重庆山地区域气象要素空间插值方法对比

doi:10.3969/j.issn.1673-503X.2022.04.007

Abstract

Abstract:

Mountainous terrain is one typical and complex land surface, and the accurate simulation and acquisition of meteorological elements over mountainous regions are facing challenges due to a limited number of meteorological stations. Based on the meteorological data over the studied area in 1999 and 2018, we used four methods of thin-plate smoothing splines (ANUS), Co-Kriging (CK), ordinary Kriging (OK), and inverse distance weighting (IDW) to spatially interpolate air temperature, precipitation, and total solar radiation on annual and monthly scales. Using a cross-validation method, mean absolute error (MAE), magnitude of relative error (MRE), and root mean square error (RMSE) are used to evaluate the interpolation accuracy and determine the optimal interpolation method for each meteorological element. The results showed that ANUS is the optimal interpolation method for air temperature and total solar radiation, while IDW is the optimal interpolation method for precipitation. The interpolation accuracy for air temperature and total solar radiation is better during the months with high air temperature and total solar radiation than that during months with their low values, and the trend is opposite for the precipitation. The interpolation accuracy for the three elements is better on an annual scale than on a monthly scale. The MRE values showed that the interpolation accuracy for the three elements is in order of air temperature > total solar radiation > precipitation, being 1.86%, 4.6%, and 6.87% on an annual scale in 1999 and being 2.79%, 5.82%, and 17.42% on a monthly scale, respectively. In 2018, the interpolation accuracy for total solar radiation is 3.03% and 4.88% on the annual and monthly scales, respectively. After using data at regional encryption stations, the interpolation accuracy for air temperature and precipitation can reach 2.03% and 11.2% on the annual scale and 3.2% and 23.14% on the monthly scale, respectively. Our research can provide scientific reference and a basis for the spatialization of meteorological elements in similar complex terrain areas.

Key words: Spatial interpolation, Meteorological elements, Cross-validation

CLC Number:

P413

Chun-hua YANG,Li ZHENG,He-qing HUANG,Bo LEI,Shuo YANG,Jian-hui LIU,Ming-yang ZHANG,Qiu-yan DUAN. Comparison of spatial interpolation methods of meteorological elements over Chongqing mountainous region[J]. Journal of Meteorology and Environment, 2022, 38(4): 57-66.

