Abstract
Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms.
Highlights
Seasonal snow cover is an important component of the Earth’s hydrologic cycle, energy balance, and climate system [1,2,3,4]
snow water equivalent (SWE), which is determined by integrating snow density over snow depth, describes how much water would be released if snowpack melted completely at once [8,9]
The accuracy, was affected by uncertainties in the assumptions. One such assumption is that snow grain size and snow density are assumed to be static in all layers of snowpack
Summary
Seasonal snow cover is an important component of the Earth’s hydrologic cycle, energy balance, and climate system [1,2,3,4]. Simplifying snowpack as one homogeneous layer may result in significant errors in snow depth retrieval Another source of uncertainty is that these algorithms did not account for the effects of forest canopy and atmosphere, which attenuate the signals emitted from the surface and emit their own energy toward the satellite. The algorithm designed for the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) accounts for the influence of forest cover and snow grain growth and takes advantage of the expanded range of channels available on the AMSR-E/2 instruments [25,26] This algorithm retrieves the snow depth from moderate snow accumulations using the 37 GHz channel and from deep snow using the 19 and 10 GHz channels. A discussion is presented in Section 4, and in Section 5 we give the conclusions of this study
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