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SMMR Snow Depth IDC Data on FTP

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SMMR Snow Depth
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Readme Contents

Data Set Overview
Sponsor
Original Archive
Future Updates

The Data
Characteristics
Source

The Files
Format
Name and Directory Information
Companion Software

The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data

Contacts
Points of Contact

References

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Data Set Overview

The data set consists of one degree by one degree gridded global monthly averaged snow depths derived from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) half degree by half degree gridded snow depth data.

The SMMR sensor was placed in an alternate-day operating pattern on 19 November 1978 due to spacecraft power limitations, providing complete global coverage every six days. Regions poleward of 72 degrees have complete coverage for each day the sensor was recording data. The SMMR data spans over the period from 1978 through 1987.

The algorithm used to retrieve snow depth on a global scale using the remotely sensed microwave signals has been developed by a group of NASA scientists (Chang et al., 1976,1982,1987,1990, 1992; Foster et al., 1984; Hall et al., 1982). Data are placed into 1/2 degree latitude by 1/2 degree longitude grid cells. SMMR data were interpolated for spatial and temporal gaps. Overlapping data in a cell from separate orbits within the same six-day period are averaged to give a single brightness temperature, assumed to be at the center of the cell. Maps are based on six-day average brightness temperature data from the middle week of each month. Oceans and bays are masked so that only microwave data for land areas are distinct.

Comparisons of SMMR snow maps with previous maps produced by NOAA/NESDIS and US Air Force Global Weather Center indicate that the total snow derived from SMMR is usually about ten percent less than that measured by the earlier products, because passive microwave sensors often can't detect shallow dry snow less than about 5 cm deep. SMMR snow depth results are especially good for uniform snow covered areas such as the Canadian high plains and Russian steppes. Heavily forested and mountainous areas tend to mask the microwave snow signatures, and SMMR snow depth derivations are poorer in those areas.

This snow depth data set supports climate modeling, snow melt run-off, and other geophysical studies(Hall 1988, Hall and Martinec, 1985; Schmugge 1980a&b). In the Northern Hemisphere, the mean monthly snow cover ranges from about 7 percent to over 40 percent of the land area, thus making snow the most rapidly varying natural surface feature. The mean monthly snow storage (excluding Greenland) ranges from about 1.5 x 1016 g in summer to about 300 x 1016 g in winter. Snow cover is a sensitive indicator of climate change, with the position of the southern boundary of snow cover in the Northern Hemisphere of particular significance as it is likely to retreat northward because of sustained climate warming (Barry, 1984; Foster 1989).

General Circulation Models (GCMs) also suggest that the amount of snowfall by latitude may change because of changes in atmospheric moisture flux with a decrease in the frequency and occurrence of snowfall in the low and middle latitudes and an increase in the high latitudes (Barry, 1985).

Energy balance studies of the Earth-atmosphere system using satellite observations indicate a net radiative energy gain between the equator and 35 latitude and a net radiative energy loss poleward of this latitude. The Arctic region is influenced by the energetic subpolar systems transporting heat and momentum into the region and it, in turn, influences the general circulation of the atmosphere by being a heat sink for the global weather machine (Vowinckel and Orvig, 1970). For a better understanding of the heat transfer between the atmosphere, the snowpack, and the ground, snow depth and snow extent must be known.

Satellite snow cover records are presently too short to determine definite trends. Continued monitoring will be needed to define snow accumulation and depletion patterns, and to detect correlations between snow cover and large-scale circulation patterns.

Sponsor
The production and distribution of this data set are being funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:

The authors wish to thank the original data producer, Dr. Alfred Chang of the Hydrological Sciences Branch at NASA Goddard Space Flight Center in Greenbelt, MD. The Distributed Active Archive Center (Code 902) at Goddard Space Flight Center, Greenbelt, MD, 20771, acquired this dataset from the National Snow and Ice Data Center (NSIDC) and put it in the present format for distribution. Goddard DAAC's share in these activities was sponsored by NASA's Earth Science enterprise.

Original Archive
The original 0.5 by 0.5 degree gridded dataset was acquired from the National Snow and Ice Data Center (NSIDC).

The information in this document has been primarily summarized from: Chang, A. T. C., J. L. Foster, D. K. Hall, H. W. Powell, and Y.L. Chien. 1992. Nimbus-7 SMMR Derived Global Snow Depth Data Set. The Pilot Land Data System. NASA/Goddard Space Flight Center. Greenbelt, MD.

