EVALUATION OF RAGWEED FORECASTING IN TULSA Estelle Levetin

  • Slides: 24
Download presentation
EVALUATION OF RAGWEED FORECASTING IN TULSA Estelle Levetin, Ph. D The University of Tulsa

EVALUATION OF RAGWEED FORECASTING IN TULSA Estelle Levetin, Ph. D The University of Tulsa

Ambrosia Pollen • Most important pollen allergen in N. A. • In Tulsa area,

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

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

Flowering in Ragweed • Controlled by photoperiod • Pollination is the same time each

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 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

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

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

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

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%)?

What conditions trigger pollen entrainment? • • No rain Sunshine Low humidity (below 75%)? Moderate to high wind speeds Afternoon temperatures below 95 o. F Morning temperatures above 65 o. F Phenological phase

What are Low, Moderate, High, and Very High Values? NAB* 1994 AAAAI Pollen and

What are Low, Moderate, High, and Very High Values? NAB* 1994 AAAAI Pollen and Spore Report Tulsa Model Low Moderate High Very High 0 -50 th 50 -75 th 75 -99 th >99 th NAB Concen 0 -9 10 -49 50 -499 >500 Tulsa Concen 0 -129 129 -284 285 -613 >613 Percentile 0 -50 th 50 -75 th 75 -90 th >90 th Tulsa Concen 0 -129 129 -284 285 -410 >411 Percentile Goal 0 -25 th 25 -50 th 50 -95 th >95 th Actual Percentile 0 -27 th 27 -47 th 47 -95 th >95 th 0 -49 50 -99 100 -489 >490 Percentile Concentration *Burge, H. A. 1992. Monitoring for Airborne Allergens. Annals of Allergy, 69: 9 -18

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

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 v R – forecast of rain (- variable amount) v T – temp outside optimum range (morning temperature < 65 F or afternoon temperature > 95 F) (- variable amount) v RH – forecast of noon relative humidity >75% (-1) v W-sp – wind speeds >15 mph (+1) v W-dir – wind from N - Aug 15 -31 or wind from S - Oct 1 -31 (+1) v Pre – Preseason weather – hot, dry July and August (-1) Forecast = PF – R – T – RH + W-sp + W-dir – Pre

NAB Pollen Categories Forecast Pollen Level Low Moderate High Very High Number of Days

NAB Pollen Categories Forecast Pollen Level 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

Ragweed Pollen Forecaster

Computer Program Evaluation • Correct forecast 34 days (49%) • Incorrect forecast 13 days

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

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

Acknowledgment The assistance of Claudia Owens, Shernell Surratt, and Christen Townsend in counting pollen

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

OKLAHOMA By A March Tulsa Pictures Tulsa TulsaOKLAHOMA By A March Tulsa Pictures Tulsa Tulsa
By Stephen STORY ELEMENTS THE MICE RAGWEED RagweedBy Stephen STORY ELEMENTS THE MICE RAGWEED Ragweed
Forecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment ForecastingForecasting Replenishment Forecasting Replenishment Forecasting Forecasting Replenishment Forecasting
PLANT AND SOCIETY ESTELLE LEVETIN KAREN MCMAHON ChapterPLANT AND SOCIETY ESTELLE LEVETIN KAREN MCMAHON Chapter
Indoor Molds and Mycotoxins Estelle Levetin Ph DIndoor Molds and Mycotoxins Estelle Levetin Ph D
Common Fungal Spores Estelle Levetin Ph D FungalCommon Fungal Spores Estelle Levetin Ph D Fungal
Taxonomy of Allergenic Fungi Estelle Levetin Ph DTaxonomy of Allergenic Fungi Estelle Levetin Ph D
forecasting longterm forecasting middleterm forecasting shortterm forecasting 2forecasting longterm forecasting middleterm forecasting shortterm forecasting 2
Rose M Washington Tulsa Economic Development Corporation TulsaRose M Washington Tulsa Economic Development Corporation Tulsa
01 ESTELLE DARCY English tutor Estelle Darcy Certified01 ESTELLE DARCY English tutor Estelle Darcy Certified
Introduction Definition Forecasting Technology Technological Forecasting Process ForecastingIntroduction Definition Forecasting Technology Technological Forecasting Process Forecasting
Forecasting Forecasting Problems and Methods New product forecastingForecasting Forecasting Problems and Methods New product forecasting
Forecasting Forecasting A Basis of Forecasting In businessForecasting Forecasting A Basis of Forecasting In business
Forecasting Forecasting A Basis of Forecasting In businessForecasting Forecasting A Basis of Forecasting In business
DEMAND FORECASTING DEMAND FORECASTING Demand forecasting means estimationDEMAND FORECASTING DEMAND FORECASTING Demand forecasting means estimation
Forecasting Models FORECASTING Forecasting is attempting to predictForecasting Models FORECASTING Forecasting is attempting to predict
Secondary Compounds and Medicinal Plants Levetin and McSecondary Compounds and Medicinal Plants Levetin and Mc
Past warming and ragweed season US National ClimatePast warming and ragweed season US National Climate
Survey of Giant Ragweed Distribution and Spread inSurvey of Giant Ragweed Distribution and Spread in
PAVEMENT EVALUATION Pavement evaluation Functional evaluation Structural evaluationPAVEMENT EVALUATION Pavement evaluation Functional evaluation Structural evaluation
MGTSC 352 Finals Notes Forecasting Forecasting Quantitative TimeMGTSC 352 Finals Notes Forecasting Forecasting Quantitative Time
Climate Forecasting Unit StateoftheArt Climate Forecasting for WindClimate Forecasting Unit StateoftheArt Climate Forecasting for Wind
3 1 Forecasting CHAPTER 3 Forecasting 3 23 1 Forecasting CHAPTER 3 Forecasting 3 2