Meteorology 356

Class Notes

11/15/04

Kris Ian Plaza

Readings:

• Section IV (Browsed--Portions Discussed);
• Section V (281-288; 292,294)
• Section VI; Section VIII
• Section VII (Browse)
• Section IX 366-373; 512-516
• Section X

Review:

Last class we review Standard deviation and coefficient of variation.

· A standard variation is a statistical measure, obtained mathematically, of variations from the mean. This works well for precipitation and expresses the range of annual precipitation values that can be expected

Example: For SF if mean is 22.68 there is a variability of +/- 6

· Coeffiecient of variation is defined as standard deviation expressed as a fraction or percentage.

Schematic Bell Curve:

· Another way of measuring average precipitation. This diagram can give meaningful information by knowing the extremes.

Seasonal Rainfall of Downtown SF:

· Study chart of rainfall in downtown SF. It turned out that blue line is average and roughly will occur 70% of the time. Red represents the extreme which is 7.63 +/- from the mean.

Rainfall Variability:

· Looked at the map of the U.S. Turned out that location affects the variability of rainfall. SF falls under Mediterranean climate

· It is typical for a Mediterranean climate rainfall to have high extremes.

· 2 components of rainfall variability

1. areal - The variation of rainfall amounts at various locations across a region for a specific time interval

2. temporal - variation of rainfall amounts at a given location across a region for a specific time interval

• Looked at the rainfall variability in California. It turned out that on the southern part of Ca. there is a 3.5 variability and if you move further south there is .5 variability. On the northern part of Ca. we have 2.5 variability. This means that embedded in our climate are extremes that can start from dry to suddenly wet seasons.

Graph of Rainfall at downtown LA compared to San Luis Obispo:

• Found out that our climate is cyclical. Both San Luis Obispo and LA have same 22 year of wet and dry cycle
• High correlation between two cities. Note that no cause and effect is implied by correlation. In order to have a correlation it needs several tests to support it

Research in UC Berkeley:

• Horrible drought in part of Africa during 1970 prompted the researchers to make a meteorological study of that area. Researchers run random correlation studies between stations that are the same general range of latitude. The result was negative correlation. They found out that if parts of the same range of latitude has wet season the other side has dry season
• Europe and Africa – inversely related
• N. America and Africa – conversely related
• N. America and Mexico – inverse correlation
• But there are times that it does not correlate maybe because of the location. Ex. San Diego and Mazatlan Mexico – lags by 7 years