Mobile, W-Band Radar during VORTEX2,” written the help of six patient co-authors and three reviewers. This paper addresses the question, “If it looks like a tornado, lasts as long as a tornado, and behaves like a tornado, but only on radar, is it still a tornado?”

We examined two cases from VORTEX2. In the first (25 May 2010 near Tribune, Kansas), both humans and radars observed a tornado under the hook echo of a supercell. Here is a photo of the Tribune tornado, along with some the W-band data Krzysztof Orzel (UMass) and I collected:

Top: W-band radar truck with Tribune tornado, and data collected.

(top) The UMass W-band radar collects data in the 25 May 2010 Tribune tornado. Krzysztof Orzel (UMass engineer) can be seen next to the truck. (bottom) W-band reflectivity (left) and Doppler velocity (right) in the intensifying Tribune tornado.

In the second (26 May 2010 near Prospect Valley, Colorado), the radars observed a feature beneath the hook echo of another supercell that looked similar to the tornado seen the previous day, none of the hundreds of storm chasers in the area, including about 100 VORTEX2 participants (some of whom can be counted among the most experienced storm chasers in the world!), reported a tornado or even a funnel cloud. (The Prospect Valley storm produced tornadoes earlier in the afternoon near DIA, but none during VORTEX2 operations.) Indeed, I didn’t even know the vortex was there until the data were post-processed, because it was too small to see on our in-cab display. Closer examination revealed seven of these tiny vortices spinning up in tornado-family-like fashion on the tip of the hook. The one pictured below (#5) was the strongest and longest-lived.
Top: W-band radar truck under Colorado supercell. Bottom: W-band radar data collected in a vortex near the tip of the hook.

(top) The UMass W-band radar collects data beneath the hook echo of the Prospect Valley storm. The black arrow indicates a small cloud base lowering that persisted for more than 30 minutes. (bottom) Radar data collected in the hook echo, showing a vortex with a weak-echo hole that lasted 8 min.

Here’s a screenshot from our in-cab Situation Awareness for Severe Storm Intercept (SASSI) display around the time the above data were collected, showing participants reporting a “small circulation” and “rising motion”. But, no one says the T-word.

SASSI screen grab from Prospect Valley

SASSI screen grab from 2240 UTC on 26 May 2010, showing VORTEX2 vehicles deployed under the Prospect Valley storm. Orange circles are dual-Doppler lobes drawn by the radar coordinator, and the purple cone denotes where the field coordinators believed the circulation would go. “UMW” is the W-band radar.

The radar presentations look similar, no? Those of us who use mobile radar data need “tornado threshold” criteria in order to determine objectively such parameters as tornado start and end times. While there is no universally accepted Doppler velocity threshold for tornadoes, the Alexander and Wurman (2008) criterion (40 m/s across <= 2 km diameter, and persisting for at least two consecutive scans) is used in a number of studies. Both the 25 May and 26 May vortices met this criterion and lasted about 8 minutes, but had it not been for radar observations, we might not have known a vortex was present on 26 May.* We appear to have caught a vortex that just barely tickled the lower end of the tornado spectrum. The surface dew point depressions were much higher - about 12 oC – than on the previous day (8 oC). We speculate that just a little additional moisture would have made this vortex visible and changed the designation of the 26 May case from non-tornadic to tornadic in the VORTEX2 logs.

One might ask, “Who cares? The Prospect Valley vortex damaged nothing and injured no one.” As a scientist, I care. Documenting these types of events with high-quality observations demonstrates that the boundary between tornadic and non-tornadic vortices is fuzzy, and that human and radar detection of tornado occurrence may not always be consistent. Since this article appeared online last week, I’ve gotten a number of e-mails from other scientists who have made similar observations of weak vortices under supercells, but who weren’t sure how to categorize them. We didn’t want to make a new category of vortex for this type of event – there are already enough animals in the zoo*,** – but in this paper we use the clunky term “sub-tornado-strength, convective-storm vortex (SCV)” to describe the Prospect Valley not-quite-tornado.

