基于机器学习的高精度高分辨率气象因子时空估计
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摘要
气象变量常作为重要的影响因子出现在环境污染、疾病健康和农业等领域,而高分辨率的气象资料可作为众多研究的基础数据,对推进相关研究的发展意义重大。本文以中国大陆为研究区域,利用2015年824个气象站点的气温、相对湿度和风速3套数据,结合不同的解释变量组合,分别构建了各自的GAM和残差自编码器神经网络(简称残差网络)模型,以10倍交叉验证判断模型是否过拟合。研究结果表明:①GAM和残差网络方法都不存在过拟合问题,同GAM相比,残差网络显著提高了模型预测的精度(3个气象因素的交叉验证CV R~2平均提高了0.21,CV RMSE平均降低了37%),其中相对湿度模型的提升幅度最大(CV R~2:0.85 vs. 0.52,CV RMSE:7.53%vs. 13.59%);②残差模型的结果较普通克里格插值结果和再分析资料更接近站点观测数据,表明残差网络可作为高分辨率气象数据研制的可靠方法。此外,研究还发现在相对湿度模型中加入臭氧浓度和气温、在风速模型中加入GLDAS风速再分析资料,可提升模型的性能。
The meteorological stations are sparsely distributed across Mainland China. In terms of generating high-resolution surfaces of meteorological parameters, the estimation accuracy of existing models is limited for air temperature, and is poor for relative humidity and wind speed(few studies reported). With the measurement data of 824 monitoring stations covering the mainland of China in 2015, this study compared the typical Generalized Additive Model(GAM) and autoencoder-based residual neural network(here after, residual network for short) in terms of predicting three meteorological parameters, i.e. air temperature, relative humidity, and wind speed. The performances of the two models were evaluated through 10-fold cross-validation. Basic variables including latitude, longitude, elevation, and the day of the year are used in the air temperature models. In addition to the basic variables, the relative humidity models use air temperature and ozone concentration as covariates, and the wind speed models use wind speed coarse-resolution reanalysis data as covariates. In our spatiotemporal models, spatial coordinates capture the spatial variation and time index of the day captures the time variation.Compared with GAM, residual network significantly improved the prediction accuracy: on average, CV(Cross Validation) R~2 of the three meteorological factors increased by 0.21, CV RMSE decreased by 37%, and the relative humidity model improved the most(CV R~2: 0.85 vs. 0.52, CV RMSE: 7.53% vs. 13.59%). With incorporation of the monthly index in the relative humidity models, the accuracy was greatly improved, indicating that the different levels of time factors are important for the relative humidity models. Furthermore, we also discussed the effectiveness and limitations of coarse resolution reanalysis data and nearest neighbor values as covariates. This study shows that the residual network model can greatly improve the accuracy of national high spatial(1 km) and temporal(daily) resolution meteorological data as opposed to traditional GAMs. Our findings provide implications for high-accuracy and high-resolution mapping of meteorological parameters in China.
The meteorological stations are sparsely distributed across Mainland China. In terms of generating high-resolution surfaces of meteorological parameters, the estimation accuracy of existing models is limited for air temperature, and is poor for relative humidity and wind speed(few studies reported). With the measurement data of 824 monitoring stations covering the mainland of China in 2015, this study compared the typical Generalized Additive Model(GAM) and autoencoder-based residual neural network(here after, residual network for short) in terms of predicting three meteorological parameters, i.e. air temperature, relative humidity, and wind speed. The performances of the two models were evaluated through 10-fold cross-validation. Basic variables including latitude, longitude, elevation, and the day of the year are used in the air temperature models. In addition to the basic variables, the relative humidity models use air temperature and ozone concentration as covariates, and the wind speed models use wind speed coarse-resolution reanalysis data as covariates. In our spatiotemporal models, spatial coordinates capture the spatial variation and time index of the day captures the time variation.Compared with GAM, residual network significantly improved the prediction accuracy: on average, CV(Cross Validation) R~2 of the three meteorological factors increased by 0.21, CV RMSE decreased by 37%, and the relative humidity model improved the most(CV R~2: 0.85 vs. 0.52, CV RMSE: 7.53% vs. 13.59%). With incorporation of the monthly index in the relative humidity models, the accuracy was greatly improved, indicating that the different levels of time factors are important for the relative humidity models. Furthermore, we also discussed the effectiveness and limitations of coarse resolution reanalysis data and nearest neighbor values as covariates. This study shows that the residual network model can greatly improve the accuracy of national high spatial(1 km) and temporal(daily) resolution meteorological data as opposed to traditional GAMs. Our findings provide implications for high-accuracy and high-resolution mapping of meteorological parameters in China.
