DOI: 10.1002/2017JD026648
Scopus记录号: 2-s2.0-85040788574
论文题名: Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau
作者: Ma Y. ; Hong Y. ; Chen Y. ; Yang Y. ; Tang G. ; Yao Y. ; Long D. ; Li C. ; Han Z. ; Liu R.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期: 2 起始页码: 814
结束页码: 834
语种: 英语
英文关键词: data merging
; dynamic Bayesian model averaging
; satellite precipitation
; Tibetan Plateau
Scopus关键词: algorithm
; artificial neural network
; Bayesian analysis
; calibration
; correlation
; data processing
; data set
; error analysis
; experiment
; kriging
; numerical model
; performance assessment
; precipitation (climatology)
; precipitation assessment
; satellite imagery
; spatiotemporal analysis
; TRMM
; China
; Qinghai-Xizang Plateau
英文摘要: Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic Bayesian model averaging (BMA) algorithm. The blended experiment was performed at a daily 0.25° grid scale for 2007–2012 among Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT and 3B42V7, Climate Prediction Center MORPHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). First, the BMA weights were optimized using the expectation-maximization (EM) method for each member on each day at 200 calibrated sites and then interpolated to the entire plateau using the ordinary kriging (OK) approach. Thus, the merging data were produced by weighted sums of the individuals over the plateau. The dynamic BMA approach showed better performance with a smaller root-mean-square error (RMSE) of 6.77 mm/day, higher correlation coefficient of 0.592, and closer Euclid value of 0.833, compared to the individuals at 15 validated sites. Moreover, BMA has proven to be more robust in terms of seasonality, topography, and other parameters than traditional ensemble methods including simple model averaging (SMA) and one-outlier removed (OOR). Error analysis between BMA and the state-of-the-art IMERG in the summer of 2014 further proved that the performance of BMA was superior with respect to multisatellite precipitation data merging. This study demonstrates that BMA provides a new solution for blending multiple satellite data in regions with limited gauges. ©2017. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114671
Appears in Collections: 气候减缓与适应
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作者单位: State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, United States; Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, China; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China; China Institute of Water Resources and Hydropower Research, Beijing, China
Recommended Citation:
Ma Y.,Hong Y.,Chen Y.,et al. Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(2)