* Dr. Chuck Doswell documents a similar case using a photograph from Dr. Bill Gallus in his essay, “What is a tornado?” Incidentally, that tornado also occurred in Colorado. The Prospect Valley vortex would not meet Chuck’s tornado definition because it caused no damage.
** CSWR documents a number of these in a recent paper in Weather and Forecasting, including what they call “marginal tornadoes.”

The situation gets serious

I am still getting my bearings back after three straight days of tornadoes. Yesterday, I watched the Moore tornado form from the controls of the KOUN radar on the north side of Norman.

It was my first time to see a tornado while not actively chasing. As I watched it track past me to the northwest and disappear into the murk, I crossed my fingers that it would spare Moore. It didn’t. It crossed I-35 between 4th and 19th street, scattering cars, houses, and people like a mammoth lawn mower. The Warren Theater, where I watched a sneak preview of the new Star Trek movie last week, was damaged and pressed into use as a triage area.

I was ready to write two relatively light-hearted posts about my chases last weekend. I just don’t feel that lighthearted anymore. A co-worker of mine lost her home, but fortunately is not injured. My husband and I are going to go through our garage tonight and gather items to donate to people a few miles away who literally lost everything they own. Honestly, how many times can a nightmare repeat itself?

New pub

This month’s issue of Monthly Weather Review contains my newest paper, EnKF assimilation of high-resolution, mobile Doppler radar data of the 4 May 2007 Greensburg, Kansas supercell into a numerical cloud model, describing the second half of my dissertation research. (Yes, I know I graduated over a year ago… Publication takes a long time, as well it should!) The take-home message of the paper is that low-level (< 1 km AGL) wind observations make more realistic analyses of supercells (and other rapily-changing atmospheric phenomena). Supercells have a lot going on under the hood – updraft and downdraft pulses, mesocyclone cycling, cold pool generation, etc. – not all of which are apparent to the naked eye or even to an advanced, 4D observing system like a radar. Computers can help fill in some of the gaps via process called data assimilation (DA).

For those not familiar with DA, it means combining atmospheric observations (such as those from a radar) with a computer-generated weather forecast in order to produce a mathematically optimal, 3D analysis of the atmospheric state. You then use the analysis to launch a new forecast. Rinse and repeat every few minutes. The end result is a series of 3D snapshots of the storm, which you can use to diagnose the storm’s inner processes. (There is a gargantuan body of theory required to combine these two very different types of input and assess the quality of the analyses. I shan’t bore you with the two semesters’ worth of details that I slogged through in grad school.)

For this study, I used an advanced DA technique called the ensemble Kalman filter (EnKF) to assimilate NEXRAD (from Dodge City, Kansas) and UMass X-Pol data collected in the Greensburg storm. In one set of experiments, I withheld the UMass X-Pol data (which were collected more frequently and closer to the surface). The mesocyclone of the simulated Greensburg storm was much stronger and more persistent in the experiments where I used the UMass X-Pol data, and the updrafts and downdrafts stronger and more compact. While we lacked independent data to use for verification, making our assessment necessarily qualitative in some regards, our results are consistent with previous DA studies using artificial, “perfect” radar observations.

Simulated Greensburg storm (reflectivity)

Here’s the simulated reflectivity in the Greensburg storm, after assimilating (left) WSR-88D data only and (right) also UMass X-Pol data. Both storms are in the same place and look similar overall…

Simulated Greensburg storm (vertical velocity, vorticity)

BUT… the velocity fields are very different! Updrafts (red) and downdrafts (blue) are more intense. Also, the vorticity bullseye corresponding to the Greensburg tornado (black contours) is much stronger.

I described in a previous post our serendipitous UMass X-Pol data collection in the Greensburg, Kansas storm of 2007, and how that evolved into a detailed case study published last year. My husband lead-authored a companion study earlier last year where he assessed whether modifications to the initial model environment changed the forecasts. (Answer: Yes. Quite a bit, in fact.) This pub completes the trifecta. As we were about to submit this paper for peer review, we made a last-minute decision to switch the DA software to a system that was more extensively tested for severe storms. Even though that added a month to the prep time, I am glad that we did, because the resulting analyses, generated from the same observations, looked markedly better.

