月平均气温物理经验统计模型优化研究
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摘要
气温是一个重要的气象参数,为了给农业研究提供更加精准且物理意义完善的气温空间分布,建立分站分月的月平均气温物理优化模型。通过融合遥感数据合成比辐射率与气温模拟结果迭代优化,克服以往模型中气温回代的缺陷,对模型进行优化,生成了2007年我国1 km×1 km分辨率的月平均气温空间分布图。分析表明,模拟结果能较好地反映气温的宏观分布趋势和局地分布特征。误差分析结果表明,迭代优化后的年平均绝对误差为0.72℃,年平均均方根误差为1.01℃。相较于IDW插值法,优化模型模拟结果精度更高,影响气温的各项因子在结果中显示的特征更明显;相对于前人的隐式统计模型,参数物理意义更加完善,精度略有提升。为气温空间分布模拟提供了一种新思路,对科学指导农业生产、合理利用农业资源具有一定的参考意义。
Temperature is a significant meteorological parameter. Physical optimization temperature model for the monthly average temperature based on Month-Station was built to provide the more accurate temperature distribution for the agricultural research. The model was optimized by the remote sensed data and the iterative optimization. Results of 2007 monthly mean temperature characteristic of 1 km × 1 km spatial resolution were showed in this paper. The results showed that the simulation results can well reflect the temperature macroscopic distribution and the local characteristics. The error analysis results showed that the annual temperature MABE is 0. 72℃ and the annual temperature RMSE is 1. 01℃. The optimization temperature model had higher accuracy than IDW interpolation method,and more obvious what each factor expressed. Compared with the previous implicit statistical model,the physical meaning ofthe parameters was more perfect and the precision was improved slightly. It provided a new idea for the simulation of temperature,which had certain reference value for agricultural production and agricultural resources.
Temperature is a significant meteorological parameter. Physical optimization temperature model for the monthly average temperature based on Month-Station was built to provide the more accurate temperature distribution for the agricultural research. The model was optimized by the remote sensed data and the iterative optimization. Results of 2007 monthly mean temperature characteristic of 1 km × 1 km spatial resolution were showed in this paper. The results showed that the simulation results can well reflect the temperature macroscopic distribution and the local characteristics. The error analysis results showed that the annual temperature MABE is 0. 72℃ and the annual temperature RMSE is 1. 01℃. The optimization temperature model had higher accuracy than IDW interpolation method,and more obvious what each factor expressed. Compared with the previous implicit statistical model,the physical meaning ofthe parameters was more perfect and the precision was improved slightly. It provided a new idea for the simulation of temperature,which had certain reference value for agricultural production and agricultural resources.
引文
[1]李军,游松财,黄敬峰.中国1961-2000年月平均气温空间插值方法与空间分布[J].生态环境学报,2006,15(1):109-114.
[2]李军,黄敬峰.山区气温空间分布推算方法评述[J].山地学报,2004,22(1):126-132.
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[4]成海民.河北省月平均气温的分布式模拟研究[D].南京信息工程大学,2012.
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[6]段春锋,缪启龙,曹雯.西北地区参考作物蒸散变化特征及其主要影响因素[J].农业工程学报,2011,27(8):77-83.
[7]徐剑波,赵凯,赵之重,等.利用HJ-1B遥感数据反演西北地区近地表气温[J].农业工程学报,2013,29(22):145-153.
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[12]Hudson G,Wackernagel H.Mapping temperature using kriging with external drift:Theory and an example from scotland[J].International Journal of Climatology,1994,14(1):77-91.
[13]Luo Z,Wahba G,Johnson D R.Spatial-Temporal Analysis of Temperature Using Smoothing Spline ANOVA.[J].Journal of Climate,1998,11(1):18-28.
[14]陈晓峰,刘纪远,张增祥,等.利用GIS方法建立山区温度分布模型[J].中国图象图形学报,1998,3(3):234-238.
[15]刘新安,于贵瑞,范辽生,等.中国陆地生态信息空间化技术研究(Ⅲ)---温度、降水等气候要素[J].自然资源学报,2004,19(6):818-825.
[16]曾燕,邱新法,何永健,等.复杂地形下黄河流域月平均气温分布式模拟[J].中国科学:地球科学,2009(6):774-786.
[17]邱新法,仇月萍,曾燕.重庆山地月平均气温空间分布模拟研究[J].地球科学进展,2009,24(6):621-628.
