1. Density of weather observing sites is inadequate over some regions
a. Oceans
b. Mexico

2. Initial condition errors
a. Errors in observations grow exponentially with time
b. Interpolation errors and round off errors grow exponentially with time

3. Boundary errors
a. Models have tough handle on atmospheric processes near the edge of forecast boundary
b. Initial condition data not as good near boundary
c. Grid spacing is further near boundaries

4. Mesoscale processes not handled well
a. Includes: sea breeze, air mass thunderstorm convection, complex topography, outflow boundaries, interacting and preset boundaries

5. Complex physical processes difficult to handle
a. Differential heating
b. Snow/ ice cover (updated weekly)
c. Evaporation
d. Terrestrial radiation

6. Unrepresentativeness error
a. Rawinsonde launched near thunderstorm or some other mesoscale phenomena

7. Vertical resolution
a. Can only see specific layers of the atmosphere (sfc, 1000, 850,700,500,upper levels)

8. Be cautious when using MOS
a. May be unreliable when weather is climatologically unusual or an ageostrophic environment (Baroclinic environment) exists
b. Highs tend to be more accurate than lows.
c. Does not account for newly fallen snow or mesoscale deep convection
d. MRF MOS tends to aim toward climatology as forecast time interval increases

9. Can often have trouble with return flow from the Gulf
a. Low level jet can advect heat, momentum and moisture a considerable distance over the course of a few hours

10. Arctic outbreaks can be underforecasted due to the extreme shallow nature of these fronts as they move into the lower latitudes

11. Computer Power

Synoptic Scale Model Advantages

1. Can compare several models to get a "composite" forecast
2. Can see large scale circulation patterns: WAA, CAA, Moisture advection, vorticity advection, jet streaks
3. Work well when forecasting for a large region
4. Can be used in conjunction with mesoscale models or mesoscale knowledge to improve the forecast the synoptic models and MOS are giving


1. Why do you suppose synoptic models have a difficulty handling storm systems that come out of Mexico and into the US?

Deficient amount of surface and upper air data over Mexico

2. The next time a shallow arctic front moves into the US, monitor how well the synoptic models handle the speed, location and temperature contrast at the surface associated with the front.

3. Why do you suppose models often have a tough handle on return flow from the Gulf and the low level jet originating from the Gulf of Mexico?

*Deficient amount of data over the Gulf of Mexico
*Wind speed of return flow can be underestimated

4. What type of weather systems do the synoptic models have a good handle on?

The larger the system the better. They have a good handle of large-scale troughs and ridges.

5. What is unrepresentativeness error?

It is an observation that does not represent the overall synoptic environment. An Example includes launching a rawinsonde into a thunderstorm.

6. Why is cloud cover significant to temperature and instability forecasts?

Cloud cover influences surface temperature. Temperature influences the stability of the atmosphere (i.e. warming the low levels of the atmosphere causes instability)

7. Why is it advantageous to have satellite data in conjunction with the other methods of meteorological data collection in developing a forecast model?

Satellite data can show mesoscale features that the synoptic models can not show