diagnosing ef scale potential using conditional probabilities

Diagnosing EF Scale Potential Using Conditional Probabilities

Adapted from material and images provided by Bryan Smith,

Rich Thompson, Andy Dean, Dr. Patrick Marsh (affiliations SPC)

Impact-Based Warnings

• “Explore an evolution of the existing NWS warning system to facilitate improved public response and decision making in the most life-threatening weather events.”

• Intended Outcomes:• reframe the warning problem and warning message in terms of societal needs

• In NWS CR in last 5 years… over 3,000 tornadoes have occurred. • 87% of those tornadoes were EF0-1 resulting in 3% of tornado fatalities (all from EF1).• 13% of those tornadoes were EF2-5 resulting in 97% of all tornado fatalities

• increase fidelity of warnings (distinguishing situational urgency by better emphasizing potentially HIGH IMPACT events)

• incrementally improve warning system (can be done within existing structure)

• conduct an initial “proof of concept” (small steps)

2

Overview of Smith, et. al. study (2012, 2014)

• Manual radar analysis– Convective mode assigned using full volumetric WSR-88D archived level II data

at beginning of each tornado event

– Low-level rotational velocity at 0.5° tilt analyzed during life span of tornado (starting one volume scan prior) and peak value recorded

• Near-storm environment– Estimated using archived SPC mesoanalysis data

• Development of conditional tornado probabilities– Box/Whisker diagrams developed that normalize dataset, distinguish between

convective modes, and distinguish between radar range from the target – Initial development of raw probabilities are range and mode independent– Raw probabilities alone are not enough for decision-makers– Normalized probabilities are derived as best fits for operational application– Forecaster expertise continues to play a key role in conceptual application

Key Definitions

– Low-Level Rotational Velocity (Vrot) – taken at the 0.5 degree slice independent of radar range. For example, dataset encompasses 1-101 miles from the radar, or 100 – 10,000 feet Above Radar Level (ARL). Peak values recorded not necessarily gate-to-gate.

– Convective Mode - determined subjectively via examination of radar signatures

– Raw Conditional Probability of Tornado Intensity – probability derived from complete, unfiltered, dataset of Maximum Vrot vs. Tornado Intensity

– Normalized Conditional Probability of Tornado Intensity – probability derived from dataset after filtering outliers and normalizing data distribution across EF scale.

January 2009 – May 2013Tornado segment data filtered by max EF-scale on hourly 40 km horizontal grid

Tornado events < 10,000 ft above radar level (1–101 mi range)

Total number of tornadoes sampled = 4378

EF-scale

Note that the raw dataset is dominated by population of EF0-1 tornadoes (almost 5X more than EF2-5)

Data includes all convective modes and 0.5 degree

samples at all ranges to 101 miles

EF0

EF3

EF2EF1

EF4+

Shaded zones indicate most probable EF scale outcome - conditional on tornado occurrence

Probabilities are based on raw, unfiltered data for the entire sample

* Probabilities are derived by accounting for each tornado (and assigned EF scale intensity) in a 10 kt Vrot bin (e.g.

50-60 kts). The derived probability for each bin is assigned to the mid point of the bin (e.g. 55kts). The total sample

size is 4378 tornadoes.

EF2-5

EF0-1

EF2-3 EF4-5

Same dataset, only within IBW Framework (Base Tier Warnings EF0-1 vs. Enhanced Tier Warnings EF2-5). Threshold where EF2-

5 tornado becomes the most probable outcome is Vrot > 60 kts.

This is very useful information. However, operationally, the use of raw probability does not tell the whole story.

EF-scale

This is because the distribution across EF scale is non-normal and is weighted toward the EF0-1 population

Standard Box and Whisker plots. Whisker tips represent 10th

and 90th

percentiles, while boxes are bounded by 1st

and 3rd

quartiles, and dash in the middle is the median

value or 2nd

quartile.

Note that ~80% of the EF2 population, and ~40% of the EF3 population, fall below the

raw conditional 60 kt threshold for EF2+ tornadoes. We need to capture more of these

events.

Instead, we can normalize the dataset by equally weighting each EF-scale bin (as in the

diagrams above) and filter the sample “outliers” outside the tips of the whiskers.

