1. Richard Grumm National Weather Service State College PA
Standardized anomalies As a means to identify high impact weather events
Richard Grumm
National Weather Service
State College PA

2. 11/11/2018
Introduction
Standardized anomalies and probabilities of standardized anomalies aid in producing forecasts of significant weather events in these examples heavy rainfall
This is of value in determining the threat and assigning confidence in the threat
We can leverage this information
Communicate in decision making
Provide confidence in the event type
And the general area to be impacted
What are standardized anomalies?
Standardized anomalies and Flooding
March 2010 (RI)  Standardized anomalies were there
May 2010 (TN)  Standardized anomalies were there
July 2010 (Pakistan) Standardized anomalies were there
Sept 2010 (PA)  yep, there too!
Like a loyal friend they help us define critical pattern
The next step is to leverage the probabilities

3. Two parts of the process
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Two parts of the process
Communicating concisely the important points
What are the most likely outcomes and the most likely impacts.
Too often we drop the ball here, mired in details
The science or nuts-bolts
Too often we get lost in these details and miss the point
Okay, some nuts and bolts

4. Defining standardized anomalies
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Defining standardized anomalies
We need several things:
The mean value (C )of each field (u,v,h,PW,MSLP)
The standard deviation of each fields (s)
And the instantaneous value of the field (F)
Thus standardized value is simply
SD = (currentValue - climateValue)/s
SD = (F – C)/ s

5. SD = (F – C)/ s We can compute this from Power from ensembles
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SD = (F – C)/ s
We can compute this from
Re-analysis data for past big/historic events
A single model
An ensemble mean
Power from ensembles
Probabilities of key areas of large SD
Relate back to fields/parameters associated with critical high impact weather
Providing us a Threat Assessment.
Threat assessment in a sense relative to climatology a gauged outcome.
Note the data are not normally distributed and though they show the potential impact/significance the changing climate can play a role in the underlying outcomes.
The real power is in ensembles to get probabilities

6. Standardized anomalies Recent floods
Historic floods like New England March 2010
Grand Ole Memphis May 2010 to string you along some more
The devastating Pakistani floods of Jul-Aug 2010.
September record rain eastern USA
Re-analysis looks
we will use March
Model forecast looks
00-hour forecasts 5 May 2010
Ensemble looks
several cases
Threats looks
Multiple events
includes probability of precipitation
Would a probability of precipitation relative to climatology be useful?
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New England March 2010

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The anomalous winds

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5 may 2010 Nashville FLoods

10. May 2010 v-winds and anomalies
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May 2010 v-winds and anomalies

11. July 2010 Pakistani floods Composite anomalies For 28-30 July 2010
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July 2010 Pakistani floods
Figure . As in Figure 4 except valid 0000 UTC July 2010.
Composite anomalies
For July 2010

12. PW anomalies and table record PW
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PW anomalies and table record PW
Date
Standardized anomaly
Value (mm)
00Z21JUL2010
3.78
71.10
18Z20JUL2010
4.07
70.50
06Z21JUL2010
3.27
68.30
06Z03AUG1953
3.14
66.90
06Z12JUL1953
3.09
66.80
06Z06AUG2010
3.00
65.30
06Z01AUG1976
2.78
65.00
06Z27JUL1966
2.76
64.80
06Z05JUL1988
3.04
64.60
06Z27AUG1997
3.52
64.50
06Z26JUN1980
3.57
64.40
12Z01AUG1976
3.24
64.30
00Z10JUL1960
2.91
64.00
00Z11JUL1960
2.84
63.90
00Z12JUL1953
00Z16JUL1958
06Z24JUL2001
2.67
18Z02JUL1983
3.40
63.80
00Z03JUL1983
63.60
18Z01AUG1976
63.50
Table 1. Top 20 highest precipitable water values at Islamabad. Data include the date, the standardized anomaly and the value of the precipitable water from the Global Re-analysis. Values over 70 mm are shaded in yellow. Return to text.

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850 hpa wind anomalies

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September 2010

15. September 2010 rain and floods
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September 2010 rain and floods

16. Threats: probabilities and patterns
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Threats: probabilities and patterns
The patterns are simple repeatable and thus of value to us.
The critical thing is to Leverage this
With probabilities of key fields to attach confidence in a significant event
Tie in QPF probabilities in events like this
To confidently anticipate an meteorologically and climatologically significant event.
Leverage EFS data and key parameters
In this case floods low level winds and PW are helpful.
To focus attention on key forecast problems fast
Highlight probability of exceedance
PW/WINDS/MSLP other fields
Use with probability of PoP for key thresholds

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Accumulated rainfall

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Accumulate rainfall

19. Meteorologoical context key parms
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Meteorologoical context key parms

20. Anomaly perspective key fields
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Anomaly perspective key fields

21. Memphis flood- 30 APR SREF qpf
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Memphis flood- 30 APR SREF qpf

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Memphis anomalies

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MEMPHIS 01 MAY SREF

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01 May qpf

25. Southern New England flood-QPF
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Southern New England flood-QPF

26. Probabilities of key anomalies
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Probabilities of key anomalies

27. Other quick examples Moscow record heat-July 2010
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Other quick examples
Moscow record heat-July 2010
Large height and 850 hPa temperature anomalies
Plume of high PW air about the ridge
Anomalies signal
Historic Mid-western Storm October 2010

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Moscow heat 101f 29 July 2010

29. 101F Moscow high confidence anomalies
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101F Moscow high confidence anomalies

30. Other quick examples-II
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Other quick examples-II
Historic Mid-western Storm October 2010
We can define anomalies to get at
Potential record low pressure
High winds about the deep cyclone
And the surge of moisture.
Show me the cold or is it the “money”

31. Anomalies with a record cyclone
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Anomalies with a record cyclone
Storm lacked deep height and thermal anomalies.

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Review
Standardized anomalies and probabilities facilitate quick identification of significant weather events
We need to leverage the probabilities
Make better threat assessments graphics, provide confidence and probabilistic decision inputs to users
Conveying the information is as important recognizing the threat.

33. 11/11/2018
Key thesis of this talk
The role of standardized anomalies in identifying the potential for high impact weather events is presented. Four historic rainfall events including the March 2010 New England Floods, the 5 May 2010 Nashville Floods, the July 2010 Pakistani Floods and the 30 September 2010 Mid-Atlantic heavy rainfall event are presented using re-analysis data. In addition to these rainfall events, the East Coast Heat wave of July 2010 and the Great Russian heat wave of July-August 2010 are presented from a standardized anomaly perspective. Each event was associated with significant standardized anomalies in key parameters forecasters often use to identify such events. Quantifying the standardized anomalies facilitates quick assessment of the potential impact of these events. Two to 3 standard deviations in the precipitable water field combined with 3 to 4 standard deviations in the wind fields often aid in quickly identifying the potential for significant heavy rainfall events. Heat events are often characterized by 1 to 2 standard deviation above normal mid-tropospheric heights and 2 to 3 standard deviation above normal low to mid-level tropospheric temperatures. A new method of displaying ensemble data is presented. This display method uses the probability distribution function (PDF) of ensemble forecasts of key standardized anomaly fields. The PDF information can facilitate the quick identification of the potential for high impact events. For heavy rainfall events, these PDF data can be used to tie the potential high impact pattern back to the high probability forecasts of heavy rainfall.