I wrote this paper during my CAPS postdoc with the able assistance of my co-authors, representing a fruitful collaboration between SoM, NSSL, and CAPS. I manually edited and dealiased all the radar data (a task that took nearly two months). I had the benefit of two astute reviewers (including my brother-in-EnKF, Dr. James Marquis) who asked some mighty tough questions. And I got to share this MWR issue with some other super scientists – Tom Galarneau, Jeff Beck, and Chris Weiss.

New pub

I’ve got a paper in this month’s Monthly Weather Review entitled Mobile, X-band, Polarimetric Doppler Radar Observations of the 4 May 2007 Greensburg, Kansas, Tornadic Supercell. This is the first of two planned papers based on my Ph.D. research, an observational study based on UMass X-Pol data that we collected in the Greensburg storm.

UMass X-Pol on 4 May 2007

UMass X-Pol on 4 May 2007. Had it not been for the blown tire, we might not have collected data in the Greensburg storm.

Operations on 4 May 2007 started off on a low note. Howie, Kery H., and I were driving the UMass X-Pol to Dodge City, Kansas on our first chase of the season, when we blew a tire. With the aid of a good samaritan who just happened to have a spare that was the right size, we limped into Protection, Kansas. Tornado reports near Arnett, OK tempted the rest of our group back south, and we figured we had a bust chase on our hands. As soon as the new tire was on, a storm erupted southwest of Protection, and we decided to collect some “consolation data” in it and shake down the system.

A local sheriff showed us to his favorite storm spotting place on a hill looking over Protection. That road was too muddy for us to use, so we moved about two miles east and deployed on a packed gravel road. As we lost daylight fast, the storm dropped a rotating wall cloud, followed by a brief tornado and an additional funnel cloud to our west. We got volumetric, polarimetric X-band data of all these features. At one point I had to back the truck up to avoid beam blockage from telephone poles along US Hwy. 160, resulting in a small gap in the data.

The Greensburg tornado (#5) illuminated by lightning. These are frame grabs from my handheld video.

The Greensburg tornado (#5) illuminated by lightning. These are frame grabs from my handheld video.

As night fell, we lost visual on the wall cloud, but the radar presentation showed a giant spiraling hook echo. We were certain there must be a tornado occurring, so we kept collecting data. We had barely any reception of weather radio, but between the lightning crackles we could make out a series of urgent tornado warnings on our target storm issuing from the NWS office in Dodge City, including indications of a large tornado. Occasionally, flashes of lightning outlined a large lowering in the cloud base to our north (see images at right). After collecting more than an hour of data, we finally shut the system off around 9:30 p.m., when the deep-cycle marine batteries on board the UMass X-Pol, used to power the antenna pedestal and computer, completely drained.

En route back to Norman around midnight, congratulating ourselves on a great first deployment of the season, my cell phone rang. Chip L. had been chasing with us earlier in the afternoon, but split off after our tire mishap. “Greensburg has been completely destroyed,” he said solemnly. As an EMT, he had gone there to assist, and witnessed near-complete devastation of a town of ~1500. We stopped at a fast food restaurant in Ada, where a TV screen in the corner of the room flickered images that looked like they’d come from a war zone. The remainder of the drive back was much quieter and more somber.

Reflectivity, Doppler velocity, and storm-relative Doppler velocity data in the Greensburg tornado

Reflectivity, Doppler velocity, and storm-relative Doppler velocity data in the Greensburg tornado

Over the next few days, back in Norman, I pored over the data, and it quickly became clear that this would be my dissertation case. We had captured not only the genesis of an EF-5 tornado, but several weaker, antecedent tornadoes. In trying to figure out why the Greensburg storm changed tornado production “modes”, a wealth of information offered itself up, and that information forms the basis for this paper. Other scientists, most notably Jana H., Howie, Mike U., Jeff H., contributed data, discussion, and analysis to the study. Two anonymous reviewers also helped me to sculpt the messy initial version into something clearer and more concise. (Word to the wise: Don’t just copy-and-paste half of your dissertation into a journal template and submit it. I was lucky the first version didn’t get rejected outright!)

