Mapping random and systematic errors of satellite-derived snow-water equivalent observations in Eurasia

James L. Foster; Chaojiao Sun; Jeffrey P. Walker; Richard Kelly; Jiarui Dong; Alfred Chang

doi:10.1117/12.564641

26 October 2004 Mapping random and systematic errors of satellite-derived snow-water equivalent observations in Eurasia

James L. Foster, Chaojiao Sun, Jeffrey P. Walker, Richard Kelly, Jiarui Dong, Alfred Chang

Proceedings Volume 5568, Remote Sensing for Agriculture, Ecosystems, and Hydrology VI; (2004) https://doi.org/10.1117/12.564641
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain

Abstract

Passive microwave sensors onboard satellites can provide global snow water equivalent (SWE) observations day or night, even under cloudy conditions. However, there are both systematic (bias) and random errors associated with the passive microwave measurements. While these errors are well known, they have thus far not been adequately quantified. In this study, unbiased SWE maps, random error maps and systematic error maps of Eurasia for the 1990-1991 snow season (November-April) have been examined. Dense vegetation, especially in the taiga region, and large snow crystals (>0.3 mm in radius), found in areas where the temperature/vapor gradients are greatest, (in the taiga and tundra regions) are the major source of systematic error. Assumptions about how snow crystals evolve with the progression of the season also contribute to the errors. In general, while random errors for North America and Eurasia are comparable, systematic errors are not as great for Eurasia as those observed for North America. Understanding remote sensing retrieval errors is important for correct interpretation of observations, and successful assimilation of observations into numerical models.

Citation Download Citation

James L. Foster, Chaojiao Sun, Jeffrey P. Walker, Richard Kelly, Jiarui Dong, and Alfred Chang "Mapping random and systematic errors of satellite-derived snow-water equivalent observations in Eurasia", Proc. SPIE 5568, Remote Sensing for Agriculture, Ecosystems, and Hydrology VI, (26 October 2004); https://doi.org/10.1117/12.564641

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