Nimbus-7 SMMR Derived Global Snow Cover Parameters

Abstract

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

Monitoring Flood and Discharge Variations in the Large Siberian Rivers From a Multi-Satellite Technique

“…In addition to streamflow and multi-satellite derived inundation extent, other datasets are thus used to help understand the response of river hydrology to inundation extent changes: (1) the basin mean monthly precipitation and temperature time series from ERA-40 reanalysis (Serreze et al 2005;Rigor et al 2000); (2) the monthly mean snow water equivalent (SWE) derived from SSM/I data (available for the period 1993-2000) Brodzik 2001, 2002;Chang et al 1987) and from station in situ observations from the Former Soviet Union Hydrological Snow Survey (available from 1993 to 1996 for the Ob and Lena watersheds only) (Krenke 2004). …”

Remote sensing of snow depth and snow water equivalent

“…Empirical techniques based on the inverse relationship between brightness temperature and snow depth were the first to be proposed in the literature (e.g., Chang et al, 1978Chang et al, , 1979Chang et al, , 1986Chang et al, , 1989Chang et al, , 1980Chang et al, , 1982Chang et al, , 1985Chang et al, , 1987aChang et al, , 1987bGrody and Basist, 1996;Goodison and Walker, 1995;Tait, 1998). In particular, because of the different sensitivity of brightness temperature to snow depth and SWE at different frequencies (e.g., K-and Ka-band, Figure 5.6), algorithms based on the direct relationship between snow depth and brightness temperature spectral difference were proposed (Chang et al, 1987a):…”

“…In particular, because of the different sensitivity of brightness temperature to snow depth and SWE at different frequencies (e.g., K-and Ka-band, Figure 5.6), algorithms based on the direct relationship between snow depth and brightness temperature spectral difference were proposed (Chang et al, 1987a):…”

“…Foster et al (2005) proposed an algorithm in which R c depends on monthly climatology and on snow classes defined following Sturm et al (1995). Grippa et al (2004) estimated seasonal SWE in Siberia, also computing R c in a dynamic fashion, and Kelly et al (2003) proposed a dynamic algorithm based on the original baseline algorithm by Chang et al (1987a), which accounts for the evolution of grain size and density through an exponential growth model for grain size and through the analysis of the outputs of an electromagnetic model.…”

Remote sensing of snow extent

“…Microwave scattering by ice crystals is frequency-dependent, enabling the use of two or more bands to estimate SWE (Chang et al, 1987;Grody and Basist, 1996), although other methods have been evaluated such as one based on the inversion of a snow emission model (e.g., Pulliainen and Hallikainen, 2001). A thorough discussion of SWE retrieval from space appears in Chapter 5 of this book.…”