|FORECAST MODEL DATA INPUT
METEOROLOGIST JEFF HABY
A forecast model is only as good as the data put into the model. The
synoptic scale models rely on data from
these sources: Rawinsondes, aircraft observations, surface observations, satellites, and other remote sensing sources.
It is over remote locations and just
downwind from remote locations
that forecast models have the greatest error. The
three most important remote location regions are mountainous terrain, desert terrain, and the oceans. Over these three
locations, surface observation stations are very scattered. Upper air data is not as dense either. The locations near
the U.S., that have sparse data, are Mexico, and the oceans.
Storm systems that move from the ocean or from Mexico
are generally not handled as well by the forecast models as they could be. Examples include low pressures moving from
Mexico and into the U.S. and a
trough of low pressure moving from
the Pacific Ocean and into the U.S. Over the last
few years, satellite data has increased the accuracy of forecast models and allowed a better sampling of remote
locations. Satellite data can also be used for numerous forecasting purposes, especially
severe weather. Examples
of meteorological data that satellites provide are
LI, cloud top pressures, sea surface temperatures and
temperature profiles of the atmosphere. Although these indices and temperature profiles are not as accurate as
the rawinsondes, the data is accurate enough to be used for forecasting purposes and adds good data to the models.
Rawinsondes can only sample at point locations, but satellites can cover a broader area more uniformly. One of
the primary limitations of satellite data is it can not detect meteorological conditions below clouds. Only in
clear sky or nearly clear sky conditions can the satellite sensor detect CAPE, LI, temperature profile of the atmosphere,
etc. You can find ALL current satellite derived products at the following web site.