地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (10): 1642-1652.doi: 10.12082/dqxxkx.2019.180603
收稿日期:
2018-11-26
修回日期:
2019-05-24
出版日期:
2019-10-25
发布日期:
2019-10-29
通讯作者:
史岚
E-mail:sl_nim@163.com
作者简介:
作者简介:史 岚(1978-),女,江苏扬州人,博士,副教授,主要从事3S技术与气象应用研究。E-mail:sl_nim@163.com
基金资助:
SHI Lan1,*(),HE Qiquan1,YANG Jiao1,WAN Yibo1,2
Received:
2018-11-26
Revised:
2019-05-24
Online:
2019-10-25
Published:
2019-10-29
Contact:
SHI Lan
E-mail:sl_nim@163.com
Supported by:
摘要:
针对目前的技术手段下难以直接获得大范围高精度精细化降水空间分布的问题,本文以闽浙赣地区为研究范围,选用GPM IMERG降水产品,综合应用地面实测降水数据以及水汽与植被指数数据,基于地理加权回归(GWR)法构建了基于水汽因子的降尺度模型,同时基于最小二乘(OLS)法构建了基于水汽因子与植被指数的对比模型,将降水产品的分辨率从0.1°提升至1 km,最终获得2015年闽浙赣地区各月精细化降水空间分布,使用验证站点实测数据进行验证。结果表明:① 构建的 3个降尺度模型中,GWR模型与2种OLS模型相比,拟合优度分别提升了102.9%和93.9%,模型降尺度结果整体优于2种OLS模型,且月际差异小,稳定性更高;2种OLS模型中,采用了水汽因子的模型拟合效果有8个月份更优;② 融合多源数据的GWR降尺度模型获得的结果在研究区内是可靠的,与GPM降水产品相比,在提升空间分辨率的同时,平均相对误差与均方根误差月均分别下降了42%和32%,精度明显改善。
史岚,何其全,杨娇,万逸波. 闽浙赣地区GPM IMERG降水产品降尺度建模与比较分析[J]. 地球信息科学学报, 2019, 21(10): 1642-1652.DOI:10.12082/dqxxkx.2019.180603
SHI Lan,HE Qiquan,YANG Jiao,WAN Yibo. Downscaling Modeling of the GPM IMERG Precipitation Product and Comparative Analysis in the Fujian-Zhejiang-Jiangxi Region[J]. Journal of Geo-information Science, 2019, 21(10): 1642-1652.DOI:10.12082/dqxxkx.2019.180603
表1
GPM原始数据与GPM融合数据精度指标"
月份 | R | MRE/% | RMSE/mm | |||||
---|---|---|---|---|---|---|---|---|
原始GPM | 融合GPM | 原始GPM | 融合GPM | 原始GPM | 融合GPM | |||
1 | 0.451 | 0.804 | 34.43 | 12.68 | 5.54 | 2.60 | ||
2 | 0.940 | 0.962 | 31.84 | 22.22 | 5.74 | 4.42 | ||
3 | 0.917 | 0.956 | 14.67 | 11.06 | 7.13 | 5.10 | ||
4 | 0.703 | 0.803 | 27.83 | 18.15 | 9.69 | 7.97 | ||
5 | 0.781 | 0.917 | 20.25 | 11.52 | 25.17 | 16.42 | ||
6 | 0.854 | 0.957 | 28.27 | 15.45 | 22.81 | 12.24 | ||
7 | 0.594 | 0.744 | 17.05 | 15.06 | 14.67 | 12.65 | ||
8 | 0.768 | 0.774 | 30.98 | 27.37 | 23.45 | 22.60 | ||
9 | 0.706 | 0.888 | 25.88 | 16.76 | 16.04 | 10.46 | ||
10 | 0.676 | 0.855 | 32.14 | 17.79 | 9.57 | 6.98 | ||
11 | 0.919 | 0.943 | 41.10 | 22.59 | 9.86 | 7.87 | ||
12 | 0.892 | 0.976 | 19.52 | 4.28 | 9.04 | 2.56 |
表2
3种模型拟合效果对比"
月份 | R2 | AIC | |||||
---|---|---|---|---|---|---|---|
OLS_NDVI模型 | OLS_PWV模型 | GWR_PWV模型 | OLS_NDVI模型 | OLS_PWV模型 | GWR_PWV模型 | ||
1 | 0.245 | 0.235 | 0.781 | 25 911.77 | 26 248.06 | 22 080.06 | |
2 | 0.766 | 0.762 | 0.946 | 30 974.77 | 31 291.86 | 26 339.71 | |
3 | 0.377 | 0.443 | 0.902 | 34 699.48 | 34 626.82 | 28 805.83 | |
4 | 0.385 | 0.391 | 0.816 | 32 379.88 | 31 632.09 | 28 647.43 | |
5 | 0.455 | 0.458 | 0.879 | 39 411.65 | 39 299.39 | 34 772.91 | |
6 | 0.373 | 0.370 | 0.912 | 40 366.09 | 40 744.17 | 34 126.50 | |
7 | 0.265 | 0.274 | 0.773 | 36 740.84 | 36 136.28 | 33 275.12 | |
8 | 0.536 | 0.548 | 0.794 | 37 655.52 | 36 931.75 | 35 307.04 | |
9 | 0.550 | 0.545 | 0.820 | 35 016.98 | 35 013.09 | 32 309.58 | |
10 | 0.229 | 0.231 | 0.817 | 31 590.47 | 31 875.78 | 27 092.22 | |
11 | 0.492 | 0.553 | 0.943 | 36 241.38 | 36 146.63 | 29 248.77 | |
12 | 0.349 | 0.446 | 0.806 | 33 279.96 | 33 033.82 | 29 497.57 |
表3
GPM原始数据与降尺度结果精度指标"
月份 | R | MRE/% | RMSE/mm | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
原始GPM | 降尺度结果 | 原始GPM | 降尺度结果 | 原始GPM | 降尺度结果 | 原始GPM | 降尺度结果 | ||||
1 | 0.451 | 0.812 | 34.43 | 13.01 | 19.19 | 8.90 | -0.609 | 0.654 | |||
2 | 0.940 | 0.958 | 31.84 | 22.78 | 19.88 | 15.80 | 0.866 | 0.916 | |||
3 | 0.917 | 0.966 | 14.67 | 10.03 | 24.71 | 15.48 | 0.829 | 0.933 | |||
4 | 0.703 | 0.849 | 27.83 | 16.86 | 33.57 | 24.04 | 0.448 | 0.717 | |||
5 | 0.781 | 0.908 | 20.25 | 11.89 | 87.18 | 59.54 | 0.601 | 0.814 | |||
6 | 0.854 | 0.962 | 28.27 | 14.97 | 79.02 | 40.01 | 0.699 | 0.923 | |||
7 | 0.594 | 0.769 | 17.05 | 13.30 | 50.81 | 41.08 | 0.335 | 0.565 | |||
8 | 0.768 | 0.801 | 30.98 | 24.15 | 81.24 | 75.94 | 0.557 | 0.613 | |||
9 | 0.706 | 0.879 | 25.88 | 16.48 | 55.58 | 37.40 | 0.492 | 0.770 | |||
10 | 0.676 | 0.856 | 32.14 | 16.52 | 33.15 | 24.44 | 0.431 | 0.691 | |||
11 | 0.919 | 0.931 | 41.10 | 21.11 | 34.15 | 29.70 | 0.823 | 0.866 | |||
12 | 0.892 | 0.974 | 19.52 | 4.56 | 31.30 | 9.25 | 0.406 | 0.948 |
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