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

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Presentation on theme: "Some Applications of Indices to Forecasting 12 th Great Divide Workshop, 10/7/2008 Matthew J. Bunkers, SOO Rapid City, SD."— Presentation transcript:

1 Some Applications of Indices to Forecasting 12 th 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” K Index (KI) Total Totals (TT) Severe Weather Threat (SWEAT) Lifted Index (LI)* Showalter Index (SI) Lapse Rate (LR)* * Can be calculated over many different layers/levels/parcels CAP Strength (700 mb LI) Relative Humidity (RH)*

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

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

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

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

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

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

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 LSCP Tornadic left-moving supercell (1-EF1) 

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

18 The Edge: 28 Feb 2007 – Eastern KS (1-EF4) 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 T adv 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 T adv MCS index and 700mb T adv 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 ) Std Dev Mean 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 T adv component  -4 to 6 (should be -2 to 2) NARR data underrepresented the operational T adv range – MCS index basically proxy for 700mb T adv – 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

30 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! …but AWIPS is okay 275° 28 kts

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.


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