Search: WFRF:(Wei Linyong)
> (2022) >
Bias correction of ...
-
Wei, LinyongHohai University
(author)
Bias correction of GPM IMERG Early Run daily precipitation product using near real-time CPC global measurements
- Article/chapterEnglish2022
Publisher, publication year, extent ...
Numbers
-
LIBRIS-ID:oai:lup.lub.lu.se:21391a58-ac0c-45f4-8c41-be9dd21b1c41
-
https://lup.lub.lu.se/record/21391a58-ac0c-45f4-8c41-be9dd21b1c41URI
-
https://doi.org/10.1016/j.atmosres.2022.106403DOI
Supplementary language notes
-
Language:English
-
Summary in:English
Part of subdatabase
Classification
-
Subject category:art swepub-publicationtype
-
Subject category:ref swepub-contenttype
Notes
-
This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
-
Jiang, ShanhuHohai University
(author)
-
Ren, LiliangHohai University
(author)
-
Zhang, LinqiHohai University
(author)
-
Wang, MenghaoHohai University
(author)
-
Liu, YiHohai University
(author)
-
Duan, ZhengLund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science(Swepub:lu)zh7785du
(author)
-
Hohai UniversityMERGE: ModElling the Regional and Global Earth system
(creator_code:org_t)
Related titles
-
In:Atmospheric Research: Elsevier BV2790169-8095
Internet link
Find in a library
To the university's database