EVALUATION OF RAGWEED EVALUATION OF RAGWEED FORECASTING IN TULSAFORECASTING IN TULSA

Estelle Levetin, PhDThe University of Tulsa

Ambrosia Pollen

• Most important pollen allergen in N.A.

• In Tulsa area, cumulative Ambrosia pollen is first or second in terms of yearly abundance

• The ability to accurately predict day to day pollen levels could provide important benefit to sensitive individuals either by avoidance or by taking prophylactic medication

Stand of Ambrosia trifida along the east bank of the Arkansas River

Flowering in Ragweed

• Controlled by photoperiod• Pollination is the same time each year at a

given location unless stressful climatic conditions influence growth and reproduction in the plants.

• Once pollination begins, pollen release and atmospheric pollen concentrations are influenced by meteorological conditions.

Pollen Forecasts from TU

• Multiple regression models• Empirical model for mountain cedar

pollen release coupled with HY-SPLIT dispersion model

• Development of ragweed forecasts– Empirical Model– Ragweed Pollen Forecaster (computer

software) generated by 6 students from Dept of Computer Science (Cyber Corp)

Air Sampling

• Burkard Spore Trap has been used for air sampling in Tulsa since Dec. 1986

• Ragweed data from 1987 to 2001 was used to determine pollen season characteristics– Start date - 5% of season

total)– End date – 95% of season

total– Typical peak date

Five-Day Running Mean of Ambrosia Levels in the Tulsa Atmosphere 1987-2001

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8/15 8/29 9/12 9/26 10/10 10/24

Po

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Five-Day Running Mean of Ambrosia Levels in the Tulsa Atmosphere 1987-2001

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8/15 8/29 9/12 9/26 10/10 10/24

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Pollen Season Characteristics

• Mean start date (5% of season total) – 27 Aug

• Mean end date (95% of season total) – 11 Oct

• Mean peak date – 10 Sep

Empirical Model

• Pollen concentrations compared with data from the National Weather Service to determine the effects of meteorological conditions on airborne pollen levels

• Empirical model was developed based on phenology and the weather forecast

• NGM-MOS 60 hour forecasts were used • Model was used to generate pollen forecasts for

the 2002 and 2003 ragweed seasons• Comparison with the atmospheric ragweed

pollen concentrations was used to evaluate the model

What conditions trigger pollen entrainment?

• No rain• Sunshine• Low humidity (below 75%)?• Moderate to high wind speeds• Afternoon temperatures below 95oF• Morning temperatures above 65oF• Phenological phase

  

Low Moderate High Very High

NAB*Percentile 0-50th 50-75th 75-99th >99th

  NAB Concen 0-9 10-49 50-499 >500

 Tulsa Concen 0-129 129-284 285-613 >613

1994 AAAAI Pollen and

Spore Report

Percentile 0-50th 50-75th 75-90th >90th

Tulsa Concen 0-129 129-284 285-410 >411

Tulsa ModelPercentile Goal 0-25th 25-50th 50-95th >95th

 Actual Percentile 0-27th 27-47th 47-95th >95th

 Concentration 0-49 50-99 100-489 >490

*Burge, H.A. 1992. Monitoring for Airborne Allergens. Annals of Allergy, 69: 9-18

What are Low, Moderate, High, and Very High Values?

Phenology Factor

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6 0.0 to 0.49 - low0.5 to 0.99 - mod1.0 to 4.89 - high>4.90 - very high

Phenology Factor

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2

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4

5

6 0.0 to 0.49 - low0.5 to 0.99 - mod1.0 to 4.89 - high>4.90 - very high

Forecasting Model• Phenology Factor (PF) – based on day in the pollen

season and 15 year mean concentration (Range 1 to 6)• Metereological forecasts from NGM-MOS 60 hr forecast

R – forecast of rain (- variable amount) T – temp outside optimum range (morning temperature < 65

F or afternoon temperature > 95 F) (- variable amount) RH – forecast of noon relative humidity >75% (-1)W-sp – wind speeds >15 mph (+1)W-dir – wind from N - Aug 15-31 or wind from S - Oct 1-31

(+1)Pre – Preseason weather – hot, dry July and August (-1)

Forecast = PF – R – T – RH + W-sp + W-dir – Pre Forecast = PF – R – T – RH + W-sp + W-dir – Pre

2002 Average Daily Ambrosia Concentration

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14008/

1/20

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8/8/

2002

8/15

/200

2

8/22

/200

2

8/29

/200

2

9/5/

2002

9/12

/200

2

9/19

/200

2

9/26

/200

2

10/3

/200

2

10/1

0/20

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10/1

7/20

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4/20

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10/3

1/20

02

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2002 Average Daily Ambrosia Concentration

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14008/

1/20

02

8/8/

2002

8/15

/200

2

8/22

/200

2

8/29

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2

9/5/

2002

9/12

/200

2

9/19

/200

2

9/26

/200

2

10/3

/200

2

10/1

0/20

02

10/1

7/20

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10/2

4/20

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10/3

1/20

02

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Comparison of 2002 Ambrosia Pollen Concentration with Pollen Forecast

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LOW LOW/MOD MOD MOD/HIGH HIGH HIGH/VERY

Comparison of 2002 Ambrosia Pollen Concentration with Pollen Forecast

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LOW LOW/MOD MOD MOD/HIGH HIGH HIGH/VERY

2003 Average Daily Ambrosia Concentration

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2003 Average Daily Ambrosia Concentration

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Comparison of 2003 Ambrosia Pollen Concentrations with Pollen Forecast

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Polle

n gr

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/m3

LOW LOW/MOD MOD MOD/HIGH HIGH HIGH/VERY VERY

Comparison of 2003 Ambrosia Pollen Concentrations with Pollen Forecast

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Polle

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LOW LOW/MOD MOD MOD/HIGH HIGH HIGH/VERY VERY

Mean Airborne Ambrosia Pollen Concentration at Each Predicted Forecast Level

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LOW LOW-MOD MOD MOD-HIGH HIGH HIGH-VERY VERY

Mean Airborne Ambrosia Pollen Concentration at Each Predicted Forecast Level

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LOW LOW-MOD MOD MOD-HIGH HIGH HIGH-VERY VERY

Forecast Pollen Level

NAB Pollen Categories

Low Moderate High Very High

Number of Days

LOW 28 9 3  

LOW TO MODERATE 11 16 7  

MODERATE 2 3 7  

MODERATE TO HIGH     15  

HIGH     16 1

HIGH TO VERY HIGH     14 2

VERY HIGH     5 1

Ragweed Pollen Forecaster

Comparison of 2003 Pollen Concentration with Computer Generated Forecast

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Low Moderate High Very High

Computer Program Evaluation

• Correct forecast 34 days (49%)• Incorrect forecast 13 days (19%)• No forecast data 23 days (32%)

– For the 47 days with data: 72% correct

Conclusions

• Empirical model accurately predicted the pollen level on 84% of the days during the 2002 and 2003 ragweed seasons (74% using NAB levels)

• Computer program needs more work• Pollen forecasts are only as accurate as the

meteorological forecasts• More research is needed on the

– effects of RH and rain on pollen release and dispersal

– influence of pre-season meteorological conditions on the seasonal pollen potential

AcknowledgmentThe assistance of Claudia Owens, Shernell Surratt, and Christen Townsend in counting pollen is greatly appreciated.