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| Hauptseite > Publikationsdatenbank > Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook > print |
| Hauptseite > Publikationsdatenbank > Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook > print |
| 001 | 842557 | ||
| 005 | 20240712100943.0 | ||
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| 100 | 1 | _ | |a Sofiev, Mikhail |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2017 |b EGU |
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| 520 | _ | _ | |a The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN). Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and shown to remain nearly optimal throughout the whole period. |
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