ENVSCI 301 Lecture Notes - Particulates, Falsifiability, Spatiotemporal Database
4 Nov 2013
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Document Summary
Representation of a system, copies of mimics something, simplification, reproduces the parts that are interested in. Model process: process of what is happening. Non-linear: some changes cause no response and some cause a huge response, hard to predict. Conceptual model; story or picture, easier to understand than words, i. e. flow chart. Mathematical models; take advantage of the absolute truths and formats that are in maths. Empirical model; observation based, don"t worry about process, start with data and build a model from there. Simulation models; virtually recreate a system in a computer. Understand model, map it back to real system and so on; surrogative reasoning. Collect a lot of data, and figure out what they tell us. Lecture 5: introduction to models and modeling part ii. Grain: resolution, how big the pixel is, how large the quadrate is, how often you take the data. Extent: total area encompassed by the study.
