Copula-based Stochastic Uncertainty Analysis of Satellite Precipitation Products

Satellite products, like all datasets, are subject to errors and uncertainties. Due to inherent biases embedded in satellite precipitation estimates, we present an error-adjustment approach based on the statistical differences between satellite precipitation products and in-situ observations (observed errors) employing two widely-used error models, namely that additive and the multiplicative error models, in an attempt to assess their suitability for the error correction of satellite-based daily precipitation estimates over northeast Austria. An error-adjustment technique based on the concept of the copula is adopted and applied to correct the supplied precipitation fields. It was found that IMERG precipitation estimates improved after error adjustment when compared to original satellite precipitation estimate (OSPE). The additive error model resulted in a better improvement by fitting the entire range of data when compared with the multiplicative error model. Moreover, the additive error model extracted the error with more accuracy and produced a better estimation of their characteristics, while the method based on the multiplicative error was less robust. However, the overall spatial dependence of the observed errors is reasonably preserved as that of the generated errors by copula. In addition, the validation results implied that the simulated realizations error-adjusted band, encompassed the observed data reasonably. Moreover, the copula-based simulations associated with the additive error model performed much better in comparison to the multiplicative error model. Overall, by using the t-copula model with an emphasis on the additive error model and imposing the simulated error fields on the OSPE, one may generate multiple realizations of precipitation fields.