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Strong Synoptic System Type Severe and Tornadic Event
17 November 2013
value of anomalies for situational awareness
By
Richard H. Grumm
National Weather Service State College, PA
Trevor Alcott
National Weather Service, Salt Lake City UT
1. Overview
A strong mid-tropospheric trough
(Fig. 1) raced across the central and
United States from 17 November
through 18 November 2013. The
fast moving short-wave was
associated with a strong 250 hPa jet
stream which had a nearly textbook
severe pattern (Fig. 1 & Fig. 6a:
Uccellini and Johnson 1979) over
the Mid-Mississippi and Ohio
Valley from 1200 UTC to 0000
UTC on 17 November 2013 (Figs.
2b-c). The flow between the deep
trough and the strong ridge over the
eastern Gulf of Mexico (Fig. 1)
Figure 3. Storm Prediction Storm reports for 17 November 2013. Data are color
resulted in a surge of warm moisture coded by event type. Courtesy of the storm predictions Center.
rich air and strong low-level flow
which produced over 580 reports of severe weather (Fig. 3) on 17 November and into early 18
November 2013.
Similar to the severe event of 1 November 2013, this case also illustrates the power of
using standardized anomalies to identify potential high impact weather events (HIWE).
Numerous studies over the past decade have shown the value of using standardized
anomalies to predict HIWEs. Grumm and Hart (2001), Hart and Grumm (2001), Stuart and
Grumm (2006), and Graham and Grumm (2010) have all shown the value of using
standardized anomalies to identify a wide range of HIWEs. The method is effective at
identifying when the pattern as predicted by a forecast system departs significantly from
normal. Though the climate data used to compute the standardized anomalies approximate
a normal distribution, the data are not normally distributed. This is a limitation in the data
that requires additional information to identify some of the extreme weather events.
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The success of the method of using standardized anomalies to identify HIWE lead to the
concept of a situational awareness table (Graham et al. 2013). In the table, key anomalies
by parameter, such as height (Z), temperature (T), moisture (q or PW), wind speed (V), and
wind components (u- and v-) were displayed and color coded to draw the forecasters
attention to the potential of extreme events. A complete list of the variables and levels the
anomalies are computed at are listed in Table 1 (Graham et al. 2013). These data were
then used to show how the historic western United States storm of January 2010 was
predicted. In addition to providing visual alerts, these data provide good input to software
to produce messages to alert users of HIWEs.
This paper will document the strong severe weather event of 17-18 November 2013. The
focus on the impacts is related to the traditional use of standardized anomalies and some of
the limitations in using these data to identify extremely anomalous stations. The concept of
using return periods and percentiles to compliment standardized anomalies is introduces as
a more advanced means to assess the potential of a HIWE. With over 580 reports of severe
weather and over 400 events in the Midwest, this was by far one of the largest November
severe events in recent history (Table 1).
2. Data and Methods
The large scale pattern was reconstructed using the 00-hour forecast of the NCEP Global
Forecast System (GFS) as first guess at the verifying pattern. The standardized anomalies were
computed in Hart and Grumm (2001). All data were displayed using GrADS (Doty and Kinter
1995). The standardized anomalies and the probability distribution functions are based on the reanalysis climate (R-Climate). Though not shown here, they could be produced from internal
model system climatologies (M-Climate).
The traditional standardized anomalies were produced from the GFS 00-hour forecast using
Climate Forecast System based means and standard deviations. The climatology spans a 30 year
period. This 30-year period was used in the situational awareness (SA) tables to show the return
period of the anomalies.
The forecasts in the SA tables were derived from the 42 member North American Ensemble
Forecast System (NAEFS). These data are displayed using the NAEFS mean fields relative to the
climate. In addition to standardized anomalies, the return period and interval of the anomalies are
produced relative to the 21-day centered values. The return period is in years such that a 30-year
return period represents the extreme in the climatological data set. The percentile data represent
where along the climatological probability distribution the current NAEFS forecast lies.
Extremes in the tails would be in the 97 to 100 percentile (MAX) or in the 3 to 0th percentile
(MIN). In addition to finding the MAX and MIN relative to the time, the MAX and MIN are
computed using the 4 climatological times of 00, 06, 12, and 18 UTC. This provides the ability
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to estimate the MAX (MIN) at the specified time or for all 4 times in the same 21-day centered
climatological window.
Storm reports were gathered from the Storm Prediction Center (SPC) and using the local archive
at the National Weather Service Office in State College, PA. These latter data were used to
produce Table 1. The data were filtered by date and region to compare this event focused over
the Mid-Mississippi and Ohio Valleys (MMV and OHV) to previous events.
