Preface |
Introduction |
Will Thunderstorms Develop?
If Thunderstorms Develop Will They Become
Severe?
Types of Severe Weather Phenomena |
Summary
Return to PCU 2
The purpose of this instructional component is to present techniques that can be used to evaluate the potential for severe convection for your region using synoptic-scale data. Using knowledge of physical processes, convective parameters, and pattern recognition, forecasters should be able to assess the threat of severe convection over their forecast region in manner similar to the Severe Weather Outlooks issued by the Storm Prediction Center (SPC). This component does not address any details concerning mesoscale processes and the continuous monitoring necessary to fine tune the spatial and temporal resolutions of severe convective forecasting. Information on mesoscale processes, storm structure, and evolution are presented in successive units of the Forecasting Severe Convection Professional Development Series.
Convective forecasting can present many challenges for you as a forecaster at Weather Forecast Office (WFO). Assessing the potential for thunderstorm occurrence in your forecast area is a routine part of the forecast preparation process. How do you decide whether to include thunderstorms into your forecast? Will thunderstorms initiate in your forecast area or will they develop outside your area of responsibility and move into your forecast area? Forecasters need to be able to assess the synoptic-scale environment to determine if current or future large scale processes and patterns are favorable for thunderstorms. The purpose of this document is to present a conceptual approach to performing a 4-D assessment of the synoptic-scale environment to evaluate the potential for thunderstorm occurrence in your region.
During the forecast preparation phase, McNulty (1995) presents four questions that must be considered by the forecaster in addressing the occurrence of thunderstorms.
For the initiation of deep, moist convection (thunderstorms) there are three primary ingredients needed (McNulty 1985, Doswell 1987, Johns and Doswell 1992):
Even though these ingredients present an overly simplistic view of thunderstorm formation, the challenge for a forecaster is diagnosing them and then evaluating how they will change in both time and space using synoptic and sub-synoptic data sets.
To assess the potential for deep, moist convection, a forecaster must be able to diagnose the current dynamic and thermodynamic structure of the troposphere and to forecast changes resulting from thermal advection, moisture advection, and vertical motion fields.
Convective instability refers to a vertical temperature structure in which the wet-bulb potential temperature decreases with height. This structure will allow the upward acceleration of rising parcels of air once these parcels move above the Level of Free Convection (LFC). Numerous parameters have been developed to measure this convective instability. A couple of the more common ones include the Lifted Index (LI) (Galway 1956) and the more inclusive measure known as Convective Available Potential Energy (CAPE). Figure 1 shows an example of CAPE from the Eta model as displayed on AWIPS using D2D.
Figure 1. Example of CAPE from Eta model available on AWIPS. Contour units are j/kg.
At times, indices may be unrepresentative of the convective potential and may not provide a complete assessment of the thermodynamic environment. Examination the vertical structure of the atmosphere via sounding analysis is typically a better and more thorough approach. Sounding analysis provides a better insight than indices alone into atmospheric processes that affect thunderstorm formation. Interactive sounding programs, as found on AWIPS, allow forecasters to modify the sounding data to reflect temperature and moisture changes that might be anticipated within the next 6-12 hours.
Sounding analysis also allows forecasters to assess the effect of near-surface Convective Inhibition Energy (CIN) (Colby 1984, Bluestein 1993) on thunderstorm development. Convective Inhibition Energy measures the energy in the negatively buoyant region through which rising parcels of air must travel before reaching the LFC. As the negatively buoyant area increases, the magnitude of the near-surface forcing must increase if rising parcels of air are to move from the boundary layer to the LFC. If the CIN is large enough, thunderstorms may be suppressed due to the inability of rising parcels to reach the LFC. With low CIN, it is easier to bring rising parcels to the LFC. Forecasters must also judge the impact of diurnal and advective processes on changes in the magnitude of the negative buoyancy.
Occasionally thunderstorms develop above a stable boundary layer where there is no obvious moisture or convergence source at the surface. These storms form above the boundary layer, often along a sloping frontal surface. These storms, known as elevated thunderstorms (Colman 1990a, b), are one of the more challenging events to anticipate because there is a lack of observed data above the surface layer to make a forecast judgement.
Data from a 4-year period were used by Colman (1990a,b) to examine the dynamic and thermodynamic environment associated with elevated thunderstorms. Colman concluded that these types of thunderstorms typically occurred above a frontal inversion with the following conditions: mid-tropospheric warm air advection; shallow fronts; stronger than normal wind shear; and extremely stable surface layer.
