Identifying and Mapping Systematic Errors in Passive Microwave Snow Water Equivalent Observations

Understanding remote sensing retrieval errors is important for correct interpretation of observations, and successful assimilation of observations into numerical models. Passive microwave sensors onboard satellites can provide global snow water equivalent (SWE) observations day or night and under cloudy conditions. However, there are errors associated with the passive microwave measurements, which are well known but have not been adequately quantified so far. This study proposes a new algorithm for passive microwave SWE retrievals that removes known systematic errors. Specifically, we consider the impact of vegetation cover and snow crystal growth on passive microwave responses. As a case study, systematic errors (difference between the old and new algorithms) are presented for the snow season 1990-91. Standard error propagation theory is used to estimate the uncertainty in the new retrieval algorithm (not shown here). An unbiased SWE dataset is produced and monthly SWE error maps (October-May) are derived for the Northern Hemisphere. The next step is to fine tune and test the bias-free algorithm, which will be applied to the combined passive microwave dataset from SMMR and SSM/I over 20 years.