1. Some Applications of Indices to Forecasting
12th Great Divide Workshop, 10/7/2008
Matthew J. Bunkers, SOO Rapid City, SD

2. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

3. A cornucopia of “indices”
Lifted Index (LI)*
Total Totals (TT)
K Index (KI)
Showalter Index (SI)
Severe Weather Threat (SWEAT)
CAP Strength (700 mb LI)
Lapse Rate (LR)*
Relative Humidity (RH)*
* Can be calculated over many different layers/levels/parcels

4. A cornucopia of “indices”
Lifted Index (LI)*
Total Totals (TT)
K Index (KI)
Showalter Index (SI)
Severe Weather Threat (SWEAT)
CAP Strength (700 mb LI)
Lapse Rate (LR)*
Relative Humidity (RH)*
Lifted Condensation Level (LCL)*
Level of Free Convection (LFC)*
Equilibrium Level (EL)*
Wet Bulb Zero (WBZ)
Melting Level (MLT)
Warm Cloud Depth (WCD)*
Precipitable Water (PW)*
Equivalent Potential Temperature (e)*
Moisture Flux “Convergence” (MFC)*
* Can be calculated over many different layers/levels/parcels

5. A cornucopia of “indices”
Lifted Index (LI)*
Convective Available Potential Energy (CAPE)*
Total Totals (TT)
Convective Inhibition (CIN)*
K Index (KI)
Bulk Richardson Number (BRN)*
Showalter Index (SI)
Bulk Richardson Number Shear (BRNSHR)
Severe Weather Threat (SWEAT)
Bulk Vertical Wind Shear*
CAP Strength (700 mb LI)
Total Vertical Wind Shear*
Lapse Rate (LR)*
Storm-Relative Wind*
Relative Humidity (RH)*
Storm-Relative Helicity (SRH)*
Downdraft CAPE (DCAPE)
Lifted Condensation Level (LCL)*
Normalized CAPE (nCAPE)*
Level of Free Convection (LFC)*
Equilibrium Level (EL)*
Wet Bulb Zero (WBZ)
Melting Level (MLT)
Warm Cloud Depth (WCD)*
Precipitable Water (PW)*
Equivalent Potential Temperature (e)*
Moisture Flux “Convergence” (MFC)*
* Can be calculated over many different layers/levels/parcels
* Can be calculated over many different layers/levels/parcels

6. A cornucopia of “indices”
Lifted Index (LI)*
Convective Available Potential Energy (CAPE)*
Total Totals (TT)
Convective Inhibition (CIN)*
K Index (KI)
Bulk Richardson Number (BRN)*
Showalter Index (SI)
Bulk Richardson Number Shear (BRNSHR)
Severe Weather Threat (SWEAT)
Bulk Vertical Wind Shear*
CAP Strength (700 mb LI)
Total Vertical Wind Shear*
Lapse Rate (LR)*
Storm-Relative Wind*
Relative Humidity (RH)*
Storm-Relative Helicity (SRH)*
Downdraft CAPE (DCAPE)
Lifted Condensation Level (LCL)*
Normalized CAPE (nCAPE)*
Level of Free Convection (LFC)*
Equilibrium Level (EL)*
Wind Index (WINDEX)
Wet Bulb Zero (WBZ)
Dry Microburst Index (DMI)
Melting Level (MLT)
Theta-E Index (TEI)
Warm Cloud Depth (WCD)*
Microburst Day Potential Index (MDPI)
Precipitable Water (PW)*
Wet Microburst Severity Index (WMSI)
Equivalent Potential Temperature (e)*
Moisture Flux “Convergence” (MFC)*
* Can be calculated over many different layers/levels/parcels
* Can be calculated over many different layers/levels/parcels

7. A cornucopia of “indices”
Lifted Index (LI)*
Convective Available Potential Energy (CAPE)*
Total Totals (TT)
Convective Inhibition (CIN)*
HI = Haines Index*
HMI = Hybrid Microburst Index
K Index (KI)
Bulk Richardson Number (BRN)*
LSI = Lid Strength Index
Showalter Index (SI)
Bulk Richardson Number Shear (BRNSHR)
DCI = Deep Convective Index
TQ Index = for “low-topped
instability”
Severe Weather Threat (SWEAT)
Bulk Vertical Wind Shear*
CAP Strength (700 mb LI)
Total Vertical Wind Shear*
Lapse Rate (LR)*
Storm-Relative Wind*
Relative Humidity (RH)*
Storm-Relative Helicity (SRH)*
Downdraft CAPE (DCAPE)
Lifted Condensation Level (LCL)*
Normalized CAPE (nCAPE)*
Level of Free Convection (LFC)*
Equilibrium Level (EL)*
Wind Index (WINDEX)
Wet Bulb Zero (WBZ)
Dry Microburst Index (DMI)
Melting Level (MLT)
Theta-E Index (TEI)
Warm Cloud Depth (WCD)*
Microburst Day Potential Index (MDPI)
Precipitable Water (PW)*
Wet Microburst Severity Index (WMSI)
Equivalent Potential Temperature (e)*
Moisture Flux “Convergence” (MFC)*
* Can be calculated over many different layers/levels/parcels