In situations where there is isentropic lift drawing warm, moist, unstable air over a more stable, boundary layer air, the scene is often set for elevated thunderstorms. In these situations, forecasters should not base stability measures on surface or boundary layer air, but on air parcels that are being lifted above the stable layer. By choosing the most unstable parcel in the lowest 300 mbs when analyzing soundings (observed or model forecasts), forecasters will have a much better opportunity of assessing the potential for convection whether it be elevated or rooted in the boundary layer.
Colman (1990a) found that conditional symmetric instability (CSI) may be a significant factor in the formation of elevated convection. Colman (1990b) concluded that elevated thunderstorms occurring in convectively stable environments are most commonly the result of frontogenetic forcing in the presence of weak symmetric stability. Analysis of gridded data or isentropic cross-sections may be the key to improving our ability to better anticipate elevated thunderstorm development.
Numerical model forecast soundings can be displayed on AWIPS using the D2D application. Forecasters can select a gridpoint and display a model forecast sounding of the thermodynamic and wind profiles which can show how key elements such lapse rates, depth of the moist layer, CIN, etc., are predicted to change with time. Model forecast soundings can also be modified to account for any model biases or adjustments in temperature or moisture that the forecaster determines appropriate.
The importance of both the horizontal and vertical distributions of moisture to thunderstorm forecasting has been recognized by forecasters at least since the early 50's (Means 1952, Beebe and Bates 1955). Traditional techniques used by NWS forecasters include analysis of isodrosotherms on surface charts and constant pressure charts. These analyses allow the identification of moisture advection and moisture gradient patterns. Moisture advection is used to anticipate short-term changes in moisture. These changes can substantially affect convective instability estimates or reinforce or modify existing moisture gradients. Advective changes may be very rapid in some situations. For example, moisture advection by the nocturnal low-level jet over the Southern and Central Plains rapidly moves low-level moisture northward to produce the "moist tongue" that is seen by forecasters as a favorable pattern for thunderstorm initiation (Miller 1972). Horizontal moisture gradients locate boundaries such as drylines (Schaefer 1974, 1986a) that are favorable for thunderstorm development (Rhea 1966, Ziegler et al. 1997 ).
Figure 2. Eta surface equivalent potential temperature available on AWIPS. The units are degrees K.
Along with analyzing for dew point temperatures, moisture can be examined in terms of mixing ratio (Bothwell 1988), and in terms of equivalent potential temperatures as seen in Figure 2 (Scofield and Robinson 1990). These parameters are more conservative relative to changes in pressure or altitude than dew point.
Mixing ratio is combined mathematically with wind data to produce fields of moisture flux convergence. Maxima in moisture flux convergence indicate where moisture advection and/or mass convergence are strongest. The use of surface moisture flux convergence can be used to identify areas of mesoscale forcing. Figure 3 shows an example of moisture flux divergence from AWIPS in which moisture convergence is occurring in the regions of negative flux divergence.
Figure 3. ETA surface moisture flux divergence available on AWIPS. The units are g/kg/12hr.
The lift needed to produce thunderstorms can be discussed in terms of a synoptic-scale component, a mesoscale component, and their interaction. Studies of upper-tropospheric divergence (McNulty 1978) and lower-tropospheric warm-air advection (Maddox and Doswell 1982) indicate that synoptic-scale upward motion is present during most thunderstorm events. This synoptic-scale lift is usually associated with middle- and upper-tropospheric troughs, jet maxima, and isentropic lift. Figure 4 shows the traditional method of overlaying of 500 mb heights and vorticity and assessing the vorticity advection to infer synoptic-scale lift. Another technique that has been discussed in the literature as a better method for evaluating synoptic-scale vertical motion is examining Q-vectors and the divergence of Q. In Figure 5, synoptic-scale vertical motion is indicated where the Q-vectors are converging together.
Figure 4. ETA 500 mb heights (solid) and vorticity (dashed) available on AWIPS. The image can be used to estimate areas of vorticity advection.
Figure 5. ETA 500-300 mb layer average Q-vectors and divergence of Q available on AWIPS. The negative dashed lines represents upward vertical motion.
By itself, synoptic-scale lift does not typically generate or trigger convection but produces an environment conducive to the development of deep, moist convection. Synoptic-scale lift alone can destabilize an atmospheric column if the column is convectively unstable and if enough time is available to accomplish the required lift. In most situations there is not enough time available to produce significant destabilization and another source of lift is needed to help focus the ascent enough to release the convective instability and initiate convection (Doswell 1987). Mesoscale lift mechanisms provide this additional source of lift.
