First of all What information you would like to retrieve from the database? And secondly What is the content of database? 

For data analysis, there are several tools are available.

If you have a large dataset available then you first have to find what type of information is needed to retrieve from it.There are many methods available in data mining like associations,clustering,classification etc.In which respect you want to analyse that data that is known only from your problem statement

If you have a large dataset available then you first have to find what type of information is needed to retrieve from it.There are many methods available in data mining like associations,clustering,classification etc.In which respect you want to analyse that data that is known only from your problem statement

you can predict CO2 concentration , rain fail prediction etc ... like problems 

Madhu,

You may want to consider using Principal Component Analysis (PCA) and/or Factor Analysis (FA) techniques to reduce/collapse the number of variables to facilitate the regression/cluster/classification, etc. analysis of your data.  I find that to be useful when working with large amounts of atmospheric and/or socio-demographic data.

Eric

can you please elaborate how you want to go for the data you are having, As if you are having more sort of numeric data in your database then you can go for the statistical analysis where you can plot various distributions for the probabilities.

After doing first round of analysis then you will get the idea about your data then according to it you can go for various clustering techniques or may be other types of techniques.

But according to me you should get some intuition about your data first then only go for standard data mining technique.

You can predict the evolution of weather in some regions, you can aggregate this data to find a synthesis of the state of the weather by month or season or year...

May be this paper could help you..

http://www.sciencedirect.com/science/article/pii/0034425788900193