Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau

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.

Snow Hydrology in the Upper Yellow River Basin Under Climate Change: A Land Surface Modeling Perspective

“…When there are more ground snow observations (i.e., snow depth, SWE, and snowmelt) available in the UYR basin at a proper resolution, the validation would be much more reasonable. Improvements of climate model simulations, bias correction methods, and the quality of in situ and satellite observations are beyond the scope of this work but are even crucial for climate change‐related studies (Ma et al, ; B. Q. Zhang et al, ), which deserve to be further explored in future studies.…”

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

“…It accounts for both spatial and temporal variability in the performances of the merging members. The DBMA-MSPD was found superior to both SMA and OOR merging methods [26].Very limited literature is available on developing and evaluating the MSPDs across Pakistan [16,22,48]. The results demonstrated significant improvements of MSPDs overall merging members.…”

“…It accounts for both spatial and temporal variability in the performances of the merging members. The DBMA-MSPD was found superior to both SMA and OOR merging methods [26].…”

“…The errors might be associated with precipitation retrieval algorithms or other factors such as the topographic, climatic and seasonal dependency of SPPs. These errors impact the quality of precipitation estimates, which propagate further into hydrological modeling and other applications [16].Several efforts, such as developing new products with high spatial and temporal resolution [23,25], reducing sampling issues, improvements in precipitation estimation algorithms, merging different SPPs using relative weights and weight estimation using the dynamic methods, have been made to address the uncertainties of existing SPPs [7,16,26]. Developing and application of a merged multi-satellite precipitation dataset (MSPD) based on different statistical models or even the same statistical model can significantly improve the accuracy of meteorological and hydrological models [27].…”

“…This method has also been successfully applied in hydrological modeling and streamflow simulations [44][45][46]. A MSPD using Dynamic BMA (DBMA) was developed recently to optimally merge four SPPs and evaluated over Tibetan Plateau both topographically and seasonally [26,47]. It accounts for both spatial and temporal variability in the performances of the merging members.…”