An operational phenological model for numerical pollen model for - PowerPoint PPT Presentation

���������������������������������������������� An operational phenological model for numerical pollen model for numerical pollen prediction Helfried Scheifinger and Elisabeth Koch ZAMG Siegfried Jäger and Uwe Berger SciCon Science Pharma Consulting GmbH Robert Neumcke mindshape freiburg

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Scheifinger 24.06.2010 Overview 1. Motivation 2. Selection of 14 phases 3. TSM problems 4. Probabilistic representation 4. Probabilistic representation 5. Spatial resolution 6. Representation on the Web Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Overview 1. Motivation 2. Selection of 14 phases 3. TSM problems 4. Probabilistic representation 4. Probabilistic representation 5. Spatial resolution 6. Representation on the Web Zentralanstalt für Meteorologie und Geodynamik

COST ES0602, COST ES0603 and WMO Joint Workshop Chemical and Biological Weather Forecasting: State of the art and future perspectives – Conclusions – Natural allergenic species, in particular, pollen, are an Natural allergenic species, in particular, pollen, are an air quality issue of major concern. Allergies are increasing globally and the projected health impacts are alarming. Still, harmful allergenic species are currently not controlled by regulatory policies and measures. Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Umweltabteilu ng COST ES0602, COST ES0603 and WMO Joint Workshop Chemical and Biological Weather Forecasting: State of the art and future perspectives – Conclusions – The adverse health effects could also be reduced by The adverse health effects could also be reduced by implementing and using forecasting and information systems for harmful allergenic species, e.g., By issuing pre-warnings for susceptible population subgroups. Such adaptation measures showed a high positive impact in several countries but their implementation requires broad-scale operational arrangements and a legislative basis. Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Wichtigkeit und Motivation 24.06.2010 Umweltabteilu ng State of the art solution Numerical pollen forecast via numerical weather forecast + atmospheric transport (chemistry) model Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Wichtigkeit und Motivation 24.06.2010 Umweltabteilu ng Main problem Example chemical weather forecast. Input: Emission inventory Output from NWF models … and it works … and it works Numerical pollen forecast. Input: No emission inventory for pollen Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Plan 24.06.2010 Umweltabteilu ng Main problem No emission inventory for pollen Solution: model pollen emission Input: Input: •Plan density distribution •Phenology •Atmospheric pollen concentration Output: •Real time pollen emission in space Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Phenological model Assumption: phenological inception of flowering = begin of potential pollen emission into the atmosphere Model inception of flowering of pollen emitting or anemophilous species Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Phenological model First step towards NPFC As stand alone real time phenological maps support qualitative pollen forecast support qualitative pollen forecast Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Overview 1. Motivation 2. Selection of 14 phases 3. TSM problems 4. Probabilistic representation 4. Probabilistic representation 5. Spatial resolution 6. Representation on the Web Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Umweltabteilu ng 14 COST 725 Phases Zentralanstalt für Meteorologie und Geodynamik

Scheifinger List of COST725 phases : 24.06.2010 Umweltabteilu ng Phase Scientific name Common name Acer platanoides First flowers open Norway mapple Aesculus hippocastanum First flowers open Horse chestnut Alnus glutinosa Beginning of flowering Black alder Alopecurus pratensis Beginning of flowering Foxtail grass Artemisia vulgaris Beginning of flowering Mugwort Betula pendula Beginning of flowering Silver Birch Corylus avellana Corylus avellana Beginning of flowering Beginning of flowering Common hazel Common hazel Forsythia suspensa First flowers open Forsythia Fraxinus excelsior First flowers open Common ash Salix caprea First flowers open Goat willow Sambucus nigra First flowers open Elder Syringa vulgaris First flowers open Lilac Tilia cordata First flowers open Small leaved lime Secale cereale (winter) First flowers open Rye Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Overview 1. Motivation 2. Selection of 14 phases 3. TSM problems 4. Probabilistic representation 4. Probabilistic representation 5. Spatial resolution 6. Representation on the Web Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilung TSM 0 t < if T T 2 { { ∑ ∑ ( ( ) ) = = b = = F F R R T T R R − − ≥ ≥ T T T T if if T T T T f f f f b b t 1 Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilung • Phenological model, TSM, 2003, Vienna • Horse chestnut beginning of flowering C days) 10000 300 ure (1/10°C) um (1/10° 8000 200 6000 100 100 Temperatu Temperature su 4000 0 2000 0 -100 1 31 61 91 121 Yearday entry date = function(mean daily temperature, begin date of summation, temperature sum at entry date, temperature threshold) Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilung Task: model fitting • Given for model fitting: T (time series of mean daily temperatures) and t 2 (entry date) • Searched for: the 3 model parameters • Searched for: the 3 model parameters values (t 1 , F, T b ) have to be chosen such that the TSM gives lowest RMSE • Solution: Model fitting via inverse techniques Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilung Model fitting via inverse techniques • Metropolis algorithm (Press et al., 1992) • + fast • - not absolutely reliable • - only one solution, unstable • - only one solution, unstable • Look Up Table (LUT) • - slow, much computing power • + never fails • + all relevant solutions, stable Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilung Model fitting via inverse techniques • Preferred LUT • visualise cost function in parameter space Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Select a phenological model 24.06.2010 Umweltabteilu ng Modelling of TSM parameters in space = = painful experience Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Subjectively selected „optimum“ temp sum commencement date (yd 61) LT mean entry date: 142 – 132 No unique degday/tempthr pair! Zentralanstalt für Meteorologie und Geodynamik

Scheifinger Methods 24.06.2010 Umweltabteilung • Example of a LUT, 2D cut • Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Zentralanstalt für Meteorologie und Geodynamik

Scheifinger 24.06.2010 Lilac bf, Birzai (Lith.), 56° N Lilac bf, Rijeka (Croatia), 45° N RMSE distribution in Birzai very different from that in Rijeka: lowest RMSE at higher temp/lower tsums in Birzai and lower temp/higher tsums in Rijeka yd 41 best temp sum commencement date for Rijeka, 20 days before Birzai Zentralanstalt für Meteorologie und Geodynamik