Updated 2 May 2013 to correct typo on date of previous low tornado count

The 12-month period from May 2012 to April 2013 was remarkable for the absence of tornado activity and tornado impacts in the United States.

We can start by looking at the number of EF1 and stronger tornadoes during that period. A final count is available through January 2013 and we have a pretty good estimate of how many occurred in February through April, although final numbers won’t be available until July. Although the 12 month total may change a little bit with the final data, it’s unlikely to change enough to affect the results here.

From May 2012-April 2013, the estimate is that there were 197 tornadoes rated EF1 or stronger. Where does that stack up historically? Well, we have pretty good data back to 1954. During that time, the previous low for (E)F1 and stronger tornadoes in a 12 consecutive calendar month period was 247, from June 1991-May 1992. The next lowest (ignoring the overlapping periods, such as April 2012-March 2013) was 270 from November 1986-October 1987. The lowest non-overlapping 12 month counts on record from 1954-present, with the starting month, are:

217 May 2012 (preliminary)
247 June 1991
270 November 1986
289 December 2001
298 June 2000

This apparent record was set less than two years after the record for most EF1+ tornadoes in a 12-month period was set, with 1050 from June 2010-May 2011. The time series showing the evolution of the number of (E)F1+ tornadoes since 1954 is below. The number of (E)F1+ tornadoes in the 12 months beginning with the time on the x-axis is plotted for every month starting in January 1954 and ending in May 2012, the most recent point.

The death toll from May 2012-April 2013 was 7. National Weather Service official statistics go back to January 1950, but we can extend that by using the work of Tom Grazulis from the Tornado Project, who has collected tornado fatality information back into the 17th century. The data are reasonably good back to 1875, but it’s still possible that there are some missed fatalities, particularly as we go back farther in time. So, where does 7 fatalities in 12 consecutive calendar months stack up? Again, here are the lowest totals, going back to 1875, for 12 consecutive months, with the starting month. (For overlapping periods, such as April 2012-March 2013 and May 2012-April 2013, only the lowest period is listed.)

5 September 1899
7 May 2012
8 August 1991
12 November 1909
12 May 1940

The 2012 tornado season in the United States got off to a quick start with well-above average numbers in January, February, and March. Later, over 80 tornadoes occurred on 14 April. Since then, the number of tornadoes in the US has been unusually low. In order to understand how low, we need to look at the long-term history of tornado occurrence. The most reliable portion of the tornado data begins in 1954 but, even after that, we have to be careful in how we interpret it. Since the mid-1950s, the number of tornadoes reported has increased by an average of 14 per year. The increase has been almost entirely in the weakest tornadoes (F0) and is highly likely that the causes are non-meteorological. We can think of this increase in the same way we think of inflation in economics and estimate its impact by adjusting historical tornado counts to account for it. This process, and how it can be applied to part of the year, is discussed here.

That inflation-adjustment process allows us to look at historical data, but a problem still remains of how to look at recent reports. Preliminary, eyewitness reports of tornadoes are collected by local National Weather Service Forecast Offices and the offices then evaluate those reports and produce a list of “final” tornado reports. This process of evaluation takes a few months to complete, so it can be challenging to answer the question “how many tornadoes occurred” shortly after an event. Over the last several years, a simple relationship between the preliminary and final reports has been observed with the number of final reports being approximately 85% of the preliminary reports. As a result, when looking at the preliminary reports in recent months, we can a pretty good estimate of the final reports simply by multiplying the preliminary reports by 0.85.

Let’s look at how many tornadoes we would expect based on the inflation-adjusted tornado count and compare this year’s tornadoes to that long-term expectation. To emphasize the small number of tornadoes since the middle of April, we’ll start on 15 April and add up the number of tornadoes each year through the end of July. In the accompanying chart, we see the distribution of the accumulated number of inflation-adjusted tornadoes as we got from 15 April-31 July. The distribution is based on the period from 1954-2011. The maximum and minimum of any of those years are shown in blue (note that the year associated with the maximum and minimum can change from day to day along the way). The heavy black line is the median of the distribution, the gray lines are the 25th and 75th percentiles (half the years will be between them), and the dashed lines are the 10th and 90th percentiles (4 out of 5 years will be between them). For comparison, the estimated number of final tornado reports from 2012 are shown in red.

Through the end of May, the tornado count for the period from 2012 goes along at approximately the 10th percentile of the long-term distribution but, after that, falls well below the previous low. To put this into perspective, the estimated number of final reports from June for 2012 is approximately 100. The previous inflation-adjusted low for any previous June is 94 in 1988. (Remember that the blue line represents the fewest number of tornadoes from any of the 58 years from 1954-2011.) The median number of June tornadoes in 1954-2011 was approximately 270.

