The statistical method known as Bayesian Rule is used to infer the likelihood of a region
being either 'melting snow' or 'other precipitation', using a priori information and posterior
conditioning. The a priori information is the first estimate of the 0 °C heights from an
outside source. This could be automatically inserted from radiosonde or NWP data, or could
come from the climatological values found in IRIS/Setup.
The likelihood of the MLHGT can be updated by each subsequent radar observation over
time building up a confidence in the estimate. If there are a high number of 'melting snow' or
'other precipitation' bins in a region, and the ratio of one condition versus the other is high,
confidence increases quickly. Likewise, if there is a low number of classifications in the
region or if the ratio of 'melting snow' to 'other precipitation' is almost equal, the confidence
remains low. Once the minimum confidence is reached the melting level height form MLHGT
is assigned to that region, else the height remains to be the climatological value. The
Bayesian inference can be conceptually understood as constructing a display of vertical
cross-sections in which the most likely position of the ML can be visually recognized by
inspection of the MeltClassifier decisions.
The regions are presented as columns of data in an Earth co-ordinate system. The user is
allowed to configure the vertical resolution and azimuthal size of each region. The columns
size in range domain are scaled to match their size in azimuth. The columns range extent
grows proportionally as the size in azimuth as distance from the radar increases. Thus the
columns are eectively the same shape throughout the domain. This is advantageous
because the statistics is improved at farther distances, due to an increasing number of data
points, which compensates for the loss in resolution by beam broadening.
Chapter 3 – Configuring IRIS Products
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