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References 46

1	支星, 谈建国, 孙兰东. 1961—2017年中国华东区域高空温度变化特征[J]. 气象与环境学报, 2021, 37 (2): 48- 55. doi: 10.3969/j.issn.1673-503X.2021.02.007
2	金巍, 刘卫华, 李晶, 等. 1951—2017年鞍山市气温变化特征及城市热岛变化[J]. 气象与环境学报, 2021, 37 (4): 78- 85. doi: 10.3969/j.issn.1673-503X.2021.04.011
3	敖雪, 赵春雨, 崔妍, 等. 中国东北地区气温变化的模拟评估与未来情景预估[J]. 气象与环境学报, 2021, 37 (1): 33- 42.
4	张仁平, 郭靖, 张云玲. 新疆草地净初级生产力(NPP)空间分布格局及其对气候变化的响应[J]. 生态学报, 2020, 40 (15): 5318- 5326.
5	潘竟虎, 徐柏翠. 中国潜在植被NPP的空间分布模拟[J]. 生态学杂志, 2020, 39 (3): 1001- 1012.
6	陈妮娜, 纪瑞鹏, 米娜, 等. 春玉米生长发育、产量和籽粒品质对减量施氮的响应[J]. 气象与环境学报, 2021, 37 (4): 86- 92. doi: 10.3969/j.issn.1673-503X.2021.04.012
7	黄丽艳, 闫巧玲, 高添, 等. 基于ALOS PALSAR雷达影像的人工林蓄积量估算——以塞罕坝机械林场华北落叶松人工林为例[J]. 生态学杂志, 2015, 34 (9): 2401- 2409.
8	梅晓丹, 毛学刚, 范文义, 等. 基于改进的MT-CLIM模型的中高纬低山丘陵区太阳总辐射模拟——以小兴安岭为例[J]. 测绘工程, 2015, 24 (6): 9- 13. doi: 10.3969/j.issn.1006-7949.2015.06.003
9	李雨鸿, 陶苏林, 李荣平, 等. 辽宁省净初级生产力时空演变及其对地形因子的响应[J]. 气象与环境学报, 2021, 37 (5): 107- 112. doi: 10.3969/j.issn.1673-503X.2021.05.016
10	谭剑波, 李爱农, 雷光斌. 青藏高原东南缘气象要素Anusplin和Cokriging空间插值对比分析[J]. 高原气象, 2016, 35 (4): 875- 886.
11	冯锦明, 赵天保, 张英娟. 基于台站降水资料对不同空间内插方法的比较[J]. 气候与环境研究, 2004, 9 (2): 261- 277.
12	任璇, 郑江华, 穆晨, 等. 不同气象插值方法在新疆草地NPP估算中的可靠性评价[J]. 草业科学, 2017, 11 (3): 439- 448.
13	王丽娜. 气象要素空间插值算法的研究及其应用[D]. 重庆: 重庆理工大学, 2014.
14	Wong K W , Wong P M , Gedeon T D , et al. Rainfall prediction model using soft computing technique[J]. Soft Computing, 2003, 7 (6): 434- 438. doi: 10.1007/s00500-002-0232-4
15	吴昌广, 林德生, 周志翔, 等. 三峡库区降水量的空间插值方法及时空分布[J]. 长江流域资源与环境, 2010, 19 (7): 752- 758.
16	钱永兰, 吕厚荃, 张艳红. 基于ANUSPLIN软件的逐日气象要素插值方法应用与评估[J]. 气象与环境学报, 2010, 26 (2): 7- 15.
17	赵美艳, 余君, 胡芸芸. 基于局部薄盘光滑样条函数的重庆地区气温空间插值[J]. 陕西气象, 2021, (1): 50- 55.
18	Burrough P A , McDonnell R A . Principles of geographical information systems[M]. New York, USA: Oxford University Press, 1998: 12- 16.
19	Atkinson P M , Lloyd C D . Mapping precipitation in Switzerland with ordinary and indicator Kriging[J]. Journal of Geographic Information and Decision Analysis, 1998, 2 (2): 72- 86.
20	李任君, 高懋芳, 李强, 等. 基于ANUSPLIN的降水空间插值方法研究[J]. 中国农业信息, 2019, 31 (1): 48- 57.
21	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.
22	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, 2006, 15 (1): 109- 114.
23	关宏强, 蔡福, 王阳, 等. 短时间序列气温要素空间插值方法精度的比较研究[J]. 气象与环境学报, 2007, 23 (5): 13- 16.
24	许民, 王雁, 周兆叶, 等. 长江流域逐月气温空间插值方法的探讨[J]. 长江流域资源与环境, 2012, 21 (3): 327- 334.
25	李艳, 朱军, 胡亚, 等. 不同插值方法模拟四川省逐月降水量的对比分析[J]. 水土保持研究, 2017, 24 (1): 151- 154.151-154, 160
26	王江, 乐章燕, 廖荣伟, 等. 中国区域温度和降水不同空间插值方法精度对比[J]. 气象与环境学报, 2016, 32 (6): 85- 93.
27	张玮玮, 张眉, 吴杨, 等. 复杂地形下浙江夏季气候要素空间插值方法评价[J]. 干旱气象, 2020, 38 (4): 674- 682.