[18]袁淑杰,谷晓平,缪启龙,等.基于DEM的复杂地形下平均气温分布式模拟研究---以贵州高原为例[J].自然资源学报,2010,25(5):859-867.
[19]姚镇海.融合遥感数据的月平均气温分布式模拟参数优化的研究[D].南京信息工程大学,2015.
[20]王丽,邱新法,王培法,等.复杂地形下长江流域太阳总辐射的分布式模拟[J].地理学报,2010,65(5):543-552.
[21]王新生,徐静,柳菲,等.近10年我国地表比辐射率的时空变化[J].地理学报,2012,67(1):93-100.
[22]刘东琦,王介民.利用ATSR/ERS-1资料研究复杂下垫面比辐射率的变化[J].大气科学,1999,23(5):613-622.
[23]梁顺林.定量遥感[M].科学出版社,2013.
[24]曾燕,邱新法,刘昌明,等.黄河流域有效辐射拟合研究[J].地理科学,2007,27(3):342-347.
[2]李军,黄敬峰.山区气温空间分布推算方法评述[J].山地学报,2004,22(1):126-132.
[3]李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000,15(3):260-265.
[4]成海民.河北省月平均气温的分布式模拟研究[D].南京信息工程大学,2012.
[5]冯美臣,肖璐洁,杨武德,等.基于遥感数据和气象数据的水旱地冬小麦产量估测[J].农业工程学报,2010,26(11):183-188.
[6]段春锋,缪启龙,曹雯.西北地区参考作物蒸散变化特征及其主要影响因素[J].农业工程学报,2011,27(8):77-83.
[7]徐剑波,赵凯,赵之重,等.利用HJ-1B遥感数据反演西北地区近地表气温[J].农业工程学报,2013,29(22):145-153.
[8]Nalder I A,Wein R W.Spatial interpolation of climatic Normals:test of a new method in the Canadian boreal forest[J].Agricultural&Forest Meteorology,1998,92(4):211-225.
[9]Jeffrey S J,Carter J O,Moodie K B,et al.Using spatial interpolation to construct a comprehensive archive of Australian climate data[J].Environmental Modelling&Software,2001,16(4):309-330.
[10]Thornton P E,Running S W,White M A.Generating surfaces of daily meteorological variables over large regions of complex terrain[J].Journal of Hydrology,1997,190(3-4):214-251.
[11]Bartier P M,Keller C P.Multivariate interpolation to incorporate thematic surface data using inverse distance weighting(IDW)[J].Computers&Geosciences,1996,22(7):795-799.
[12]Hudson G,Wackernagel H.Mapping temperature using kriging with external drift:Theory and an example from scotland[J].International Journal of Climatology,1994,14(1):77-91.
[13]Luo Z,Wahba G,Johnson D R.Spatial-Temporal Analysis of Temperature Using Smoothing Spline ANOVA.[J].Journal of Climate,1998,11(1):18-28.
[14]陈晓峰,刘纪远,张增祥,等.利用GIS方法建立山区温度分布模型[J].中国图象图形学报,1998,3(3):234-238.
[15]刘新安,于贵瑞,范辽生,等.中国陆地生态信息空间化技术研究(Ⅲ)---温度、降水等气候要素[J].自然资源学报,2004,19(6):818-825.
[16]曾燕,邱新法,何永健,等.复杂地形下黄河流域月平均气温分布式模拟[J].中国科学:地球科学,2009(6):774-786.
[17]邱新法,仇月萍,曾燕.重庆山地月平均气温空间分布模拟研究[J].地球科学进展,2009,24(6):621-628.
[18]袁淑杰,谷晓平,缪启龙,等.基于DEM的复杂地形下平均气温分布式模拟研究---以贵州高原为例[J].自然资源学报,2010,25(5):859-867.
[19]姚镇海.融合遥感数据的月平均气温分布式模拟参数优化的研究[D].南京信息工程大学,2015.
[20]王丽,邱新法,王培法,等.复杂地形下长江流域太阳总辐射的分布式模拟[J].地理学报,2010,65(5):543-552.
[21]王新生,徐静,柳菲,等.近10年我国地表比辐射率的时空变化[J].地理学报,2012,67(1):93-100.
[22]刘东琦,王介民.利用ATSR/ERS-1资料研究复杂下垫面比辐射率的变化[J].大气科学,1999,23(5):613-622.
[23]梁顺林.定量遥感[M].科学出版社,2013.
[24]曾燕,邱新法,刘昌明,等.黄河流域有效辐射拟合研究[J].地理科学,2007,27(3):342-347.