Nonparametric Estimation of Conditional Transition Probabilities in a ...
Diagnosing EF Scale Potential Using Conditional Probabilities
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Transcript of 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)
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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
1) Use your situational awareness of the
mesoscale and near-storm environments.
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.
Diagnosing/Anticipating the Range of Possibilities
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
Operational Application
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.
Diagnosing/Anticipating the Range of Possibilities
Operational Application
3) Use your understanding of the character of the
low level circulation.
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.
Diagnosing/Anticipating the Range of Possibilities
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.
Operational Application
4) Use your understanding of raw and normalized
probabilities of conditional tornado intensity.
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.
Diagnosing/Anticipating the Range of Possibilities
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
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)
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
EF2 damage (5JUN2009)
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
EF3 damage (15MAR2012)
Vrot = 73.3
Max grid STP = 6.6
Outlook = MDT 5%
Watch = SVR
EF4 damage (26JUN2010)
Vrot = 76.8
Max grid STP = 0.4
Outlook = SLGT 5%
Watch = TOR
EF3 damage (27JUL2010)
Vrot = 69.5
Max grid STP = 7.8
Outlook = MDT 15% SIG
Watch = PDS TOR
EF3 damage (10APR2011)
Vrot = 88.9
Max grid STP = 10.9
Outlook = HIGH 30% SIG
Watch = PDS TOR
EF3 damage (15APR2012)
Vrot = 108.5
Max grid STP = 6.1
Outlook = HIGH 30% SIG
Watch = PDS TOR
EF3 damage (2MAR2012)
Vrot = 54.4
Max grid STP = 5.8
Outlook = SLGT 5%
Watch = TOR
EF3 damage (12AUG2011)
Vrot = 81.6
Max grid STP = 5.5
Outlook = HIGH 30% SIG
Watch = PDS TOR
EF4 damage (2MAR2012)
Vrot = 84.8
Max grid STP = 8.8
Outlook = MDT 10% SIG
Watch = TOR
EF4 damage (17JUN2010)
Vrot = 70.7
Max grid STP = 7.0
Outlook = MDT 15% SIG
Watch = PDS TOR
EF2 damage (11APR2011)
Vrot = 47.6
Max grid STP = 4.5
Outlook = SLGT 10% SIG
Watch = TOR
EF4 damage (17JUN2010)
Vrot = 62.7
Max grid STP = 14.2
Outlook = MDT 10%
Watch = TOR
EF3 damage (10APR2011)
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
EF2 damage (27APR2012)
Vrot = 99.0
Max grid STP = 7.8
Outlook = MDT 10% SIG
Watch = TOR
EF5 damage (22MAY2011)
Vrot = 84.0
Max grid STP = 9.6
Outlook = SLGT 5%
Watch = TOR
EF3 damage (28MAY2013)
Vrot = 94.7
Max grid STP = 0.8
Outlook = SLGT 2%
Watch = SVR
EF2 damage (23JUN2012)
Vrot = 93.8
Max grid STP = 6.0
Outlook = MDT 10% SIG
Watch = TOR
EF3 damage (28MAY2013)
Vrot = 73.9
Max grid STP = 6.0
Outlook = MDT 10% SIG
Watch = PDS TOR
EF3 damage (20JUN2011)