文档介绍：Section 12 Air Quality Forecasting Tools
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工作报告
Background
Forecasting tools provide information to help guide the forecasting process.
Forecasters use a variety of data products, information, tools, and experience to predict air quality.
Forecasting tools are built upon an understanding of the processes that control air quality.
Forecasting tools:
Subjective
Objective
More forecasting tools = better results.
.au/air/AAQFS
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工作报告
Background
Persistence
Climatology
Criteria
Statistical
Classification and Regression Tree (CART)
Regression
works
Numerical modeling
Phenomenological and experience
Predictor variables
Fewer resources, lower accuracy
More resources, potential for higher accuracy
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工作报告
Selecting Predictor Variables (1 of 3)
Many methods require predictor variables.
Meteorological
Air Quality
Before selecting particular variables it is important to understand the phenomena that affect pollutant concentrations in your region.
The variables selected should capture the important phenomena that affect pollutant concentrations in the region.
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Selecting Predictor Variables (2 of 3)
Select observed and forecasted variables. Predictor variables can consist of observed variables (., yesterday’s ozone or concentration) and forecasted variables (., tomorrow’s maximum temperature).
Make sure that predictor variables are easily obtainable from reliable source(s) and can be forecast.
Consider uncertainty in measurements, particularly measurements of PM.
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工作报告
Selecting Predictor Variables (3 of 3)
Begin with as many as 50 to 100 predictor variables.
Use statistical analysis techniques to identify the most important variables.
Cluster analysis is used to partition data into similar and dissimilar subsets. Unique (., dissimilar) variables should be used to avoid redundancy.
Correlation analysis is used to evaluate the relationship between the predictand (., pollutant levels) and various predictor variables.
Step-wise r