Future Updates
Goddard DAAC will update this data set as new data are processed and made available at NSIDC.

The Data

Characteristics

Source
The Scanning Multichannel Microwave Radiometer operated on NASA's Nimbus-7 satellite (Chang, 1982) for more than eight years, from 26 October 1978 to 20 August 1987, transmitting data every other day. Intended to obtain ocean circulation parameters such as sea surface temperatures, low altitude winds, water vapor and cloud liquid water content on an all-weather basis (Oakes et al., 1989), the SMMR is a ten channel instrument capable of receiving both horizontally and vertically polarized radiation. The instrument could deliver orthogonally polarized antenna temperature data at five microwave wavelengths, 0.81, 1.36, 1.66, 2.8 and 4.54 cm.

A parabolic antenna 79 cm in diameter reflected microwave emissions into a five-frequency feed horn. The antenna beam maintained a constant nadir angle of 42 degrees, resulting in an incidence angle of 50.3 degrees at Earth's surface. The antenna was forward viewing and rotated equally +/- 25 degrees about the satellite subtrack. The 50 degree scan provided a 780 km swath of the Earth's surface. Scan period was 4.096 seconds.

Conversion of the raw radiometric readings to microwave brightness temperatures involved correcting for actual antenna patterns, including sidelobe effects, as well as separating out the horizontal and vertical polarization components of each of ten channels of radiometric data (Gloersen et al, 1980, Han, 1981).

After launch, the prelaunch constants were updated by checking against earth targets of known properties - open, calm sea water with clear skies or light clouds, and consolidated first-year sea ice. The brightness temperatures were verified by comparison with brightness temperatures obtained from airborne radiometer with all SMMR channels during Nimbus 7 underflights. The underflights were particularly important, since extrapolation from the laboratory cold reference of 100 degrees Kelvin to the postlaunch value of 30 degrees Kelvin cannot be done with complete confidence.

The Files
The global snow data set contains global gridded snow depth estimates. Data in each file progresses from North to South and from West to East beginning at 180 degrees West and 90 degrees North. Thus first point represents the grid cell centered at 89.5 degree North and 179.5 West. Grids with missing values are filled with missing value code ( -999.9). This data set consists of 106 monthly mean data files for the period from October 1978 through August 1987. Format

Data Files

Name and Directory Information

Naming Convention:

The file naming convention for the SMMR Snow Depth files is

smmr_snw.depth.1nmegl.[yymm].ddd

where:
smmr_snw = data product designator: SMMR snow
depth = parameter name: snow depth
1 = number of levels
n = vertical coordinate, n = not applicable
m = temporal period, m = monthly
e = horizontal grid resolution, e = 1 x 1 degree
gl = spatial coverage, gl=global land
yy = year
mm = month
ddd = file type designation, (bin=binary, ctl=GrADS control file
Directory Path

/data/inter_disc/hydrology/smmr_snow/yyyy

where yyyy is year.

Companion Software
Several software packages have been made available on the CIDC CD-ROM set. The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS meta-data files (.ctl) have been supplied for each of the data sets. A GrADS gui interface has been created for use with the CIDC data. See the GrADS document for information on how to use the gui interface.

Decompression software for PC and Macintosh platforms have been supplied for datasets which are compressed on the CIDC CD-ROM set. For additional information on the decompression software see the aareadme file in the directory:

software/decompression/

Sample programs in FORTRAN, C and IDL languages have also been made available to read these data. You may also acquire this software by accessing the software/read_cidc_sftwr directory on each of the CIDC CD-ROMs

The Science
Theoretical Basis of Data
Microwave radiometery is useful as a remote sensing tool because the emissivity of an object depends on its composition and physical structure. Thus, determination of emissivity provides information on the physical properties of the emitting medium. The equivalent temperature of the microwave radiation thermally emitted by an object is called its brightness temperature (Tb). It is expressed in units of temperature (Kelvin) because for microwave wavelengths, radiation emitted from a perfect emitter is proportional to its physical temperature. An object's emissivity is determined by measuring the brightness temperature radiometrically and by measuring the physical temperature in some manner (Foster et al. 1984).

Microwave emission from a layer of snow over a ground medium consists of emission by the snow volume and emission by the underlying ground. Both contributions are governed by the transmission and reflection properties of the air-snow and snow-ground interfaces, and by the absorption or emission and scattering properties of the snow layer (Stiles et al. 1981).

