Forecasting tomorrow`s weather - Little Shop of Physics

How can I
forecast
tomorrow’s
weather?
A laboratory experiment from the
Little Shop of Physics at
Colorado State University
Overview
While there are considerable difficulties in forecasting the day-to-day weather accurately in a
quantitative sense, it is relatively easy to make
your own qualitative forecast. A relatively good
forecast can be made one day in advance based
on knowledge of the weather conditions the
day before. By completing the basics of this
exercise, you should be able to make an informed decision as to whether it will rain, the
relative intensity of the rain, and whether it will
be hot or cold. The more advanced areas of
the following activities will help you to develop
a more quantitative forecast.
Theory
CMMAP
Reach for the sky.
Necessary materials:
• NASA Hurricane tracking activity packet
• An example station plot map with sea level pressure contours
• Access to the past few days of weather analyses,
including surface data with station plots as well as
upper-level data
• Access to current local model output statistics
(MOS)
The example station plot map can cover various US
locations and can be created at
http://vortex.plymouth.edu/surface-u.html
Recent weather analyses can be found at the above
address or at http://weather.unisys.com/archive/
Most information for a qualitative forecast can
Local MOS can be obtained from
be seen on a simple weather map created by
http://www.weather.gov/mdl/synop/products.php
taking observations at many different places at
the same time. If you see a low pressure area
that has been moving eastward toward your current location, what type of weather might you expect? If
a cold front is moving southward out of Canada, what would you soon expect the temperature to do? If it is going to be cloudy
tomorrow, will it be warmer or colder than it
was today? Often the best qualitative forecast follows from persistence. If it is warm
and sunny today, and it was warm and sunny
yesterday, the odds are pretty good that it
will be warm and sunny tomorrow, that is,
unless you see something else heading in on
your weather map.
You can do a lot to forecast tomorrow’s weather just by looking at a
weather map.
1
In order to predict specific quantities in a
forecast, such as temperature, humidity, and
rainfall, we rely on computer models. Numerical weather prediction (NWP) uses
a system of equations that describe the be-
havior of the atmosphere. NWP models read in the current state of the weather from observations as an
initial condition and then calculates the solutions to the equations that will tell us what the weather will be
a few seconds into the future. Then it uses that information and repeats the calculations many times until
it gives a quantitative forecast for a few days into the future.
Meteorologists like to run many versions of the same forecast with slight differences in the initial conditions to account for a lack of perfect observations. The multiple forecast runs are then averaged together
as a summary of the predicted state of the atmosphere. Added to this information is information about
how the weather acted the last time that similar weather conditions existed. So if the weather five years
ago was very close to what it is like today, it is likely that tomorrow’s weather will be very much like what
happened five years ago, too. This summary is known as Model Output Statistics (MOS). MOS is
often called “model guidance” because forecasters will use the generated numbers as a guide to make
their forecast. The results are never perfect, and different models give different forecasts, too. Therefore,
forecasting usually takes some intuition, which is why most people can read the different MOS data sets
and come up with reasonable forecasts.
Doing the Experiment
This experiment includes a number of activities that progressively lead from qualitative to quantitative
forecasting.
Part I: Hurricane Tracking
• In this section, students will use satellite images to predict when and where a hurricane will hit.
• Big idea: Observations can lead too future predictions.
• The hurricane tracking packet includes a hurricane tracking chart and satellite images of Hurricane Georges moving through the Caribbean Sea at 8 different points in time.
• Begin by placing an “X” on the hurricane tracking chart for the first 4 positions of the hurricane.
• Connect the Xs with a dark or colored line.
• Based on what you have just drawn, see if you can predict where and when the hurricane will
hit the United States.
• Add the next day’s position to your chart and reevaluate your prediction. Has your predicted
time and location of landfall changed?
• Keep adding positions, and see where and when the hurricane made landfall.
• For further inquiry:
• Ask the students how this image was made. Is it radar, visible, infrared?
• Discuss the structure of the hurricane. Are the eye, eye-wall, or spiral rain bands visible?
Where would you expect the most damage from this storm? i.e., where is the storm surge
strongest?
• If hurricanes are a particular threat in your area, a discussion of the differences between a
hurricane watch and warning may be warranted. A few examples are listed on the hurricane tracking chart.
• If societal impacts are a focus of the course, research the history of this storm and its aftermath and others for comparison.
Part II: Analyze Your Own Weather Map From Station Plots
• Obtain a map of station plots as directed under Necessary Materials.
• Explain the coding of the station plots to your students.
• Details can be found at http://www.hpc.ncep.noaa.gov/dailywxmap/wxsymbols.html
• Read the data off of the station plot map and determine the location of cold and warm fronts
based on noted precipitation, wind shifts, pressure troughs, and rapid spatial temperature
changes.
