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A Real-Time Automated Method to Determine Forecast Confidence Associated with Tornado Warnings Using Spring 2008 NWS Tor

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John Cintineo Cornell University Travis Smith Valliappa Lakshmanan Kiel Ortega NOAA - NSSL. A Real-Time Automated Method to Determine Forecast Confidence Associated with Tornado Warnings Using Spring 2008 NWS Tornado Warnings. Background.

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john cintineo cornell university travis smith valliappa lakshmanan kiel ortega noaa nssl
John Cintineo

Cornell University

Travis Smith

Valliappa Lakshmanan

Kiel Ortega

NOAA - NSSL

A Real-Time Automated Method to Determine Forecast Confidence Associated with Tornado WarningsUsing Spring 2008 NWS Tornado Warnings
background
Background
  • Warning Decision Support System – Integrated Information (WDSS-II)
    • Uses merged, multi-sensor CONUS radar network
    • combines model, lightning, and GOES satellite data
    • Short-term severe weather forecasting products
  • Objective:
    • To examine how WDSS-II products can be used as predictors for issuing NWS tornado warnings.
    • Assign objective probabilities to warnings based on varying the attribute threshold.
radar derived products storm environment data
Environmental Shear

Storm Relative Flow 9-11km AGL

Storm Relative Helicity 0-3km

CAPE, CIN

LCL min height

SATELLITE:

IR band-4 min temp. (cloud tops)

Total of 23 products

Radar-derived productsStorm Environment Data
  • Maximum Expected Size of Hail (MESH)
  • Probability of Severe Hail (POSH)
  • Severe Hail Index (SHI)
  • Vertically Integrated Liquid (VIL)
  • Area of VIL +30
  • Echo Tops of 50, 30, & 18dBZ
  • 3-6 km & 0-2 km Azimuthal Shear
  • Lowest level max dBZ
  • Reflectivity at 0C, -10C, & -20C
  • Overall max reflectivity
  • Height of 50dBZ above 253K isotherm
slide4

Methodology:

    • Investigated archived NWS spring 2008 CONUS tornado warnings with WDSS-II radar-derived products
    • Each storm attribute maximum (or minimum) values computed every 1 minute of the warning
    • Compared attribute values from the issuance of the warning (initial values) and the expiration of the warning (lifetime max/min).
    • Composite time series of each attribute
    • Warnings broken down by verified vs. unverified
    • Verification data obtained from the Storm Prediction Center’s storm data (preliminary).
    • storm environment data provided by 20-km RUC model.
slide5

Dataset:

    • 2 May – 1 July for 0-2 km Azimuthal shear, VIL, Area of VIL +30, and reflectivity products
    • 15 May to 1 July for 3-6 km Azimuthal shear
    • 20 random days for Storm Environment attributes
    • NB: for 1 May – 10 May 0-2 km Azimuthal shear is replaced by 0-3km Azimuthal shear

1,617 Tornado WarningsFrequency of Hits = 0.256 (414 verified warnings)False Alarm Ratio = 0.744 (1,203 unverified warnings)Average Warning Duration: 38.6 mins

initial 0 2 km azimuthal shear
Initial 0-2 km Azimuthal Shear

UNVERIFIED VERIFIED

Mean: 0.0053 s^-1 Mean: 0.0078 s^-1

SD: 0.0044 s^-1 SD: 0.0053 s^-1

lifetime max 0 2km azimuthal shear
Lifetime Max 0-2km Azimuthal Shear

UNVERIFIED VERIFIED

Mean: 0.0078 s^-1 Mean: 0.0109 s^-1

SD: 0.0051 s^-1 SD: 0.0055 s^-1

initial vertically integrated liquid vil
Initial Vertically Integrated Liquid (VIL)

UNVERIFIED VERIFIED

Mean: 27.76 kg/m^2 Mean: 34.44 kg/m^2

SD: 20.46 kg/m^2 SD: 18.66 kg/m^2

lifetime maximum vil
Lifetime Maximum VIL

UNVERIFIED VERIFIED

Mean: 37.00 kg/m^2 Mean: 46.35 kg/m^2

SD: 20.27 kg/m^2 SD: 18.05 kg/m^2

initial 0 2 km az shear s 1

Initial Vertically Integrated Liquid (kg/m^2)

y >= 60 60 > y >= 40 40 > y >= 20 y < 20

CONDITIONAL PROBABILITY CONTINGENCY TABLE

Initial 0-2 km Az. Shear (s^-1)