8. A cornucopia of “indices”
Lifted Index (LI)*
Convective Available Potential Energy (CAPE)*
Total Totals (TT)
Convective Inhibition (CIN)*
HI = Haines Index*
HMI = Hybrid Microburst Index
K Index (KI)
Bulk Richardson Number (BRN)*
LSI = Lid Strength Index
Showalter Index (SI)
Bulk Richardson Number Shear (BRNSHR)
DCI = Deep Convective Index
TQ Index = for “low-topped
instability”
Severe Weather Threat (SWEAT)
Bulk Vertical Wind Shear*
CAP Strength (700 mb LI)
Total Vertical Wind Shear*
Indices of Indices (“Inbreeding”)
Lapse Rate (LR)*
Storm-Relative Wind*
Energy-Helicity Index (EHI)*
Relative Humidity (RH)*
Storm-Relative Helicity (SRH)*
Vorticity Generation Parameter (VGP)*
Downdraft CAPE (DCAPE)
Supercell Composite Parameter (SCP)*
Lifted Condensation Level (LCL)*
Normalized CAPE (nCAPE)*
Significant Tornado Parameter (STP)*
Level of Free Convection (LFC)*
Significant Hail Parameter (SHIP)
Equilibrium Level (EL)*
Wind Index (WINDEX)
Significant Severe Parameter (SSP)
Wet Bulb Zero (WBZ)
Dry Microburst Index (DMI)
Strong Tornado Parameter (STP)
Melting Level (MLT)
Theta-E Index (TEI)
Warm Cloud Depth (WCD)*
Microburst Day Potential Index (MDPI)
Precipitable Water (PW)*
Wet Microburst Severity Index (WMSI)
Equivalent Potential Temperature (e)*
Moisture Flux “Convergence” (MFC)*
* Can be calculated over many different layers/levels/parcels

9. A cornucopia of “indices”
Lifted Index (LI)*
Convective Available Potential Energy (CAPE)*
Total Totals (TT)
Convective Inhibition (CIN)*
HI = Haines Index*
HMI = Hybrid Microburst Index
K Index (KI)
Bulk Richardson Number (BRN)*
LSI = Lid Strength Index
Showalter Index (SI)
Bulk Richardson Number Shear (BRNSHR)
DCI = Deep Convective Index
TQ Index = for “low-topped
instability”
Severe Weather Threat (SWEAT)
Bulk Vertical Wind Shear*
CAP Strength (700 mb LI)
Total Vertical Wind Shear*
Indices of Indices (“Inbreeding”)
Lapse Rate (LR)*
Storm-Relative Wind*
Energy-Helicity Index (EHI)*
Relative Humidity (RH)*
Storm-Relative Helicity (SRH)*
Vorticity Generation Parameter (VGP)*
Vorticity Generation Parameter (VGP)*
Downdraft CAPE (DCAPE)
Supercell Composite Parameter (SCP)
Supercell Composite Parameter (SCP)*
Lifted Condensation Level (LCL)*
Normalized CAPE (nCAPE)*
Significant Tornado Parameter (STP)*
Level of Free Convection (LFC)*
Significant Hail Parameter (SHIP)
Equilibrium Level (EL)*
Wind Index (WINDEX)
Significant Severe Parameter (SSP)
Wet Bulb Zero (WBZ)
Dry Microburst Index (DMI)
Strong Tornado Parameter (STP)
Melting Level (MLT)
Theta-E Index (TEI)
Warm Cloud Depth (WCD)*
Microburst Day Potential Index (MDPI)
Precipitable Water (PW)*
Wet Microburst Severity Index (WMSI)
Equivalent Potential Temperature (e)*
Mesoscale Convective System Forecast Index (MCS Index)  a recent index published in WAF (2007)
Moisture Flux “Convergence” (MFC)*
 This list is not nearly exhaustive!
* Can be calculated over many different layers/levels/parcels

10. What’s a forecaster to do?

11. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

12. Attributes of indices Doswell and Schultz (2006)
“On the Use of Indices and Parameters in Forecasting Severe Storms”
Electronic Journal of Severe Storms Meteorology

13. Benefits of indices Can summarize large amounts of data
Can quickly draw attention to “critical” areas for further diagnosis
Both are attractive when under time pressure