The intensity of microwave radiation emitted through and from a snowpack depends on physical temperature, grain size, density, and underlying surface conditions of the snowpack. In general, the microwave emissivity of snow increases when liquid water is present in the snow; snow often exists near its melting point and, as one of the most unstable natural substances, is subject to extreme structural changes occurring with freeze-thaw cycles.

Recognizing the microwave signatures of the many forms of snow comes with understanding the way snow's permittivity changes through the various stages of metamorphism. A material's dielectric properties are characterized by the dielectric constant, a measure of the material's response to an applied electric field. This response combines the wave's propagation characteristic (velocity and wavelength) in the material with the energy losses in the media.

Snow parameters significantly affecting microwave sensor response are: liquid water content, crystal size, depth and water equivalent, stratification, snow surface roughness, density, temperature and soil state, moisture, roughness and vegetation. For example, the dielectric constants of water and ice are so different that even a little melting causes a strong microwave response. The low dielectric constant for snow also provides sufficient contrast with bare ground in the brightness temperature range for snowfield monitoring (Rango et al 1979).

Radiation emerging from a snowpack can be derived by solving radiative transfer equations (Chandrasekhar 1960, England 1975, Chang et al. 1987, Tsang and Kong 1977) and using them to calculate brightness temperatures with different physical parameters. When radiometric measurements of brightness temperature are made at more than one microwave wavelength or polarization, it's possible to deduce additional information about the medium. This potential provides the rationale for the development of inversion techniques that calculate desired physical parameters from brightness temperatures measured at multiple wavelengths and polarizations (Gloersen and Barath 1977).

Algorithms to evaluate and retrieve snow cover and snow depth have been derived from research using a combination of microwave sensors aboard satellites, aircraft, and trucks, as well as in situ field studies. A method relating microwave radiometric data to snow cover and snow depth is to examine the differences between the brightness temperature observed for snow-covered ground and that for snow-free ground.

Algorithm Development:

Currently, several algorithms are available to evaluate and retrieve snow cover and snow depth parameters for specific regions and specific seasonal conditions. These algorithms have been derived from research using a combination of microwave sensors aboard satellites, aircraft, and trucks, as well as in situ field studies. A straightforward method to relate microwave radiometric data to snow cover and snow depth is to examine the differences between the brightness temperature observed for snow covered ground and that for snow free ground. The general form of a snow cover algorithm is:

Delta Tsc = Fsc - Fsc=0

where

Delta T = change in brightness temperature
Fsc= observed radiometric value for snow covered terrain
Fsc=0= observed radiometric value for snow-free terrain

F may be either the brightness temperature at a single frequency or a more complicated expression involving the brightness temperature at several frequencies or polarizations (Hallikainen and Jolma, 1987).

Efforts have been made by several investigators to produce a reliable global snow algorithm (Kunzi et al., 1982; Hallikainen, 1984; Chang et al., 1987). The monthly snow cover and snow depth maps produced for this data set were generated by using the algorithm developed by Chang et al. (1987) that prescribes a snow density of 0.30 g/cubic centimeter and a snow grain size of 0.3 mm for the entire snowpack. The difference between the SMMR 37 GHz and 18 GHz channels is used to derive a snow depth-brightness temperature relationship for a uniform snow field:

SD = 1.59 * (Tb18H - Tb37H)

where SD is snow depth in cm, H is horizontal polarization, and 1.59 is a constant derived by using the linear portion of the 37 and 18 GHz responses to obtain a linear fit of the difference between the 18 GHz and 37 GHz frequencies. If the 18 GHz brightness temperature (Tb18H)is less than the 37 GHz brightness temperature(Tb37H), the snow depth is zero and no snow cover is assumed.

Evaluation of similar algorithms shows that only those that include the 37 GHz channel provide adequate agreement with manually measured snow depth and snow water equivalent values. It may be noted that the T b18H - Tb37H often gives better results than the 37 GHz channel alone. Using the 18 GHz channel reduces the snow temperature, ground temperature, and atmospheric water vapor effects on brightness temperatures.

The SMMR instrument was not designed to last a decade. The characteristics of the SMMR instrument have been changing through the years. These changes in instrument behavior have affected the calibration of the SMMR measurements. To understand the long-term variations of the calibrated SMMR brightness temperatures, the monthly means and the standard deviations of the brightness temperatures over global ocean areas have been analyzed.