Mesoscale lifting mechanisms create near-surface convergence which forces boundary layer air upward. This low-level convergence and upward lift must be strong enough to carry the near-surface air to the LFC or higher. In situations where the negative buoyancy is very weak or absent, the role of the mesoscale lifting mechanism is more to initiate and maintain the supply of near-surface air to the developing storm.
The near-surface mesoscale lift is provided by three main features: boundaries, differential heating, and wind interactions with topography. Examples of features producing mesoscale sources of lift include fronts, dry lines, convective outflow boundaries (see Figure 6), convergence zones associated with sea- and lake-breezes, and orographically generated convergence zones..
Figure 6. An example of an outflow boundary as seen in visible satellite imagery available on AWIPS. The plotted data are from surface-based METAR observations using standard notation.
The identification of near-surface lifting mechanisms is frequently the key to anticipating the occurrence of thunderstorms in a specific location during a specific time frame. Examining a surface analysis combined with satellite imagery, radar, and derived fields (e.g., LAPS on AWIPS) can assist forecasters in timely identification of mesoscale convergence sources.
Once a forecaster decides that the potential for thunderstorms exist, the next questions that need to be asked are:
1) Will the thunderstorms become severe?
2) Is the environment conducive to producing large
hail, damaging winds and/or tornadoes?
Studies (McNulty 1988, Johns and Doswell 1992) indicate that there are two basic ingredients that need to be evaluated to help one differentiate a severe thunderstorm environment from a non-severe thunderstorm environment. These are instability and vertical wind shear. The importance of mid-level dry air or the intrusion of dry air at the mid-levels has been discussed as a primary ingredient, but largely it's a factor in determining the magnitude of convective instability. Mid-level dry air can aid in the development of strong convective wind gusts. The entrainment of dry air into a rain-saturated downdraft results in evaporative cooling that enhances the negative buoyancy of the downdraft.
Extreme convective instability, when released, provides the positive buoyancy needed to develop strong thunderstorm updrafts and large hail. Although extreme instability has not been explicitly defined, experience at the Storm Prediction Center indicates that LIs less than -8 deg C or CAPE values greater than 4000 j/kg represent thresholds of extreme instability.
Strong vertical wind shears have been recognized for several decades as a precursor to severe weather occurrence (Miller 1972, Schaefer 1986b). Theoretical and numerical modeling studies (Weisman and Klemp 1982, 1984, Rotunno and Klemp 1985) have demonstrated the relationship between the vertical wind shear and storm types. Low-level storm relative helicity (SREH) has been used to forecast the potential for mid-level mesocyclones in supercell thunderstorms (Brooks et al. 1993; Brooks et al. 1994a; Brooks et al. 1994b).
It has been recognized for years (Morgan and Summer 1985) that a necessary ingredient for the development of large hail is a strong updraft. Over the years techniques used to estimate potential hail size have been largely based on the positive buoyancy of rising air parcels (Fawbush and Miller 1953, Foster and Bates 1956). However, updraft strength by itself is not a sufficient indicator that large hail will develop. Hail development and size attained appear to be greatly affected by variations in storm-scale wind structures (see Nelson 1983).
A factor that can influence hail size at the ground is the effect of melting as hailstones fall through the freezing level to the ground. The melting process can be affected by the following (Johns and Doswell 1992):
The height of the wet-bulb temperature of 0 deg C (WBZ) has been correlated with the occurrence of hail. It approximates the freezing level height of downdraft air, within which the hailstone is likely to be found. The higher this level is, the longer the melting process can operate. Miller (1972) showed that a large majority of the reported surface hail occurred when the height of the WBZ is between 5000 ft and 12,000 ft above ground level (AGL). Large hail is most likely when the WBZ is between 7000 ft AGL 11,000 ft AGL.
Forecasting the occurrence of hail using synoptic-scale tools is a difficult problem. While radar techniques and hail-size estimation algorithms are helpful in short term forecasting, they are not very useful when assessing the threat of hail 12 to 24 hours in the future. While parameter evaluation can be useful in assessing the potential for hail, pattern recognition can be of some help, although pattern recognition plays a much larger role in the forecasting of damaging winds and tornadoes. One pattern typically associated with hail development is Miller's "cold core low" pattern. If sufficient moisture and instability are present near the cold core, low-topped thunderstorms can result with hail often being observed. In cold core patterns, the common occurrence of hail at the surface is aided by low WBZ heights and low mean temperatures below the WBZ height..