3. Pattern over the region
The large scale pattern as analyzed by the GFF 00-hour forecasts (Fig. 1) showed the deep 500
hPa trough which rapidly moved across the central United States from 16/0000 through 18/1200
UTC. The 500 hPa height anomalies were on the order of -1s below normal. This trough
combined with the positive height anomalies over the Gulf of Mexico produced a strong gradient
between the two systems resulting in the strong 250 hPa jet with +1 to +2s above normal winds
in the jet as it swept through the MMV on 17 November 2013 (Fig. 2).
The strong southerly flow between the two upper-level systems opened the Gulf of Mexico
allowing a flow of warm air and deep moisture (Fig. 4) into the region; the fuel for convection.
The airmass was unseasonably warm and moist just ahead of the upper-level short-wave was
accompanied by a strong 850 hPa jet (Fig. 5) and had a surge of CAPE with CAPE in the 600 to
1200JKg-1 range. CAPE is close to 0 during November so these numbers are quite impressive
and most likely represent values well at the extreme tails of CAPE climatology for midNovember.
4. Forecasts and standardized anomalies to identify extreme events
Tabular data of the anomalies as forecast from the NAEFS showed the potential for a surge of
strong winds from 850 to 250 hPa, high PW air, and a deep trough with several days lead-time
(Figs. 7 & 8). Figure 7 shows the standardized anomalies for each variable while Figure 8 shows
the return period of the anomalies in years, based on a 30-year base climatology. The wind and
moisture anomalies for 17 November over the eastern United States were forecast to have return
periods of 5-10 years from forecasts initialized at 14/0000 UTC. The return periods increased
dramatically for the moisture field (Q) and wind fields (V) from forecasts issued at 15/0000
UTC.
The larger anomalies on 15 November (Fig. 8) relative to those on 14 November (Figs. 7 ) show
the impact of averaging 42 forecasts and how the ensemble mean sharpens as the forecasts
converge toward a solution. The NAEFS meridional winds from 15 November valid at 1800
UTC 16 November show the strong 250 hPa winds and the strong low-level jet over the central
United States (Fig. 9).
5. Summary
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Surges of deep warm moist air can produce conditions in November more similar to the warm
season. When conditions with strong winds, strong shear, and relatively high CAPE are present
they can and they do produce late season severe events. The conditions on 17 November in terms
of the 250 hPa jet (Fig. 2), the 850 hPa jet (Fig. 5), the PW (Fig. 4) and the CAPE (Fig. 6)
suggest this was an excellent set up for a late season severe weather event. The pattern was
similar to the jet pattern and convergence of ingredients outlined by Uccellini and Johnson
(1979), with the added benefit of knowledge related to how strong the forecast jet and moisture
plume were relative to climatology. These additional climatological data can aid in anticipating
high impact weather events (HIWE). Related to severe weather, this additional information may
aid in identifying meteorologically and climatologically significant events.
The future of forecasting HIWE and automated procedures to produce alerts of such events will
improve as the R-Climate and M-Climate probability distribution functions are employed. These
data should improve identifying when the modeling system, relative to climate is producing a
high end event. The use of M-Climate data may aid in identifying when the model is predicting
an extreme event it the modeling system atmosphere, which is often indicative of the potential
for a record event in the real (observed) atmosphere.
6. Acknowledgements
The Pennsylvania State University for gridded data access and the Mid-Atlantic River forecasts
and Trevor Alcott for return period data and images.
7. References
Associated Press, 2013a: More than 80 evacuated in Louisville floods and similar stories 6-7
October 2013.
Associated Press, 2013b: South Dakota: Cattle Loss from Blizzard Estimated above 10,000 and
similar stories 6 to 14 October 2013.
Bodner, M. J., N. W. Junker, R. H. Grumm, and R. S. Schumacher, 2011: Comparison of
atmospheric circulation patterns during the 2008 and 1993 historic Midwest floods. Natl. Wea.
Dig., 35, 103-119. [Abstract]
Coniglio, M. C., D. J. Stensrud, and M. B. Richman, 2004: An observational study of derecho-producing
convective systems. Wea. Forecasting, 19, 320-337.
Duke, J. W., and J. A. Rogash, 1992: Multiscale review of the development and early evolution of the 9
April 1991 derecho. Wea. Forecasting, 7, 623-634.
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Doty, B.E. and J.L. Kinter III, 1995: Geophysical Data Analysis and Visualization using GrADS.
Visualization Techniques in Space and Atmospheric Sciences, eds. E.P. Szuszczewicz and J.H.
Bredekamp, NASA, Washington, D.C., 209-219.
Graham, R.A, T. Alcott, N. Hosenfeld, and R. Grumm, 2013: Anticipating a rare event utilizing
forecast anomalies and a situational awareness display: The Western Region US Storms of 18-23
January 2010, BAMS, 2013.