2
• For further inquiry:
• Have students contour the sea level pressure or temperature surfaces before analyzing the
map for fronts.
Part III: Tracking Day-to-Day Weather
• In this section, you will compile a few days of national weather maps and satellite images (optional) in order to make a qualitative forecast for a location of your choosing.
• Collection of the maps can be done on a daily basis, making predictions for the next day based
on new observations or you can collect archived maps.
• Show the students each weather map in order to get a feel for how weather systems move across
the United States. Pay particular attention to cold and warm fronts, wind pattern, pressure patterns, and any precipitation.
• Make a prediction based on the movement of the weather like you did in Part I for the hurricane. Make sure to verify your prediction the following day.
• For further inquiry:
• Look at upper-level wind and height charts and determine wether surface cyclones will intensify based on upper-level convergence and divergence.
Part IV: Decoding Model Output Statistics
• Obtain MOS from at least two different models from the website listed under Necessary Materials.
• Explain the MOS coding system to the students.
• For details on this visit: http://www.weather.gov/mdl/synop/mavcard.php
• Compare the statistics given by each the models. How are they similar? How do they differ?
• Based on the information obtained in Part III and from the MOS data, make a quantitative
forecast for tomorrow’s high and low temperatures, averaged sustained and gusting wind
speeds, wind direction, sky conditions, and chance of precipitation. What are the chances of a
thunderstorm tomorrow?
Summing Up
After completing this set of exercises, you should have a feel for how meteorologists and you can forecast
tomorrow’s weather. It turns out that all you really have to do is read a map or some model data.
For More Information
CMMAP, the Center for Multi-Scale Modeling of Atmospheric Processes: http://cmmap.colostate.edu
Little Shop of Physics: http://littleshop.physics.colostate.edu
3
Weather Forecasting - Qualitative
 Most information for a qualitative forecast can be seen
on a simple weather map
 If you see a low pressure area that has been moving
eastward towards Colorado, what type of weather might
you expect?
 If a cold front is moving southward through Wyoming,
what would you expect the temperature to do?
 If it’
it’s going to be cloudy tomorrow, will it be warmer or
colder than it was today?
 Often the best forecast is persistence: if it’
it’s warm and
sunny today, and it was warm and sunny yesterday, the
odds are pretty good that it will be warm and sunny
tomorrow (unless you know something else)
How can I forecast
tomorrow’s weather?
Related Questions
Part I: Hurricane Tracking
In this section, students will use satellite images to predict when and where a hurricane
will hit the coast. BIG IDEA: Observations can lead too future predictions. The hurricane
tracking packet includes a hurricane tracking chart and satellite images of Hurricane
Georges moving through the Caribbean Sea at 8 different points in time.
1. Begin by placing an “X” on the hurricane tracking chart for the first four positions
of the hurricane.
2. Connect the Xs with a dark or colored line.
3. Based on what you have just drawn, see if you can predict where and when the
hurricane will hit the United States.
Prediction #1:
4. Keep adding positions from positions 5-8 one at a time, and see where and when
the hurricane made landfall each time you update your tracking chart.
Prediction #2:
Prediction #3:
Prediction #4:
Prediction #5:
5. Has your predicted time and location of landfall changed?
6. How this image was made. Is it radar, visible, infrared?
7. What do the colored parts of the hurricane indicate?
Station Plots
Wind speed,
direction, and peak
gust
Temperature (oF)
G23
Station
Pressure
72
Visibility
(miles)
998
9
Current
Weather
Dewpoint
(oF)
3 hour
pressure
change
-16
45
Sky
Cover
FNL
.09
Station
ID
3 hour
precipitation
Reading Station Plots
 Pressure
 000 to 499: 1000.0 mb to 1049.9 mb
 500 to 999: 950.0 mb to 999.9 mb
 Pressure Tendency
 In tenths of a mb change in 3 hours
 E.g., -32 is a drop of 3.2 mb in 3 hrs
 Temperature in oF
Reading Station Plots
Reading Station Plots
Station Plot
Weather Forecasting - Quantitative
 In order to predict specific quantities in a forecast
(temperature, humidity, rainfall) we rely on computer
models
 Numerical weather prediction (NWP) uses a system of
equations that describes the behavior of the atmosphere
 NWP model uses the current state of the atmosphere as
its initial condition and steps through a small time step,
recalculating every number for each step until the
forecast time is reached
So why aren’t forecasts always right?
 Observations aren’
aren’t good enough!
 A model is only as good as its initial conditions
 Even having an observation for every square meter of the planet would leave out
smaller details…
details…and we don’
don’t have even close to that many observations
 Computers aren’
aren’t fast enough!