Once thunderstorms develop, the ingredients needed to produce damaging winds at the surface are those that promote strong downdrafts. The production of strong downdrafts have been attributed to precipitation drag, negative buoyancy caused by evaporative cooling, and downward transfer of momentum from middle levels of the atmosphere (Foster 1958). In downdrafts, the drag from water loading combines with the effects of negative buoyancy to enhance downdrafts.
A certain class of thunderstorm which produce wind gusts known as downbursts (Fujita 1976) can often cause severe damaging winds. Downbursts are classified as either microbursts or macrobursts depending upon their size, and as either wet or dry depending upon the environment that produced them. In most situations the environmental shear is weak, so an examination of the thermodynamic pattern can be used to identify when strong downbursts might occur.
Dry microbursts (Caracena et al. 1983; Wakimoto 1985) environments are typically associated with:
Wet microbursts profiles (Atkins and Wakimoto 1991) typically display high moisture values through a deep, surface-based layer, with the top of the moist layer sometimes extending up to 500 mb. During the day as heating occurs, a dry adiabatic lapse rate can develop and extend up to around 850 mb. Atkins and Wakimoto (1991) examined the vertical profiles of equivalent potential temperatures and found that on active wet microbursts days the difference in theta-e between the surface and mid-levels (600-700 mb) was consistently greater than 20 degrees K. On thunderstorms days when no wet microbursts activity was observed, theta-e differences were less than or equal to 13 degrees K.
A common convective structure associated with damaging winds that occurs in moderate to strong wind shear environments is the bow echo (Fujita 1978). Bow echoes are typically 20-120 km (10-65 nm) long bow-shaped systems which are noted for producing swaths of damaging winds. The curved bow echo structure represents the diverging outflow winds associated with a strong downdraft. It is indicative of mesoscale organization, enhanced outflow and damaging (downburst) winds. Bow echoes can develop from isolated storms or from pre-existing lines and may contain embedded supercells. For multiple bow echoes which are found in a squall line, the radar signature is referred to as a line echo wave pattern (LEWP). Occasionally, a persistent large-scale bow echo or series of bow echoes produces a succession of downbursts that affect a widespread area (or swath). This larger-scale wind event has been called a family of downbursts clusters (Fujita and Wakimoto 1981), or more recently, a derecho (Johns and Hirt 1987).
Numerical simulations (Weisman, 1993) suggest that long-lived bow echoes develop in environments that include CAPEs of at least 2000 j/kg and vertical wind shears of at least 20 m/s over the lowest 2.5 - 5 km AGL. Weisman further suggests that long-lived bow echoes (LBEs) favor environments where the majority of the shear is confined in the lowest 2.5 km. However, a more recent study by Evans (1998) examined 115 proximity soundings associated with observed long-lived bow echoes and found that LBEs were often observed to occur in very weak low level shear and within strong deep-layer shear. Evans found that more than half of the 115 soundings in his sample had 0-2 km shear less than 10 m/s, and less than half had a majority of the deep layer (0-6 km) shear confined to the lowest 2 km. These findings suggest that there is not just one distinct environment that produces LBEs. Other favorable environments also exist that have different CAPE and shear regimes.
There are two basic synoptic patterns (Johns 1993) associated with bow-echo development:
The warm season pattern is most commonly observed during the late Spring and Summer and is usually associated with progressive derechos. The dynamic pattern may occur at any time of the year, but appears to be least common during the middle and late Summer. Long-lived serial derechos have been associated with the dynamic pattern.
Browning (1964) presented a conceptual model of the airflow within specific types of thunderstorms which he called "supercells". Since that time, improvements in radar technology and numerical model simulations have made it possible to understand and redefine basic features, structure, and physical processes that occur with supercell thunderstorms (Ray et al. 1975, Klemp 1987, Weisman and Klemp 1984).
The basic definition of a supercell is a convective storm with a mesocyclone. A generally accepted definition of a mesocyclone is a cyclonic circulation associated with a thunderstorm updraft that has vorticity > 10 -2 s -1 , a lifetime on the order of tens of minutes, and is present through a significant depth of the convective's storm depth (Moller et al. 1994). Even though supercell thunderstorms are relatively rare, the ability to forecast the location and timing of them is significant because of the substantial amount of property damage and deaths they produce in comparison to other severe weather phenomena.
