Bias Correction of Long-Term Satellite Monthly Precipitation Product (TRMM 3B43) over the Conterminous United States
Hashemi, Hossein LU
; Nordin, Matias
; Lakshmi, Venkat
; Huffman, George J.
and Knight, Rosemary
(2017)
In Journal of Hydrometeorology
18(9).
p.2491-2509
- Abstract
- The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998–2015).However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias... (More)
- The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998–2015).However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias between the satellite and ground-based estimates is compared with mean surface temperature and elevation, respectively. A weak linear relationship is observed between the bias and temperature but a moderate inverse linear relationship is observed between the bias and elevation. Based on these observations, a linear model is developed for the TRMM 3B43–PRISM bias and elevation. The developed model is calibrated and validated using Monte Carlo cross validation with 25% of the available data as a calibration set and the remaining 75%of the data as a validation set. The estimated model parameters are then used in a correction formula for the TRMM 3B43 dataset for elevations above 1500m above mean sea level. The corrected TRMM 3B43 product is verified for the high-elevation regions over the entire United States as well as in two high-elevation local regions in the western United States. The results show a significant improvement in the accuracy of the monthly satellite product in the high elevations of the United States. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2c435118-e206-4b75-b038-ec5af767da18
- author
- Hashemi, Hossein
LU
; Nordin, Matias
; Lakshmi, Venkat
; Huffman, George J.
and Knight, Rosemary
- organization
- publishing date
- 2017-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- TRMM TMPA, Bias correction, PRISM, Satellite
- in
- Journal of Hydrometeorology
- volume
- 18
- issue
- 9
- pages
- 19 pages
- publisher
- American Meteorological Society
- external identifiers
-
- scopus:85030097237
- wos:000417351800009
- ISSN
- 1525-7541
- DOI
- 10.1175/JHM-D-17-0025.1
- project
- Development of tools for improved groundwater management using satellite imagery, field data, and hydrological modeling
- language
- English
- LU publication?
- yes
- id
- 2c435118-e206-4b75-b038-ec5af767da18
- date added to LUP
- 2017-09-27 11:25:47
- date last changed
- 2023-10-05 16:48:51
@article{2c435118-e206-4b75-b038-ec5af767da18,
abstract = {{The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998–2015).However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias between the satellite and ground-based estimates is compared with mean surface temperature and elevation, respectively. A weak linear relationship is observed between the bias and temperature but a moderate inverse linear relationship is observed between the bias and elevation. Based on these observations, a linear model is developed for the TRMM 3B43–PRISM bias and elevation. The developed model is calibrated and validated using Monte Carlo cross validation with 25% of the available data as a calibration set and the remaining 75%of the data as a validation set. The estimated model parameters are then used in a correction formula for the TRMM 3B43 dataset for elevations above 1500m above mean sea level. The corrected TRMM 3B43 product is verified for the high-elevation regions over the entire United States as well as in two high-elevation local regions in the western United States. The results show a significant improvement in the accuracy of the monthly satellite product in the high elevations of the United States.}},
author = {{Hashemi, Hossein and Nordin, Matias and Lakshmi, Venkat and Huffman, George J. and Knight, Rosemary}},
issn = {{1525-7541}},
keywords = {{TRMM TMPA; Bias correction; PRISM; Satellite}},
language = {{eng}},
number = {{9}},
pages = {{2491--2509}},
publisher = {{American Meteorological Society}},
series = {{Journal of Hydrometeorology}},
title = {{Bias Correction of Long-Term Satellite Monthly Precipitation Product (TRMM 3B43) over the Conterminous United States}},
url = {{http://dx.doi.org/10.1175/JHM-D-17-0025.1}},
doi = {{10.1175/JHM-D-17-0025.1}},
volume = {{18}},
year = {{2017}},
}