Processing Sequence and Algorithms
Nimbus-7 SMMR flight data were received by the Meteorological Operations Control Center (MetOCC). The user-formatted output tape from MetOCC was then transferred to and processed by the Science and Applications Computer Center. Two calibrated brightness temperature tapes, CELL-ALL and TCT (Temperature Calibrated Tape) were produced. CELL-ALL data were gridded according to SMMR spatial resolution while TCT data retained footprint configuration. TCTs were used for the snow parameters.

Brightness temperatures on CELL-ALL tapes were selected for each channel from all ocean areas between 60 degrees N and 50 degrees S and 600 km away from land masses. Daytime and nighttime data were separated. The means and standard deviations of the brightness temperatures for each month from January 1979 to October 1985 were calculated. The statistics of this analysis are available in Fu et al., 1988.

Resampling of 0.5x0.5 degree gridded dataset to 1x1 degree grid:

For consistency with the other data sets in the Goddard DAAC's Climatology Interdisciplinary Data Collection, the SMMR snow data received from NSDIC were reformatted at the DAAC from the original one-byte unsigned integer into 32-bit floating point quantities and regridded to 1 x 1 degree from their original 0.5 x 0.5 degrees.

Since in the original data, grid elements span from 85 degree North to 85 degree South (array dimension 720x340), and the grid elements could have the following values:

following steps were performed in the regridding process:

  1. The original data of array size 720x340 was copied to an array of dimension (720x360) starting from the (720x10+1) cell of the new array. The fill value -999.9 was assigned for first and last ten (half degree) latitude bands. We refer this larger array as new original data.

  2. A temporary 1 degree longitude by half degree latitude grid (array dimension 360 x 360) was defined. Starting with the first latitude band in the new original data set (89.5N to 90N), the first pair of grid cells (cells 1 and 2) was averaged and assigned to the value of the first temporary cell, and average of the next pair of new original data cells was assigned to second cell of the temporary array.

  3. In step 2, if either of the original 0.5 degree cells is a mask value (other than 3-250), then no average is performed and the temporary cell is assigned the mask value of the unfilled 0.5 degree cell. If both of the original cells have different mask values and if any of the contributing cell was masked for water (mask value 255), then the new cell value was assigned 255 fill value. Similarly if either of the cell had value 253 (no data available or quality flagged error) then the new cell was assigned the fill value -999.9.

  4. Steps 2 and 3 were repeated for the remaining pairs of 0.5 grid cells (along the longitude) of the first latitude band in the new original data set.

  5. Steps 2 through 4 were performed for the remaining 179 half degree width latitude bands in the new original data set to arrive at a temporary array of size 360 x 360 (1 degree longitude by 0.5 degrees latitude)

  6. The entire procedure above was repeated in the latitudinal direction using the same grid cell averaging scheme to arrive at the final 360 x 180 (1 degree longitude by 1 degree latitude) array.

  7. Thus the value of the final element is an average of four (two along longitude and two along latitude) original elements. The presence of a fill value -999.9 dominates over the masks or values of the other three participating elements. In the absence of -999.9 element, the presence of water (mask 255) dominates over other values (3 to 250, or 254).

  8. At the end the numbers 255 representing the water have been changed to -99.0 in order to differentiate water from the permanent ice better, since values 254 and 255 are very close.
  9. For conformity to existing criteria, and gif images, created from the resultant files, were visually inspected to assure that the data was free of artifacts introduced by these procedures.

Scientific Potential of Data
In the Northern Hemisphere, the mean monthly snow cover ranges from about seven percent to over 40 percent of the land area, making snow the most rapidly varying natural surface feature. This variability means that snow is a sensitive indicator of climate change, depending on temperature, precipitation and solar radiation for existence. Yet, once on the ground, snow influences each of these climatic factors, with important economic consequences: moisture stored in winter snowpack supplies as much as one third of the world's irrigation waters.

Snow cover and depth change rapidly over large areas during fall buildup and spring melt. To adequately forecast and model these changes, accurate snow and ice observations are needed, and long-term data bases of snow parameters must be collected. To understand heat transfer between the atmosphere, snowpack and ground, snow depth and snow extent must be known.

Although the microwave snow products are not yet being used in an operational mode, several ongoing studies, described below, point out the potential uses of this microwave snow data set.

Climate Modeling Studies:

The mechanics of Earth's atmospheric circulation are highly complex and only partially understood, which makes numerical simulation difficult. Hence, it is difficult to describe rigorously the role of snow as it affects global climate, and it is hard to ascertain the causes of a particular deficiency in a model's climate simulation because of the complicated interactions that take place. In the case of snow, sorting out cause and effect can be particularly trying. Its existence depends on factors such as temperature, precipitation, and solar radiation, but once present, snow cover can influence each of these factors (Broccoli, 1985). Many global climate models (GCMs) have treated snow as a uniform feature; i.e., with a uniform albedo and a uniform coverage from year to year. This is not a good depiction of the physical situation. Snow cover and depth change rapidly over large areas during fall buildup and spring melt and, until recently, the capability did not exist to recognize these changes.

For over 25 years, efforts have been made to construct GCMs for use in both forecasting and climate modeling projects. During this period, great strides were made in improving the accuracy of numerical forecasts as well as in the quality of climate model simulations. To do this work, accurate snow and ice observations are needed to provide boundary conditions for atmospheric GCMs, to initialize forecast models, and to validate forecast and climate model simulations (Robock, 1980). At present, the most suitable snow cover record for validation of GCMs is the NOAA satellite-derived snow cover data base. This data base has been used to a limited extent in model validation (Kukla, et al., 1985).

Some GCMs also predict the mass of snow on Earth's surface from a snow mass budget equation that includes the processes of snowfall, snow melt, and sublimation. Generally, the snow layer is considered to have uniform properties over its entire depth within a model grid box, and the surface albedo is taken to be a function of the depth of snow and the type of underlying surface. GCMs calculate snow accumulation as the result of precipitation from clouds. In GCMs, snow ablation occurs only as a result of above-freezing temperature.

The observed water equivalent of snow is required to validate the surface snow mass simulated by GCMs. Such observations were made locally for Europe, North America, and elsewhere from climatological records and are archived in various reports. But passive microwave data from sensors such as SMMR and SSM/I may provide a more realistic synoptic representation of the snow water equivalent (Foster and Rango, 1989).

In addition, the snow extent data derived from passive microwave satellites may be useful for input to GCMs because the scale of the SMMR data is such that it can easily be made compatible with typical GCM grid scales, and data can be acquired through cloud cover and darkness. SMMR and AVHRR derived data on snow are being used in several different versions of GCMs to analyze the influence of snow on the global climate. Three of these models are the Goddard Laboratory for Atmospheric Sciences (GLAS) 4th Order GCM, the National Center for Atmospheric Research (NCAR) Community Climate Model (Dickinson, 1983), and the Goddard Institute for Space Studies (GISS) GCM (Hansen et al., 1983). Currently, realistic satellite derived values of snow extent and snow water equivalent are being used in the models to study interannual changes in the output of each GCM. Preliminary results for the Northern Hemisphere indicate that, as expected, there are some disagreements between the climatologically -derived and the satellite-derived snow distributions. However, overall patterns are basically the same.

Snow Melt Runoff Studies:

Satellite microwave data have been used to evaluate the average areal water equivalent of snow cover in the mountainous Colorado River Basin in the western U.S. It has been shown that satellite microwave data, even at very poor resolution, can be used to obtain information about average basin snow water equivalent. The microwave approach has certain advantages including an all-weather observation capability, an ability to make areal measurements, and a data measurement capability in remote, inaccessible regions. Difficulties in using the microwave approach that arise from alternating dry and wet snowpack conditions are minimized by using nighttime data. In a study by Rango et al. (1989), an average snow water equivalent for a basin 3,419 km2 in area was obtained using the difference in microwave brightness temperatures of the 37 and 18 GHz channels. In two test years (1986 and 1987), the microwave determined average basin snow water equivalent on April 1 was within 15 percent of the actual observed value as derived from stream flow measurements. The approach is not yet ready for true operational use because it needs additional tests in other years and in other basins. But as resolutions improve with future sensors, the advantages of the microwave measurements will be more significant, especially in data sparse regions. The improved microwave data could be used on smaller basins and for determining snow water equivalent of individual elevation zones. Such data could be used for selecting elevation zone snow cover depletion curves in particular years for use in snow melt runoff forecasts, or to directly provide areal water equivalent data to snow melt runoff models (Rango et al., 1989).