Tornadoes can be subdivided into two basic groups: supercell tornadoes and non-supercell tornadoes (Wakimoto and Wilson 1989, Doswell and Burgess 1993). Most of the numerical studies and research has focused on tornadoes associated with supercell thunderstorms. From the results of numerical studies, research, radar and visual observations, it is believed that supercells are associated with most strong (F2-F3) and violent (F4-F5) tornadoes. However, not all thunderstorms with mesocyclones result in tornadoes. In fact, studies indicate that less than 50% of supercells actually produce tornadoes (Burgess et al. 1993).
Most tornado forecasting has centered around predicting environments favorable for supercells. Only in recent years has the research and technological advances allowed forecasters to attempt to distinguish between tornadic and non-tornadic mesocyclone environments. This section will focus on operational forecast tools that can be used to help diagnose mesocyclone environments and furthermore to aid the forecaster in distinguishing between tornadic and non-tornadic environments.
The genesis of mesocyclones and associated tornadoes is described as a three-stage process (Davies-Jones and Brooks 1993). The first stage is associated with the development of a mid-level mesocyclone (3-7 km AGL) through the generation and tilting of low-level streamwise vorticity into a thunderstorm updraft. The second stage involves the development of a low-level mesocyclone. The low-level mesocyclone does not simply build downward from the mid-levels, but appears to be linked to tilting of low-level baroclinic vorticity (Davies-Jones and Brooks 1993, Rotunno 1993). The final stage is the development of a tornado. In recent years, most of the research suggests that the occurrence of tornadoes in supercells is associated with the development of a low-level mesocyclone.
Numerical modeling studies by Rotunno and Klemp (1985), Davies-Jones and Brooks (1993), Wicker and Wilhelmson (1995), and Wicker (1996) have discussed similar mechanisms in the development of the low-level mesocyclone, but present subtle differences in the development of the tornado. Brooks et al. (1994a,b) discussed the role of the mid-level winds in the evolution and development of low-level mesocyclones. They hypothesized that differences in the distribution of precipitation within a supercell, resulting from changes in the storm-relative winds, was responsible for changes in low-level mesocyclone development. Davies-Jones and Brooks (1993) discussed how the presence of evaporating precipitation associated with the rear-flank downdraft was important to the development of the low-level mesocyclone. Therefore, when the mid-level storm-relative winds are weak, much of the precipitation falls near the thunderstorm updraft resulting in a strong cold outflow undercutting the updraft thereby shortening the life-cycle of the low-level mesocyclone. On the other end of the spectrum, if the storm-relative winds are too strong, the precipitation is carried away from the updraft and no low-level mesocyclone develops. Their findings suggests that in order for long-lived tornadic mesocyclones to develop there needs to a balance between the mid-level storm-relative winds, storm-relative environmental helicity, and low-level absolute relative humidity.
Vertical wind shear through a substantial depth of the atmosphere has been shown to be one of the most critical environmental factors affecting the development and evolution of thunderstorms (e.g., Weisman and Klemp 1982, 1984, Rotunno and Klemp 1985, Weisman 1993). Even as far back as the 1950s, it was recognized that changes in wind speed and direction from the surface to the upper levels of the atmosphere influenced the type of severe weather phenomena observed. Over the years, a variety of parameters have been developed to assist forecasters in evaluating vertical wind shear and stability in the atmosphere. A brief discussion of those that have operational applicability to forecasting mesocyclones and tornadoes will be presented in the following text.
Weisman and Klemp (1982, 1984) formulated a relationship between wind shear and buoyancy referred to as the Bulk Richardson Number (BRN). They showed that it could be used to delineate between different storm types such as supercell and multicell storms. The results of their work concluded that modeled supercells were likely when 5 < BRN < 50, and multicell storms were likely when BRN > 35, when CAPE values ranged between 1500 and 3500 j/kg.
Vertical wind shear and storm type can be evaluated from wind hodographs. Weisman and Klemp (1986) summarize the results of Chisholm and Renick (1972) that illustrate the typical wind hodographs (see Figure 7) associated with single-cell, multicell, and supercell thunderstorms. Hodographs that display a straight-line shear profile with strong shear favor the development of splitting storms with the left-moving storm rotating anticyclonically and the right-moving storm rotating cyclonically. Clockwise (counterclockwise) curved hodographs with strong shear favor the development of a right-moving cyclonic (left-moving anticyclonic) supercells.
