摘要
能见度对于生产生活安排具有重要的指导意义,目前我国的能见度监测网络站点覆盖相对稀疏,观测数据具有空间离散性和局地性。利用集合深度学习模型融合多源数据估计能见度,该集合模型包括了深度神经网络模型、随机森林模型、梯度强化模型和广义线性模型四种机器学习器,融合了气象观测、大气成分观测、模式模拟和土地利用类型等多源数据,并利用Barnes客观分析进一步消除深度学习融合误差,开发出空间连续的能见度网格数据集。该数据的空间分辨率为12km,经过独立样本评估,融合数据的R^2为0.61。相比较于空间插值和线性模型等方法,提出的方法具有更好的准确度,且具有很好的空间解析力,可进一步用于开发更高分辨率数据。该方法具有较高的计算效率和较好的数据兼容性,可以部署于业务化平台中可靠运行。
Ground visibility had significant influences on transportation and human outdoor activities.Stationary visibility observations are usually spatially sparse,inadequate to meet the demand for complete spatial coverage data.To tackle the problem,we put forward a novel data fusion framework by incorporating an ensemble deep learning method and a Barnes objective analysis approach.The ensemble deep learning method works to generate high-quality first-guess field by building complex linkages between data sets.The Barnes objective analysis method was used to remove remaining residual errors and biases.Furthermore,it was designed to be hierarchical to better address the environmental factors such as particulate concentrations and relative humidity that greatly affect visibility in a sub-layer.The data fusion framework is able to include multiple data sources of different types,i.e.gridded WRF meteorological predictions,CMAQ air quality predictions,stationary meteorological and atmospheric composition observations and other supporting land use/cover data sets.The model was implemented to generate gridded hourly visibility data sets in China.The fused gridded data was evaluated against observations from independent monitors,with an R^2=0.61.Comparatively the R2 values of interpolation approach and linear regression fusion were respectively 0.55 and 0.57.Besides,our gridded fusion data have much more and detailed spatial information than that of the smoothed interpolation data.Our data fusion approach is relatively easy to implement in operational system and also has good extensibility to handily include more data sets.
作者
吕宝磊
胡泳涛
李林
梁海河
刘钧
王晓江
Lü Baolei;Hu Yongtao;Li Lin;Liang Haihe;Liu Jun;Wang Xiaojiang(Huayun Sounding Meteorological Technology Company,Ltd.,Beijing 102299;School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,Georgia 30332;Beijing Meteorological Service,Beijing 100089;Meteorological Observation Center,China Meteorological Administration,Beijing 100081;China Huayun Meteorological Technology Group,Beijing 100081)
出处
《气象科技进展》
2018年第6期77-82,共6页
Advances in Meteorological Science and Technology