July was even more remarkable than June. Only 24 preliminary reports were received, leading to an expected number of final reports of a little over 20. The lowest number of inflation-adjusted tornado reports from 1954-2011 is 73 (1960). Even without inflation adjustment, the fewest number of tornadoes in any July in that time period is 42 (1960), emphasizing the extraordinary nature of this July. The median number of July tornado reports is about 150.

When we look at the whole period from 15 April-31 July, the median tornado count in the record is 850, compared to 2012, with a little under 300. The 850 represents almost 2/3 of the usual annual total of about 1300. One way of thinking about the late spring and early summer tornado season is that the atmosphere missed more than 40% of a typical year’s tornadoes in 3 1/2 months. Compared to 2003, the comparable period in 2012 had more than 900 fewer tornadoes. 2011 had the second highest number of tornadoes in this part of the year, so in the last two years, the US has experienced the extreme high end of the distribution of the number of tornadoes and the extreme low end of the distribution.

Bill has been involved with the National Weather Service Forecast Offices for more than 20 years, most recently serving as the Meteorologist-In-Charge at the Fort Worth Forecast Office. He has also worked in New York City, Lansing, Mich., and Kansas City as well. Bunting is no stranger to Norman, however, as he worked as a forecaster at the Norman Forecast Office from 1990-1993. As the operations chief, Bunting supervises the 22 forecasters, is responsible for the forecasts that leave the office, and generally oversees the forecast office.

Bunting received his bachelor’s degree in meteorology and psychology from The University of Oklahoma in 1984. On returning to Norman Bunting said, “I’ve always admired the Storm Prediction Center’s expertise and innovation in terms of  forecasts they provide, and the opportunity to be a part of this group was one I couldn’t pass up.”

Bunting said he was “very much looking forward to the collaborative opportunities” between the Weather Forecast Office, National Severe Storms Laboratory, and other service branches here in the Norman office.

Bunting is taking over this position from Dave Imy, who retired in December.

We can look at the record of tornado deaths, discussed here, dating back to 1875. The last time there were no deaths in the month of May was in 2005. Prior to that, it was 1994. Overall, there have been 15 years in the 138 years of the record (1875-2012) with no deaths in the month of May, so we’d expect that to happen about once every decade.

May 2012 stands in dramatic contrast to May 2011, when 178 people died in tornadoes, 158 of them in the Joplin, Missouri tornado of 22 May. 178 deaths is the fifth highest death toll in the period 1875-2012, and the largest since 211 people died in 1933. The deadliest May on record was 1896, when 502 people were killed, including 255 in the Saint Louis, MO-East Saint Louis, MO tornado of 27 May. Adjusted for wealth of the country, that tornado was the costliest in US history, with damage adjusted to 2011 dollars of over $6 billion.

Storm of the Month is a program for National Weather Service forecasters to share weather events with other offices and improve user proficiency. Forecast offices across the country sign up to present a weather event and discuss in a live online session how they have used dual-polarization to their benefit.  The peer-to-peer storytelling sessions are audio recorded, presented in a webinar format, and followed by a 15 minute question and answer session with the presenting forecasters. All Storm of the Month presentations are later uploaded to the Web site as virtual lessons. The audio recording is accompanied by a visual presentation as well as an overview and explanation of concepts brought up during the question and answer period.

“Storm of the Month has not only served as an effective learning tool but also has generated enthusiasm for dual-polarization,” said Jami Boettcher of the WDTB, “It can be viewed as a communication model applicable to other technology implementation efforts.” The concept is also being used with the NOAA Hazardous Weather Test Bed as their experiment results are being published online in a similar manner.

The Storm of the Month series has proven to be very successful in improving user proficiency.  The high attendance of this increasingly popular program has been beneficial for not only the forecasters and other users but ultimately the public as well.  Boettcher said, “If a forecaster has more confidence about what a threat is, if they understand the threat better, they will communicate that threat better to the public.”

The webinar series can be viewed at http://www.wdtb.noaa.gov/courses/dualpol/SOTM/index.html. More information about dual polarization radar is available at http://www.nssl.noaa.gov/research/radar/dualpol.php.

Eric L. Ice — Clerical/ Administrative

Nancy Beck — Outstanding Community Service

Tanyelle M. Casper — Outstanding Customer Service

Steven Smith — Supervisory

Christina Horvat — Supervisory (Dept. of Defense)

William H. Greenwood — Administrative, Technical, & Professional, GS-8  and below

Mark Wakeam — Administrative, Technical, & Professional, GS-8 and below (Dept. of Defense)

Terrell “B” Ballard — Administrative, Technical, & Professional, GS-9 and above

Darcelle “Darcy” Saxion — Administrative, Technical, & Professional, GS-9 and above (Dept. of Defense)

Washington Coast Team- Outstanding Team

Terrell “B” Ballard was chosen from ten nominees as the winner for the Administrative, Technical, & Professional, GS-9 and above category. “B,” as he is known around the office, is the team leader and recognized expert for the radar technicians who perform both emergency and routine maintenance on the 160 NEXRAD radar systems world-wide.  He is known as a “hands on” leader and is the lead on all of the most difficult on-site repairs.  Ballard also assists with the 24-hour customer service hotline that provides technical assistance via phone with a focus on repairing radars after major equipment failures. The fleet of current radars has a goal of 96 percent operational availability and Ballard’s team is directly credited with keeping that metric at an amazing 99 percent. He is also active in his community as a member of both the Masons and Shriners.

