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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 l.jpg

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 l.jpg
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 l.jpg

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 l.jpg

  • 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 l.jpg

  • 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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg

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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
Initial Max LL Reflectivity

Mean: 49.88 dBZ Mean: 55.09 dBZ

SD: 14.63 dBZ SD: 11.02 dBZ


Lifetime max ll reflectivity l.jpg
Lifetime Max LL Reflectivity

Mean: 56.86 dBZ Mean: 60.51 dBZ

SD: 10.39 dBZ SD: 7.11 dBZ


Initial dbz @ 20c l.jpg
Initial dBZ @ -20C

Mean: 46.58 dBZ Mean: 53.08 dBZ

SD: 14.21 dBZ SD: 10.78 dBZ


Lifetime max dbz @ 20c l.jpg
Lifetime Max dBZ @ 20C

Mean: 52.75 dBZ Mean: 57.86 dBZ

SD: 11.97 dBZ SD: 8.43 dBZ



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