引文
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(1)http://data.cma.cn/。
(2)http://www.resdc.cn/。
[2] Gao M, Yin L, Ning J. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis[J]. Atmospheric Environment, 2018,184:129-139.
[3] Li L, Lurmann F, Habre R, et al. Constrained mixed-effect models with ensemble learning for prediction of nitrogen oxides concentrations at high spatiotemporal resolution[J]. Environmental Science&Technology, 2017,51(17):9920-9929.
[4]王小燕,赵晓宇,陈恢富,等.江汉平原小麦孕穗期空气相对湿度升高的产量效应[J].中国农业科学,2014,47(19):3769-3779.[Wang X Y, Zhao X Y, Chen H F, et al. Characteristics of air moisture and the effects of high air moisture at booting stage on grain yield of wheat in Jianghan plain[J].Scientia Agricultura Sinica, 2014,47(19):3769-3779.]
[5]孙丽娜,孙瑞红,仇贵生,等.相对湿度对苹小卷叶蛾实验种群的影响[J].应用生态学报, 2014,25(12):3587-3592.[Sun L N, Sun R H, Qiu G S, et al. Influence of relative humidity on the Adoxophyes orana experimental population[J]. Chinese Journal of Applied Ecology, 2014,25(12):3587-3592.]
[6]赵琴,潘静,曹兵,等.气温升高与干旱胁迫对宁夏枸杞光合作用的影响[J].生态学报,2015,35(18):6016-6022.[Zhao Q, Pan J, Cao B, et al. Effects of elevated temperature and drought stress on photosynthesis of Lycium barbarum[J]. Acta Ecologica Sinica, 2015,35(18):6016-6022.]
[7] Bo Y C, Song C, Wang J, et al. Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease(HFMD)in Mainland China[J]. BMC Public Health, 2014,14(1):358.
[8]谢今范,刘玉英,王玉昆,等.东北地区风能资源空间分布特征与模拟[J].地理科学,2014,34(12):1497-1503.[Xie J F, Liu Y Y, Wang Y K, et al. Spatial distribution characteristics of wind resource and its simulation in northeast China[J]. Scientia Geographica Scinica, 2014,34(12):1497-1503.]
[9]任永建,刘敏,袁业畅,等.湖北省风能资源的高分辨率数值模拟试验[J].自然资源学报,2012,27(6):1035-1043.[Ren Y J, Liu M, Yuan Y C, et al. The high resolution numerical simulation of wind energy resource in Hubei Province[J]. Journal of Natural Resources, 2012,26(6):1035-1043.]
[10]于洋,卫伟,陈利顶,等.黄土高原年均降水量空间插值及其方法比较[J].应用生态学报,2015,26(4):999-1006.[Yu Y, Wei W, Chen L D, et al. Comparison on the methods for spatial interpolation of the annual average precipitation in the Loess Plateau region[J]. Chinese Journal of Applied Ecology, 2015,26(4):999-1006.]
[11]王国泰,张守平,杨清伟,等.基于空间插值方法的重庆降水信息展布[J].南水北调与水利科技,2018,16(3):18-23.[Wang G T, Zhang S P, Yang Q W et al. Precipitation information distribution in Chongqing based on spatial interpolation method[J]. South-to-North Water Transfers and Water Science&Technology, 2018,16(3):18-23.]
[12]林忠辉,莫兴国,李宏轩,等.中国陆地区域气象要素的空间插值[J].地理学报,2002,57(1):47-56.[Lin Z H, Mo X G, Li H X, et al. Comparison of three spatial interpolation methods for climate variables in China[J]. Acta Geographica Sinica, 2002,57(1):47-56.]
[13]张仁平,张云玲,郭靖,等.新疆地区降水分布的空间插值方法比较[J].草业科学,2018,35(3):521-529.[Zhang R P,Zhang Y L, Guo J, et al. Comparison of spatial interpolation methods for precipitation distribution in Xinjiang region[J]. Pratacultural Science, 2018,35(3):521-529.]
