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Alaskan heat episode of 16-19 June 2013-Draft
By
Trevor Alcott
National Weather Service Western Region, Salt Lake City UT
And
Richard H. Grumm
National Weather Service State College, PA
Abstract:
A strong 500 hPa ridge developed over Alaska from 15-20 June 2013. The 500 hPa heights
peaked around +4above normal 17-18 June 2013. Despite these relative values, data from the
climate forecast system indicated that the 500 hPa heights were higher than 500 hPa heights
observed in the climate forecast system since 1979. A similar signature was observed in the 700
hPa temperature field. The forecast and observed 700 hPa temperatures were at the extreme tail
of the probability distribution function relative to the climate forecast system.
The resulting large ridge with above normal heights and temperatures lead to a multi-day heat
episode over Alaska. Temperatures at several climate sites exceed 90F on at least 1 day and
several locations had high temperatures in excess of 80F for three consecutive days, a true
Alaskan heat wave.
The results shown here suggest that for extreme events and heat episodes that the traditional
standardized anomaly provides insights into extreme events. However, the skewed distribution of
the height and temperature fields reveals that where a forecast lies relative to the probability
distribution can provide a clearer signal when extreme weather events approach historic levels.
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1. Overview
An unseasonably strong ridge affected Alaska in June 2013. The 500 hPa heights +2 to +4
above normal as the ridge peaked in intensity around mid-June. Beneath the ridge, most of
Alaska experienced a heat episode and several locations set all-time record highs. The
standardized anomalies (Hart and Grumm 2001; Grumm and Hart 2001) provided some insights
into the potential for a heat episode. Standardized anomalies offer clues to potential extreme and
near record events and have proven successful in identifying historic heat waves (Grumm 2011)
and broad range of high impact events (Hart and Grumm 2001; Graham and Grumm 2010).
Over the course of the past 14 years, standardized anomalies have proven useful in aiding
forecasters in distinguishing high-end weather events. Surges of high precipitable water (PW)
accompanied by strong low-level winds with large u- and v-wind anomalies (Stuart and Grumm
2006; Bodner et al 2011;Junker et al 2009;Junker et al 2008;Grumm 2011) have been used to
diagnose and forecast extreme rainfall events. The same concepts have been applied to predicted
heat waves (Grumm 2011) and significant East Coast snow events (Stuart and Grumm 2007).
Many of the aforementioned studies (Graham and Grumm 2011; Hart and Grumm 2001) were
biased toward the larger anomalies being associated with deep troughs and cyclonic events. Due
to the general lack of large anomalies with anticyclones, heat waves and major heat waves rarely
show up in lists of extreme weather events when magnitude of the standardized anomaly is used
to identify large events. This implies that the anomalies associated with high impact weather
events are skewed and biased toward events with deep cyclones or strong pressure gradients.
The European Center for Medium-Range forecasting (ECMWF) developed an extreme forecast
index (EFI: Legg and Mylne 200;Lalaurette 2003). The EFI is based on an internal ensemble
forecast system (EFS) climatology. The emphasis is on when key fields such as model
quantitative precipitation (QPF), winds, temperatures, and other variables depart for the 15-year
ensemble forecast. The data are displayed and the index is computed using the model probability
distribution function (PDF). This facilitates identifying when a parameter is forecast to exceed
the internal model climatology (M-Climate) and thus when the EFS may be forecasting a near
extreme, extreme, or potentially extreme event. Using the comparative PDF the data can identify
record high temperatures and extreme 500 hPa events where the standardized anomaly approach
might not facilitate identifying an extreme event.
This paper examines the heat episode of June 2013 as it affected Alaska. The larger scale pattern
from a standardized anomaly construct is presented. These data clearly show the successful
forecast of the heat episode with 6 days of predictability and a coherent signal of a strong ridge
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Date
New
Record
High
High Tied
Record High
Total
Record
High Max
Tied Record
High max
Total2
Highs over 80
14-Jun
1
0
1
1
0
1
0
15-Jun
0
0
0
1
0
1
0
16-Jun
4
0
4
0
0
0
2
17-Jun
9
1
10
2
2
4
9
18-Jun
6
1
7
3
0
3
8
19-Jun
1
1
2
0
2
2
1
Table 1. NCDC climate data showing record high maximums and record high minimums for June 2013.