Congratulations to Ballard and all award nominees. Your hard work and excellence in the community does not go unnoticed!

Nationwide, the month of May in 2011 broke the record for the deadliest since 1933, with 178 lives claimed from major disasters in Joplin and Oklahoma alone.  The best way to protect your family from disaster is to be prepared. Evaluate your severe weather procedures before there is an immediate threat. Refresh the memory of everyone in your household about your plan and ensure all are comfortable executing the plan in a timely manner if an emergency situation arises.

Rick Smith, warning coordination meteorologist at the National Weather Service Forecast Office in Norman, Okla., urges awareness and preparedness.

“In Oklahoma it is not a question of if it’s going to happen, it’s when,” Smith said. “As we saw on May 24, 2011, it only takes one day to have a huge impact.”

As Smith pointed out, this year has served as a contrast to the active tornado season of 2011 but there are still plenty of opportunities for threatening conditions. It is essential to stay alert and informed about the weather even when conditions don’t appear to be particularly threatening.  A severe weather situation can change very quickly and unpredictably. Are you ready?

NSSL Mesonet vehicle at the Exploratorium

The partnership is the result of a five-year educational grant with NOAA to co-develop interactive exhibits, learning experiences and professional development workshops for the learning institution.

NSSL retired researcher Dave Rust shared his thunderstorm electricity expertise and his skill at creating weather measuring instruments.  Dave pioneered the use of free-flying balloons and mobile laboratories to make observations, significantly advancing thunderstorm science.

Susan Cobb, NSSL meteorologist and science writer, shared her experience that includes forecasting for locations all over the world, and writing about weather science for all audiences. Susan worked with visitors to understand, experience and forecast weather in the San Francisco area and around the world.

Sean Waugh is a graduate student at the University of Oklahoma and an instrumentation specialist working with the NOAA NSSL.  He helped design and build seven Mobile Mesonets, storm research cars outfitted with weather instruments, computers, and communications equipment. Sean gave personal tours of the Mobile Mesonet and focused on ways NSSL collects data to learn more about storms.

The NOAA National Severe Storms Laboratory’s mission to improve our knowledge of severe weather and to develop new tools to better forecast and warn of its hazards has endured since its establishment in 1964. The Exploratorium first opened in 1969 and welcomes more than 500,000 visitors each year.

The bi-monthly charts on the left show tornado events summed for each period with the years increasing along the positive y-axis. The charts on the right use the same bi-monthly tornado sums for each period but years are re-ordered and ranked based on MEI. The strongest bi-monthly El Niño events are at the top of the y-axis while the strongest La Niña events are at the bottom of the y-axis. Years/months between the two extremes are ENSO neutral, or transition periods between the two phases.

Cook and Schaefer, 2008, found an increase in cool-season tornado events during ENSO cool-phase (La Niña) and this finding is borne out to some extent by the charts for the two bi-monthly periods of Dec-Jan and Jan-Feb. These two overlapping cool-season periods exhibit some correlation between summed tornadoes and MEI with no more than 12 percent of the variance (R²) in the summed tornadoes being accounted for by ENSO phase strength (stronger La Niña ~= more U.S. tornado activity).

The weak connection between ENSO cool-phase strength and more tornadoes is maintained, but to a lesser extent, for the Feb-Mar and Mar-Apr bi-monthly tornado events with R² values of 6 percent and 10 percent, respectively. All other bi-monthly periods throughout the year show very little or no correlation with ENSO phase strength indicating that there are clearly many other positive and negative influences that modulate tornado activity in the U.S. through most of the year. (The R², or proportion of variance, value is indicated in the upper left of each chart for each bi-monthly period where the years are ordered by ranked MEI value and is also indicated in the caption for each of these charts.)

It is interesting to note that in a couple of the strongest cool-season El Niño cases (1983 and 1998) tornado events appear to be above average when compared to other cool-season El Niño cases. This is especially true of the strongest El Niño case (based on MEI) of Dec 1982 through Jan 1983 (Dec-Jan right-hand chart below). Thus, a very strong El Niño (ENSO warm phase based on MEI) during the cool season does not necessarily result in weakly suppressed downstream tornado activity in the U.S. but may actually act to support more vigorous larger scale storm systems that, in turn, result in more tornadoes.

