# Presentation: 701315

## Developing Forecasting Tools

- OVERVIEW
- Acquiring historical data
- Sources
- Data issues
- Understanding the processes
- Climatology
- Weather typing
- Scatter plots
- Case studies

- Creating forecasting tools
- Range of tools
- Strengths/weaknesses
- How to develop a regression equation

## Air Quality Data Sources

- Air Quality Data
- In-house (local data)
- EPA’s AIRS (regional data) www.epa.gov/airs
- AIRNow (regional images) www.epa.gov/airnow
- Special Studies
- TexAQS 2000
- Southern Oxidant Study
- Gulf of Mexico AQ Study
- NARSTO-Northeast
- Others
- Contacts:
- NOAA
- DOE

- Source: www.cgenv.com/Narsto/

## Meteorological Data Sources

- Historical meteorological data
- Soundings and surface observations
- Regional climate centers wrcc.sage.dri.edu/rcc.html
- University web sites weather.uwyo.edu/upperair/sounding.html
- Data and weather maps
- Climate Prediction Center
- National Climatic Data Center  lwf.ncdc.noaa.gov/oa/ncdc.html
- NOAA’s ARL www.arl.noaa.gov/ready/arlplota.html
- Trajectories
- NOAA ARL www.arl.noaa.gov/ready/hysplit4.html

**72558 OAX Omaha Observations at 12Z 27 Jan 2002**
- -----------------------------------------------------------------------------
-    PRES   HGHT   TEMP   DWPT   RELH   MIXR   DRCT   SKNT   THTA   THTE   THTV
-     hPa     m      C      C      %    g/kg    deg   knot     K      K      K
- -----------------------------------------------------------------------------
-  1000.0     56
-   966.0    350    5.2   -5.8     45   2.58    190     10  281.1  288.6  281.6
-   963.0    375    7.4   -3.6     46   3.06    193     12  283.6  292.5  284.1
-   954.0    452    8.2   -3.8     42   3.04    204     20  285.2  294.1  285.7
-   935.8    610    8.3   -4.3     41   2.98    225     35  286.9  295.7  287.4
-   925.0    705    8.4   -4.6     39   2.95    230     33  287.9  296.7  288.4
-   902.3    914   15.0   -8.0     20   2.33    240     26  296.8  304.1  297.2

## Data Issues:  Sample Size

- At least 3 to 5 years of data
- Be aware of changes in emissions (fuel changes, new sources)

- Number of days exceeding the federal 8-hr ozone standard of 0.085 ppm in the Sacramento Metropolitan Air Quality Management District.  The solid line indicates a five-year moving average.

## Data Issues: Monitoring Network

- Does the monitoring network capture the peak concentrations?
- What types of monitors exist?
- Street, neighborhood, urban
- Background, downwind, rural

## Data Issues:  Quality Assurance

- Understanding the quality of the data
- Range checks (maximum and minimum)
- Nearby site comparisons
- Pollutant comparisons (NO + NO2 = NOx)
- If data types are merged into a database, perform spot checks comparing original data to database

## Data Issues:  Quality Assurance

- Main and Roberts (2000)

- Example of problems encountered with AIRS data.  The top figure shows ozone concentrations at a site that normally experiences 10 times these concentrations in July.  Inspection of the data showed that either the AIRS units code or decimal point indicator code was in error.  The bottom plot shows anomalously high ozone concentrations on the morning of the 11th; the high ozone value appears erroneous.  (Level 0, AIRS data)

## Data Issues:  Quality Assurance

- Example of problems encountered with AIRS data.  The figure shows that there were an abnormally large number of zero ozone concentrations at a site during a few years possibly indicating monitor or reporting problems.  (Main and Roberts, 2000).