Data includes a list of dates in June 2013 and the number of record high maximums and record high
minimums set by date. The highs over 80 value shows the number of sites each day which set new
records had daily maximums of 80F or greater. For each record type the new record and tied records for
the date are shown.
with about 10 days of predictability. A re-analysis based (R-Climate) EFI approach is presented
to show how similar to an M-Climate based EFI, R-Climate based EFI indicated that this was an
extremely anomalous 500 hPa ridge, despite the modest 500 hPa and 700 hPa height and
temperature anomalies respectively. The R-Climate and M-Climate approach offer an
opportunity to improve forecasting synoptically forced high impact events and lend themselves
well to automate decision support systems applications.
2. Methods and Data
The large scale pattern was constructed using the GFS 00-hour forecasts which were compared
to the standardized anomalies for key fields as described by Hart and Grumm (2001). The
pattern with standardized anomalies was extracted from the Climate Forecast System (CFS) and
compared to the 30-year climatology. Gridded forecasts and CFS data relative to standardized
anomalies were plotted using GrADS (Doty and Kinter 1995).
Forecasts from the NCEP Global Ensemble Forecast System (GEFS), Global Forecast System
(GFS), and North American Ensemble Forecast System (NAEFS) were used to examine how
well the heat episode was predicted.
The NAEFS data was plotted against a 30-year climate which included the probability
distribution function (PDF) at each grid point. The relative value of the forecast parameter was
plotted as to its location in the PDF. The displays using the PDF were made using PYGRIB and
only extreme events, in the lower 10th or upper 90th percentile were displayed. In section 6 a
comparison of PDF and standardized anomalies is presented these data were also displayed using
PYGRIB.
The high temperature data for the period of the heat episode were retrieved from the National
Climatic Data Center. These data were plotted and used to produce tables showing the records
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set and locations where temperatures were above 80F. The term heat episode is used here as no
site in Alaska was able to meet the heat wave criteria of 3 consecutive days of 90F or greater
high temperatures.
3. Pattern Overview
The 500 hPa pattern over Alaska and the northeastern Pacific basin (Fig. 1) shows the evolution
of the large 500 hPa ridge over Alaska from 0000 UTC 15 June through 0000 UTC 20 June
2013. The 500 hPa height anomalies peaked 17-18 June when the anomalies were +3 to +4
above normal. The accompanying 850 hPa temperatures (Fig. 2) showed 850 hPa temperatures
too were in the +3 to +4 range, but the peak lagged the 500 hPa height anomalies by about 1
day.
The precipitable water (PW: mm) and PW anomalies showed (Fig. 3) the well-known pattern
associated with heat waves with a plume of high PW air on the west side of the ridge (Grumm
2013). The PW values in west-central Alaska peaked at +3 to +4 above normal at 0000 UTC 16
June (Fig. 3b) and remained above normal through about 0000 UTC 18 June 2013 (Fig. 3d). The
plume of high PW air was associated with a strong 850 hPa southerly jet (not shown) and 250
hPa jet (Fig. 4) over the northern Pacific which brought the high PW air from lower latitudes into
Alaska.
The National Climatic Data Center (NCDC) climate sites in Alaska (Table 1) indicated that the
heat episode peaked between 16-18 June where 4-10 Stations tied or broke record high
temperatures for the date. Fairbanks, Alaska reached 91F on 20 June 2013. Only 2 sites reached
or exceed 90F including Chulitina River on 17 June (Table 2) and Fairbanks on 19 June (not shown).
The high temperatures over much of central Alaska were over 80F on 17 June (Fig. 05). Chulitina River
exceeded 80F on 3 consecutive days (Tables 2-4) including 16, 17, and 18 June 2013 but only exceeded
90F on 17 June. Thus, the term heat episode is used here as the definition of a heat wave includes
3 or more consecutive days of 90F or greater temperatures.