28	Collins F C. A Comparison of spatial interpolation techniques in temperature estimation[D]. Blacksburg, Virginia: Virginia Tech, 1995.
29	Dubois G . Spatial interpolation comparison 97:Foreword and introduction[J]. Journal of Geographic Information and Decision Analysis, 1998, (2): 1- 10.
30	Bates D M , Lindstrom M J , Wahba G . Gcvpack-routines for generalized cross validation[J]. Communications in Statistics-Simulation and Computation, 1987, 16 (1): 263- 297.
31	Hutchinson M F. ANUSPLIN version 4.3[M]. Canberra, Australia: The Australia National University, 2004.
32	刘志红, LiL T, McVicarT R, 等. 专用气候数据空间插值软件ANUSPLIN及其应用[J]. 气象, 2008, 34 (2): 92- 100.
33	刘志红, McVicarT R, LiL T, 等. 基于ANUSPLIN的时间序列气象要素空间插值[J]. 西北农林科技大学学报: 自然科学版, 2008, 36 (10): 227- 234.
34	李章林, 王平, 张夏林. 距离幂次反比法的改进与应用[J]. 金属矿山, 2008, (4): 88- 92.
35	郭婧. 基于AMMRR插值法的中国草地综合顺序分类图的研制[D]. 兰州: 甘肃农业大学, 2011.
36	汤国安, 杨昕. ArcGIS地理信息系统空间分析实验教程[M]. 北京: 科学出版社, 2006: 309- 428.
37	朱会义, 贾绍凤. 降雨信息空间插值的不确定性分析[J]. 地理科学进展, 2004, 23 (2): 34- 42.
38	李朝奎, 陈良, 王勇. 降雨量分布的空间插值方法研究——以美国爱达荷州为例[J]. 矿产与地质, 2007, 21 (6): 684- 687.
39	徐翔, 许瑶, 孙青青, 等. 复杂山地环境下气候要素的空间插值方法比较研究[J]. 华中师范大学学报: 自然科学版, 2018, 52 (1): 122- 129.
40	翁笃鸣, 罗哲贤. 山区地形气候[M]. 北京: 气象出版社, 1990.
41	曾燕, 邱新法, 潘敖大, 等. 地形对黄河流域太阳辐射影响的分析研究[J]. 地球科学进展, 2008, 23 (11): 1185- 1193.
42	廖荣伟, 曹丽娟, 张冬斌, 等. 中国地面气温和降水网格化数据精度比较[J]. 气象科技, 2017, 45 (2): 364- 374.
43	Smith R B . The influence of mountains on the atmosphere[J]. Advances in Geophysics, 1979, 21, 87- 230.
44	鲁振宇, 杨太保, 郭万钦. 降水空间插值方法应用研究——以黄河源区为例[J]. 兰州大学学报: 自然科学版, 2006, 42 (4): 11- 14.
45	郑小波, 罗宇翔, 于飞, 等. 西南复杂山地农业气候要素空间插值方法比较[J]. 中国农业气象, 2008, 29 (4): 458- 462.
46	彭红兰, 刘芳, 朵海瑞, 等. 三江源地区温度和降水量空间插值方法比较[J]. 安徽农业科学, 2010, 38 (18): 9646- 9649.9646-9649, 9680

年份	MAE/℃				MRE/(%)				RMSE/℃
年份	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW
1999	0.31	0.6	0.61	0.52	1.86	3.57	3.60	3.09	0.38	1.00	0.97	0.90
2018	0.35	0.59	0.86	0.71	2.03	3.48	5.02	4.18	0.42	0.80	1.06	0.94

年份	MAE/℃				MRE/(%)				RMSE/℃
年份	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW
1999	85.56	88.86	79.57	76.64	7.67	7.96	7.13	6.87	120.57	116.98	104.1	103.52
2018	123.59	132.3	128.18	120.12	11.52	12.33	11.95	11.2	148.42	158.51	155.82	140.25

年份	MAE/℃				MRE/(%)				RMSE/℃
年份	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW	ANUS	CK	OK	IDW
1999	171.03	174.91	174.77	162.97	4.6	4.71	4.7	4.39	203.37	204.55	204.49	196.43
2018	117.48	131.17	128.25	147.69	3.03	3.38	3.31	3.81	150.94	149.66	147.6	178.46

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Comparison of spatial interpolation methods of meteorological elements over Chongqing mountainous region

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