Introduction

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow, which can cover >50% of the Northern Hemisphere land surface during winter (Reference Armstrong and BrodzikArmstrong and Brodzik, 1999; Reference Frei and RobinsonFrei and Robinson, 1999) is responsible for the largest annual and interannual differences in land surface albedo. Surface temperature is highly dependent on the presence or absence of snow cover, and temperature trends have been shown to be related to changes in snow cover (Reference Groisman, Karl and KnightGroisman and others, 1994). Realistic simulation of snow cover in climate models is essential for correct representation of the surface energy balance, as well as for understanding winter water storage and predicting year-round runoff. The lack of meteorological and snow data to execute, calibrate and validate snow-covermodels is a major obstacle to improved simulations (Reference Barron, Hartmann, King, Shimel and SchoeberlBarron and others, 1999).

When snow covers the ground, some of the microwave energy emitted by the underlying soil is scattered by the snow grains. Therefore, when moving from snow-free to snow-covered land surfaces, a sharp decrease in emissivity and associated brightness temperatures provides a nearly unambiguous indicator of the presence of dry snow (Reference MätzlerMätzler, 1994). In addition, snow exhibits what is termed a negative spectral gradient which means that as the microwave frequency increases (e.g. from 19 to 37 GHz), the emissivity and associated brightness temperatures decrease. Nearly all other land-surface types exhibit a positive spectral gradient (Reference MätzlerMätzler, 1994). Various theoretical and empirical studies have demonstrated that the amount of scattering can be correlated with snow mass and specific wavelength. From this basic relationship, regional algorithms have been developed which indicate the presence of snow and compute snow water equivalent (SWE) (Reference Chang, Foster and HallChang and others, 1987; Reference GoodisonGoodison, 1989; Reference NaglerNagler, 1991; Reference Grody and BasistGrody and Basist, 1996; Reference HiltbrunnerHiltbrunner, 1996; Reference TaitTait, 1998; Pullainen and Hallikainen, 2001). Nearly all of these algorithms have been developed and tested for dry-snow conditions only. SWE cannot be determined when the snow is wet (i.e. liquid water is present on the snow grain surface) because wet snow is primarily an emitter with respect to microwave energy, so the information derived from the characteristic scattering signature of dry snow is lost. However, it is still possible to detect the presence of wet snow due to its distinct polarization difference (Reference Walker and GoodisonWalker and Goodison, 1993; Reference MätzlerMätzler, 1994).

Data Sources

Comparison Of Microwave And Visible Snow Data

In this phase of our study we evaluate the overall capability of the passive-microwave data to map snow-covered area through comparison with the EASE-Grid version of the NOAA Northern Hemisphere snow-extent data. This involves the comparison of data derived from two completely different sensor systems and analysis techniques. The NOAA dataset is the result of the manual interpretation of visible satellite data, while the microwave data are the result of the consistent application of individual numerical algorithms throughout the brightness-temperature time series.

For the period 1978–99, both passive-microwave and visible datasets show a similar pattern of interannual variability, and both indicate maximum extents consistently exceeding 40610⁶ km² (Fig. 1). The visible data typically show higher variability in the departures from the monthly means, while the long-term trends based on the departures indicate similar decreases in Northern Hemisphere snow extent of approximately 0.25% per year (NOAA) and 0.13% per year (passive microwave) (Fig. 2). In order to provide a single example for initial illustration, the Reference Chang, Foster and HallChang and others (1987) algorithm has been used to compute snow-covered area for the SMMR period, and a modified version of this same algorithm has been used during the SSM/I period in Figures 1–3. In this example, and throughout this study, only brightness temperatures from cold orbits (night or early morning) are used as input to the algorithms, in order to reduce the probability that the microwave sensor will be viewing a wet-snow surface.

Fig. 1.

Northern Hemisphere snow-covered area derived from visible (NOAA) and passive-microwave (SMMR and SSM/I) satellite data, 1978–99.

Fig. 2.

(a) Visible-derived (NOAA) and (b) passive-microwave-derived (SMMR and SSM/I) snow-covered area departures from monthly means for Northern Hemisphere, 1978–99.

Fig. 3.

Northern Hemisphere mean monthly snow-covered area derived from visible (NOAA) and passive microwave (SMMR and SSM/I), and the difference between the two (visible minus passive microwave), 1978–99.

When the monthly climatologies are compared (Fig.3) it becomes clear that the agreement between the two techniques varies depending on the time of the year. During the shallow-snow conditions of early winter (October– December) the algorithms tested consistently indicate less snow-covered area than the visible data. This underestimate of snow extent most likely results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by this and other algorithms which rely primarily on the 19 and 37 GHz channels. Reference Robinson, Dewey and HeimRobinson and others (1993) and Reference BrownBrown (2000) indicate that the threshold for snow detection in the NOAA dataset is approximately 2.0 cm. In addition, because visible data are better suited to detecting shallow snow, they also monitor the fluctuating snow-extent edge more precisely, resulting in greater spatial variability over time, as shown in Figure 2a. The inclusion of the 85 GHz channel, as shown below, with the associated enhanced scattering response, improves the microwave capability to map shallow snow. Although the 85 GHz channel is affected by atmospheric conditions, a cloud-clearing filter can be applied which selects the day with the clearest atmosphere during the monitoring period (Reference NaglerNagler, 1991) or which screens out precipitating clouds (Reference Grody and BasistGrody and Basist, 1996).

