Deep Convection: Forecast Parameters - PowerPoint PPT Presentation

1 / 27
About This Presentation
Title:

Deep Convection: Forecast Parameters

Description:

Title: PowerPoint Presentation Last modified by: test Created Date: 1/1/1601 12:00:00 AM Document presentation format: On-screen Show (4:3) Other titles – PowerPoint PPT presentation

Number of Views:110
Avg rating:3.0/5.0
Slides: 28
Provided by: claspage
Category:

less

Transcript and Presenter's Notes

Title: Deep Convection: Forecast Parameters


1
Deep Convection Forecast Parameters
2
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
3
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

4
CAPE
  • We estimate the total buoyancy force available
    to
  • accelerate an updraft air parcel by computing
    the
  • Convective Available Potential Energy (CAPE)
  • Recall water-loading and entrainment are always
  • neglected in its calculation (sometimes
    moisture)
  • Directly related to maximum updraft velocity
  • Values
  • Range 0 5000 J/kg
  • Typical 800 3000 J/kg

5
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

6
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)

7
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

8
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

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
9
CIN
  • We estimate the total buoyancy force available
    to
  • decelerate an updraft air parcel by computing
    the
  • Convective Inhibition (CIN)
  • 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 0 500 J/kg

10
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
  • 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

11
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

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
12
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
  • 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 20 to 14
  • Typical 2 to 6

13
Bulk Wind Shear
  • Simple parameter used to characterize the
  • wind shear in the layer most relevant to
  • storm structure and evolution
  • Low values denote environments favoring
  • the formation of ordinary (single) cells
  • High values denote environments favoring
  • the formation of supercells
  • Often calculated in the 0-6 km AGL layer
  • Helps distinguish between ordinary,
  • multicell, and supercell formation
  • Values
  • Range 0 60
  • Typical 5 30

14
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

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
15
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
  • Low values are indicative of environments
  • in which shear related processes will
  • play a significant role
  • Helps distinguish between supercell and
  • multicell formation
  • Values

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
16
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

17
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

18
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

Good for tornado forecasts
19
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

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
20
Energy Helicity Index (EHI)
  • Parameter used to characterize the likelihood
  • of supercell formation and tornadic activity
  • 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 0.0 30.0
  • Typical 0.0 5.0
  • gt 0.5 Supercells Likely
  • gt 1.0 Tornadoes Likely

21
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

22
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

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
23
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)
  • Developed by Thompson et al. (2003)
  • Values gt 1.0 suggest supercell formation is
    likely
  • Significant Tornado Parameter (STP)

24
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

25
SPC Sounding Analysis
26
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
  • Lets look at these simulations in detail

27
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 rear- flank 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.
Write a Comment
User Comments (0)
About PowerShow.com