The 500 hPa heights and height anomalies (Fig. 6) and the 850 hPa temperatures and temperature
anomalies (Fig. 7) show that 500 hPa heights peaked over 5820 m over central Alaska during the
period of record heat from 0000 UTC 17 to 1200 UTC 18 June 20131. The 500 hPa height
anomalies peaked in the CFSV2 data in the +3 to +4 range. The 850 hPa temperatures (Fig. 7)
showed the same lag indicated in the 24 hour perspective. The 850 hPa temperatures peaked in
the 18 to 20C range at 0000 UTC though 1200 UTC 18 June 2013 with standardized anomalies
around +4 above normal (Figs. 6d-e).
4. PDF analysis of the event
1
6-hourly data showed a 5820 m high at 1800 UTC 16 June.
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Figure 8 shows the North American Ensemble Forecast System (NAEFS) 500 hPa height
forecasts leading up to the event and the verifying GFS 00-hour forecast. The GFS analysis
indicated that the 500 hPa heights over most of Alaska were extreme end of the CFS 500 hPa
height climatological PDF at 0000 UTC and all time periods in the CFS centered on 18 June.
The analysis clearly shows that the 500 hPa heights analyzed represented a record event relative
to the CFS..
Figure 9 shows the NAEFS 700 hPa temperature forecasts and the verifying GFS 00 hour
forecasts. These data too show that the 700 hPa temperatures at 0000 UTC 18 June were at alltime values for the time of year. Comparing the analysis to the PDF clearly shows this was the
record event at both 500 hPa and 700 hPa in terms of height and temperature extremes
respectively. Relative to traditional anomalies, these data can highlight extreme and in this case,
record events.
5. Ensemble forecasts
NCEP GEFS forecast of 500 hPa heights for 6 forecast cycles valid at 0000 UTC 18 and 0000
UTC 19 June 2013 are shown in Figures 10 & 11. These data show that forecasts of a ridge over
Alaska had at least 7 days of lead-time (Fig. 10a). The convergence of forecasts on a strong ridge
with 500 hPa height anomalies in excess of +3 above normal showed about 5 days of
predictability. An examination of intervening GEFS runs suggested about a 5.5 to +6 day leadtime for a strong ridge (not shown).
Forecasts valid at 0000 UTC 19 June (Fig. 11), the afternoon hours where the highest
temperatures over the widest region were experience, showed a similar success as those valid 24
hours earlier. Once the ridge was established in the GEFS the predictions of its duration were
relatively good and in this timeframe the ridge showed good predictability at least 8 days in
advance and the strong ridge was predicted with about 6-7 days lead-time.
The 850 hPa forecasts for the 0000 UTC 18 June (Fig. 12) and 19 June (Fig. 13) showed similar
skill. The 850 hPa temperature anomalies in several forecasts showed a wide area of +3 to +4
850 hPa temperature anomalies (Figs. 11d-f) and a few areas where the 850 hPa temperature
anomalies were forecast to over +5 above normal. The broad region of over +4 850 hPa
temperature anomalies were in the forecasts valid at 0000 UTC 18 June 2013 too (Fig. 13).
These latter forecasts lacked the extreme areas of over 5 850 hPa temperature anomalies.
6. Forecast relative to the full PDF
The 500 hPa and 700 hPa forecasts from the NAEFS (Figs. 8 & 9) showed the evolution of the
forecast of a record event over 10 days. Due to position and intensity issues in the forecasts, the
500 hPa heights were unable to show a strong signal until about 144 hours prior to the event. It
should be noted that the NAEFS ensemble mean 500 hPa heights at 240 hours forecast the
heights in the 90th percentile relative to the CFS climatology. The 96-hour and shorter forecasts
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all predicted a record event in terms of 500 hPa heights and the GFS analysis verified this record
forecast.
The 700 hPa temperature forecasts showed a similar pattern with the ensemble mean forecast
close to 90th percentile outcome 10 days in advance and a 97.5 percentile event, in the correct
approximate location, about 6 days in advance of what was a record event in terms of 700 hPa
temperatures. The verifying analysis suggests that the 500 hPa height forecasts were more
successful than the 700 hPa temperature forecasts.
