James L. Foster

Nimbus-7 SMMR Derived Global Snow Cover Parameters

Snow covers about 40 million km 2 of 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. INTRODUCTION Remotely acquired microwave data in conjunction with essential ground observations will most likely lead to advanced extraction of snow properties beyond conventional techniques. Landsat visible and near-infrared data have recently become near operational for use in measurements of snow covered areas (Rango 1975, 1978). Operational NOAA satellites provide continuous global coverage with 4 km spatial resolution. Both Landsat and NOAA data acquisition are hampered by cloud cover, sometimes at critical times when a snowpack is ripe and ready to melt. Furthermore, information on water equivalent, free water content and other snowpack properties germane to accurate snow melt run-off prediction is not currently available using visible and near-infrared data because only surface and near surface snow contribute to the measured reflectances. Microwave remote sensors which have the capability to penetrate the snowpack and respond to variations in snow properties, could provide information about snow depth and snow water equivalent (Ran go and others 1979; Chang and others 1982). However, due to the coarse spatial resolution of the present microwave radiometers, combinations of vegetation, terrain and snow information within a pixel greatly complicate the retrieval algorithm development. Algorithms need to be developed that are specific to physiographic areas like the Colorado River basin and the north slope of Alaska. These algorithms will take into account additional parameters related to microwave signatures. Until these algorithms are operational, the use of remotely collected microwave data for global quantitative snowpack analysis will not be operational due to the complexities involved in the data analysis. MICROWA VE EMISSION FROM SNOW Microwave emission from a layer of snow over ground consists of two parts: (1) emission by the snow volume and (2) emission by the underlying ground . Snow particles act as scattering centers for microwave radiation from a snowpack. The scattering effect which redistributes the upwelling radiation according to snow thickness and crystal size, provides the physical basis for microwave detection of snow. Mie scattering theory is used to account for the energy redistribution by snow crystals . Although the snow crystal usually is not spherical in shape, its ensemble scattering properties can be mimicked by spheres (Chang and others 1976). Theoretical computations indicate that scattering by individual snow crystals can be the dominant modification factor of upwelling 37 GHz (0.8 cm) radiation in the dry snow cases (Chang and others 1982). The effect of scattering is lessened by using the longer wavelengths. Fig.1 shows the calculated brightness temperatures versus snow water equivalent for SMMR frequencies . The effective microwave penetration depth into a dry snowpack, typically 10-100 times the wavelength, depends on the wavelength used and the characteristic crystal size of the snowpack . When the wavelength is much larger than the crystal size (> 5 cm), absorption will be the dominant effect. The brightness temperature will resemble the physical temperature of the snowpack. When the wavelength is comparable to the snow crystal size « I cm), scattering becomes the dominant effect. Nimbus-7 SMMR is a five frequency, dualpolarized microwave radiometer which measures the upwelling microwave radiation at 6.6, 10.7, 18.0, 21.0, and 37.0 GHz (4 .6, 2.8, 1.7, 1.4 and 0.8 cm) while scanning 25 0 to either side of the spacecraft (approximately 780 km swath width) with a constant incidence angle of approximately 50 0 with respect to the Earths surface. The spatial resolution varies from 25 km for the 37 GHz (0.8 cm) to 150 km for the 6.6 GHz (4.6 cm). A detailed description of this instrument can be found in Gloersen and Barath (1977). The Nimbus-7 satellite was launched on October 24, 1978, into a sun-synchronous polar orbit with local noon/ midnight equatorial crossing. Using the multifrequency analysis approach, one may make inferences regarding not only the thickness of the snowpack, but the underlying soil (wet versus dry) condition. The shorter wavelengths, such as 0.8 cm (37 GHz), sense near surface (0-50 cm) temperature and emissivity, and

A prototype AMSR-E global snow area and snow depth algorithm

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

Abstract While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithrns performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 1996 algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorith-rn and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithln performs better in North America in each month than does the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al. (1987)algorithm is in closer accord to the SDC than is the GSFC 1996 algorithm.

Snow cover and snow mass intercomparisons of general circulation models and remotely sensed datasets

Abstract Confirmation of the ability of general circulation models (GCMs) to accurately represent snow cover and snow mass distributions is vital for climate studies. There must be a high degree of confidence that what is being predicted by the models is reliable, since realistic results cannot be assured unless they are tested against results from observed data or other available datasets. In this study, snow output from seven GCMs and passive-microwave snow data derived from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are intercompared. National Oceanic and Atmospheric Administration satellite data are used as the standard of reference for snow extent observations and the U.S. Air Force snow depth climatology is used as the standard for snow mass. The reliability of the SMMR snow data needs to be verified, as well, because currently this is the only available dataset that allows for yearly and monthly variations in snow depth. [The GCMs employed in this investigation are the United Ki...

Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover Densities in Central Alaska

Field and aircraft measurements were acquired in April 1995 in central Alaska to map snow cover with MODIS Airborne Simulator (MAS) data, acquired from high-altitude aircraft. The Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) is a 36-channel system that will be launched on the EOS-AM-1 platform in 1999. A vegetation-density map derived from integrated reflectances (Ri), from MAS data, is compared with an independently-produced vegetation type and density map derived from Thematic Mapper (TM) and ancillary data. The maps agreed to within 13%, thus corroborating the effectiveness of using the reflectance technique for mapping vegetation density. Snow cover was mapped on a 13 April 1995 MAS image, using the original MODIS prototype algorithm and an enhanced MODIS prototype algorithm. Field measurements revealed that the area was completely snow covered. With the original algorithm, snow was mapped in 96% of the pixels having <50% vegetation-cover density according to the Ri map, while in the areas having vegetation-cover densities ⩾50%, snow was mapped in only 71% of the pixels. When the enhanced MODIS snow-mapping algorithm was employed, 99% of the pixels having <50% vegetation-cover density were mapped, and 98% of the pixels with ⩾50% vegetation-cover density were mapped as snow covered. These results demonstrate that the enhanced algorithm represents a significant improvement over the original MODIS prototype algorithm especially in the mapping of snow in dense vegetation. The enhanced algorithm will thus be adopted as the MODIS at-launch snow-cover algorithm. Using this simple method for estimating vegetation density from pixel reflectance, it will be possible to analyze the accuracy of the MODIS snow-cover algorithm in a range of vegetation-cover in places where information on vegetation-cover density is not available from ground measurements.

Snow water equivalent estimation by microwave radiometry

Abstract Snow water equivalent (SWE) is one of the most important parameters for accurate prediction of snowmelt runoff. Conventionally, SWE is monitored using observations made at widely scattered points in or around specific watersheds. Remote sensors, which provide data with better spatial and temporal coverage, can be used to improve the SWE estimates. Microwave radiation, which can penetrate through a snowpack, may be used to infer the SWE. Calculations made from a microscopic scattering model are used to simulate the effect of varying SWE on the microwave brightness temperature. Data obtained from truck mounted, airborne and spaceborne systems from various tests sites have been studied. The simulated SWE compares favorably with the measured SWE for dry snowpacks. In addition, whether or not the underlying soil is frozen or thawed may be discriminated using the polarization information obtained by spaceborne sensors.

Snow Cover and Temperature Relationships in North America and Eurasia

Abstract In this study the snow cover extent during the autumn months in both North America and Eurasia has been related to the ensuing winter temperature as measured at several locations near the center of each continent. The relationship between autumn snow cover and the ensuing winter temperatures was found to be much better for Eurasia than for North America. For Eurasia the avenge snow cover extent during the autumn explained as much as 52% of the variance in the winter (December-February) temperatures compared to only 12% for North America. However, when the average winter snow cover was correlated with the average winter temperature it was found that the relationship was better for North America than for Eurasia. AS much as 46% of the variance in the winter temperature was explained by the winter snow cover in North America compared to only 12% in Eurasia.

Development of a technique to assess snow-cover mapping errors from space

Following the December 18, 1999, launch of the Earth Observing System (EOS) Terra satellite, daily snow-cover mapping is performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using moderate resolution imaging spectroradiometer (MODIS) data. This paper describes a technique for calculating global-scale snow mapping errors and provides estimates of Northern Hemisphere snow mapping errors based on prototype MODIS snow mapping algorithms. Field studies demonstrate that under cloud-free conditions, when snow cover is complete, snow mapping errors are small (<1%) in all land covers studied except forests, where errors are often greater and more variable. Thus, the accuracy of Northern Hemisphere snow-cover maps is largely determined by percent of forest cover north of the snowline. From the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the authors classify the Northern Hemisphere into seven land-cover classes and water. Estimated snow mapping errors in each of the land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. The resulting average monthly errors are expected to vary, ranging from about 5-10%, with the larger errors occurring during the months when snow covers the boreal forest in the Northern Hemisphere. As determined using prototype MODIS data, the annual average estimated error of the future Northern Hemisphere snow-cover maps is approximately 8% in the absence of cloud cover, assuming complete snow cover. Preliminary error estimates will be refined after MODIS data have been available for about one year.

EFFECTS OF FOREST ON THE SNOW PARAMETERS DERIVED FROM MICROWAVE MEASUREMENTS DURING THE BOREAS WINTER FIELD CAMPAIGN

Passive microwave data have been used to infer the areal snow water equivalent (SWE) with some success. However, the accuracy of these retrieved SWE values have not been well determined for heterogeneous vegetated regions. The Boreal Ecosystem-Atmosphere Study (BOREAS) Winter Field Campaign (WFC), which took place in February 1994, provided the opportunity to study in detail the effects of boreal forests on snow parameter retrievals. Preliminary results reconfirmed the relationship between microwave brightness temperature and snow water equivalent. The pronounced effect of forest cover on SWE retrieval was studied. A modified vegetation mixing algorithm is proposed to account for the forest cover. The relationship between the microwave signature and observed snowpack parameters matches results from this model.

A blended global snow product using visible, passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.