Values

Deep Convection: Forecast Parameters

Mesoscale

M. D. Eastin

Deep Convection: Forecast Parameters CAPE and CIN Lifted Index Bulk Vertical Wind Shear Bulk Richardson Number (BRN)

Storm Relative Environmental Helicity (SREH) Energy Helicity Index (EHI) Other parameters…

Effective use of the Parameters CAPE / Shear “Phase Space”

Mesoscale

M. D. Eastin

Mesoscale Forecasting Two Step Process: 1. Convective Outlook • Use available synoptic and mesoscale observations to forecast synoptic-scale regions where severe weather development is possible • NOAA Storm Prediction Center (SPC) [ http://www.spc.noaa.gov ] • Issue Mesoscale Discussions and 3-day Convective Outlooks • Issue Fire Weather Outlooks • Issue Watches for Severe Thunderstorms and Tornados 2. Nowcasts • Monitor the available mesoscale observations in order to update or alter forecasts for potential severe weather as needed • Once convection has developed, continuous monitoring of storm type, structure, evolution, and motion with Doppler radar is required

• Local NWS Forecast Offices [ http://www.nws.noaa.gov/ ] • Monitor the Local WSR-88D radar • Issue Warnings for Severe Thunderstorms, Tornadoes, Flash Floods, Winter Storms, Freezes, and High Surf Mesoscale

M. D. Eastin

CAPE • We estimate the total buoyancy force available to accelerate an updraft air parcel by computing the Convective Available Potential Energy (CAPE) EL

CAPE 



LFC

g

Tpar  Tenv Tenv

dz

• Recall water-loading and entrainment are always neglected in its calculation (sometimes moisture)

• Directly related to maximum updraft velocity

wmax 

2CAPE

Values: Range: Typical: “Small” “Moderate” “Large” Mesoscale

0 – 5000 J/kg 800 – 3000 J/kg < 1000 J/kg 1000 – 2500 J/kg > 2500 J/kg

common severe weather threshold

M. D. Eastin

CAPE Surface Based CAPE (SBCAPE): • Computed using only the surface temperature and moisture observations Strengths: • Integrates through the entire atmospheric depth Weaknesses: • Does not account for layers that are not well mixed • May grossly underestimate instability when strong nocturnal inversion exists (morning soundings) • Overestimates instability when very shallow moist layers are present • Assumes no mixing between the parcel and environment • Neglects water loading

Mesoscale

M. D. Eastin

CAPE Mean Layer CAPE (MLCAPE): • Computed using the lowest 100 mb AGL mean layer temperature and moisture observations

Strengths: • Most realistic for convection originating near the surface • Incorporates daytime boundary layer mixing • Helps remove any nocturnal boundary layer effects Weaknesses: • Underestimates instability if convective initiation is elevated (e.g. behind a cold/warm front) • Assumes no mixing between the parcel and environment • Neglects water loading

Mesoscale

M. D. Eastin

CAPE Most Unstable CAPE (MUCAPE): • Computed using the temperature and moisture observation pair within the lowest 300 mb AGL that results in the most unstable parcel Strengths: • Most realistic for elevated convection • Helps remove the nocturnal boundary layer effect

Weaknesses: • Underestimates instability if convective initiation is near the surface • Assumes no mixing between the parcel and environment • Neglects water loading

Mesoscale

M. D. Eastin

CAPE Climatology of Ordinary – Supercell CAPE: • Rasmussen and Blanchard (1998) • Computed MLCAPE for over 6000 soundings taken at 0000 UTC during 1992 • Stratified the soundings as follows: TOR: Sounding associated with a tornadic supercell SUP: Sounding associated with a non-tornadic supercells that produced large hail ORD: Soundings associated with thunderstorms not containing severe winds, large hail, or a tornado

• Performed a simple statistical analysis on the MLCAPE distribution

Mesoscale

Boxes denote 25th and 75th percentile Horizontal bar denotes median value Lines extend to the 10th and 90th percentiles Values at each percentile are shown M. D. Eastin

CIN • We estimate the total buoyancy force available to decelerate an updraft air parcel by computing the Convective Inhibition (CIN) LFC

CIN 



g

T par  Tenv

SFC

Tenv

dz

• CIN is often the result of a capping inversion • Represents the amount of energy that must be given to a parcel (overcome) to reach the LFC • In general, CIN is bad… Values: Range: Typical: “Small” “Moderate” “Large” Mesoscale

0 – 500 J/kg 20 – 200 J/kg < 10 J/kg 10 – 50 J/kg > 50 J/kg

common severe weather threshold M. D. Eastin

CIN Strengths: • Helps discriminate between deep convection (low CIN) and no convection (high CIN) • Can relate CIN to the required vertical velocity needed to overcome the negative area:

wrequired lift 

2CIN 

Weaknesses: • Can be difficult to assess the required lift when no mesoscale boundaries are present • Very sensitive to changes in BL temperature and moisture values and thus CAPE choice • Assumes parcel mixing with the environment • Neglects water loading

