Post-Processing of Numerical Ozone Model Forecasts:

The Land-Sea Problem

Bill RyanDepartment of Meteorology

The Pennsylvania State Universitywfr1@psu.edu

2010 International Workshop on Air Quality Forecasting ResearchQuebec City, Quebec

The Problem: False Alarms of High O3

• Operational air quality forecasters along the I-95 Corridor in the northeastern US are increasingly reliant upon photo-chemical models for O3 forecast guidance.– Significant recent changes in regional emissions have reduced skill of

guidance methods that require training period.• Overall model performance in this region is good but suffers from

frequent “false alarms” of unhealthy O3 levels.

• The false alarm rate is primarily a function of strong forecast O3 gradients along land-sea boundaries.

• Numerical guidance output (1200 UTC initialization) arrives close (within minutes in some cases) to forecast deadlines. Forecasters require a simple, quick method to deal with this issue.

Example of the Land-Sea O3 Gradient

NOAA-EPA Numerical Ozone Forecast Model (www.weather.gov/aq)

Pattern Recurs in Certain Locations

Not an Issue with PM2.5 Forecasts

Daily maximum 8-Hour AverageOzone (ppbv), NOAA-EPA Model

24-hour (midnight-midnight)average PM2.5 (µg/m3)

Complicating the Issue: Sea Breezes Can Drive Steep Coastal O3 Gradients

In this case (August 11, 2010) sea breeze circulations developed north of afrontal boundary and re-circulated the previous day’s polluted air mass.

MODIS/Aqua~ 1730 UTC

Simplest Application: Philadelphia Forecast Area

The Philadelphia Metro Forecast Area, roughly enclosed in rectangle, is peripherallyaffected by modeled sea breeze-O3 effects, primarily in southern NJ

How Do US Operational Forecasters Receive Local Scale Model Forecast Information?

Model forecast output is automaticallygenerated by NOAA-EPA by extracting

peak O3 concentrations over land areas within designated warning areas

(using zipcodes) that are used foremail/Web notification. This is posted in

timely fashion at AirNow Tech(http://www.airnowtech.org/).

Millville, NJ monitor location shown at left.

Interstates and zip code boundaries shown.

Millville

Forecast Peak O3 Frequently Skirts Coastal Areas

Simplest post-processing method toremove land-sea effects is

to extract O3 forecast at locationsnear monitors and/or away from the

boundary. This can be quicklydone using point data extraction from the

NOAA forecast web site.

Results of Simple Method (2010)

• Removing coastal zip codes from the Millville forecast reduces bias at that location from 17 ppbv to 9 ppbv.

• Because this location is often the location of peak modeled O3 in the Philadelphia metro area, Code Orange model forecast frequency in PHL is reduced from 44 to 20 days (Threshold for Code Orange - Air Quality Alert is 76 ppbv 8-hour average) .

• Of the 20 forecast Code Orange cases, 16 observed Code Orange in PHL in 2010.

Effect on Metropolitan Scale Forecasts (2010)

• When this simple post-processing is applied to the PHL metro domain, numerical model skill equals, or exceeds, expert forecast skill and is a large improvement on earlier forecast methods (not shown).

Hit False Alarm Threat0

0.10.20.30.40.50.60.70.80.9

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ForecastModel

Simple Approach Less Useful When Monitors are Located Immediately Along Sea-Land Boundary

Southern Delaware forecastarea includes monitor alongcoast at Lewes.

Bias Correction Post-Processing

• Use of Sonoma Tech Air Quality MOS (AQMOS)– http://aqmos.sonomatech.com/index.cfm

• Not a true MOS, method is long term bias removal with additional correction in higher O3 cases (hierarchal bias correction method).

• Applied to southern Delaware in 2010, reduces overall forecast bias by 4.6 ppbv.

• Also, reduces number of false alarms of Code Orange O3 (76 ppbv) from 19 to 5.

• At cost of 4 additional missed Code Orange cases although “close” forecasts (71,74,75 ppbv).

More Difficult Application: Baltimore Metropolitan Area

In 2010, the monitor at Edgewood,located northeast of Baltimore,reached the Code Orange rangeon 75% of all Code Orange days

in the Baltimore region.

On half of the Code Orange days,the three bayside monitors

were the only locations reaching the alert threshold.

An Example of the Bay Breeze Effect on O3 inthe Baltimore Metro Area

2200 UTC hourly O3 observations and wind barbs, June 25, 2009.

Note on Determining Air Quality Forecast Skill in the mid-Atlantic

• False alarms can be due to factors other than water-land effects.

• A frequent problem is forecasting the timing and extent of convection.

• At right, a late day thunderstorm on June 3 limited O3 to 74 ppbv.

• A set of 7 convection cases excluded in further analysis of 2010 results.

Model Forecast O3 in Baltimore

• The problem is solving the “false alarm” issue in non-convective cases.

• Using raw model output, 26 false alarm cases in 2010.

• Using the simple method of excluding near-water zipcode locations reduces false alarms to 19.

Further Post-Processing Approaches

• Due to the close proximity of emissions sources to the sea-land boundary, improvement by removing bay side grid points (simple method) is useful but limited.

