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[CIDC FTP Data]
[SMMR Snow Depth IDC Data on FTP]
Data Access
SMMR Snow Depth
[rule]
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
* Parameters: snow depth
* Units: centimeters
* Typical Range: 3 to 100 (0 = land with no snow or snow less
than 2.5 cm)
* Temporal Coverage: October 1978 through August 1987
* Temporal Resolution: Monthly mean
* Spatial Coverage: Global
* Spatial Resolution: 1 degree x 1 degree
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
* File Size: 259200 bytes, 64800 data values
* Data Format: IEEE floating point notation
* Headers, trailers, and delimiters: none
* Fill Value: -999.9 (no data available or data failed quality
filters)
specific grid values used as mask
water: -99.0
permanent ice: 254
* Image orientation: North to South
Start position: (179.5W, 89.5N)
End position: (179.5E, 89.5S)
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:
* 255 -- water
* 254 -- permanent ice
* 253 -- no data available, or data failed quality filters
* 252 -- unused
* 251 -- unused
* 3 - 250 -- snow depth in centimeters
* 0 -- no snow, or snow less than 2.5 cm
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)
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