Geophysical Studies:

Any redistribution of water mass over Earth causes slight changes in Earth's rotation because of the exchange of angular momentum between the solid Earth and the hydrosphere. The buildup and disappearance of snow excites polar motion producing a shift in the position of the rotation axis relative to a fixed geographic axis. The polar motion consists mainly of an annual wobble and a 14-month Chandler wobble. The annual wobble is a forced motion caused primarily by seasonal changes in Earth's atmosphere and hydrosphere. In the course of the annual wobble, the rotational axis describes a somewhat elliptical path about the fixed geographic axis of perhaps four meters (Chao et al., 1987).

Until recently, monthly measures of polar motion and global snow volume were too inexact to be able to determine the effect of snow on Earth's rotation. However, with the launch of the Lageos satellite in 1976, which can measure polar motion accurately, and the Nimbus satellite in 1978 (SMMR), it is now possible to assess and monitor the effects of changes in the distribution of snow mass on Earth's surface. Chao et al. (1987) used the Lageos and Nimbus data sets to compute the snow load excitation of the annual wobble of Earth's rotation axis. It was found that the snow load excitation has an amplitude that is some 30 percent of the total annual wobble excitation, thus it represents a significant geophysical contribution (Chao et al., 1987).

Agricultural Studies:

There is potential for using passive microwave data to detect areas of winter kill. Winter kill results when grain crops planted in fall (e.g., winter wheat) are damaged or killed because there was insufficient snow cover to insulate the young plants from subfreezing temperatures. Winter kill is most often experienced in the Great Plains of the U.S. and Canada and in the steppe areas of the Soviet Union. With adequate snow cover the damage attributable to winter kill is minimized even during very cold winters. Microwave maps of North America and Eurasia are useful in discerning areas of meager snow cover and depth and thus may be used as an indirect means to assess winter kill losses (Goodison et al., 1986, Foster et al. 1983). In the future, microwave data on snow depth and snow cover may be included as an additional input to improve the performance of the models currently being used to forecast winter kill potential.

Validation of Data
Extensive validation of the SMMR-derived data on snow cover and snow depth (Cavalieri, 1988) is essential and will lead to the development of more accurate and reliable algorithms.

There are, of course, complications that arise when one tries to apply an algorithm based on average snow conditions to specific regions where the climate, snowpack structure, and vegetation cover may differ. Studies using radiative transfer modeling and SMMR data demonstrate that snowpack structure significantly influences the microwave emission. Depth hoar, at the base of some snowpacks (Benson et al., 1975), consists of large snow grains that are effective scatterers of microwave radiation at the 37 and 18 GHz frequencies. These large grains cause a reduction in the microwave emission from the entire snowpack (Hall et al., 1986). Additionally, in dense coniferous forests the greater emission from the trees may overwhelm the emission from the underlying ground. Thus, the microwave brightness temperature of the snowpack is higher than if no trees were present (Hall et al., 1982; Hallikainen, 1984). Also, microwave radiation at 37 GHz is nearly transparent to shallow (<5 cm) dry snow, which results in underestimates of snow extent and snow volume in the vicinity of the snow boundary.

Seasonal and annual variability in snow extent have been measured from SMMR data as well as Advanced Very High Resolution Radiometer (AVHRR) data collected on the NOAA satellites, but the error bands are lacking for both products. The SMMR and NOAA products agree fairly well, but the SMMR data produce consistently lower snow covered area estimates than do the NOAA data. For example, snow covered area in the Northern Hemisphere for January 1984 is 39.3 x 106 km2 and 45.5 x 106 km2 as measured from the SMMR and NOAA data respectively, a difference of about 16 percent. The error in the SMMR-derived snow depths is more difficult to determine because there is no reliable data set with a spatially dense enough network with which to compare the SMMR-derived snow depths on a hemispheric basis. The only other data set available with which to derive global snow volume is the data set produced by the Rand Corporation. The monthly averaged Rand data set was constructed by using climatological averages from meteorological station data. But preliminary comparisons between the SMMR and the Rand data sets for snow volume in the Northern Hemisphere indicate that the data sets are comparable. For March, the snow volume is 290 x 1016 and 364 x 1016g as determined from the SMMR and Rand data sets respectively. This is a difference of about 20 percent. The error bands are unknown and may be large; however, this SMMR temporal data set is the only source of monthly snow volume currently available (Chang et al., 1992).