## Data Issues:  Miscellaneous

- Expect several different formats (significant effort is spent assembling a data set)
- Seek to standardize units (ex., ppm or μg/m3, m/s or mph)
- Carefully examine time standards and conventions
- Time zones (UTC, LST, LDT)
- Validation times for model data
- Time stamp (begin hour, end hour, middle)
- AQ data are generally in local standard or daylight begin hour
- Meteorological data are mixed, but UTC is common
- Matching data (00Z Day 2 = Afternoon Day 1 USA)
- Continually examine data quality

## Understanding the Processes

- Some Basic Analyses
- Climatology of the air quality
- Day-of-week distribution
- Diurnal pattern
- Number of exceedances (by day, site, month, year)
- Maximum concentrations (duration and location)
- Weather typing
- Scatter plots
- Case studies

## Climatology

- Columbus 8-hr Ozone Exceedances by Day of Week (1996-2000)

- Days

## Weather Typing

- Weather Typing:  Relating generalized synoptic weather patterns to air quality conditions
- Types of weather patterns
- 500-mb pattern
- Surface pressure pattern
- Determine patterns on all days, not just high days
- Pay special attention to ramp-up and cleanout days; these are usually the most difficult to predict

## Weather Typing:  Surface Patterns

- MacDonald et al., 1998
- Comrie and Yarnal, 1992

## Weather Typing:  Upper-air Patterns

- Cairo, Egypt PM10 forecasting program
- Weather processes are the same
- Reviewed twice daily (00Z and 12Z) 500-mb weather maps from 1999
- Classified the synoptic weather into nine categories

## Result:  several different types of large-scale weather patterns can produce PM episodes in Cairo

- Weather Typing:  Upper-air Patterns

## Weather Typing

- Relate weather events to air quality episodes
- Examine relationship among 500-mb heights, temperature, ozone

## Scatter Plots

- Qualitatively and quantitatively relates predictor variables with air quality
- Positive and negative relationships
- R2 is the variance explained

- R2 = 1.0 perfect
- R2 = 0.67 moderate correlation
- R2 = 0.21 low correlation

## Case Studies

- Provide insight into important processes
- How to conduct a case study:
- Select a few air quality episodes; include ramp-up and cleanout days
- Review locations, magnitude, and duration of poor air quality
- Review surface and upper-air weather patterns
- Create back trajectories using NOAA’s ARL Hysplit trajectory model to estimate transport
- Estimate upwind air quality to help quantify local versus background contributions (ex., AIRNow maps)
- Review the twice daily temperature soundings to help estimate vertical mixing

## Case Study - Egypt

- Time series plot of PM10 concentrations in Cairo in October 1999.  The October 20-25, 1999, episode is outlined (Dye et al., 2000).

- Select episodes to study; include ramp-up and cleanout days

- October 23 – high PM10

## Case Study - Egypt

- Review surface and upper-air weather patterns

## Case Study - Egypt

**  **Review surface and upper-air weather patterns

## Case Study - Egypt

- Review trajectories to estimate transport

## Case Study - Egypt

- Review upper-air soundings to estimate vertical mixing

## Case Study Results

- Episode Characteristics
- Weak upper-level troughs passed over and north of Egypt
- The troughs lowered surface pressures to the north, thereby weakening the northerly winds, but winds still remained light to moderate from the north
- Aloft temperatures warmed, the capping inversion lowered, and vertical mixing was reduced
- Despite the moderately strong northerly winds, the reduced vertical mixing allowed for high PM10 concentrations
- The episode ended when a strong trough followed by a ridge passed over Egypt, increasing the northerly winds and weakening the inversion

## Forecasting Tools

- Forecasting tools can be
- Qualitative	“hot temperatures  high ozone”
- Quantitative 	“wind speeds <2.5 m/s = 50 μg/m3 PM2.5”
- Tools
- Help or aid in decision making processes
- Help forecasters to reach a consensus
- Tools should be used in conjunction with human experience

**Notes:**
Infer cause and effect from correlations/associations seen in the data

## Creating Forecasting Tools

- Tool development is a function of:
- Amount and quality of data (AQ and meteorological)
- Resources for development (human, software, computing)
- Resources for operations (human, software, computing)
- Types of tools:
- Persistence
- Climatology
- Criteria, Thresholds, Rules of thumb
- Regression equations
- Classification and Regression Trees (CART)
- Neural networks
- Fuzzy logic
- Numerical modeling
- Conceptual and experience

- Fewer resources, lower accuracy** **

- More resources, better accuracy

## Example Tool:  Criteria

- Table of meteorological values associated with high ozone episodes
- Simple forecasting tool; modest accuracy
- Compare forecasted value to criteria