 In order to truly create a perfect forecast, one would have to use the exact
equations on a really, really small spatial scale
 In order to create a model that will create a 12 hour forecast in less than 12
hours, we must approximate certain parts of equations and run the model on a
grid with spacing of multiple kilometers
 ~1.2 million grid points x thousands of calculations > 1 Billion calculations per time step
 More than 1 Quadrillion calculations per time step at 1 meter resolution (1012)
 More than 4 Quadrillion calculations to simulate 12 hours
 Forces us to make approximations to the equations that govern the atmosphere
 Little bit of error added at each time step
 Chaos reigns supreme!
 Ever heard of the butterfly effect? It’
It’s more than just a movie
Chaos Theory
 Also known as the “Butterfly Effect”
Effect”, coined by Dr.
Edward Lorenz in the 60’
60’s (he’
(he’s still teaching at MIT)
 Basically says that even the most insignificant change to
initial conditions will magnify into drastic changes
 The smallest disturbance will eventually grow into a large difference –
this limits the range of forecasts to just a few days
 Even if the initial conditions and computing power were
perfect,
perfect, chaos theory would limit us to a reasonable
range of about 2 weeks
 Computers cannot possibly predict the movement of a
butterfly, or how hard you step on the gas pedal
So How far have we come?
 100 years ago we just looked to the west.
 The first attempt at numerical weather prediction
by Lewis Fry Richardson was done by hand.
 On a WWI battlefield as part of an ambulance unit in
Northern France
 Just wanted to predict the pressure change over the
next six hours
 Calculation took him six weeks!
weeks!
 And he was off by over 140 mb…
mb…
Richardson’s Forecast Factory
How far have we come?



In 1937, the US started using weather balloons.
It wasn’
wasn’t until WWII that the existence of a jet stream was confirmed.
In 1948, ENIAC was put together by John von Neumann in a 30 by
50 room.
 In April 1950, the first 24-hr forecast was attempted.
 Took more than 24 hours due to breakdowns
 8 years later, forecasts began to show signs of skill.
 As it stands today, we can have a great deal of faith in a weather
forecast out to about 3 days.
 Just 30 years ago, we could only do 2 days
 Beyond that, the accuracy drops dramatically
 If you see a 15 day forecast…
forecast…don’
don’t believe it
MOS
 The output from a model can be shown in the form of
Model Output Statistics (MOS)
 MOS is a summary of the predicted condition of the
atmosphere at each forecast time
 MOS is often called “model guidance”
guidance”, because
forecasters will use the generated numbers as a guide to
make their forecast
 MOS isn’
isn’t perfect though, and forecasting takes some
intuition
The premise behind MOS
 The (preceding) models produce output describing the
weather over No. America and vicinity at grid points or in
“wave space”
space”.
 Conditions for this time of year at a specific city may
have occurred similarly in the historic past (i.e., old
climate data are used).
 Forecasts of current conditions can be made for a city
using the current model output and based on the historic
weather outcomes.
Downfalls of MOS
 There isn’
isn’t just one model that is used for NWP
 Multiple models are used that have differences in
resolution and in the equations used and assumptions
made
 The models never agree on everything
 A good forecaster will look at multiple model predictions
and have a feel for which model performs the best under
certain circumstances
Station Name – Artesia, NM
Date and Time the Model was run
Decoding MOS – Max and Min
Temperature
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
15 18
84
76
OV
23
09
85
75
OV
22
09
36
2
42/
42/
7 6
7 7
N N
0
0
4
7
N
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
5 4 4 4
7 7 7 7
N N N N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
Decoding MOS – 3 hourly
temperature forecasts
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
15 18
84
76
OV
23
09
85
75
OV
22
09
36
2
42/
42/
7 6
7 7
N N
0
0
4
7
N
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
5 4 4 4
7 7 7 7
N N N N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
Decoding MOS – Cloud Cover
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
7
7
N
15 18
84
76
OV
23
09
85
75
OV
22
09
36
2
42/
42/
6
7
N
0
0
4
7
N
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
5 4 4 4
7 7 7 7
N N N N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
Decoding MOS - Cloud Cover
• CL = Clear, no clouds
• SC = Scattered, between clear and sky
1/2 filled with clouds
• BK = Broken, 1/2 - totally cloudy
• OV = Overcast
Decoding MOS – Winds
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
9/04/2003
0600 UTC
/SEPT 6
15 18 21 00 03 06
89
84 85 83 80 79 77
76 75 74 74 75 75
OV OV OV OV OV OV
23 22 24 24 23 24
09 09 08 04 04 04
36
55
46
66
2
4
4
4
42/ 0 80/ 0 37/ 0
42/ 0
93/ 0
7 6 4 5 4 4 4
7 7 7 7 7 7 7
N N N N N N N
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
Meteorology Wind Direction
180 = From
the South
090 = From
the East
270 = From
the West
000 = From
the North
Decoding MOS – Chance of
measurable precipitation
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
9/04/2003
0600 UTC
/SEPT 6
15 18 21 00 03 06
89
84 85 83 80 79 77
76 75 74 74 75 75
OV OV OV OV OV OV
23 22 24 24 23 24
09 09 08 04 04 04
36
55
46
66
2
4
4
4
42/ 0 80/ 0 37/ 0
42/ 0
93/ 0
7 6 4 5 4 4 4
7 7 7 7 7 7 7
N N N N N N N
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
Decoding MOS – Quantitative
Precipitation Forecast
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
CIG
7 7 7 6 6 7 7
VIS
7 7 7 7 7 7 7
OBV
N N N N N N N
T06, T12:
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
7
7
N
15 18
84
76
OV
23
09
85
75
OV
22
09
36
42/
42/
7 6
7 7
N N
2
0
0
4
7
N
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
5 4 4 4
7 7 7 7
N N N N
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
6 5 5 7 7
7 7 7 7 7
N N N N N
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
5
7
N
probability of thunderstorms / severe t-storms over 6, 12h
Decoding MOS – Precip.