In the years since that night, I’ve driven through Greensburg several times, and have been continually amazed by the community’s resilience and resurgence. When the time came, I dedicated my dissertation research to all the victims of the Greensburg tornado. Although improved scientific understanding can never undo the pain to the Greensburg community, I’d like to think that this study was one small, positive thing that came of an otherwise wholly devastating event.

No Time Toulouse

Météo France's Toulouse C-band radar

Météo France's Toulouse C-band radar

Sorry it’s been a little quiet around here over the last month, but I think I had a good reason. I was getting ready for my first European radar conference: 7th European Conference on Radar in Meteorology and Hydrology in Toulouse. I’d never set foot in France before, and wanted to put my best one forward!

At the Météo France conference center, I presented a talk (about GBVTD analysis of W-band data we collected during VORTEX2) and two posters (both on EnKF assimilation of mobile radar data in supercells). I reconnected with domestic and international colleagues, as well as making some new acquaintances. Between sessions, we had receptions and banquets at several Toulouse landmarks, including City Hall and the 800-year-old Hotel Dieu.

Poster session at ERAD 2012

The poster session of ERAD 2012, held in an air-conditioned tent.

I got to see updated versions of some research presented at last fall’s AMS Radar Meteorology Conference in Pittsburgh, as well as some intriguing new work from my European contemporaries. (There weren’t many tornado talks, but there aren’t as many tornadoes in Europe, after all!) On the final day, there were a couple of talks about radar-based aeroecology (detection and characterization of birds, bats, insects, etc.). Fascinating stuff. Biologists are finding gold in the data that we usually ditch in QC!

An evening stroll down the streets of Toulouse

An evening stroll down the streets of Toulouse

Outside the conference, downtown Toulouse was visually pleasing and gastronomically amazing. I took relaxed strolls through the streets and gardens in the evenings, admiring the wrought iron balconies and old chuches, nibbling cheese, and sipping wine. Oh, and taking in Euro Cup matches with the locals, too! The people were, by and large, friendly, and most of the waitstaff at restaurants spoke enough English to get our orders right. I visited 13th-century cathedrals, open-air markets, stunning museums, historic hotels, and verdant gardens.

I figured out early in my stay that I couldn’t possibly pack in all the activities I wanted to do in one week. It’s just as well, because I kept getting lost! And of all the cities I’ve visited, Toulouse was by far the best city to get lost in, slow down, and enjoy.

I’ve returned to find summer baking Oklahoma in earnest. It may not be too long before we dust off the dust devil chasing gear again!

New pub

I am a co-author on a paper in this month’s issue of Monthly Weather Review, entitled Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones. Dan ran a set of NCOMMAS ensemble forecasts of the Greensburg storm, assimilating reflectivity and velocity data from the Dodge City, Kansas WSR-88D (KDDC). He varied both the 0-3 km AGL velocity profile in the initial model environment to reflect the onset of a low-level jet, and also cut loose forecasts at different times to see how the vorticity swaths changed. Not surprisingly, the forecasts are better when the lead time decreases. However, there are still issues with the simulated Greensburg storm moving too quickly toward the east, possibly as a consequence of cold pool buildup.

We consider this paper a proof-of-concept study in support of the Warn-on-Forecast project. It demonstrates probabilistic forecasting of a tornadic mesocyclone’s track using operationally available data, albeit not in a real-time framework.

My contributions to this study were the dealiased KDDC data, the low-level VAD wind profiles, some of the graphics, and of course, help with the writing. (Fortunately, Dan is a good writer and didn’t need much help!)

One minor erratum – I just noticed that my listed affiliation is partly incorrect. I do work for CAPS, but my previous affiliation was CIMMS, not NWC. That’s an “oops” that I should have caught during the editing process. Fortunately, it doesn’t impact the science!

Is it time to modify the (Enhanced) Fujita scale paradigm?

I received many thoughtful and passionate responses to my previous post regarding the upgrade of the El Reno / Piedmont /Guthrie tornado in Oklahoma to EF-5 based, in part on radar observations of 60 m AGL wind speeds. As I noted there/then, the EF scale, as was the F-scale before it is a damage scale, not a wind speed scale. Some have argued that, for this reason, actual wind speed measurements should have no bearing on the EF-scale rating, while others have argued that we should try to incorportate wind speed measurements in EF-scale ratings whenever they are available.

