Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude
Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However,...
Main Authors: | Hongyi Li, Yang Zhang, Huajin Lei, Xiaohua Hao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/15/8/2180 |
Similar Items
-
Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau
by: Yibo Ding, et al.
Published: (2024-08-01) -
Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau
by: Peng Bai, et al.
Published: (2018-08-01) -
Refining daily precipitation estimates using machine learning and multi-source data in alpine regions with unevenly distributed gauges
by: Huajin Lei, et al.
Published: (2025-04-01) -
The influence of the precipitation recycling process on the shift to heavy precipitation over the Tibetan Plateau in the summer
by: Boyuan Zhang, et al.
Published: (2023-01-01) -
Bias Correction of IMERG Satellite Precipitation in the Central and Eastern Qinghai-Xizang Plateau based on Quantile Delta Mapping Method
by: Juan DU, et al.
Published: (2024-04-01)