TY - GEN
T1 - Error analysis of ensemble multi-satellite precipitation datasets over the Tibetan Plateau
AU - Liu, Ronghua
AU - Ma, Yingzhao
AU - Yang, Yuan
AU - Han, Zhongying
AU - Tang, Guoqiang
AU - Liu, Qi
AU - Hong, Yang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Robust validation of the ensemble precipitation datasets was vital for assessing and then improving the quality and confident application in water energy cycle related research. In this study, the performance of the Ensemble Multi-Satellite Precipitation Datasets using the Dynamic Bayesian Model Averaging scheme (EMSPD-DBMA) was evaluated and compared against the rain gauge networks, as well as the Day-1 Level 3 products of the newly launched of Global Precipitation Measurement (GPM) mission over the Tibetan Plateau (TP). Comprehensive analyses of daily precipitation estimates for 2000-2015 showed that the EMSPD-DBMA products showed a reliable performance in estimating the precipitation regimes in the past 16 years over the TP, where the averaged correlation coefficients and relative bias (RB) were 0.532 and -8.9%, respectively. The EMSPD-DBMA datasets appreciably showed better correlations and lower errors than that of GPM in the summer season of 2014 and 2015, though with very similar spatial patterns. Moreover, the ensemble products EMSPD-DBMA could significantly improve the rainfall detection around 1.3 times GPM-era products at the survey periods.
AB - Robust validation of the ensemble precipitation datasets was vital for assessing and then improving the quality and confident application in water energy cycle related research. In this study, the performance of the Ensemble Multi-Satellite Precipitation Datasets using the Dynamic Bayesian Model Averaging scheme (EMSPD-DBMA) was evaluated and compared against the rain gauge networks, as well as the Day-1 Level 3 products of the newly launched of Global Precipitation Measurement (GPM) mission over the Tibetan Plateau (TP). Comprehensive analyses of daily precipitation estimates for 2000-2015 showed that the EMSPD-DBMA products showed a reliable performance in estimating the precipitation regimes in the past 16 years over the TP, where the averaged correlation coefficients and relative bias (RB) were 0.532 and -8.9%, respectively. The EMSPD-DBMA datasets appreciably showed better correlations and lower errors than that of GPM in the summer season of 2014 and 2015, though with very similar spatial patterns. Moreover, the ensemble products EMSPD-DBMA could significantly improve the rainfall detection around 1.3 times GPM-era products at the survey periods.
UR - https://www.scopus.com/pages/publications/85036671147
U2 - 10.1109/IGARSS.2017.8128047
DO - 10.1109/IGARSS.2017.8128047
M3 - Conference contribution
AN - SCOPUS:85036671147
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4684
EP - 4687
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -