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On possibilities of assimilation of near-real-time pollen data by atmospheric composition models

机译：大气成分模型同化近实时花粉数据的可能性

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Assimilation of pollen observations for increasing accuracy of the deterministic model forecast and reanalysis faces several roadblocks. The most-evident problem today is the long delay of the data because of the manual character of the observations. Automatic monitors are about to eliminate this issue but more are on the way. This paper shows that the classical assimilation of the model state, i.e. pollen concentrations, has very little effect on the forecasts. Due to short relaxation time of the system, the updates generated by the assimilation are forgotten within a few hours. In a search of approaches with a longer-lasting impact, a numerical experiment is conducted assimilating the total seasonal pollen emission, which controls the overall season severity. It turned out to be a prominent example of parameters affecting the model predictions over the long period-in the retrospective simulations. It remains to be demonstrated that this parameter can substantially benefit from assimilation of the near-real-time data that are becoming available from the automatic pollen monitors.
机译：花粉观测的同化升高确定性模型预测和再分析的准确性面临若干路障。今天最明显的问题是由于观察的手工性质，数据的长期延迟。自动监视器即将消除此问题，但更多在路上。本文表明，模型状态的经典同化，即花粉浓度，对预测影响很小。由于系统的短暂放松时间，同化产生的更新在几个小时内被遗忘。在寻找具有更持久的影响的方法中，对数值实验进行了同化整个季节性花粉排放，这控制了整个季节严重程度。事实证明，它是影响长期内模型预测的参数的突出示例 - 在回顾性模拟中。它仍然值得证明该参数可以显着受益于从自动花粉监视器获得的近实时数据的同化。

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