Mesoscale

M. D. Eastin

CIN Climatology of Ordinary – Supercell CIN: • Rasmussen and Blanchard (1998) • Computed CIN for over 6000 soundings taken at 0000 UTC during 1992 • Stratified the soundings as follows: TOR: Sounding associated with a tornadic supercell SUP: Sounding associated with a non-tornadic supercells that produced large hail ORD: Soundings associated with thunderstorms not containing severe winds, large hail, or a tornado

• Performed a simple statistical analysis on the CIN distribution

Mesoscale

Boxes denote 25th and 75th percentile Horizontal bar denotes median value Lines extend to the 10th and 90th percentiles Values at each percentile are shown M. D. Eastin

Lifted Index (LI) • Simple parameter used to characterize the instability of an environment • Raise a surface parcel to its LCL, and then moist adiabatically to 500 mb

LI  Tenv  Tparcel • Limited by only estimating buoyancy at one level (can be misrepresentative) • The more negative the LI, the greater the chance for deep convection and severe weather Values: Range: Typical:

+20 to –14 +2 to –6 20 Supercells

Mesoscale

M. D. Eastin

Bulk Wind Shear Climatology of Ordinary – Supercell Bulk Shear: • Rasmussen and Blanchard (1998) • Computed Bulk Shear for over 6000 soundings taken at 0000 UTC during 1992 • Stratified the soundings as follows: TOR: Sounding associated with a tornadic supercell SUP: Sounding associated with a non-tornadic supercells that produced large hail ORD: Soundings associated with thunderstorms not containing severe winds, large hail, or a tornado

• Performed a simple statistical analysis on the Bulk Shear distribution

Mesoscale

Boxes denote 25th and 75th percentile Horizontal bar denotes median value Lines extend to the 10th and 90th percentiles Values at each percentile are shown M. D. Eastin

Bulk Richardson Number (BRN) • Simple parameter used to characterize the relative importance of buoyancy and vertical wind shear processes in controlling storm structure and evolution

BRN 

CAPE 1 U2 2

• Low values are indicative of environments in which shear related processes will play a significant role  Helps distinguish between supercell and multicell formation Values:

Range: 1 – 4000 Typical: 10 – 400 < 50 > 50 Mesoscale

Supercells possible Multicells possible

BRN Shear (½U2): One half the square of the vector difference between the mean 0-6 km AGL wind and the mean 0-0.5 km AGL wind Provides a measure of the “bulk” wind shear

M. D. Eastin

Bulk Richardson Number (BRN) Strengths: • Combines both instability and vertical shear into a single parameter • Provides estimate of rotation potential without considering storm motion Weaknesses: • No measure of directional shear • Does not account for hodograph curvature • Same weaknesses as for CAPE • Does not account for the vertical distribution of instability • No indication or measure of CIN

Mesoscale

M. D. Eastin

Storm-Relative Helicity (SREH) • Parameter used to characterize the strength of the low-level, storm-relative speed and directional shear

 The greater the “helicity”, the more likely the updraft will rise in a helical manner and produce a mesocyclone  Helps forecast supercell formation  Calculation method (see pp. 230-213 in text):

• Estimate storm motion • Compute storm-relative winds • SREH is two times (2×) the total area swept out by the storm-relative winds between 0-3 km AGL (green area) Values: Range: 0 – 1000 Typical: 50 – 400 > 100 Supercells Possible > 200 Supercells Likely Mesoscale

M. D. Eastin

Storm-Relative Helicity (SREH) Strengths: • Provides estimate of rotation potential while also considering storm motion • Does account for both total shear and hodograph curvature • Can discriminate between supercells and tornadic supercells Weaknesses:

• Very sensitive to the storm motion • Very sensitive to change in low-level wind vectors

Note: SREH can be computed for a variety of depth or layers. Common layers are: 0-3 km 0-2 km 0-1 km Mesoscale

Good for supercell forecasts Good for tornado forecasts M. D. Eastin

Storm-Relative Helicity (SREH) Climatology of Supercell –Tornado SREH: • Rasmussen and Blanchard (1998) • Computed SREH for over 6000 soundings taken at 0000 UTC during 1992 • Stratified the soundings as follows: TOR: Sounding associated with a tornadic supercell SUP: Sounding associated with a non-tornadic supercells that produced large hail ORD: Soundings associated with thunderstorms not containing severe winds, large hail, or a tornado

• Performed a simple statistical analysis on the SREH distribution (0-3 km layer)

Mesoscale

Boxes denote 25th and 75th percentile Horizontal bar denotes median value Lines extend to the 10th and 90th percentiles Values at each percentile are shown M. D. Eastin

Energy Helicity Index (EHI)  Parameter used to characterize the likelihood of supercell formation and tornadic activity

EHI 

CAPE  SREH 160,000

• The divisor of 160,000 is simply used to obtain a manageable number • CAPE is computed using the mean layer (ML) • SREH is computed from either the 0-1 km, 0-2 km, or the 0-3 km layer. Values: Range: Typical:

