复杂山区地形条件下ERA5再分析地表气温降尺度方法

doi:10.12082/dqxxkx.2022.210386

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 750-765.doi: 10.12082/dqxxkx.2022.210386

复杂山区地形条件下ERA5再分析地表气温降尺度方法

于法川¹(), 祝善友¹^,^*(), 张桂欣², 朱佳恒¹, 张南³, 徐永明¹

1.南京信息工程大学遥感与测绘工程学院,南京 210044
2.南京信息工程大学地理科学学院,南京 210044
3.河北省气象台, 石家庄 050021

收稿日期:2021-07-11 修回日期:2021-09-22 出版日期:2022-04-25 发布日期:2022-06-25
通讯作者: ^*祝善友（1977— ）,男,山东日照人,教授,博导,研究方向为热红外遥感基础理论与应用、生态遥感。 E-mail: zsyzgx@163.com
作者简介:于法川（1997— ）,男,山东菏泽人,硕士生,研究方向为遥感气象应用。E-mail: yufc@nuist.edu.cn
基金资助:
国家重点研发计划项目(2019YFC1510203);国家自然科学基金项目(42171101);国家自然科学基金项目(41871028);河北省技术创新引导计划项目(19975414D)

A Downscaling Method for Land Surface Air Temperature of ERA5 Reanalysis Dataset under Complex Terrain Conditions in Mountainous Areas

YU Fachuan¹(), ZHU Shanyou¹^,^*(), ZHANG Guixin², ZHU Jiaheng¹, ZHANG Nan³, XU Yongming¹

1. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Hebei Meteorological Observatory, Shijiazhuang 050021, China

Received:2021-07-11 Revised:2021-09-22 Online:2022-04-25 Published:2022-06-25
Supported by:
National Key Research and Development Program of China(2019YFC1510203);National Natural Science Foundation of China(42171101);National Natural Science Foundation of China(41871028);S&T Program of Hebei(19975414D)

摘要/Abstract

摘要：

高时空分辨率的气温栅格数据是多种地学模型和气候模型的重要输入。山区地形复杂,气温空间异质性强,如何获取高时空分辨率的山区地表气温数据一直是研究热点与难点。本文选择地形复杂的河北省张家口市作为试验区,基于局部薄盘样条函数对ERA5再分析日均近地表气温（2 m高度）进行空间插值,并利用随机森林算法,结合少量气象站观测气温数据、地形地表参数数据构建日均气温订正模型和气温逐时化模型,实现空间分辨率由0.1 °（约11 km）到30 m的逐时气温降尺度,最后将该模型拓展应用于其他时间与区域,检验本文发展的降尺度方法在没有站点观测数据条件下的时空移植性。结果显示,本文降尺度方法得到的高时空分辨率山区气温数据精度较高,1月均方根误差（RMSE）平均值为2.4 ℃,明显优于气象站点插值结果,且气温相对高低的空间分布更为合理、纹理更加丰富;将该方法应用到其他时间与区域的RMSE平均值分别为2.9 ℃与2.5 ℃,均小于再分析资料直接插值所产生的误差。研究结果总体表明,在气象站点较少甚至没有时,可利用本文方法通过ERA5再分析气温准确获取复杂地形条件下的山区高时空分辨率气温数据。

关键词: 复杂地形, 插值, Landsat, SRTM, ERA5, 局部薄盘样条函数, 随机森林算法, 统计降尺度

Abstract:

Gridded land surface air temperature with high spatial and temporal resolution is an important input parameter for various geospatial and climate models. Due to the complex terrain and strong spatial heterogeneity of air temperature in mountainous areas, how to obtain surface air temperature data with higher spatial and temporal resolution has always been a research hotspot and difficult question. By selecting the complex terrain region of Zhangjiakou, Hebei province as the experimental area, we first interpolate daily average air temperature at the height of 2 m from ERA5 reanalysis dataset based on local thin plate spline function. Then, by using random forest algorithm, combined with a small amount of weather observation data, terrain data, and land surface parameters, this research constructs the daily average temperature correction model and the hourly temperature estimation model to downscale ERA5 reanalysis air temperature with the resolution of 0.1 ° (about 11 km) to 30 m resolution. Finally, the model is extended and applied to other times and regions without field observation to test the generalizability of the developed downscaling method. The results show that the accuracy of the downscaling method is high, and the mean Root Mean Square Error (RMSE) for January is about 2.4 ℃, which is better than the interpolation results using data measured from meteorological stations. The spatial distribution of the air temperature derived by the developed method is more reasonable, and the texture is more abundant. The average RMSE values of other time and region are 2.9 ℃ and 2.5 ℃, respectively, which are smaller than the RMSE using direct interpolation of reanalysis data. The overall results show that the developed method can be used to downscale the ERA5 reanalysis data to obtain accurate air temperature data with higher spatial and temporal resolution in mountainous areas when there are few meteorological stations.