5 kt 15 kt 25 kt 35 kt 45 kt 55 kt 65 kt 75 kt 85 kt 95 kt 105 kt0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

EF4+EF2-3EF0-1

EF2-5EF0-1

EF2-3

EF4+

Using the normalized and filtered data set, we can derive a set of “Normalized” probabilities for conditional tornado intensity. The “normalized”

threshold where a EF2-5 tornado is the most probable outcome is 45 kts (conditional on tornado occurrence).

Standard Box and Whisker plots. Whisker tips represent 10th

and 90th

percentiles, while boxes are bounded by 1st

and 3rd

quartiles, and dash in the middle is the median value

or 2nd

quartile.

For the Filtered Population…

45 kt threshold captures 100% of EF3+ tornadoes and ~75% of the EF2

population.

However, it also captures ~7% of the EF0 population and ~23% of the EF1

population.

EF-scale

In both the scatter plots and box/whisker diagrams there is significant overlap of EF1 and EF2 tornadoes between our normalized 45 kt and raw 60 kt decision thresholds. Because of this, a clean threshold is unattainable. This is

where forecaster expertise becomes most important in the warning decision process.

Now applied to the non-filtered data set:

The 45 kt threshold captures over 92% of EF3+ tornadoes and nearly

67% of the EF2 population.

And also captures ~10% of the EF0 population and ~33% of EF1

tornadoes.

Operational Application

1) Use your situational awareness of the

mesoscale and near-storm environments.

2) Use your understanding of convective modes.

3) Use your understanding of the character of the

low level circulation.

4) Use your understanding of raw and normalized

probabilities of conditional tornado intensity.

Character of Low Level Circulation

Consideration of Convective Mode

Use raw and normalized probabilities of

conditional tornado intensity

Understand mesoscale and near-storm

environment

1 2

3

Diagnosing/Anticipating the Range of Possibilities

4

Diagnose and Anticipate Most Probable Category of

Tornado Intensity (EF0-1 vs EF2-5)

Operational Forecasting Application



a) Examine CAPE/Shear relationships for

environments favorable for supercell

development.

b) Examine SPC mesoscale analysis for

environments favorable for larger tornadoes

(e.g. Sig TOR Parameter – STP).

c) Be aware of low level boundaries conducive for

rapid tilting and/or stretching of local vorticity

maxima…. and LCL heights for estimates of

cloud base.


Neighborhood max value (dark bounded B/W plot)

vs.

grid value (gray shaded B/W plot)

Neighborhood value STP = within 185 km radius,

Grid value STP= within 40km x 40 km grid space

STP vs. EF-scale


2) Use your understanding of convective mode

a) RM Supercells are most likely to produce tornadoes

that require enhanced tags.

b) QLCS storms that produce significant tornadoes

appear to do so with lower Vrot thresholds than RM

Supercells. (possibly due to enhanced forward

motion vector contributions on right flanks of low

level circulations).

c) Circulations in disorganized convection are unlikely

to produce significant tornadoes that need

enhanced tornado tags.





a) Anticipate how convergent low level circulations will

behave given the near-storm environment.

b) Be cognizant of radar range from the target. For

close-in storms be sure to sample as close to the

cloud base as possible for storms that are not yet

tornadic. Use the 0.9 slice if necessary.

c) Study uses both broad Vrot maxima and Gate-to-

Gate Vrot maxima, depending on which is strongest

for a given case. Gate-to-Gate Vrot maxima should

operationally command more weight and a lower

Vrot threshold for EF2+ events.


1933Z 0.5

slice

1933Z 4.0

slice

Example of a 0.5 degree convergent rotation below a broad 4.0 degree

rotating mesocyclone. Prominent BWER evident in the lower right. This

storm is intensifying and will soon produce a

tight GTG low level circulation and eventually an EF4 tornado.




a) Keep in mind these are conditional probabilities, but also

remember that lead time is important and use as many tools

as possible to help anticipate tornado occurrence and

potential intensity. You do not have to wait for a report of a

tornado before issuing a “CONSIDERABLE DAMAGE

THREAT” tag.

b) Once a decision is made that a tornado is likely, use the Vrot

threshold of 45 knots as the initial point where you should

start seriously thinking about a “CONSIDERABLE DAMAGE

THREAT” tag.

c) Use the Vrot threshold of 60 knots as the point where you

should definitely issue a “CONSIDERABLE DAMAGE

THREAT” tag.


d) For warning decisions between these conditional thresholds, forecaster judgment should be exercise

based on your knowledge of 1) near-storm environment, 2) convective mode, 3) character and evolution

of the low level circulation.

Re-Capping: Operational Application



2) Use your understanding of convective modes.





Character of Low Level Circulation

Consideration of Convective Mode

Use raw and normalized probabilities of

conditional tornado intensity

Understand mesoscale and near-storm

environment

1 2

3


4

Diagnose and Anticipate Most Probable Category of

Tornado Intensity (EF0-1 vs EF2-5)

Recently published work

Wea. Forecasting (2012)– Demonstrated a relationship between environment, convective

mode, mesocyclone strength, and tornado damage intensity

EJSSM (2013)

– Displayed spatial distributions of supercell-related parameters

Wea. Forecasting (2013)

– Tornado warning performance (POD and lead-time) related to convective mode and supercell-related parameters

The following slides show some snapshots of

recent significant tornadoes.

(slides courtesy of Rich Thompson, SPC).

Vrot = 84.5 kt

Max grid STP = 13.1

Outlook = SLGT 5%

Watch = TOR

EF3 damage (17JUL2011)

Vrot = 40.8

Max grid STP = 1.9

Outlook = SLGT 5%

Watch = SVR

EF2 damage (20JUN2010)

Vrot = 46.7

Max grid STP = 3.6

Outlook = SLGT 10%

Watch = TOR


Vrot = 81.6

Max grid STP = 4.6

Outlook = MDT 10% SIG

Watch = TOR

EF4 damage (19MAY2013)

Vrot = 69.0

Max grid STP = 5.0

Outlook = SLGT 10%

Watch = TOR

EF2 damage (22MAR2011)

Vrot = 51.5

Max grid STP = 1.2

Outlook = SLGT < 2%

Watch = SVR


Vrot = 73.3

Max grid STP = 6.6

Outlook = MDT 5%

Watch = SVR


Vrot = 76.8

Max grid STP = 0.4

Outlook = SLGT 5%

Watch = TOR

EF3 damage (27JUL2010)

Vrot = 69.5

Max grid STP = 7.8


Watch = PDS TOR

EF3 damage (10APR2011)

Vrot = 88.9

Max grid STP = 10.9

Outlook = HIGH 30% SIG

Watch = PDS TOR


Vrot = 108.5

Max grid STP = 6.1


Watch = PDS TOR


Vrot = 54.4

Max grid STP = 5.8

Outlook = SLGT 5%

Watch = TOR

EF3 damage (12AUG2011)

Vrot = 81.6

Max grid STP = 5.5


Watch = PDS TOR


Vrot = 84.8

Max grid STP = 8.8


Watch = TOR


Vrot = 70.7

Max grid STP = 7.0


Watch = PDS TOR


Vrot = 47.6

Max grid STP = 4.5

Outlook = SLGT 10% SIG

Watch = TOR


Vrot = 62.7

Max grid STP = 14.2

Outlook = MDT 10%

Watch = TOR


Vrot = 69.5

Max grid STP = 3.9

Outlook = SLGT 5%

Watch = TOR

EF4 damage (29FEB2012)

Vrot = 65.6

Max grid STP = 3.6

Outlook = SLGT 2%

Watch = SVR


Vrot = 99.0

Max grid STP = 7.8


Watch = TOR


Vrot = 84.0

Max grid STP = 9.6

Outlook = SLGT 5%

Watch = TOR


Vrot = 94.7

Max grid STP = 0.8

Outlook = SLGT 2%

Watch = SVR


Vrot = 93.8

Max grid STP = 6.0


Watch = TOR


Vrot = 73.9

Max grid STP = 6.0


Watch = PDS TOR


diagnosing ef scale potential using conditional probabilities

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