Transcript Sounding Analog Retrieval System SARS

Sounding
ounding Analog
S
Analog Retrieval
Retrieval System
S ystem
Ryan Jewell
Storm Prediction Center
Norman, OK
What is SARS?
SARS is a forecast system based on sounding analogs.
The algorithm matches forecast soundings to a large
database of proximity soundings associated with severe
weather.
SARS finds matches using a small number of parameters
and parameter ranges determined by a calibration process.
What is SARS?
Name inspired by MARS – Map Analog Retrieval System
Greg Carbin – http://www.spc.noaa.gov/exper/mref_mars/
Safe to use!
Used experimentally at the SPC.
Integrated into NSHARP (Sounding displays)
RUC and NAM plan view display (Model Grids)
Types of SARS
Two types:
and
HailHail
SARS
canSupercell/Tornado
forecast:
Probability
of SIG (≥ soundings
2.00”) hail.(Observed)
Hail: 11481)Severe
hail proximity
2) Maximum
hail size
(≥ 0.75”)
.
Supercell
Tornado: expected
938 Supercell
proximity
soundings
(RUC) (Under Development)
Matching Sounding Database
Severe Hail Proximity Soundings
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Includes 1148 observed hail soundings 1989-2006.
Within 100 nm and +/- 2.5 hrs either side of 2330Z (21-02).
Had to be in same air mass as storm.
Modified for surface conditions (if needed).
Thrown out if contaminated by outflow, etc.
Expansion of dataset used in Jewell and Brimelow
(WAF 2009).
Matching Sounding Database
Assume dataset is “representative.”
Spans all seasons
18 years of data
All regions of the CONUS
A function of climatology and quality of soundings.
1989 - 2006
SARS Calibration Method
Determine matching parameters and ranges
Matching Parameters – Relevant parameters
associated with severe storms (various measures of
Desired Result
= The
of matches
instability
andmajority
shear associated
withagree
hail). on a particular
1
type and magnitude of severe weather, and it verifies.
2
Define initial ranges for each parameter to be used in
search. (Example +/- 500 CAPE)
If a sounding is associated with 3.00” hail, most of the SARS
matches
should
be very
largeindependently
hail.
3
Test each
sounding
against the
database, analyze matches.
4
Adjust parameters and ranges until the desired result is
received.
Example – Calibration for hail SARS.
Remove 1 sounding…test against remaining soundings (1147).
Calculate skill scores for parameter set #1 and range combination # 1...
Test various combinations of parameters and parameter ranges.
8 different parameters with 5 ranges each = 58 or 390,625 combinations.
SARS Matching Parameters
Final list of matching parameters
(out of about 20)
Most Unstable (MU) CAPE
Mixing Ratio of MU Parcel
700-500 mb Lapse Rate
500 mb Temperature
0-6 km Bulk Shear
Notably showed little or no skill:
Freezing Level
Wet Bulb Zero Heights
0-3 Storm Relative Helicity (SRH)
SARS Parameters Ranges
Significant Hail Parameter Ranges
(Resulted in best skill scores)
MUCAPE +/- 40%
Mixing Ratio of MU Parcel +/- 2.0 g/kg
700-500 mb Lapse Rate +/- 1.5 C/km
500 mb Temperature +/- 7 C
0-6 km Bulk Shear +/- 9 m/s
Large ranges, but all 5 must overlap.
Performance
SARS SIG Hail Algorithm
SARS Skill Scores
Significant Hail (≥ 2.0”) Forecast
Total Soundings = 1148
Hit
Miss
False
Alarm
486
84
97
Correct
Null
No Matches
Found
475
*5
CSI
TSS
POD
FAR
0.729
0.683
0.853
0.166
NOTE: Highest skill score AND highest % with matches
* 1 Tie
SARS Skill Scores
Significant Hail Forecast - Filtered
Remove Golf Ball (1.75”) and 2.00” reports (near 2” threshold)
Total Soundings = 889
Hit
Miss
False
Alarm
477
28
61
Correct
Null
No Matches
Found
318
*4
CSI
TSS
POD
FAR
0.843
0.784
0.945
0.113
NOTE: Highest skill score AND highest % with matches
* 1 Tie
Performance
SARS SIZE Algorithm
Mean value of SARS binned by report size – Observed vs. Forecast
MEAN STDEV: 0.43”
0.68
r2= 0.47
Correlation (Filtered): 0.75
r2= 0.56
Correlation (All):
SARS MATCHING EXAMPLES
(one HAIL of a year!)
National Record - July 23, 2010 – Vivian, SD
Contours = # Matches
Color Fill = % that are SIG (≥ 2.00”)
RUC Model
Mean SARS Hail Size (inches)
RUC Model
GRIDDED SARS EXAMPLE
KS Record (dia) - Sep 15, 2010 – Wichita, KS
7.75” Hail
2.75” Hail
4.25” Hail
RUC Model
Contours = # Matches
CIN
Color Fill = % that are SIG (≥ 2.00”)
CIN
CIN
RUC Model
Mean SARS Hail Size (inches)
CIN
CIN
CIN
RARE EVENTS
RUC Model
Contours = # Matches
Color Fill = % that are SIG (≥ 2.00”)
Put AZ Hail Case HERE
Mean SARS Hail Size (inches)
Put AZ Hail Case HERE
RUC Model
Contours = # Matches
Color Fill = % that are SIG (≥ 2.00”)
RUC Model
SUPERCELL SARS
Forecast Soundings
F5 Tornado
F4 Tornado
F4 Tornado
F5 Tornado
SARS Summary
The SARS method can be applied to various types of severe weather
(hail, tornado, wind).
SARS forecasts storm REPORTS! Local biases in reporting WILL be
reflected in SARS!
SARS may miss rare events if they have not been accounted for in
the database, but may also find rare events and heighten awareness.
SARS is conditional…cannot predict whether storms will form
(capping, forcing issues).
And oh, by the way…accuracy of SARS heavily depends upon the
forecasts models.
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ND Record - Jul 14, 2010 – Sioux County, ND
1.75” Hail
1.75” Hail