Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models. This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990–1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements. Published by Elsevier Inc.

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