Estimating precipitation errors using spaceborne surface soil moisture retrievals



[1] Limitations in the availability of ground-based rain gauge data currently hamper our ability to quantify errors in global precipitation products over data-poor areas of the world. Over land, these limitations may be eased by approaches based on interpreting the degree of dynamic consistency existing between precipitation estimates and remotely-sensed surface soil moisture retrievals. This paper demonstrates how such an approach can be implemented using a Kalman filter tuning procedure to reliably estimate daily rainfall errors in global precipitation products without reliance on ground-based rainfall observations.