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EGDI Algorithm
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Algorithm Description
The EGDI algorithm has the purpose of aiding with the forecasting
of tropical and subtropical convection. It plots a set of 6
parameters: (1) An enhanced version of the GDI, the E-GDI,
(2) low-level flow, (3) low-level water vapor flux divergence
and convergence, (4) upper- tropospheric flow,
(5) upper tropospheric divergence and (6) GDI advection
and flux vectors. The role of each component is described in the following:
(1) E-GDI (shaded):
The Enhanced Galvez-Davison Index (EGDI) is an improved version of the
Galvez-Davison Index (GDI) developed to improve the performance of the
GDI, especially in the deep tropics.
As the
GDI,
it evaluates the environment's potential to sustain
different types of tropical convection.The enhancement
consists of minor adjustments based upon four additional
variables that affect tropical convection. These are:
low-level moisture convergence, upper-tropospheric convergence,
upper-tropospheric relative humidity and precipitable water.
Important: The E-GDI is under development, so the calculation
algorithm and online grids are only in the experimental phase.
Forecasting practices at the International Desks have, so far,
found that the E-GDI often beats the GDI as a predictor for
tropical convection. This observation is,
however, only in qualitative stage at this point.
(2) Low-level flow (black):
In the NGDI algorithm, the low-level flow consists of a layer-average
of the winds between 1000 and 850 hPa. Streamlines are shown in black,
and barbs are plotted when layer-averaged winds exceed 10 kt.
The field allows to find surface feature that modulate convection
such as fronts, shear lines, the Intertropical Convergence Zone
and perturbations in the trades among others. Wind barbs also
allow to evaluate - qualitatively- potential regions of
convergence or divergence, orographic forcing, shear
and low-level jet dynamics among others.
(3) Low-level water vapor flux divergence (green and brown):
This parameter consists on a layer-average of water vapor flux
divergence. The 950-700 hPa layer is selected among other
layers by matching areas with convective development in satellite
imagery. Moisture convergence (green contours) relates to low-level
moisture inflow into the column and to dynamically-induced ascent.
The latter is a trigger mechanism for convection. Conversely,
moisture divergence (brown contours) favors sinking
motion and dissipation of convection.
(4) Upper-tropospheric flow (gold):
Consist on a layer-average of the flow between 400 and 200 hPa.
The purpose is to show the positions of upper troughs and ridges.
Wind speeds are not included in the plot to avoid
overcrowding the graphics.
(5) Upper-tropospheric divergence (thick yellow contours):
Enhanced divergence in the 400-200 hPa layer is shown in
thick yellow contours. High values generally relate to
regions of enhanced dynamically-driven ascent. Note
that thunderstorms in the model can also produce enhanced
divergence. This makes awareness about a slight bias
towards the convective parameterization important.
(6) GDI advection and flux (red contours and arrows):
Red contours show areas of positive GDI advection.
This quantity is calculated only for GDI>30. Regions with
GDI<30 are set to zero. The threshold of 30 is chosen because
higher values often indicate a potential for thunderstorms.
So the red GDI advection contours mean areas into which the
potential for thunderstorms is being transported. As such,
these environments are likely to become increasingly favorable
for deep convection during the forecast time and following hours.
GDI flux vectors are also plotted. Both advection and
flux are calculated using the flow averaged over
the cumulonimbus layer (850-200 hPa). In this calculation,
more weight is given to low- and mid-tropospheric flow,
where the bulk of water vapor and cloud liquid water lie.
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