Let’s climb into our “way back machine” and go back to 1971. (Granted, this precedes my own birth by nearly a decade, but I digress.) Dr. Ted Fujita was motivated by the question, “How fast are tornado winds?” Doppler weather radar was still in its infancy, photogrammetry was only possible with high-quality, well-documented film, and in situ measurements of the winds were, logistically, all but impossible to collect (despite valiant attempts to do so). The way I see it, Dr. Fujita asked, “What evidence for wind speeds do tornadoes most consistently leave behind?” His answer: Damage. In 1971, in a paper proposing the Fujita scale, he writes,

“…one may be able to make extremely rough estimates of wind-speed ranges through on-the-spot inspection of storm damage. For instance, the patterns of damage caused by 50 mph and 250-mph winds are so different that even a casual observer can recognize the differences immediately. The logic involved is that the higher the estimate accuracy the longer the time required to make the estimate. Thus a few weeks of time necessary for an estimate with 5-mph accuracy can be reduced drastically to a few seconds if only a 100 mph accuracy is permissible in order to obtain a large number of estimates with considerably less accuracies… high wind-speed ranges result in characteristic damage patterns which can be distinguished by trained individuals with the help of damage specifications…”

Fujita clearly spells out his rationale for the scale; his strategy was to use damage as a proxy for wind speeds in the absence of near-surface wind speed measurements. Forty years later, thanks to innovations like miniaturized, in situ probes and mobile Doppler radar, obtaining near-surface wind speeds in tornadoes is not so far-fetched. Because only a handful of such instruments exist, and deployments are challenging (the presence of a mere tree or building between the radar and tornado can compromise the measurements), we are still not collecting near-surface wind speed measurements in tornadoes with any consistency. And, we are finding that the wind speed bins don’t always match up with the damage indicators in the EF scale.

In my opinion, this means the scale needs to be made more flexible, or possibly supplemented by a wind measurement-based alternative (i.e. two ratings, one damage based and one measurement-based). One could envision expanding the EF-scale into a second dimension (i.e. an EF matrix), the second dimension only expanded if reliable wind measurements (M) are available, and collapsed if they are not. The El Reno / Piedmont / Guthrie tornado would, for example, be rated EF-4 based on its damage, but M-5 based on the RaXPol wind measurements extrapolated to the surface via an objective method.

What I outline above is merely my own half-baked idea, and I am eager to hear other suggestions from people closer to the subject area. I am not a tornado damage expert; I am an observationalist. Keeping the damage-based scale certainly has its merit, primarily in the interest of maintaining consistency with the last 40 years of records (fraught with uncertainty though it may be; see Doswell and Burgess 1988). However, a blanket disregard for reliable remote or in situ wind measurements seems unwise, when obtaining tornado wind speeds was precisely Dr. Fujita’s objective.


EF-5 upgrade based on mobile Doppler radar data

The El Reno / Piedmont / Guthrie tornado was upgraded to EF-5* this afternoon, based in part on measured RaXPol Doppler velocities of over 210 mph.

Here’s the relevant portion of the NWS Public Information Statement:

EVENT DATE: MAY 24, 2011

I’m not certain if this is the first time mobile radar data have been used to upgrade a tornado rating, but it’s certainly an unusual occurrence. (If you know of such an instance, please post a comment!) EF-5 tornadoes are extremely rare events, mobile radar data collection in them, even rarer, and crucial near-surface wind measurements, rarer still. The Doppler velocities in the upgraded EF-5 tornado were collected at 60 m AGL, according to my former officemate and Ph.D. candidate, Jeff Snyder. Since RaXPol is such a new radar, he and other members of Howie’s team have been double- and triple-checking their measurements throughout the past week. So far, I’m told, the data are of reliable quality. But, the data will still have to be subjected to the scientific peer-review process in more formal studies yet to be composed.

