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1、工作报告,1,Section 12Air Quality Forecasting Tools,工作报告,2,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 th
2、e processes that control air quality. Forecasting tools: Subjective Objective More forecasting tools = better results.,www.epa.vic.gov.au/air/AAQFS,工作报告,3,Background,Persistence Climatology Criteria Statistical Classification and Regression Tree (CART) Regression Neural networks Numerical modeling P
3、henomenological and experience Predictor variables,工作报告,5,Selecting Predictor Variables (2 of 3),Select observed and forecasted variables. Predictor variables can consist of observed variables (e.g., yesterdays ozone or PM2.5 concentration) and forecasted variables (e.g.,tomorrows maximum temperatur
4、e). Make sure that predictor variables are easily obtainable from reliable source(s) and can be forecast. Consider uncertainty in measurements, particularly measurements of PM.,工作报告,6,Selecting Predictor Variables (3 of 3),Begin with as many as 50 to 100 predictor variables. Use statistical analysis
5、 techniques to identify the most important variables. Cluster analysis is used to partition data into similar and dissimilar subsets. Unique (i.e., dissimilar) variables should be used to avoid redundancy. Correlation analysis is used to evaluate the relationship between the predictand (i.e.,polluta
6、nt levels) and various predictor variables. Step-wise regression is an automatic procedure that allows the statistical software (SAS,Statgraphics, Systat, etc.) to select the most important variables and generate the best regression equation. Human selection is another means of selecting the most im
7、portant predictor variables.,工作报告,7,Common Ozone Predictor Variables,工作报告,8,Common PM2.5 Predictor Variables,工作报告,9,Assembling a dataset,Determine what data to use What data types are needed and available What sites are representative What air quality monitoring network(s) to use (for example, conti
8、nuous versus passive or filter) What type of meteorological data are available (surface, upper-air, satellite, etc.) How much data is available (years),工作报告,10,Assembling a dataset,Acquire historical data including Hourly pollutant data Daily maximum pollutant metrics, such as Peak 1-hr ozone Peak 8
9、-hr average ozone 24-hr average PM2.5 or PM10 Hourly meteorological data Radiosonde data Model data Meteorological outputs MM5/TAPM Other Surface and upper-air weather charts HYSPLIT trajectories,工作报告,11,Assembling a dataset,Quality control data Check for outliers Look at the minimum and maximum val
10、ues for each field; are they reasonable? Check rate of change between records at each extreme. Time stamps Has all data been properly matched by time? Time series plots can help identify problems shifting from UTC to LST. Missing data Is the same identifier used for each field? I.e., 999. Units Are
11、units consistent among different data sets? I.e., m/s or knots for wind speeds. Validation codes Are validation codes consistent among different data sets? Do the validation codes match the data values? I.e., are data values of 999 flagged as missing?,工作报告,12,Tool development is a function of Amount
12、 and quality of data (air quality and meteorological) Resources for development Human Software Computing Resources for operations Human Software Computing,Forecasting Tools and Methods (1 of 2),工作报告,13,Forecasting Tools and Methods (2 of 2),For each tool What is it? How does it work? Example How to
13、develop it? Strengths Limitations,Ozone = WS * 10.2 +,工作报告,14,Persistence (1 of 2),Persistence means to continue steadily in some state. Tomorrows pollutant concentration will be the same as Todays. Best used as a starting point and to help guide other forecasting methods. It should not be used as t
14、he only forecasting method. Modifying a persistence forecast with forecasting experience can help improve forecast accuracy.,Persistence forecast,工作报告,15,Persistence (2 of 2),Seven high ozone days (red) Five of these days occurred after a high day (*) Probability of high ozone occurring on the day a
15、fter a high ozone day is 5 out of 7 days Probability of a low ozone day occurring after a low ozone day are 20 out of 22 days Persistence method would be accurate 25 out of 29 days, or 86% of the time,Peak 8-hr ozone concentrations for a sample city,工作报告,16,Persistence Strengths,Persistence forecast
16、ing Useful for several continuous days with similar weather conditions Provides a starting point for an air quality forecast that can be refined by using other forecasting methods Easy to use and requires little expertise,工作报告,17,Persistence Limitations,Persistence forecasting cannot Predict the start and end of a pollution episode Work well under changing weather conditions when accurate air quality predictions can be most critical,工作报告,18,Climatology,Climatology is the study of average and ex