Along with the seasonal variations, the data show that the monthly mean brightness temperatures have systematic biases between daytime and nighttime for most channels. There are also patterns of increasing or decreasing monthly mean brightness temperatures throughout the first 48 months. Starting in the fifth year, some of these patterns changed.

Similar analyses were performed for the brightness temperatures over land. The statistics of the analyses are available in Fu et al., 1988. Plots of monthly mean brightness temperatures can also be found there. The averaged temperatures over land are mostly stable, although the standard deviations are, as expected, larger than those over the ocean because of the greater scene variability over land (Fu et al., 1988).

Contacts

Points of Contact
For information about or assistance in using any DAAC data, contact

EOS Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)

The original Global Snow Depth Data Set (on 0.5 by 0.5 degree grid) can be accessed from the NSIDC via this document SMMR Global Snow Depth Data (Binary data files)

or via FTP

ftp daac.gsfc.nasa.gov
login: anonymous
password: < your internet address >
cd /data/inter_disc/hydrology/smmr_snow/original

For algorithm questions related to original data, please contact
Data Producers:

Dr. Alfred Chang
Hydrological Sciences Branch, Code 974
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Internet: achang@rainfall.gsfc.nasa.gov
301-286-8997 (voice)
301-286-1758 (fax)

References
Barry, R. G. 1984. Possible carbon dioxide-induced warming effects on the cryosphere. In: Climate Changes on a Yearly to Millennial Basis. eds. N. A. Morner and W. Karlen, pp. 571 to 604. Hingham:D. Reidel.

Barry, R. G. 1985. Snow cover, sea ice, and permafrost. In Glaciers, Ice Sheets, and Sea Level: Effect of a CO2-Induced Climatic Change, pp. 241 to 247. Washington:Dept. of Energy.

Benson, C., B. Holmgren, R. Timmer, G. Weller, and S. Parrish. 1975. Observations on the seasonal snow cover and radiation climate at Prudhoe Bay, Alaska, during 1972. Ecological Investigations of the Tundra Biome in the Prudhoe Bay Region, Alaska. ed. J. Brown, University of Alaska Special Report No. 2, pp. 13-50.

Broccoli, A. 1985. Characteristics of seasonal snow cover as simulated by GFDL climate models. In Glaciological Data Report GD--18, Snow Watch '85, pp. 241-248.

Cavalieri, D. J. 1988. NASA Sea Ice and Snow Validation Program. NASA Technical Memorandum 100706.

Chandrasekhar, S. 1960. Radiative Transfer. New York:Dover.

Chang, A. T. C., P. Gloersen, T. Schmugge, T. T. Wilheit, and H. J. Zwally. 1976. Microwave emission from snow and glacier ice. J. Glaciol. 16:23.

Chang, A. T. C., J. L. l, D. K. Hall, A. Rango, and B. K. Hartline. 1982. Snow water equivalent estimation by microwave radiometry. Cold Reg. Sci. Technol. 5:259-267.

Chang, A. T. C., J. L. Foster, and D. K. Hall. 1987. Nimbus-07 SMMR derived global snow cover parameters. Ann. Glaciol. 9:39-44.

Chang, A. T. C., J. L. Foster, and D. K. Hall. 1990. Satellite estimates of Northern Hemisphere snow volume. Remote Sensing Letters, International Journal of Remote Sensing 11:1:167-172.

Chang, A. T. C., J. L. Foster, D. K. Hall, H. W. Powell, and Y.L. Chien. 1992. Nimbus-7 SMMR Derived Global Snow Depth Data Set. The Pilot Land Data System. NASA/Goddard Space Flight Center. Greenbelt, MD.

Chang, H. D. 1982. User's Guide for Scanning Multichannel Microwave Radiometer (SMMR) Instrument First-Year Antenna Temperature Data Set. Washington:SASC.

Chao, B., W. P. O'Connor, A. T. C. Chang, D. K. Hall, and J. L. Foster. 1987. Snow-load effect on the Earth's rotation and gravitational field, 1979-1985. J. Geophys. Res. 92:9415-9422.

Dickinson, R. E. 1983. Land surface processes and climate surface albedos and energy balance. Advances in Geophysics 25:305-353.

England, A. W. 1975. Thermal microwave emission from a scattering layer. J. Geophys. Res. 80(32):4484-4496.

Foster, J. L. 1989. The significance of the data of snow disappearance on the Arctic tundra as a possible indicator of climate change. Arctic and Alpine Res. 21:1:60-70.

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