Doppler radar cross-section of the Greensburg tornado

Pseudo-RHI of (top) uncalibrated reflectivity factor and (bottom) Doppler velocities collected in the 4 May 2007 Greensburg, Kansas tornado. Note the weak-echo column down the center of the funnel, indicative of centrifuging of hydrometeors and debris. Also note that no data were collected at altitudes below 1.2 km AGL. From my Ph.D. dissertation. Data collected by UMass X-Pol.

For comparison, on 3 May 1999, a DOW measured winds over 300 mph in the Moore/Bridge Creek, OK F-5 tornado. In a 2002 paper about that data set, it was noted that lofted/centrifuged debris could actually contaminate the velocity measurements near the surface. In the Greensburg, KS, EF-5 tornado, which I studied as part of my dissertation research, Doppler velocities exceeded 180 mph, but only well above the surface. (We deployed too far away from the Greensburg tornado to collect data in that crucial near-surface layer – see the figure at right.)

Remember that the EF scale is not a wind scale. The wind speeds are estimates based on damage (which is the only evidence tornadoes consistently leave behind for us to study), rather than the other way around. For this reason, there may be forthcoming disagreements as to whether Doppler radar measurements can even be used to make an EF-scale determination. Stay tuned…

* An explanation of the EF scale (and how it differs from the original Fujita scale) can be found here.

Correction: The Mulhall, OK tornado was F-4, not F-5, and the 300+ mph measurement was in the Moore/Bridge Creek, OK tornado. Thanks to Roger Edwards and Mike Coniglio for the corrections!

RaXPol: Radar envy

Yesterday saw the arrival in Norman of Howie Bluestein’s new Rapid-Scan, X-band, Polarimetric mobile Doppler radar (RaXPol for short). It’s a radar primarily intended for tornado research, but which also has a myriad of other potential applications.

Yes, we’ve had radars mounted on trucks since the mid-1990s. What makes RaXPol special? Watch this:

That video clip is not sped up; that really is an 8-foot dish rotating at 180 degrees per second! By gradually changing the elevation angle, it can potentially collect a full atmospheric volume of polarimetric data in less than 30 seconds. Why is that important? Tornadoes can change drastically on time scales of only a few seconds, so the faster scientists can collect volumes, the more information we’ll have about those rapid changes. The polarimetric capability will allow researchers to distinguish different types of hydrometeors, debris, and other scatterers in supercells and tornadoes.

One might expect the entire truck to wobble with a giant antenna swinging around on its bed. The engineers addressed that issue from the design stages. As can be seen in the video clip, the entire truck remains surprisingly static, even without the hydraulic levelers deployed. Seasick crew members will not be an issue.

And as for the problem of “beam-smearing” (insufficient dwell time) that might result from such a rapidly rotating antenna, the engineers implemented a multi-frequency Tx/Rx system. Conventional Doppler radar transmits pulses a single frequency, then “listens” for the echo of the transmitted signal. Imagine someone striking a single piano key, then listening for the echo of that note. In contrast, RaXPol transmits consecutive pulses at slightly different frequencies, then listens for the returned signal from all of them simultaneously. In the piano analogy, instead of striking only one key, you would sweep your fingers over several keys, then listen for the combined echoes of all the different notes. Dr. Andy Pazmany explains in this presentation how this “frequency hopping” technique works.

RaXPol panoramaRaXPol was constructed at Prosensing in Massachusetts, funded by a Major Research Instrumentation (MRI) grant* from the National Science Foundation. It will be maintained by the Atmospheric Radar Research Center at OU. It will remain in Norman year-round, and theoretically be available for operations outside of the “normal” chase season (April – May – June), such as hurricane deployments.

I feel a little silly blogging about this radar, because I’m not going to be using it. (That job belongs to Howie’s current crop of grad students.) But, I’ve been hearing about this radar for three years, ever since Howie’s first “woof” when he heard that the grant proposal had been funded, and I’ve never been ashamed to geek out over a shiny new instrument! I can’t wait to see what data the students end up collecting.

* In the abstract, Howie mentions two female Ph.D. students. I was one of them!

Update: The OU College of Atmospheric and Geographic Sciences put out a press release about the RaXPol!