As illustrated below, values are least reliable where surface observations are sparse and
the background model forecasts of related variables such as precipitation are biased.
Not exact matches
My
background is in economic and fiscal
modelling and
forecasting.
Mark brings his highly accomplished
background in budgeting,
forecasting, financial
modeling as well as technical systems evaluation and analyses to his role at Tavistock Restaurant Collection.
Elsewhere, the
background forecast model plays a stronger role, helping values of surface air temperature to be derived from other types of observation, such as sea - surface temperatures and winds.
(
Background chorus) They
forecast things for foolish kings and not precocious toddlers They are the very
model for all Modern Climate Modellers.
This is why there is little faith placed in CAGW
forecasts, any one who knows anything about how the weather really works, understands the real drivers are not even understood enough to used in
models yet, and with out considering the
background patterns of the seasonal, annual, decadal trends that determine how the weather works, are even used in weather
forecasting, in a viable active method, why should ANY confidence be placed in CAGW long range unverifiable
modeled forecasts?
ERA - Interim combines information from meteorological observations with
background information from a
forecast model, using the data assimilation approach developed for numerical weather prediction.
Elsewhere, the
background forecast model plays a stronger role, enabling values of surface relative humidity to be derived less directly from other types of assimilated observation.
Values of relative humidity over sea are taken from the
background forecast model not the analysis, for consistency with what is done for temperature.
Their methodology combines information from weather observations with
background information provided by a
forecast model.