Key words: complex terrain, Interpolation, Landsat, SRTM, ERA5, Thin plate spline function, random forest algorithm, statistical downscaling

于法川, 祝善友, 张桂欣, 朱佳恒, 张南, 徐永明. 复杂山区地形条件下ERA5再分析地表气温降尺度方法[J]. 地球信息科学学报, 2022, 24(4): 750-765.DOI:10.12082/dqxxkx.2022.210386

YU Fachuan, ZHU Shanyou, ZHANG Guixin, ZHU Jiaheng, ZHANG Nan, XU Yongming. A Downscaling Method for Land Surface Air Temperature of ERA5 Reanalysis Dataset under Complex Terrain Conditions in Mountainous Areas[J]. Journal of Geo-information Science, 2022, 24(4): 750-765.DOI:10.12082/dqxxkx.2022.210386

图/表 16

图1

表1

图2

表2

日均气温插值订正所用的地形地表参数

参数名称	参数描述	参考文献
NDSI	$NDSI = (ρ Green - ρ Swir 1) / (ρ Green + ρ Swir 1)$	[36]
NDVI	$NDVI = (ρ NIR - ρ Red) / (ρ NIR + ρ Red)$	[37]
Albedo	$ρ short = 0.356 × ρ Coastal + 0.13 × ρ Green + 0.373 × ρ Red + 0.085 × ρ NIR + 0.072 × ρ Swir 2$	[38]
LST'	大气与地表结合部的温度状况,和气温具有较高的相关性	[39-40]
DEM	通过有限的高程数据对地面地形的数字化模拟。气温随高程的增加而降低	[41-42]
Slope	坡面的垂直高度和水平方向的距离之比	[43]
Aspect	坡面在水平面上的投影方向	[43]
SVF	$SVF = 1 - ∑ i = 1 n si n 2 β ∙ α 2 π$	[44]

表2

表3

图3

表4

图4

图5

表5

图6

图7

表6

表7

表8

表9

参考文献 44

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覆盖范围	轨道号	成像日期
方法研究试验区	124_031	2020-01-31
	124_032	2020-01-31
	125_031	2020-01-22
	125_032	2020-01-22
模型移植检验区	122_032	2020-01-01

指标	月均气温	日均气温	逐时气温
r	0.88	0.80	0.71
MAE/℃	2.40	2.70	3.10
RMSE/℃	3.00	3.30	3.80

数据	MAE	MBE
0.1°分辨率再分析气温	3.88	3.53
降尺度后30 m分辨率气温（训练集）	1.22	-0.09
降尺度后30 m分辨率气温（验证集）	2.13	0.47

指标	TPS-RF	TPS-Sta	TPS-Line
RMSE/℃	2.68	4.17	2.92
MAE/℃	2.13	3.61	2.33
r	0.82	0.84	0.79

是否使用站点观测数据	方法	RMSE/℃	MAE/℃	r
否	TPS-era5	3.93	3.14	0.74
否	TPS-RF-time	2.89	2.35	0.77
是	TPS-RF	2.37	1.88	0.80
	TPS-Line	2.63	2.10	0.73
	TPS-Sta	3.50	3.10	0.87

复杂山区地形条件下ERA5再分析地表气温降尺度方法

A Downscaling Method for Land Surface Air Temperature of ERA5 Reanalysis Dataset under Complex Terrain Conditions in Mountainous Areas

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摘要/Abstract

引用本文

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图/表 16

参考文献 44

相关文章 15

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是否使用站点观测数据	方法	RMSE/℃	MAE/℃	r
否	TPS-era5	2.69	2.12	0.72
否	TPS-RF-area	2.41	1.93	0.73
是	TPS-RF	2.00	1.53	0.76
	TPS-Line	2.00	1.56	0.74
	TPS-Sta	2.25	2.00	0.86

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