Amount
 0 = no precipitation
 1 = 0.01 to 0.09 inches
 2 = 0.10 to 0.24 inches
 3 = 0.25 to 0.49 inches
 4 = 0.50 to 0.99 inches
 5 = 1.00 to 1.99 inches
 6 = 2.00 inches or greater
Decoding MOS – Local forecast
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
9/04/2003
0600 UTC
/SEPT
15 18 21 00 03
89
84 85 83 80 79
76 75 74 74 75
OV OV OV OV OV
23 22 24 24 23
09 09 08 04 04
36
55
66
2
4
4
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
6
06 09 12
76
77 77 79
75 75 76
OV OV OV
24 21 22
04 06 06
46
44
68
4
2
5
/
15 18 00
91
85 88 82
75 74 74
OV OV OV
25 25 26
11 11 06
35 30
54
4 0
4
06
78
74
BK
24
05
15
0
High Temperature: 89F
Low Temperature: 77F
Precipitation Category: Cat-4
Decoding MOS – Zone Forecast
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
15 18
84
76
OV
23
09
85
75
OV
22
09
36
2
42/ 0
42/ 0
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
Tonight: Mostly cloudy with scattered showers and thunderstorms. Lows in the mid 70s.
Light southeast winds. Chance of rain 30%.
Tomorrow: Mostly cloudy with scattered to numerous showers and thunderstorms. Highs
near 90. Light southwest winds. Chance of rain 70%.
Tomorrow Night: Mostly cloudy with scattered showers and thunderstorms. Lows in the
mid 70s. Light southwest winds. Chance of rain 70%.
Decoding MOS – Zone Forecast
KMIA
AVN MOS GUIDANCE
DT /SEPT 4
/SEPT 5
HR
12 15 18 21 00 03 06
X/N
89
TMP 79 85 86 84 81 80 79
DPT 77 76 75 75 75 76 76
CLD BK BK BK OV OV OV OV
WDR 00 19 17 17 14 13 16
WSP 00 01 04 03 01 01 02
P06
20
44
25
P12
45
Q06
0
2
0
Q12
1
T06
33/ 0 64/ 0 21/ 0
T12
66/ 0
9/04/2003
09 12
77
79 80
76 77
OV OV
17 20
05 03
20
30
0
1
19/ 0
15 18
84
76
OV
23
09
85
75
OV
22
09
36
2
42/ 0
42/ 0
0600 UTC
/SEPT 6
21 00 03 06
89
83 80 79 77
74 74 75 75
OV OV OV OV
24 24 23 24
08 04 04 04
55
46
66
4
4
4
80/ 0 37/ 0
93/ 0
09 12
76
77 79
75 76
OV OV
21 22
06 06
44
68
2
5
37/ 0
/
15 18 00 06
91
85 88 82 78
75 74 74 74
OV OV OV BK
25 25 26 24
11 11 06 05
35 30 15
54
4 0 0
4
39/ 0 22/ 0
58/ 0 66/ 0
Tonight: Mostly cloudy with scattered showers and thunderstorms. Lows in the mid 70s.
Light southeast winds. Chance of rain 30%.
Tomorrow: Mostly cloudy with scattered to numerous showers and thunderstorms. Highs
near 90. Light southwest winds. Chance of rain 70%.
Tomorrow Night: Mostly cloudy with scattered showers and thunderstorms. Lows in the
mid 70s. Light southwest winds. Chance of rain 70%.
Description of MOS Alphanumeric Message
Definitions of Categorical Elements