Small fonts - This option simply controls what size and
type of fonts are used to label contour lines on analysis charts. The same
thing, except with "small fonts" unchecked and "negative fonts"
Negative fonts - Contour labels are plotted as white on
black, rather than black on white.
Transparent fonts - Controls whether the map below bleeds
through contour labels.
Specifies which mathematical algorithm is to be used to analyze the data
- Nearest neighbor — This is by far the quickest, and most users will use
this setting. Values are mapped to the closest gridpoint, then are
mathematically expanded to neighboring unfilled gridpoints in a cyclical manner.
Afterwards, a selective smoothing operator smooths gridpoints, performing more
smoothing iterations performed on gridpoints that are further from
representative data areas and less or no smoothing passes where data is close to
a gridpoint. Strengths: It's fast and always works. Weaknesses: Data void
border areas look linear, and data void interior areas can look pie-shaped.
- Weighted — Digital Atmosphere looks at the
neighboring stations around a gridpoint, giving it an average of those values
around it with more emphasis on the values at closer stations.
- Barnes — The Barnes method, the cornerstone of
the MCIDAS and GEMPAK weather workstations, has a number of advantages:
versatility, simplicity, and speed. Each gridpoint is assigned a
meteorological value which is the result of a Gaussian distance-dependent
weighting function. A difference field is calculated, which determines how far
off from reality the analysis field appears to be, then a difference
correction is applied to the analysis field. This takes a relatively long time
to compute, but is one of the more accurate analysis algorithms.
- Cressman — Each gridpoint is assigned a meteorological value which
is the result of a distance-dependent weighting function. The Cressman analysis
method is similar to the Barnes technique, however it makes successive
corrections using a smaller and smaller radius of influence to eliminate all
errors. It takes more time, but the results are good. In data-sparse areas, it
is common for there to be erroneous contours, especially if Digital Atmosphere
is tasked to determine the best station spacing. Strengths: It's
sophisticated and flexible, and it may retain more mesoscale structure.
Weaknesses: It can't resolve short-wavelength
components, it can require a large amount of time, and it can fail with
extreme data distribution.
These are used to control the performance of the
selected analysis type. Certain choices may be grayed out depending on the
analysis type selected and whether "automatic smoothing" is in effect.
- Smoothing coefficient - Sets the mathematical value of "s" for the smoothing
equation. This affects what kind of wavelengths will be filtered in the
- Smoothing passes - Determines how many times a
smoothing operator will operate whenever it is called. With this, you can set
how "smoothed" the maps will look when the analysis is complete. This should
be set to "1" or to a low non-zero value.
- Surface radius -Determines a radius of influence
used in the Weighted, Cressman, and Barnes analysis method for surface data.
This value should approximate the average spacing between surface stations.
- Upper radius - Determines a radius of influence
used in the Weighted, Cressman, and Barnes analysis method for upper air data.
This value should approximate the average spacing between upper air
- Gamma - Determines a gamma used in the Barnes
analysis method for upper air data.
- Reduction -
Determines a reduction value used in the Cressman analysis
method for upper air data.