• Skill decreases quickly if further exclude near-bayside locations.• Analysis of 2010 Cases (a very active O3 season) gives some

hints:– On average, observed Code Orange cases feature a stronger modeled

gradient from bay side to inland, but too much variability to use operationally (CART method).

– Most useful method combines the magnitude of modeled bayside O3 and the mean domain modeled O3.

Forecast Results for Code Orange Threshold (Air Quality Alert, 8-hour O3 ≥ 76 ppbv, 2010)

Bias False Alarm Hit Cnull Accuracy Heidke Threat0.00

0.20

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NAQC POST PERS

Blue: Model Forecast (NAQC)Red: Post-Processed Model (POST)Green: Persistence (PERS)

Summary of Baltimore Results

• Pro: Removal of bias and reduction of false alarm rate.

• Hit rate also reduced but, at 75%, approximately equal to, or better than, historical expert forecast accuracy.

• Small (~5%) improvement in skill scores.• Half of the misses and false alarms occur in

range [74, 78] ppbv (close calls).

Application to Connecticut Ozone

The majority of high O3 events occur at coastalmonitors (62%). Only 5 of 24 observed Code Orangedays did not have a coastal monitor above thethreshold (76 ppbv, 8-hour average).

Background on Model Performance in CT

• The NOAA-EPA model, on a statewide domain basis, did a reasonable job in 2010. Over-prediction bias of 5 ppbv (~10%).

• Bias correction, on the seasonal or synoptic scale, slightly improves forecasts but has little impact at the Code Orange (Air Quality Alert) threshold.

• Hit rate of 84% but high false alarm rate (34%). High bias for warning forecasts.

• Expert forecast, typical of most locations along I-95, has fewer false alarms but many more “misses”. Low bias for warning forecasts.

Behavior of Model Forecasts in Long Island Sound Similar to Chesapeake Bay/Baltimore

• Used NOAA-EPA model forecasts for locations of all CT monitors.

• As in Baltimore, exploratory analysis with CART suggests domain wide average peak O3 ([65-70 ppbv])and/or land-sea gradient ([15-20 ppbv]) are useful thresholds for analysis.

Results of Post Processed Forecasts (Statewide)

NAQC Mean Mean+Grad Forecast0

0.1

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HitFalse AlarmThreat

Model forecasts (NAQC) have a high hit and false alarm rate (high bias) whilethe expert forecast (Forecast) is the reverse. The two post-processed forecasts

Mean and Mean plus Gradient straddle the bias threshold but provide better skill scores (Threat Score, or CSI shown above in green.

Summary and Conclusions

• In populated NE US Corridor, numerical model O3 forecast guidance is useful but has high false alarm rate due to modeled steep O3 gradients along land-sea boundaries.

• A variety of simple post-processing methods, available within operational time constraints, can limit false alarm frequency.

• Efficacy of methods vary from location to location and include:– Removal of coastal locations– Bias correction (of various lengths)– Domain mean peak concentrations and/or strength of gradient

Acknowledgements

• This research is supported by ongoing grants from the Delaware Valley Regional Planning Commission (including the State of Pennsylvania Department of Environmental Protection) and the State of Delaware Department of Natural Resources and Environmental Control.

• Assistance for this presentation also provided by Sonoma Technology (Dianne Miller and Jessica Johnson) and Michael Geigert of the Connecticut Department of Environmental Protection.

Additional Slides

Preliminary Comments

• This presentation is from the operational forecaster’s perspective.

• Air quality forecasting in the US is a state/local government responsibility.

• The air quality forecasting community in the US is therefore non-centralized and diverse in terms of expertise, experience and resources.

Sea Breeze Fronts Can Lead to High O3

NOAA-EPA Model O3 forecast forAugust 11, 2010 (1200 UTC run)

HPC Surface Analysis, 1800 UTC,August 11, 2010

Hi-Res NAM Forecast:Sea Breeze Front Forming Behind Frontal Boundary

Sea Breeze is Observed

Sea Breeze Verified

(left) 3-hour temperature changeand winds, 1900 UTC, August 11

Figure Courtesy:SPC Mesoscale Analysis

http://www.spc.noaa.gov/exper/mesoanalysis/

Example of Mean Domain Model O3 and Observations

Scatter plot of observed peakO3 and mean model

Air Quality Index (AQI) forin CT.

CT Domain Wide Code Orange Threshold Forecasts: Model and Expert

Bias Hit False Alarm0

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1.41.28

0.840000000000001

0.34

0.710000000000001

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NAQC Forecast

Mean Mean+Grad0.600000000000001

0.700000000000001

0.800000000000001

0.900000000000001

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Connecticut Miss and False Alarm Cases

• Missed cases (4) were marginal Code Orange cases (76-78 ppbv) with only one hour exceedance in each case.

• Three of seven False Alarms were “close calls” (≥72 ppbv) and remaining four were False Alarms under any of the post-processing methods.

• False Alarms: 6/20 (lingering clouds), 7/20 (clouds/frontal boundary), 8/1 (SE winds), 8/31(?).