Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau. Issue 2 (23rd January 2018)

• Record Type: Journal Article
• Authors: Ma, Yingzhao; Hong, Yang; Chen, Yang; Yang, Yuan; Tang, Guoqiang; Yao, Yunjun; Long, Di; Li, Changmin; Han, Zhongying; Liu, Ronghua
• Is Part Of: Journal of geophysical research. Volume 123:Issue 2(2018)
• Page Start: 814
• Page End: 834
• Publication Date: 2018-01-23
• Subjects: dynamic Bayesian model averaging -- satellite precipitation -- Tibetan Plateau -- data merging; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD026648
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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. Key Points: A new solution for merging multisatellite precipitation data sets The dynamic BMA algorithm proved to be the most robust Dynamic BMA weights for varying spatial and temporal scales