- _Strengths_
-   Easy to develop
-   Quick to run
-   Conceptual
- _Weaknesses_
-   Qualitative
-   Does not provide	concentrations

## Example Tool:  CART

- Classification and Regression Trees
- Software develops the decision tree with human guidance
- CART splits data sets into similar and dissimilar groups

**Ozone **
**(Low–High) **

_**Variables:**_
*T850* - 12Z 850 MB temp
*DELTAP* - the pressure difference between the base and top of the inversion
*MI0* - Synoptic weather potential (scale from 1-low to 5-high).
*FAVGTMP* - 24-hour average temperature at La Paz
*FAVGRH* - 24-hour average relative humidity at La Paz.

- CART classification PM10 in Santiago, Chile

-                 **Node x**
**Variable and criteria**
**STD  = Standard deviation **
**Avg = Average PM****10**** (ug/m****3****)**
**N = number of cases in ****	****	****node**

- Cassmassi, 1999

- Is forecasted temperature at
- 850 mb  10.5°C ?

- Yes

- No

- Example Tool:  CART

## Example Tool:  Numerical Modeling

- _Strengths_
-   Based on atmospheric and 		chemistry physics
-   Provides forecast in areas 		without monitors
-   Helps further understand 		processes
- _Weaknesses_
-   High level of expertise and 		funding needed to develop, 		operate, and improve
-   Requires substantial computer 	resources

- Numerically model the processes
- Requirements:
- Gridded emission inventory
- Meteorological model forecasts
- Other supporting data
- Land use
- Boundary conditions (air quality)
- Photochemical model
- Computer resources
- More sources of uncertainty

## Research and operational modeling being conducted by a number of organizations:  Environment Canada, MCNC, NOAA, Ohio State, Sonoma Technology, Inc., SUNY-Albany, Washington University

- Example Tool:  Ozone Modeling

## 1-hr ozone concentrations in central California on September 20, 2001

- Forecast

- 1-hr ozone concentrations in central California on September 20, 2001

- Sonoma Technology, Inc.

- Example Tool:  Ozone Modeling

## Creating a Forecasting Tool:  Regression

- Several steps
- Determine what to predict
- Identify predictor variables
- Perform statistical analysis to select key predictor variables
- Develop regression equation
- Verify forecast

## Creating a Forecasting Tool: Regression

- Determine what to predict (predictand)
- 8-hr ozone concentration
- Daily peak (maximum) concentration
- At one site or all sites
- Recommend:  regional daily maximum
- Identify predictor variables
- Use conceptual understanding
- Create scatter plots of predictand with each predictor variable
- Check accessibility of variables selected
- Check physical relationship between each variable and ozone concentration

## Perform statistical analyses to identify key predictor variables

**Cluster Analysis**
-      Partition data into similar and dissimilar subsets

**Correlation Analysis**
-      Evaluate association among variables

**Step-wise Regression**
-      Automate selection of significant variables

**Human Selection**
-      Identify a limited number of variables based on
-      above analyses

- Creating a Forecasting Tool: Regression

## Develop regression equation
Use statistical software or MS Excel (LINEST function)
Evaluate several equations to strike a balance between variable accessibility, equation accuracy, and physical meaning
Example:  Columbus, next-day equation

- Use statistical software or MS Excel (LINEST function)
- Evaluate several equations to strike a balance between variable accessibility, equation accuracy, and physical meaning
- Example:  Columbus, next-day equation

*Tmax*	=	maximum temperature in oF
*Trange*	=	daily temperature range
*WS1to6**	*=	average wind speed from 12 a.m. to 		9 a.m. in knots
*V925*	=	V component of the 925-mb wind at 00Z
*RH*	=	relative humidity at the surface at 00Z
*WS500**	*=	wind speed at 500 mb at 00Z

- O3 = exp(2.421 + 0.024*Tmax + 0.003*Trange - 0.006*WS1to6 + 0.007*V925 - 0.004*RH - 0.002*WS500)

- Creating a Forecasting Tool: Regression

## Summary

- Creating Forecasting Tools
-   Data availability and quality
-   Develop understanding
-   Develop tools
-   The more tools the better

- Next Steps
- Verification of forecasts
- Questions