Maps and Images

To the right is an example of a surface data map.  I have labeled it with the four types of air masses which impact weather.  Maritime polar masses come from the cold areas of ocean.  They tend to hold cold moist air.  Continental polar masses develop over cold portions of land mass.  They care cold dry air.  Continental tropical masses form over hot areas of land mass.  The air here is
usually hot and dry.  Maritime tropical
masses are located over hot areas of ocean.
This air is most often
hot and moist.

This chart is an expansion of the above principles.  You can see here how the different types of air masses can form in different places and take different paths.  This is important to understand when predicting weather.  For instance if you live on the East Coast you are likely to experience three different types of air masses and each one effects weather in unique ways.

To the right is a map I made which shows wind speed and direction across the country.  This kind of information is very helpful for locating fronts and cells.  When you observe the wind directions across a wide area you can look for rotation indicating high pressure or low pressure cells.

These three maps are part of a micro-climate analysis the whole class developed.  Each teal dot or black flag represents a manual atmospheric reading taken at that point by someone in the class.  After we combined all the data I used that data to project approximations across the entire campus area.  The top one shows temperature across the campus, the darker the color the warmer the temperature.  The middle one shows wind speed across the campus.  The bottom one shows temperature across campus again but it includes readings take above exhaust vents across campus.  This is an incredibly powerful analysis tool looking at temperature.  I can clearly see how geographic features affect the temperature such as where the river and upper campus area(located on a hill) caused the temperatures to be lower.  The same could be done for wind speed or I could compare the two together and see how wind is affected by the temperature for instance I can see the high wind areas are also low temperature areas.  The bottom micro-climate map is useful to illustrate the importance of data discrimination.  The temperature taken above vents was significantly higher than the rest of the air because of this the projection the program made, by creating averages across space, were skewed higher than the ambient atmosphere actually was.  This principle should be thought of when considering the broad forecasts presented by national broadcasters.  If you are in a small town or remote area they may not have a weather station there so the projection between their sources of data may be skewed by heat islands or other factors.