x < 0.004 0.004 <= x < 0.008 0.008 <= x < 0.012 x >= 0.012

summary
Summary
  • Provide warning guidance for the NWS
  • Once a NWS tornado warning is issued, WDSS-II can automatically assign a probability that it will verify, in real-time
  • More years of warning data will lead to a better climatology of warning probabilities
  • With more warning data, create a contingency table based on 3 or 4 of the best predictors
  • Forecasters can use such probability data to reduce their FAR
future avenues of research
Future avenues of research
  • Extend the data set to include past springs
  • Examine environment just outside the warning polygons (to capture the entire storm)
  • Compare spring and fall tornado warnings
  • Compare attributes in tornado and severe T-storm warnings
  • Compare warning data based on region
  • Investigate warnings issued in watches, and those outside of watches
summary17
Summary
  • Provide warning guidance for the NWS
  • Once a NWS tornado warning is issued, WDSS-II can automatically assign a probability that it will verify, in real-time
  • More years of warning data will lead to a better climatology of warning probabilities
  • With more warning data, create a contingency table based on 3 or 4 of the best predictors
  • Forecasters can use such probability data to reduce their FAR
acknowledgements
Acknowledgements
  • Travis Smith
  • Lak
  • Kiel Ortega
  • Owen Shieh
  • This research was supported by an appointment to theNational Oceanic and Atmospheric AdministrationResearch Participation Program through a grant award to Oak Ridge Institute for Science and Education.
references
References
  • Erickson, S. A., Brooks, H., 2006: Lead time and time under tornado warnings: 1986-2004. 23rd Conference on Severe Local Storms
  • Guillot, E., T. M. Smith, Lakshmanan, V., Elmore, K. L., Burgess, D. W., Stumpf, G. J., 2007: Tornado and Severe Thunderstorm Warning Forecast Skill and its Relationship to Storm Type.
  • Lakshmanan, V., T. M. Smith, K. Cooper, J. J. Levit, G. J. Stumpf, and D. R. Bright, 2006: High-resolution radar data and products over the Continental United States. 22nd Conference on Interactive Information Processing Systems, Atlanta, Amer. Meteor. Soc.
  • Lakshmanan, V., T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, 2006: A real-time, three dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity and derived products. Weather and Forecasting 21, 802-823.
  • Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007: The warning decision support system - integrated information (WDSS-II). Weather and Forecasting 22, 592-608.
  • Ortega, K. L, and T. M. Smith, 2006: Verification of multi-sensor, multi-radar hail diagnosis techniques. 1st Severe Local Storms Special Symposium, Atlanta, GA, Amer. Meteo. Soc.
  • Ortega, K. L., T. M. Smith, G. J. Stumpf, J. Hocker, and L. López, 2005: A comparison of multi-sensor hail diagnosis techniques. 21st Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Amer. Meteo. Soc., P1.11 - CD preprints.
  • Witt, A., Eilts, M., Stumpf, G. J., Johnson, J. T., Mitchell, D. E., Thomas, K. W., 1998: An Enhanced Hail Detection Algorithm for the WSR-88D.
initial 3 6 km azimuthal shear
Initial 3-6 km Azimuthal Shear

UNVERIFIED VERIFIED

Mean: 0.0054 s^-1 Mean: 0.0076 s^-1

SD: 0.0041 s^-1 SD: 0.0046 s^-1

lifetime max 3 6 km azimuthal shear
Lifetime Max 3-6 km Azimuthal Shear

UNVERIFIED VERIFIED

Mean: 0.0084 s^-1 Mean: 0.0112 s^-1

SD: 0.0049 s^-1 SD: 0.0052 s^-1

initial max ll reflectivity
Initial Max LL Reflectivity

Mean: 49.88 dBZ Mean: 55.09 dBZ

SD: 14.63 dBZ SD: 11.02 dBZ

lifetime max ll reflectivity
Lifetime Max LL Reflectivity

Mean: 56.86 dBZ Mean: 60.51 dBZ

SD: 10.39 dBZ SD: 7.11 dBZ

initial dbz @ 20c
Initial dBZ @ -20C

Mean: 46.58 dBZ Mean: 53.08 dBZ

SD: 14.21 dBZ SD: 10.78 dBZ

lifetime max dbz @ 20c
Lifetime Max dBZ @ 20C

Mean: 52.75 dBZ Mean: 57.86 dBZ

SD: 11.97 dBZ SD: 8.43 dBZ

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