Graham, R A. and R. H. Grumm, 2010: Utilizing Normalized Anomalies to Assess Synoptic-Scale
Weather Events in the Western United States. Wea. Forecasting, 25, 428-445.
Grumm, R.H. and R. Hart. 2001: Standardized Anomalies Applied to Significant Cold Season
Weather Events: Preliminary Findings. Wea. and Fore., 16,736–754.
Grumm, Richard H., 2011a: The Central European and Russian Heat Event of July–August 2010.
Bull. Amer. Meteor. Soc., 92, 1285–1296.
Grumm, R.H. (2011b) New England Record Maker rain event of 29-30 March 2010. NWA. Electronic
Journal of Operational Meteorology, EJ4 12: 1-31.
Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synoptic
scale events objectively. Mon. Wea. Rev., 129, 2426–2442.
Hinrichs, G., 1888: Tornadoes and derechoes. Amer. Meteor. J., 5, 306-317, 341-349.
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rainbands. J. Atmos. Sci, 39,280-295.
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clouds and precipitation in mid-latitude cyclones. Part I: A case study of a cold front; J. Atmos. Sci., Vol.
37, p. 568 – 596.
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Forecasting, 2, 32-49.
Junker, N.W, M.J.Brennan, F. Pereira,M.J.Bodner,and R.H. Grumm, 2009:Assessing the Potential
for Rare Precipitation Events with Standardized Anomalies and Ensemble Guidance at the
Hydrometeorological Prediction Center. Bulletin of the American Meteorological Society,4 Article:
pp. 445–453.
Junker, N. W., R. H. Grumm, R. Hart, L. F. Bosart, K. M. Bell, and F. J. Pereira, 2008: Use of
standardized anomaly fields to anticipate extreme rainfall in the mountains of northern California.
Wea. Forecasting,23, 336–356.
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Lin, Y. and K.E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: development
and applications. Preprints, AMS 19th Conference on Hydrology, San Diego, CA. Paper 1.2.
Maddox,R.A., C.F Chappell, and L.R. Hoxit. 1979: Synoptic and meso-alpha aspects of flash
flood events. Bull. Amer. Meteor. Soc., 60, 115-123.
Matejka, T.J, R.A. Houze, and P.V. Hobbs: 1980: Microphysics and dynamics of clouds associated
with mesoscale rainbands in extratropical cyclones. Quart. J. R. Met Soc., 106,29-26.
RUTLEDGE S. A. and HOBBS P. V. (1983): The mesoscale and microscale structure and organization of
clouds and precipitation in mid-latitude cyclones. Part XII: A diagnostic modeling study of precipitation
development in narrow cold-frontal rainbands; J. Atmos. Sci., Vol. 41, p. 2949 - 2972
Stuart, N.A and R.H. Grumm 2006: Using Wind Anomalies to Forecast East Coast Winter Storms.
Wea. and Forecasting,21,952-968.
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implications for the development of severe convective storms. MWR,107,682-703
Storm Reports
434
277
252
225
216
209
111
91
66
65
52
51
50
43
41
Date
11/17/2013
11/10/2002
11/10/1998
11/6/2005
11/9/2000
11/11/2002
11/15/2005
11/7/1996
11/15/1989
11/14/2011
11/30/1991
11/16/2005
11/10/1975
11/16/1988
11/22/2010
Table 1. List of severe events over the central US by event dates
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Figure 1. GFS 00-hour forecasts of 500 hPa heights (m) and and standardized anomalies (shaded) in12 hour increments from a) 0000 UTC 16
November 2013 through f) 1200 UTC 18 November 2013. Return to text.
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Figure 2. As in Figure 1 except for 250 hPa winds and total wind anomalies. Return to text.
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Figure 4. As in Figure 1 except for GFS 00-hour forecasts of precipitable water (mm) and precipitable water anomalies from a) 0600 UTC 17 to f) 1200 UTC 18
November 2013. Return to text.
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Figure 5. As in Figure 4 except for 850 hPa total wind and total wind anomalies. Return to text.
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Figure 6. As in Figure 5 except for GFS 0-hour forecast of CAPE (JKg-1) contours every 600JKg-1 and shaded as in the color bar. Return to text.
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Figure 7. The situational awareness tables from the NAEFS initialized at 0000 UTC 14 November 2013. For each time and variable the standardized
anomalies are shown The largest (by absolute value) anomaly is shown for each variable. Most variables include several pressure levels with the
exception of sea-level pressure (SLP), precipitable water (PW) and integrated vertical water transport (IVT) . Return to text.
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Figure 8. As in Figure 7 except for the return interval in years for each variable. Return to text.
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Figure 9. NAEFS winds and wind return periods in days valid at 1800 UTC 17 November showing the winds showing the winds at each
pressure level and the departure of this winds from normal in days. Return to text.