14. Index limitations
Not necessarily forecast parameters; may be diagnostic (e.g., SPC meso page)
Diagnostic variables give current state (≠ /t), where  = STP, SCP, CAPE, etc.
Most indices are not rigorously developed or validated – arbitrarily combined variables

15. Index limitations
Can lead to faulty perceptions of atmosphere via over-simplification
Little value in isolation; different combos can produce similar values
Flavor of the parameter? (e.g., EHI and its inputs)
Constituents can evolve quasi-independently
Action often occurs at “The Edge” – next three slides

16. The Edge: 20 Jun 2006 – Rushville, NE
Tornadic left-moving supercell
LSCP
(1-EF1)

17. The Edge: 16 Sep 2006 – Rogers, MN
(1-EF2)

18. The Edge: 28 Feb 2007 – Eastern KS
Important to train new forecasters not to focus on bulls-eyes.

19. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

20. Example of testing an index
Jirak and Cotton (2007, WAF)
MCS index for conditional development of MCSs
Function of “best” LI, 0-3km shear, and 700mb Tadv
Convert terms to standard normal and summed
Appears to be physically based
Developed using NARR data
Tested at WFO Rapid City and found problems
Operational datasets produced different results
Didn’t implement at our office

21. MCS index on WES Image = MCS index White lines = 700mb Tadv
MCS index and 700mb Tadv looked very similar
Was “verified” with 20 cases

22. MCS index testing Let LI vary from +3 to -12 (C)
Let shear vary from 0 to 25 (m s-1)
Let Tadv vary from -1 to +2 (C hr-1)
Mean
Std Dev
Means and standard deviations based on NARR dataset (JC07).

23. Three terms of MCS index
Using JC07’s equation and reasonable ranges for the 3 terms
Ideally all 3 lines should be the same

24. MCS index summary LI component  -2 to 2 Shear component  -2 to 2
Tadv component  -4 to 6 (should be -2 to 2)
NARR data underrepresented the operational Tadv range
MCS index basically proxy for 700mb Tadv
Conclusion: not suitable for operations (authors updating to use stddev of range)

25. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

26. The STP index Thompson et al. (2003, WAF)
Significant Tornado Parameter (STP)
Mean-layer CAPE (MLCAPE, lowest 100mb)
0-6km shear vector magnitude (SHR6)
0-1km storm-relative helicity (SRH1)
Mean layer LCL (MLLCL, lowest 100mb)

27. Let’s test this Estimate valid ranges and calculate each term
For example: MLCAPE ~ 100 to 5000 J kg-1
Term 1 thus ranges from 0.1 to 5
(100/1000) = 0.1
(5000/1000) = 5

28. Versions of the STP

29. Versions of the STP

30. Versions of the STP
If you use them, know your indices!

31. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

32. Supercell Composite Parameter (SCP)
Function of MUCAPE, Eff. Shear, and Eff. SRH
Can run similar tests as for the STP
Testing suggests SCP can be misleading

33. SCP Potential Pitfall
Dominant right-mover

34. Modeling Results SRH (RM) = 62 SRH (LM) = -226
Hodograph didn’t turn enough in lowest 36 km to strongly favor LM.
Environmental heterogeneity and low-level wind variability also factors.

35. Outline Make note of several “indices”
Discuss utility & attributes of indices (+ / -)
Show several examples of testing indices for operations – implications for training

36. Example of coord system sensitivity
SWEAT Index (SW): May 2001 OK case
SW = 331
What if 850 wspd = 15 kts? (SW = 429)
Now what if wdir 30 to left (SW = 331)

37. Supercell motion example: BUFKIT
Bunkers et al. (2000)
Non-weighted MW for supercell motion, every 500 meters
BUFKIT
Uses ALL data for MW; produces low-level bias
Supercell motion often too slow…so beware of BUFKIT algorithm!

38. SCM: Excel vs. BUFKIT 12-kt difference between the two!
275° 28 kts
12-kt difference between the two!
…but AWIPS is okay

39. Summary for indices
Look at the raw data (e.g., surface maps, soundings, 0-1km shear, MLLCL, etc.)
View the indices’ constituent components (e.g., 4-panel mode)…”STP = 2 means what?”
Test new indices before implementing them in operations (e.g., the MCS index)
Folly to develop indices away from operations

40. One final thought
“The author’s most regrettable severe storm forecast mistakes have arisen from ignoring data that were relevant to the daily diagnosis…and/or failing to complete the diagnosis on what initially appeared to be a benign weather day.”
– Al Moller (2001, Severe Convective Storms Monograph)
Analysis and diagnosis of observational data is critical – yet this has become a lost art.