For additional information please contact Greg Carbin at SPC.

References:

Wolter, K., and M. S. Timlin, 1998: Measuring the strength of ENSO events – how does 1997/98 rank? Weather, 53, 315-324.

Cook, A.R., and J.T. Schaefer, 2008: The Relation of El Niño – Southern Oscillation (ENSO) to Winter Tornado Outbreaks. Mon. Wea. Rev., 136, 3121-3137.

Jan-Feb (Years in order)	Jan-Feb (Ranked by MEI), R2=12%
Feb-Mar (Years in order)	Feb-Mar (Ranked by MEI), R2=6%
Mar-Apr (Years in order)	Mar-Apr (Ranked by MEI), R2=10%
Apr-May (Years in order)	Apr-May (Ranked by MEI), R2<1%
May-Jun (Years in order)	May-Jun (Ranked by MEI), R2=2%
Jun-Jul (Years in order)	Jun-Jul (Ranked by MEI), R2<1%
Jul-Aug (Years in order)	Jul-Aug (Ranked by MEI), R2<1%
Aug-Sep (Years in order)	Aug-Sep (Ranked by MEI), R2<1%
Sep-Oct (Years in order)	Sep-Oct (Ranked by MEI), R2=1%
Oct-Nov (Years in order)	Oct-Nov (Ranked by MEI), R2<1%
Nov-Dec (Years in order)	Nov-Dec (Ranked by MEI), R2<1%
Dec-Jan (Years in order)	Dec-Jan (Ranked by MEI), R2=12%

That's no storm. That's a wind farm.

It’s time for yet another podcast of… That Weather Show… brought to you by the NOAA Weather Partners.  I’m Angelyn Kolodziej.

It’s always raining near Spearville, Kansas.  At least it appears to be when forecasters like Larry Ruthi look at radar displays.  Turns out, what looks like thunderstorms are actually rotating wind turbine blades.  Wind farms are popping up all over as renewable energy becomes a high priority for the nation.  But when they’re built near existing radars, new challenges arise for the NOAA National Weather Service.

Larry has worked at the Dodge City, Kansas Weather Forecast Office for more than sixteen years and has plenty of experience with this issue.

Wind Farm near Spearville, KS

Ruthi: “A wind farm on radar looks very much like an echo from a precipitation target or a shower or thunderstorm.  As the meteorological target moves through a wind farm – particularly if its close to the radar site – the return from the wind farm and the blades that are rotating around the antennas on the wind farm will mix with the return from the showers of t-storms and its a particular problem if its a severe weather event.”

So what’s causing this?  Let’s step back for a minute and talk about how the radar works.  A signal is sent out into the atmosphere.  That signal bounces off things like raindrops or hailstones and returns to the antenna.  The data are displayed for weather forecasters to use.  But the signal also reflects from non-weather objects like trees and mountains.  By detecting the lack of motion, radar can easily filter out these non-moving objects.  However, wind farms near the radar create a unique challenge.

Ruthi: “The towers don’t move but the blades rotate around the tops of those towers.  It makes it very difficult for our algorithm to filter those out.  So there’s a difficult conundrum that we’re dealing with there in trying to filter out the non-meteorological targets and still retaining as best we can the returns that we have from the meteorological targets.”

Wind turbine blade clutter near Montezuma, Kansas resembles a tornado signature.

The cluttered displays can be misleading for anyone using the radar data.  They can also affect the accuracy of forecasts.  Severe weather events like microbursts and tornadoes are sometimes difficult to identify.  Estimating rainfall in the area is also a challenge.

To work around this issue, forecasters must know the location and characteristics of wind farms on radar.  Scanning at higher altitudes can give forecasters a view above the rotating turbine blades.  Additional cooperative efforts may also be possible.

Ruthi: “There’s perhaps an opportunity to work together with the wind farm owners and managers to perhaps – for a short period of time each year when severe thunderstorms affect the wind farm area – to shut down the turbines that we would then be able to examine uncontaminated returns throughout the entire radar umbrella and make valid decisions without the problem of dealing with the wind farms.”

As for future wind farm development, it’s all about location, location, location.  Research shows wind farm impacts generally decrease the farther they are from the radar.  Reaching out to developers at early planning stages is a key focus for the National Weather Service.  By working together, everyone wins.  Larry and other forecasters have a clear view of the weather.  Developers build wind farms in strategic places.  And the public has access to cheaper, cleaner energy AND the best weather information possible.

Thanks for listening to another podcast of… That Weather Show… brought to you by the NOAA Weather Partners.

For more information about wind farm interaction with NEXRAD radar, visit: http://www.roc.noaa.gov/WSR88D/WindFarm/WindFarm_Index_GreatFalls.aspx?wid=*