0.0 – 30.0 0.0 – 5.0 > 0.5 Supercells Likely > 1.0 Tornadoes Likely

Note: These rough criteria are valid for all SREH layers (0-1, 0-2, or 0-3 km) Mesoscale

M. D. Eastin

Energy Helicity Index (EHI) Strengths: • Incorporates instability, vertical shear magnitude, shear curvature, and storm motion into a single parameter • Most effective discriminator for significant tornadoes associated with supercells Weaknesses:

• Same weaknesses as for CAPE • Very sensitive to the storm motion • Very sensitive to change in low-level wind vectors

Mesoscale

M. D. Eastin

Energy Helicity Index (EHI) Climatology of Ordinary – Supercell EHI: • Rasmussen and Blanchard (1998) • Computed EHI for over 6000 soundings taken at 0000 UTC during 1992 • Stratified the soundings as follows: TOR: Sounding associated with a tornadic supercell SUP: Sounding associated with a non-tornadic supercells that produced large hail ORD: Soundings associated with thunderstorms not containing severe winds, large hail, or a tornado

• Performed a simple statistical analysis on the EHI distribution (0-3 km layer)

Mesoscale

Boxes denote 25th and 75th percentile Horizontal bar denotes median value Lines extend to the 10th and 90th percentiles Values at each percentile are shown M. D. Eastin

Other Parameters  A number of additional forecast parameters have been developed (and are regularly used) at the SPC to issue severe weather watches: Supercell Composite Parameter (SCP)

 MUCAPE   0  3km SREH   BRN Shear      SCP   1   2 2 2 2   1000 J kg   100m s   40 m s  • Developed by Thompson et al. (2003) • Values > 1.0 suggest supercell formation is likely Significant Tornado Parameter (STP)

 MLCAPE   0  1km SREH   0  6km Shear  (2000 MLLCL)          STP   1   2 2 2 2 1500m  1000 J kg   100m s   20 m s    • Developed by Thompson et al. (2003) • Values > 1.0 suggest strong (EF2-EF5) tornado formation is likely Additional Forecast Parameters: Mesoscale

Useful Severe Weather Forecast Parameters M. D. Eastin

Effective Use of Parameters Use the forecast parameters as they were intended!!!  Use CAPE (any variety), CIN, and LI to determine the likelihood of deep convection • Large CAPE, low CIN, and low LI indicates an increased chance  Use Bulk Shear, BRN, and SCP to identify the storm type (ordinary, multicell, supercell)  Use SREH to determine which multicells or supercells will develop rotating updrafts • Larger values indicate an increased chance for large hail and/or tornadoes  Use EHI and STP to determine which multicells or supercells will develop tornadoes • Larger values indicate an increased chance for stronger tornadoes • Values are positively correlated with tornado strength  Use DCAPE to determine if any cell type will develop strong downdrafts • Larger values increase the chance of severe straight-line winds Remember:

These parameters and their numerical criteria are only guidelines based on statistical analysis (i.e., In the long run, they work) Pay very close attention to the “weaknesses” for each parameter

Mesoscale

M. D. Eastin

SPC Sounding Analysis

Mesoscale

M. D. Eastin

CAPE / Shear Phase Space  Joe Klemp and Morris Weisman (NCAR) conducted a series of numerical simulations (each yellow dot) whereby the model was initialized with different environments that characterize the wide spectrum of CAPE and vertical wind shear values that are often observed  One result was a “phase space” diagram that helps forecast the convective storm type on any given day  Many other aspects of how vertical shear and buoyancy processes influence storm structure and evolution were discovered from these simulations • Let’s look at these simulations in detail… Complete the UCAR COMET Module: A Convective Storm Matrix http://www.meted.ucar.edu/convectn/csmatrix/

Mesoscale

M. D. Eastin

References Brooks, H. E., C. A. Doswell, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606-618. Brooks, H. E., and R. B. Wilhelmson, 1993: Hodograph curvature and updraft intensity in numerically modeled supercells. J. Atmos. Sci., 50, 1824-1833. Doswell C. A., 1991: A review for forecasters on the applications of hodographs to forecasting severe thunderstorms. National Weather Digest, 16, 2-16. Droegemeier, K. K., aS. M. lazarus, and R. Davies-Jones, 1993: The influence of helicity on numerically simulated convective storms. Mon. Wea. Rev., 121, 2005-2029. Lilly, D. K., 1986: The structure, energetics and propagation of rotation convective storms. Part II: Helicity and storm stabilization. J. Atmos. Sci., 43, 126-140. Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2002: Direct surface thermodynamic observations within the rearflank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 1692-1721. Rasmussen, E.N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 1148-1164. Thompson, R. L., R. Edwards, J. A. Kart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 1243-1261 Thompson, R. L., and C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102-115. Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504-520. Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 2479-2498.

Mesoscale

M. D. Eastin