As the snow cover continues to build during January– March, as well as on into the melt season, agreement between the two data types improves (Fig. 3). Algorithms utilizing vertical polarizations frequently indicate the presence of snow over some snow-free desert soils, apparently due to the enhanced scattering response of the vertical channels. However, in this typical example shown in Figure 3, the microwave data indicate statistically significantly less snow-covered area than the visible data for all months except April, with a mean difference for the monthly climatologies of 2.7×10⁶ km², ranging from 8.0610⁶ km² in November to 0.4610⁶ km² in August.

Microwave Algorithm Comparisons

In this phase of the study we apply digital image comparison techniques to a multi-year, time-series analysis of several different algorithms. The ultimate goal of this study is to determine when the differences between the algorithm output and the validation data are random and when they are systematic. In the case of systematic differences, the patterns are being correlated with the specific effects of land-cover type, atmospheric conditions and snow structure. Because we compare algorithm output with continuous records of station data, we are able to identify any seasonal or interannual patterns in the accuracy of the algorithms.

Snow-covered area

The following is a brief overview of our current algorithm-comparison study for snow extent. Figure 4 contains comparisons of the NOAA weekly snow extent and four passive-microwave algorithms (Reference Chang, Foster and HallChang and others, 1987; Reference GoodisonGoodison, 1989; Reference NaglerNagler, 1991; Reference Grody and BasistGrody and Basist, 1996). It should be noted that none of these algorithms was originally developed for hemispheric-scale application, and reference to the algorithms by author name is for convenience only. However, because these algorithms individually represent examples which include both mid- and high-frequency channels, vertical and horizontal polarizations and polarization-difference approaches, they allow us to evaluate the relative merits of these different approaches at the hemispheric scale.

Fig. 4.

Typical seasonal comparisons ((a) fall, (b) winter, (c) spring) of NOAA weekly snow extent and four passive-microwave algorithms. Light grey areas in each image represent snow-covered area. The dotted line in the NOAA images represents the area within which the surface temperatures at the time of the satellite overpass had not been above freezing for the week. The heavy black line in the passive-microwave images is the boundary of the NOAA snow extent for the week.

In Figure 4, the dotted line in the NOAA images represents the area within which the surface temperatures (NCEP (U.S. National Centers for Environmental Prediction) Re-analysis data) at the approximate time of the satellite overpass had not been above freezing for the week. This information allows us to focus our analysis on those regions where a sub-freezing snow cover can be expected. The black line in the passive-microwave images corresponds to the NOAA snow extent for that week. While each season is represented by only one example week, these are typical of the results which we obtained from comparisons covering several years, as is shown later in Figure 5.

Fig. 5.

(a) Time-series comparison of snow extent from NOAA vs Chang (SMMR, 1978–87) and NSIDC1 (SSM/I, 1987–

SWE

In this phase of the study a topographically consistent subset of the FSUHS data was selected for the validation. This subset (45–60° N, 25–45° E) has the highest station density (approximately one transect per 100 km gridcell) and is primarily composed of non-complex terrain (grassland steppe) with maximum elevation differences of <500m. We have developed a processing environment and output format to compare the various algorithms with the station SWE data. For each station file this involves the combination of the daily brightness-temperature files for the observation date and for the previous 2 days to provide complete spatial coverage for the satellite data. SWE for all pixels containing at least one transect measurement is compared with the output from the respective algorithms. Figure 6 shows a time-series comparison of station data with a single algorithm (Chang and others, 1987) averaged over the total study area for a 12 year period. Figure 7 shows a single-year comparison of three different algorithms. (During 1989, 85V data were not available after 31January, hence output from the Nagler algorithm terminates on that date.) These preliminary results represent only the initial phase of our efforts to evaluate the accuracy of the microwave SWE algorithms.

Fig. 6.

Average of total study-area (FSUHS subset) SWE vs passive-microwave SWE using horizontally polarized difference algorithm, 1978–90.

Fig. 7.

Average of total study area (FSUHS subset) SWE vs passive-microwave SWE from three algorithms, 1988–89.

Conclusions and Future Work

Results from the current phase of our study indicate that horizontal polarization-based algorithms (e.g. Chang), while apparently underestimating snow extent during early winter, appear to provide the best overall estimates as winter progresses. Vertical-polarization-based algorithms (e.g. Goodison) provide similar results but with the tendency to overestimate snow extent in the presence of desert soils and/or frozen ground. Algorithms which include the 85 GHz channel (e.g. Grody and Nagler) are capable of detecting shallow snow but in their current form may frequently overestimate snow extent. As the snow cover becomes deeper and the layered structure more complex, all algorithms tend to show improved agreement with the NOAA data.

With respect to SWE, results indicate a general tendency for the algorithms tested thus far to underestimate values obtained from surface station measurements. Unlike snow extent, differences between the validation data and the microwave algorithms appear to be generally consistent throughout the winter season. Underestimates of SWE increase significantly as the forest-cover density begins to exceed 30–40%. Because of the detailed land-cover data available for this validation area, we will be able to evaluate the results of algorithm adjustments based on fractional forest cover as proposed earlier by Reference Chang, Foster and HallChang and others (1996). In addition to the time-series comparisons shown in Figures 6 and 7, our analysis is also based on image-subtraction time-series comparisons which allow evaluation of both temporal and spatial differences. Coupled with additional data on topography, vegetation cover, surface air temperature and snow structure, these spatial comparisons viewed throughout the winter season will provide valuable insight into the actual causes of the observed differences. Future analysis will continue the comprehensive multi-year comparison of at least these four algorithms with the FSUHS data, as well as other surface station measurements in the United States and Canada, and the development of a methodology that will combine visible and passive-microwave data to provide an optimal satellite-derived snow product.