[14]姜晓剑,刘小军,黄芬,等.逐日气象要素空间插值方法的比较[J].应用生态学报,2010,21(3):624-630.[Jiang X J,Liu X J, Huang F, et al. Comparison of spatial interpolation methods for daily meteorological elements[J]. China Journal of Applied Ecology, 2010,21(3):624-630.]
[15]陈思宁,郭军.不同空间插值方法在区域气温序列中的应用评估:以东北地区为例[J].中国农业气象,2015,36(2):234-241.[Chen S N, Guo J. Evaluation of different spatial interpolation methods in regional temperature sequence:A case study in northeast China[J]. China Journal of Agrometeorology, 2015,36(2):234-241.]
[16]彭彬,周艳莲,高苹,等.气温插值中不同空间插值方法的适用性分析——以江苏省为例[J].地球信息科学学报,2011,13(4):539-548.[Peng B, Zhou Y L, Gao P, et al.Suitability assessment of different interpolation methods in the gridding process of station collected air temperature:A case study in Jiangsu Province, China[J]. Journal of Geo-information Science, 2011,13(4):539-548.]
[17]李军龙,张剑,张丛,等.气象要素空间插值方法的比较分析[J].草业科学,2006(8):6-11.[Li J L, Zhang J, Zhang C, et al. Analyze and compare the spatial interpolation methods for climate factor[J]. Pratacultural Science, 2006(8):6-11.]
[18]李伟,李庆祥,江志红.用Kriging方法对中国历史气温数据插值可行性讨论[J].南京气象学院学报,2007(2):246-252.[Li W, Li Q X, Jiang Z H. Discussion on feasibility of gridding the historic temperature data in China with kriging method[J]. Journal of Nanjing Institute of Meteorology, 2007(2):246-252.]
[19] Xu C D, Wang J F, Li Q. A new method for temperature spatial interpolation based on sparse historical stations[J].Journal of Climate, 2017,31(5):1757-1770.
[20] Wang J F, Xu C, Hu M, et al. Global land surface air temperature dynamics since 1880[J]. International Journal of Climatology, 2018,381:E466-E474.
[21] Wang J F, Xu C D, Hu M G, et al. A new estimate of the China temperature anomaly series and uncertainty assessment in 1900-2006[J]. Journal of Geophysical Research:Atmospheres, 2014,119(1):1-9.
[22]解恒燕,张深远,侯善策,等.降水量空间插值方法在小样本区域的比较研究[J].水土保持研究,2018,25(3):117-121.[Xie H Y, Zhang S Y, Hou S C, et al. Comparison research on rainfall interpolation methods for small sample areas[J]. Research of Soil and Water Conservation, 2018,25(3):1-9.]
[23]马秀霞,黄领梅,沈冰.陕西省月平均气温空间插值方法研究[J].水资源与水工程学报,2017,28(5):100-105.[Ma X X, Huang L M, Shen B Study on spatial interpolation method of monthly mean temperature in Shanxi Province[J]. Journal of Water Resource&Water Engineering,2017,28(5):100-105.]
[24]沈红,刘文兆,张勋昌.黄土高原气象要素栅格化方法的研究[J].西北农林科技大学学报(自然科学版),2010,38(4):99-106.[Shen H, Liu W Z, Zhang X C. Studying the methods for rasterizing meteorological variables in the Loess Plateau[J]. Journal of Northwest A&F University(Nat. Sci. Ed.), 2010,38(4):99-106.]
[25]李月臣,何志明,刘春霞.基于站点观测数据的气温空间化方法评述[J].地理科学进展,2014,33(8):1019-1028.[Li Y C, He Z M, Liu C X. Review on spatial interpolation methods of temperature data from meteorological stations[J]. Progress in Geography, 2014,33(8):1019-1028.]
[26]周婷婷,陈文惠.基于MODIS数据和气象观测数据的气温空间插值方法比较[J].地理科学进展,2011,30(9):1143-1151.[Zhou T T, Chen W H. Comparation of the temperature spatial interpolation methods based on MODIS data and meteorological observation data[J]. Progress in Geography, 2011,30(9):1143-1151.]
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