An examination of the GFS and GEFS forecasts is used to illustrate the limitations of predicting
record events at longer ranges. The 144 hour GFS and GEFS control forecasts (Fig. 14) show the
differences in the pattern within the two forecasts systems and the GEFS ensemble mean verse
the GFS. The spaghetti plot from all members and the GFS shows the differences. The key to a
successful forecasts of a record event within an ensemble forecast system requires that the
system correctly predicted the feature, the geographic location of the feature, and the intensity of
the feature. In these 144 hour forecasts the GFS and GEFS correctly forecast a ridge over central
Alaska. There were slight differences in the intensity and location of the ridge (lower panel).
These issues in the GEFS and CMC EFS (shown), likely lead to the strong ridge in the NAEFS
over Alaska (Fig. 8) at 144 hours and the heights in the 97 percentile. The 240 hour GEFS (Fig.
15) showed far more uncertainty with the location and intensity of the ridge over central Alaska.
Thus, the NAEFS showed as strong ridge and high end event, though less confidence in the
event.
A comparison of the NAEFS 500 hPa heights and 700 hPa temperatures valid at 0000 UTC 18
June 2013 showing the forecasts of the standardize anomalies and using the PDF illustrates the
power of the PDF in forecasting extreme events. These data show that over portions of central
Alaska, the NAEFS was forecasting both 500 hPa heights (Fig. 18) and 700 hPa temperatures
(Fig. 19) which exceed all the analyzed values in the CFSR since 1979. The NAEFS forecasts,
relative to the CFSR climatology, were predicting an historic event. The standardized anomalies
for this event showed at +3 and +5event for 700 hPa temperatures and 500 hPa heights
respectively.
These data suggest that meteorological data are highly skewed and in ridges and high
temperatures the PDF is better able to identify extreme events than standardized anomalies. The
data also imply that use of climate data relative to ensemble forecasts contains uncertainty
information when the system is forecasting a relative extreme event. The 240 hour forecasts
correctly predicted the ridge and that the heights in the ridge would be in the 90th percentile. Due
to uncertainty, this signal appeared weak, though as the forecasts converged these skewed
forecasts proved to have correctly predicted a strong ridge, underestimating the actual intensity
7. Summary
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A record heat event impacted Alaska in 14-19 June 2013 and the number of record high temperatures tied
or broken peaked on the 17th and 18th of June. The data presented here indicate that the NCEP GEFS and
NAEF when displayed using the 30-year CFS data, forecast a potential extreme weather event. The 500
hPa height, 850 hPa temperature and 700 hPa temperature anomalies all indicated values at least +3
above normal. The CFS and 00-hour GFS data were used to verify the forecasts of the heights and
temperatures within the Alaskan ridge. These data indicated a successful forecast of an anomalously
strong ridge with above normal temperatures.
The GEFS forecasts of the 500 hPa heights and height anomalies (Figs. 10 & 11) along with the forecasts
of the 850 hPa temperatures and temperature anomalies (Fig. 12 & 13) implied that the GEFS correctly
predicted the Alaskan heat episode. The skill in the general pattern at 500 hPa was on the order of 8 days.
The skill in predicting the potential for both 500 hPa heights and 850 hPa temperatures to have anomalies
on the order of +3 to +5 above normal showed considerable lead-time, on the order of 6-7 days. Other
modeling and ensemble prediction systems showed comparable skill at forecasting the strong ridge and
resulting warm episode. This event and the ridge associated with central European heat wave of 2010
(Grumm 2011) suggest that larger scale ridges are relatively well predicted by synoptic scale models and
EFS.
The standardized anomaly approach provided useful insights into the potential for a significant heat
episode over Alaska. When used with EFS data, the standardized anomalies provide at least 6 days of
lead-time for the onset of this record event. However, the standardize anomalies were primarily on the
order of +3 to +4 above normal in the CFS and most EFS’s. Unlike deep cyclones and in regions of
strong gradients, it is often difficult to get large standardized anomalies, much over +4 to +5 in ridges
and features more typically associated with droughts and heat waves. Cases such as this show why an EFI
and more PDF based approach may add information to the forecast, analysis, and decision making
processes associated with extreme synoptic events.
