Triple Collocation Analysis of Satellite Precipitation Estimates over Australia

Abstract

The validation of precipitation estimates is necessary for the selection of the most appropriate dataset, as well as for having confidence in its selection. Traditional validation against gauges or radars is much less effective when the quality of these references (which are considered the ‘truth’) degrades, such as in areas of poor coverage. In scenarios like this where the ‘truth’ is unreliable or unknown, triple collocation analysis (TCA) facilitates a relative ranking of independent datasets based on their similarity to each other. TCA has been successfully employed for precipitation error estimation in earlier studies, but a thorough evaluation of its effectiveness over Australia has not been completed before. This study assesses the use of TCA for precipitation verification over Australia using satellite datasets in combination with reanalysis data (ERA5) and rain gauge data (AGCD) on a monthly timescale from 2001 to 2020. Both the additive and multiplicative models for TCA are evaluated. These results are compared against the traditional verification method using gauge data and Multi-Source Weighted-Ensemble Precipitation (MSWEP) as references. AGCD (KGE = 0.861), CMORPH-BLD (0.835), CHIRPS (0.743), and GSMaP (0.708) were respectively found to have the highest KGE when compared to MSWEP. The ranking of the datasets, as well as the relative difference in performance amongst the datasets as derived from TCA, can largely be reconciled with the traditional verification methods, illustrating that TCA is a valid verification method for precipitation over Australia. Additionally, the additive model was less prone to outliers and provided a spatial pattern that was more consistent with the traditional methods.