An Improved Spatial Downscaling Procedure for TRMM 3B43 Precipitation Product Using Geographically Weighted Regression
(2015) In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(9). p.4592-4604- Abstract
Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To... (More)
Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To obtain the monthly 1 km precipitation, two monthly downscaling strategies (annual-based fraction disaggregation method and monthly based GWR method) were evaluated. All analysis was tested in Gansu province, China with a semiarid to arid climate for three typical years. Validation with measurements from 24 rain gauge stations showed that the proposed GWR method performed consistently better than the UR and MR methods. Two monthly downscaling methods were efficient in deriving the monthly precipitation at 1 km. The former method faces the challenge of precipitation spatial heterogeneity and the derived monthly precipitation heavily depends on the annual downscaled results, which could lead to the accumulation of errors. The monthly based GWR method is suitable for downscaling monthly precipitation, but the accuracy of original TRMM 3B43 data would have large influence on downscaling results. It was demonstrated that the proposed method was effective for obtaining both annual and monthly TRMM 1 km precipitation with high accuracy.
(Less)
- author
- Chen, Cheng ; Zhao, Shuhe ; Duan, Zheng LU and Qin, Zhihao
- publishing date
- 2015-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Disaggregation, geographically weighted regression (GWR), multivariate regression (MR), satellite precipitation, validation
- in
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- volume
- 8
- issue
- 9
- article number
- 7134719
- pages
- 13 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:84933556755
- ISSN
- 1939-1404
- DOI
- 10.1109/JSTARS.2015.2441734
- language
- English
- LU publication?
- no
- id
- e8cb0f18-d51c-43d0-bfdc-1988ad78669a
- date added to LUP
- 2019-12-22 20:28:50
- date last changed
- 2022-04-18 19:49:16
@article{e8cb0f18-d51c-43d0-bfdc-1988ad78669a,
abstract = {{<p>Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To obtain the monthly 1 km precipitation, two monthly downscaling strategies (annual-based fraction disaggregation method and monthly based GWR method) were evaluated. All analysis was tested in Gansu province, China with a semiarid to arid climate for three typical years. Validation with measurements from 24 rain gauge stations showed that the proposed GWR method performed consistently better than the UR and MR methods. Two monthly downscaling methods were efficient in deriving the monthly precipitation at 1 km. The former method faces the challenge of precipitation spatial heterogeneity and the derived monthly precipitation heavily depends on the annual downscaled results, which could lead to the accumulation of errors. The monthly based GWR method is suitable for downscaling monthly precipitation, but the accuracy of original TRMM 3B43 data would have large influence on downscaling results. It was demonstrated that the proposed method was effective for obtaining both annual and monthly TRMM 1 km precipitation with high accuracy.</p>}},
author = {{Chen, Cheng and Zhao, Shuhe and Duan, Zheng and Qin, Zhihao}},
issn = {{1939-1404}},
keywords = {{Disaggregation; geographically weighted regression (GWR); multivariate regression (MR); satellite precipitation; validation}},
language = {{eng}},
month = {{09}},
number = {{9}},
pages = {{4592--4604}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}},
title = {{An Improved Spatial Downscaling Procedure for TRMM 3B43 Precipitation Product Using Geographically Weighted Regression}},
url = {{http://dx.doi.org/10.1109/JSTARS.2015.2441734}},
doi = {{10.1109/JSTARS.2015.2441734}},
volume = {{8}},
year = {{2015}},
}