The PDF of the 500 hPa heights from the 42-member NAEFS system (Fig. 8) showed that a broad region
of central Alaska was forecast to experience 500 hPa heights that were higher than the highest 500 hPa
heights in the CFSR over a 30-year period for forecasts of less than 144 hours in duration. Unlike the
standardized anomalies, these data showed that the 500 hPa heights in the NAEFS were forecast to be
higher than observed since 1979. Even at 240 hours, the heights in the NAEFS were forecast to be in the
90th percentile. The signal looked weak relative to the strong and coherent signal in the shorter range
forecasts.
Relative to traditional anomalies, these PDFs of the forecast, relative to the CFS climatology can
help identify the potential for both high impact and record events. This case suggests that
i.
ii.
iii.
iv.
PDF data can show both the potential for a high and a record event.
A weak signal at long ranges, of a high impact event can be useful data.
Higher anomalies and PDFs near the tails in the ensemble mean contain
confidence information when the EFS is forecasting a high-end event.
The lack of a signal at longer ranges does not facilitate knowing weather
uncertainty or conditions close to normal produce the resulting signal.
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v.
Probabilistic displays of the data, such as the probability of 90 (10),95 (5) ,97 (3)
and 100 (0) percent could be of value and would provide additional confidence
information.
The term heat episode is used here as the definition of a heat wave includes 3 or more
consecutive days of 90F or greater temperatures. In Alaska, high temperature of 90F or greater
are difficult to achieve and reaching these values for 3 consecutive days is likely even more
difficult to achieve. Thus, for some regions of the world, based on latitude and altitude a more
generic definition of may be required. Though the extreme heat achieved in Alaska in June 2013
was not likely a high impact heat event relative to heat waves such as the Central European Heat
Wave of 2010 (Grumm 2011) or the 2003 western European heat wave.
8. Acknowledgements
Randy Graham for feedback and information related to rare events.
9. References
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.
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.
Grumm, R.H. 2011: New England Record Maker rain event of 29-30 March 2010. NWA,
Electronic Journal of Operational Meteorology, EJ4.
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., 2011: The Central European and Russian Heat Event of July–August 2010.
Bull. Amer. Meteor. Soc., 92, 1285–1296.
Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synoptic
scale events objectively. Mon. Wea. Rev., 129, 2426–2442.
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.
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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.
Knight, P., J. Ross, B. Root, G.S. Young, R.H. Grumm, 2005: Fingerprinting significant weather events.
Proceedkngs of the Fourth Conference on Artificial Intelligence, San Diego, CA, January 9-13, 2005.
Stuart, N. and R. Grumm 2009, "The Use of Ensemble and Anomaly Data to Anticipate
Extreme Flood Events in the Northeastern United States",NWA Digest,33, 185-202.
Stuart, N. and R. Grumm 2009, "The Use of Ensemble and Anomaly Data to Anticipate Extreme
Flood Events in the Northeastern United States", 33, 185-202.
Stuart,N.A and R.H . Grumm 2007: Using Wind Anomalies to Forecast East Coast Winter
Storms.Wea. and Forecasting, 21,952-968.
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Figure 1. Return to text.
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Figure 2. Return to text.
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Figure 3. Return to text.
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Figure 4. Return to text.
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Figure 5. The 24 hour maximum temperatures (F) from the Pennsylvania State University ewall-site for the period covering
17 June 2013. Return to text.
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Location
CHULITNA
RIVER
GLENNALLEN
KCAM
NORTH POLE
TONSINA
SKAGWAY
COLLEGE
OBSERVATORY
Lat
Lon
POR
Max
Difference
Date
Old record
Date2
62.83
-149.91
32
91.0°F
12
6/17/2013
79.0°F
6/17/1983
62.11
-145.53
38
87.1°F
6.1
6/17/2013
81.0°F
6/17/1986
64.76
61.65
59.45
-147.33
-145.17
-135.31
43
45
59
86.0°F
86.0°F
84.9°F
5
4
2
6/17/2013
6/17/2013
6/17/2013
81.0°F
82.0°F
82.9°F
6/17/2005
6/17/1907
6/17/1948
64.86
-147.85
63
84.0°F
0
6/17/2013
84.0°F
6/17/1986
JUNEAU
58.3
-134.41
33
82.9°F
0.9
6/17/2013
82.0°F
6/17/1967
DOWNTOWN
CRAIG
55.48
-133.14
31
81.0°F
7.1
6/17/2013
73.9°F
6/17/2005
WISEMAN
67.42
-150.11
33
80.1°F
2
6/17/2013
78.1°F
6/17/1948
ELFIN COVE
58.19
-136.34
37
72.0°F
5.1
6/17/2013
66.9°F
1989-0
Table 2. As in Table 1 for daily data for 17 June 2013 including the location, latitude, longitude, the length of the
climate period of record, the attained maximum temperatures, how much the daily maximum exceed the
previous record by and the date of the current and previous records. Return to text.