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Figure 7. Typical wind hodographs for (a) single cell, (b) multicell, and (c) supercell thunderstorms. Adapted from Chisholm and Renick, 1972.
Storm-relative helicity (SREH) (Davies-Jones et al. 1990) has been used to identify environments that may produce thunderstorms with rotating updrafts. Helicity is the product of the storm-relative winds and the streamwise vorticity calculated over the lowest 0-2 km or 0-3 km depth of the atmosphere. Generally, by examining regions where CAPE values are positive and SREH values are greater than 100 m2/s2, one can identify areas where supercell thunderstorms are likely if convection develops. Brooks et al. (1993) discuss that, although SREH is a useful indicator of mid-level mesocyclone environments, it is not very effective in distinguishing between tornadic and non-tornadic mesocyclones. Figure 8 is an example of a hodograph showing the 0 to 3 km storm relative helicity (hatched area) as depicted by the interactive skew-T found on AWIPS.
Figure 8. Eta 12-hr forecast hodograph for a designated grid point available on AWIPS.
Johns et al. (1993) constructed a scatter diagram that related 0-2 km helicity to CAPE for 242 strong and violent tornadoes. These data showed an inverse relationship between helicity and CAPE. In general, as the 0-2 km helicity increased (decreased), CAPE values decreased (increased). Hart and Korotky (1991) used these results to combine the effects of buoyant energy and helicity into one parameter known as the Energy-Helicity Index (EHI). Studies by Davies (1993) and Rasmussen and Blanchard (1998) show a statistical correlation between EHI and significant tornado occurrence. EHI values greater than 2.0 can indicate a good potential for tornadic supercells.
Recently the Bulk Richardson Number (BRN) shear has been
examined to see whether it can
help discriminate between tornadic and non-tornadic mesocyclonic
environments (Stensrud et
al. 1997, hereafter SCB97, Thompson 1998). The BRN shear is the
denominator of the BRN
(Weisman and Klemp 1982) and is defined as the magnitude of the
difference between the 0-6
km density-weighted mean wind and the density-weighted mean wind
in the lowest 0.5 km.
Results from the Stensrud et al. (1997) mesoscale model
simulations suggested that
values of BRN shear between 40 and 100
m2/s2 were associated with
storms that produced low-level mesocyclones while values of BRN
shear below 40 m2/s2
were associated with primarily bow echoes and straight-line wind
storms. They
concluded that the potential for tornadic supercells may exist by
identifying regions that
contain values of positive CAPE, SREH values greater than
100 m2/s2,
and BRN shear values greater than 40
m2/s2. Although the
results from their study are encouraging, their work was based on
a small sample size (9
events), and the parameters were computed from a mesoscale model,
not actual sounding
data. Thompson (1998) study yielded similar results with
tornadic storms occurring
in areas where the BRN shear values ranged between 25 to 100
m2/s2.
However Thompson's study showed a clustering of BRN
shear values between 20
to 50 m2/s2 for non tornadic
supercells with 39% of the non
tornadic cases exceeding the lower bound of 40
m2/s2 discussed
in SCB97.
A word of caution needs to be mentioned concerning the
calculations of the BRN shear
values used in the aforementioned studies. Thompson's BRN
shear values were
calculated from Eta model PCGRIDDS data, whereas the BRN shear
values used in SCB97 were
calculated from the Pennsylvania State University-NCAR Mesoscale
Model (MM4). Also
the vertical resolution of the Eta PCGRIDDS data is much coarser
than MM4 model and can
introduce errors in BRN shear calculations.
This discussion has focused on assessing the synoptic environment to determine if current and/or future large scale processes are favorable for convection. Specifically it has addressed four basic questions that should be considered during a forecast shift:
Before a forecaster can make a good assessment of the convective potential, he/she must have a clear picture of what is currently happening in both the synoptic and mesoscale perspectives. A 4-dimensional analysis of observed data such as surface, upper air, satellite, etc., is essential in identifying whether the necessary convective ingredients are coming together in the same geographical region at the same time.
Much of this discussion has focused on evaluating physical processes and parameters on the synoptic-scale with the understanding that forecasting convection involves processes that spread over several temporal and spatial scales. In order to perform a regional assessment for the threat of severe convection, one needs to have a thorough understanding of mesoscale processes and how they relate to storm structure and evolution. This material will be presented in successive units as part of the Forecasting Severe Convection Professional Development Series.
Original material 10/06/99 ... format updated 9/01/00