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

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

Two parts of the process 11/11/2018 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

Defining standardized anomalies 11/11/2018 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

SD = (F – C)/ s We can compute this from Power from ensembles 11/11/2018 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

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 30-31 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? 11/11/2018

11/11/2018 New England March 2010

11/11/2018 The anomalous winds

11/11/2018 5 may 2010 Nashville FLoods

May 2010 v-winds and anomalies 11/11/2018 May 2010 v-winds and anomalies

July 2010 Pakistani floods Composite anomalies For 28-30 July 2010 11/11/2018 July 2010 Pakistani floods Figure . As in Figure 4 except valid 0000 UTC 28-30 July 2010. Composite anomalies For 28-30 July 2010

PW anomalies and table record PW 11/11/2018 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.

11/11/2018 850 hpa wind anomalies

11/11/2018 September 2010

September 2010 rain and floods 11/11/2018 September 2010 rain and floods

Threats: probabilities and patterns 11/11/2018 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

11/11/2018 Accumulated rainfall

11/11/2018 Accumulate rainfall

Meteorologoical context key parms 11/11/2018 Meteorologoical context key parms

Anomaly perspective key fields 11/11/2018 Anomaly perspective key fields

Memphis flood- 30 APR SREF qpf 11/11/2018 Memphis flood- 30 APR SREF qpf

11/11/2018 Memphis anomalies

11/11/2018 MEMPHIS 01 MAY SREF

11/11/2018 01 May qpf

Southern New England flood-QPF 11/11/2018 Southern New England flood-QPF

Probabilities of key anomalies 11/11/2018 Probabilities of key anomalies

Other quick examples Moscow record heat-July 2010 11/11/2018 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

11/11/2018 Moscow heat 101f 29 July 2010

101F Moscow high confidence anomalies 11/11/2018 101F Moscow high confidence anomalies

Other quick examples-II 11/11/2018 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”

Anomalies with a record cyclone 11/11/2018 Anomalies with a record cyclone Storm lacked deep height and thermal anomalies.

11/11/2018 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.

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 30-31 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.