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Location
Lat
Lon
POR
Max
Difference
NORTH POLE
64.76
-147.33
41
88.0°F
5.1
CHULITNA
62.83
-149.91
32
87.1°F
8.1
RIVER
TONSINA
61.65
-145.17
45
87.1°F
4.2
GLENNALLEN
62.11
-145.53
38
84.9°F
2
KCAM
SKAGWAY
59.45
-135.31
57
84.9°F
0
ALYESKA
60.96
-149.11
39
82.9°F
10.9
WISEMAN
67.42
-150.11
34
82.9°F
0.9
Table 4. As in Table 3 except for 18 June 2013. Return to text.
Location
CHULITNA
RIVER
SKAGWAY
Date
6/18/2013
Old record
82.9°F
Date2
6/18/1996
6/18/2013
79.0°F
6/18/2011
6/18/2013
82.9°F
6/18/2005
6/18/2013
82.9°F
6/18/2005
6/18/2013
6/18/2013
6/18/2013
84.9°F
72.0°F
82.0°F
6/18/1961
6/18/2005
6/18/1948
Lat
Lon
POR
Max
Difference
Date
Old record
Date2
62.83
-149.91
31
89.1°F
9
6/16/2013
80.1°F
6/16/2002
59.45
-135.31
58
87.1°F
2.2
6/16/2013
84.9°F
6/16/2002
6/16/2013
62.1°F
6/16/2006
6/16/2013
69.1°F
6/16/1944
ELFIN
58.19
-136.34
37
78.1°F
16
COVE
CRAIG
55.48
-133.14
31
73.9°F
4.8
Table 3. As in Table 3 except for 16 June 2013. Return to text.
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Figure 6. Return to text.
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Figure 7. Return to text.
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Figure 8. NAEFS forecasts and GFS analysis of 500 hPa heights (m) and the percentile of the 500 hPa verse the climatological
500 hPa height probability density function. Data show NAEFS forecasts at a) 240 hours, b) 192 hours, c) 144 hours, d) 96
hours, f) 48 hours, and d) the verifying GFS 00-hour forecasts. Return to text.
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Figure 9. As in Figure 8 except for 700 hPa temperatures. Return to text.
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Figure 10. GEFS forecasts of 500 hPa heights and 500 hPa height anomalies (shaded) from 6 GEFS forecasts valid at 0000 UTC 18 June 2013 from GEFS
forecasts initialized at a) 1200 UTC 11 June, b) 1200 UTC 13 June, c) 1200 UTC 14 June, d) 1200 UTC 15 June, e) 1200 UTC 16 June, f) 1200 UTC 17
June 2013. Return to text.
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Figure 11. As in Figure 9 except valid at 0000 UTC 19 June 2013. Return to text.
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Figure 12. As in Figure 9 except for GEFS forecasts of 850 hPa temperatures ( C) and 850 hPa temperature anomalies. Return to text.
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Figure 13. As in Figure 9 except valid at 0000 UTC 19 June 2013. Return to text.
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Figure 15.
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Figure 16.
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Figure 15. As in Figure 13 except for 700 hPa temperature and comparative
temperature anomalies. Return to text.
Figure 14. NAEFS 500 hPa heights valid at 0000 UTC 18 June 2013. Upper
panels shows the NAEFS forecasts of 500 hPa heights relative to the 30-year
CFSR data. The lower panels shows the 500 hPa heights with the traditional
standardized anomalies. Return to text.