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Chapter 13 Weather Forecasting and Analysis. Weather forecasting by the U.S. government began in the 1870s when Congress established a National Weather Service under the authority of the Army Signal Corps. The National Oceanic and Atmospheric Administration

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Chapter 13

Weather Forecasting and Analysis


Weather forecasting by the U.S. government

began in the 1870s when Congress established

a National Weather Service under the authority

of the Army Signal Corps.

The National Oceanic and Atmospheric Administration

(NOAA) was established in 1970 to include a number of

environmental agencies, including the

National Weather Service.


The Meteorological Service of Canada (MSC),

located in Downsview, Ontario,assumes all

forecasting duties for that country and

provides local and regional information

to its 14 regional weather centers.


Forecasts based on long-term information in

the absence of any data about current weather

are called climatological forecasts.

The reliability of a climatological forecast

depends on year-to-year variability

in weather conditions for the forecast day.


A persistence forecast relies completely on

current conditions with no reference to climatology.

This simple procedure might work for a little while

but will eventually fail to catch changes in weather.


The analog approach tries to recognize

similarities between current conditions and

similar well-studied patterns from before,

assuming that what happened sometime

in the past provides a clue about the future.


Numerical weather forecasting is based on

computer programs that attempt to mimic the

actual behavior of the atmosphere.

The numerical models typically used in

weather forecasting are very large and can

only be run on the most powerful computers,

so-called supercomputers.


Quantitative forecasts specify the “amount”

of the forecast variable. For example,

forecasts of the expected high or low

temperature are quantitative because

a value for the forecast variable is provided.


Qualitative forecasts provide only a

categorical value for the predicted variable.

For example, in a forecast of rain,

the predicted variable is assigned

to a particular class or category.


In a probability forecast, the chance of

some event is stated.

The most common example is the

probability-of-precipitation forecast

(PoP forecast).


Forecast quality refers to the

agreement between forecasts and observations.

Forecast value refers to the utility

of a forecast and necessarily depends

on the application of a forecast to

a particular problem or decision.


The quality of a forecast concerns

forecast accuracy.

That is, on average, how close is

the forecast value to the true value?


Forecast bias concerns systematic

over- or under-prediction.

A biased forecast method is

one whose average forecast is

above or below the true average.


The mean absolute error (MAE), ignores

the sign (positive or negative) of the errors.

That is, over- and under-predictions

are treated the same.


Forecast skill is defined as the improvement

a method provides over what can be obtained

using climatology, persistence, or some other

“no-skill” standard.


Hydrogen-filled balloons carry weather

instrument packages called radiosondes.

Radiosondes tracked by radar are called rawinsondes.


The general procedure for

all numerical models includes

the following three phases:

The analysis phase, in which observations

are used to supply values corresponding

to the starting state of the atmosphere

for all the variables carried in the model.


The prediction phase begins with values

delivered by the analysis phase using

governing equations to obtain new values

a few minutes into the future.

The process is then repeated,

using the output from the first step

as input for the next set of calculations.


In the post-processing phase,conditions

forecast by the model at regular intervals

are represented in grid form for mapping

and other display purposes.


Ensemble forecasting involves a number of

different model runs performed for

the same forecast period starting with

slightly different initial values.


If two model runs are made with

slightly different initial values,

the results might be very different

after a week or so.

This behavior is now known to be

typical of many natural and human systems,

and is referred to as chaos.


Long-range forecasts include climatology, statistics,

numerical models, and subjective judgment to produce

forecasts for periods ranging from a week to

the limits of technical feasibility.


Surface maps of prevailing conditions present

a general depiction of sea level pressure distribution

and the location of frontal boundaries.


Station models offer detailed knowledge of the

conditions at a particular location with over a dozen

weather elements represented on each station model.


Visible images view the atmosphere by registering

the intensity of reflected shortwave radiation and

are available only during the daytime.


Infrared images are based on measurements of

longwave radiation emitted (not reflected) from below.


Radar images observe the internal cloud conditions

by measuring the amount of radiation backscattered

by precipitation (both liquid and solid).


Vertical profiles of temperature

and dew point observed by

radiosondes are plotted on thermodynamic diagrams

(pseudo-adiabatic charts).

On the Stuve chart, air temperature is scaled along the horizontal axis and pressure is on a nearly

logarithmic vertical axis. The straight, solid lines are dry adiabats and the dashed, slightly curved lines are wet adiabats, showing temperature changes in a

rising saturated parcel.


The lifted index combines the

average humidity in the lowest kilometer

of the atmosphere, the predicted

maximum temperature for the day,

and the temperature at the 500 mb level

into a single number.

The K-index uses values of temperature

and dew point at the surface and the

850, 700, and 500 mb levels.


The next chapter examines human effects:

air pollution and heat islands.