Application of global snow model for the estimation of snow depth in the UK

• Published in: Meteorology and atmospheric physics 2009-10, Vol.105 (3-4), p.181-190
• Main Author: Butt, Mohsin Jamil
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Exact sciences and technology; External geophysics; Math. Appl. in Environmental Science; Meteorological satellites; Meteorology; Microwave radiation; Microwaves; Original Paper; Radiative transfer; Remote sensing; Remote sensing systems; Scientific imaging; Snow; Snow cover; Snow depth; Snowpack; Terrestrial Pollution; Waste Water Technology; Water content; Water Management; Water Pollution Control
• ISSN: 0177-7971; 1436-5065
• DOI: 10.1007/s00703-009-0042-7

Microwave imagery can be used successfully for mapping of snow and estimation of snow pack characteristics under almost all weather conditions. This research is a contribution to the field of space borne remote sensing of snow by means of passive microwave data imagery. The satellite data are acquired from the Special Sensor Microwave Imager (SSM/I). The SSM/I is a four frequency seven channels dual polarization (except 22 GHz which is only vertically polarized) scanning radiometer with channels located at 19, 22, 37, and 85 GHz frequencies. A radiative transfer theory based model is used to estimate the snow cover characteristics of different snow pack types in the UK. A revised form of the Chang et al. (Nord Hydrol 16:57-66, 1987) model is used for this purpose. The revised Chang model was calibrated for global snow monitoring and takes into account forest fractional coverage effects. Snow cover characteristics have significant effects on up-welling naturally emitted microwave radiation through the processes of forward scattering. The up-welling signal is more complex for snow covers that consist of free liquid water content. The aim of this study is to test the global snow depth model for the UK snow cover. The Chang model predicted snow depth bias results for January, February, and March 1995 are −1.26, −0.35, and −0.63 cm, respectively. Similarly, the Chang model Mean Absolute Error (MAE) for January, February, and March 1995 have values 2.88, 2.38, and 1.91 cm, respectively. These results show that the Chang model underestimates the snow depth prediction for all the case studies. The results of this study led us to the conclusion that the global snow models (Chang model) when applied for the retrieval of local snow depth estimation (UK snow cover) underestimate snow depth.