Infotech Oulu, University of Oulu, Oulu, Finland.
Finnish Meteorological Institute, Helsinki, Finland.
PLoS One. 2019 Feb 7;14(2):e0211702. doi: 10.1371/journal.pone.0211702. eCollection 2019.
Mobile, vehicle-installed road weather sensors are becoming ubiquitous. While mobile sensors are often capable of making observations on a high frequency, their reliability and accuracy may vary. Large-scale road weather observation and forecasting are still mostly based on stationary road weather stations (RWS). Though expensive, sparsely located and making observations on a relatively low frequency, RWS' reliability and accuracy are well-known and accommodated for in the road weather forecasting models. Statistical analysis revealed that road weather conditions indeed have a great effect on how the observations of mobile and stationary road weather temperature sensors differ from each other. Consequently, we calibrated the observations of mobile sensors with a linear mixed model. The mixed model was fitted fusing ca. 20 000 pairs of mobile and RWS observations of the same location at the same time, following a rendezvous model of sensor calibration. The calibration nearly halved the MSE between the observations of the mobile and the RWS sensor types. Computationally very light, the calibration can be embedded directly in the sensors.
移动的、车载安装的道路天气传感器正变得无处不在。虽然移动传感器通常能够进行高频次的观测，但它们的可靠性和准确性可能会有所不同。大规模的道路天气观测和预报仍然主要基于固定的道路天气站（RWS）。尽管 RWS 昂贵、稀疏分布且观测频率相对较低，但它们的可靠性和准确性是众所周知的，并在道路天气预报模型中得到了考虑。统计分析表明，道路天气条件确实对移动和固定道路天气温度传感器的观测结果之间的差异有很大影响。因此，我们使用线性混合模型对移动传感器的观测值进行了校准。该混合模型是通过融合大约 20000 对在同一地点同时进行的移动和 RWS 观测值拟合的，遵循传感器校准的会合模型。校准将移动传感器和 RWS 传感器类型之间的观测值均方误差（MSE）几乎减半。该校准计算量非常小，可以直接嵌入到传感器中。
