home
***
CD-ROM
|
disk
|
FTP
|
other
***
search
/
NASA Climatology Interdisciplinary Data Collection
/
NASA Climatology Interdisciplinary Data Collection - Disc 1.iso
/
readmes
/
readme.isccp_d2
< prev
next >
Wrap
Text File
|
1998-03-04
|
44KB
|
1,000 lines
[CIDC FTP Data]
[ISCCP D2 IDC Data on FTP]
Data Access
ISCCP D2 Cloud Data
Mean
Mean cloud Mean cloud Mean cloud top mean cloud cloud
fraction(%) top pressure temperature optical water
thickness
path
Clear sky surface reflectance Clear sky surface temperature
IR low cloud IR low cloud top IR low cloud top
fraction(%) pressure temperature
IR mid cloud IR mid cloud top IR mid cloud top
fraction(%) pressure temperature
IR high cloud IR high cloud top IR high cloud top
fraction(%) pressure temperature
Cumulus,liquid, Stratocumulus,liquid, Stratus,liquid,
low cloud fraction(%) low cloud fraction(%) low cloud fraction(%)
Cumulus, ice, Stratocumulus,ice , Stratus,ice,
low cloud fraction(%) low cloud fraction(%) low cloud fraction(%)
Altocumulus,liquid, Altostratus,liquid, Nimbostratus,liquid,
mid cloud fraction(%) mid cloud fraction(%) mid cloud fraction(%)
Altocumulus,ice, Altostratus,ice, Nimbostratus,ice,
mid cloud fraction(%) mid cloud fraction(%) mid cloud fraction(%)
Cirrus Cirrostratus
high cloud high cloud Deep convective
fraction(%) fraction(%) high cloud fraction(%)
TOVS TOVS
Ice/snow TOVS TOVS precipitable precipitable
cover surface near-surface water(1000- 685 water (685 -
pressure air temperature
mb) 310 mb)
[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
[rule]
Data Set Overview
Cloud cover is an extremely important climate parameter. Though
only some clouds bring precipitation, all affect the heat exchange
between the Sun, Earth and cold space, and they are also quite
variable in time, from region to region, and in the effects they
produce. Clouds modulate the solar irradiance incident on the
Earth's surface (the insolation) and this affects the productivity
of plants both on land and in the water as well as the surface
temperature and heat budget. Numerous efforts to produce cloud
climatologies from both surface and satellite observations have
been made. At present the most important of these is the ongoing
International Satellite Cloud Climatology Project (ISCCP). A
combination of satellite-measured radiances, TOVS atmospheric
temperature/humidity and ice/snow data are used by ISCCP to
produce a global dataset on cloud and surface variables. An
overview of the Project and the data products is given in Rossow
and Schiffer (1991); the algorithm and its effectiveness are
described by Rossow and Garder (1993a&b) while Rossow et al.
(1993) compare the resulting products to other cloud
climatologies. The ISCCP D-series, which is described in this
readme and more extensively in Rossow et al. (1996), is a revised
version of the C-series dataset. The ISCCP D2-series products are
gridded data averaged over each month. These data set(presently
covering the period 1986-1987 & 1989-1992) are originally produced
on an equal area map grids which has a constant 2.5 degree
latitude increments and varaiable longitude increments ranging
from 2.5 degree at the equator to 120 degree at the pole. The
Goddard DAAC has regridded a subset (36 out of the original 130
parameters) of these dataset to 1x1 degree equal angle grid for
inclusion in the Interdisciplinary data collection.
Sponsor
The production and distribution of this data set are 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 William B. Rossow, and the
Goddard Institute for Space Studies (GISS),New York, NY,
USA, for the production of this data set, and the
Distributed Active Archive Center (Code 902) at the
Goddard Space Flight Center, Greenbelt, MD, 20771, for
putting these data in their present format and
distributing them. These distribution activities were
sponsored by NASA's Earth Science enterprise.
Original Archive
The original ISCCP D2 cloud data set was produceded by the Goddard
Institute for Space Studies at New York, NY. This data set in its
original format can be obtained from NASA Langley Research Center,
Distributed Active Archive Center . This is the long term archive
for the data and also the source for detailed information
concerning the ISCCP D series and other data products.
Note: The format of the data in the GISS and LaRC archives is
different than that stored at the Goddard DAAC. The Goddard DAAC
regridded the original equal area grid to a 1 by 1degree (lat/lon)
equal angle grid. For more details see Processing Sequence and
Algorithms.
Future Updates
Additional years of the ISCCP D-2 data are being processed.
The Data
The monthly mean data is presented on 1x1 degree
latitude-longitude world grid that starts at (89.5N, 179.5W) and
runs eastward and southward to latitude 89.5 S.
The original ISCCP D1 (daily) and D2 (monthly mean) products were
calculated on an approximately equal area world grid (280x280
km^2) which is equivalent to a 2.5x2.5 degree latitude-longitude
grid at the equator. The latitude band widths were fixed to 2.5
degree and grid size along the longitude was varied to provide an
integer number of cells in a latitude zone and grid cell area as
close to an equatorial grid cell as possible. Map grids started
from south pole to north pole. In each latitude zone, all
longitudes were indexed in order from the Greenwich meridian
eastward(0 - 360 degree) before going to the next latitude zone.
Here we have interpolated a subset (36 out of the original 130
parameters) of the equal area monthly mean products to a 1x1
degree grid for easy comparison to the other Interdisciplinary
Data Collection products.
Characteristics
* Parameters:
Parameter Description Range Units
cldfrc mean cloud fraction 0.5 - percent
100
cldprs mean cloud top pressure 35 - millibars
985
cldtmp mean cloud top temperature 180 - Kelvin
320
cldtau mean cloud optical 0.09 - dimensionless
thickness 50
cldwpt mean cloud water path 0 - g/m^2
400
srfref mean clear sky surface 0.0 - fraction
reflectance 1.1
srftmp mean clear sky surface 199 - Kelvin
temperature 325
irlfrc infrared cloud fraction, 0 - 93 percent
for low-level clouds
infrared cloud top
irlprs pressure, for low-level 550 - millibars
clouds 1000
infrared cloud top
irltmp temperature, for low-level 225 - Kelvin
clouds 315
irmfrc infrared cloud fraction, 0 - 82 percent
for mid-level clouds
infrared cloud top
irmprs pressure, for mid-level 435 - millibars
clouds 680
infrared cloud top
irmtmp temperature, for mid-level 211 - Kelvin
clouds 287
irhfrc infrared cloud fraction, 0 - 84 percent
for high-level clouds
mean infrared cloud top
irhprs pressure, for high-level 20 - millibars
clouds 495
infrared cloud top
irhtmp temperature, for 180 - Kelvin
high-level clouds 264
clfrc cumulus (liquid) low-level 0 - 80 percent
cloud fraction
stratocumulus
sclfrc (liquid)low-level cloud 0 - 88 percent
fraction
slfrc stratus (liquid)low-level 0 - 50 percent
cloud fraction
cifrc cumulus (ice)low-level 0 - 72 percent
cloud fraction
stratocumulus
scifrc (ice)low-level cloud 0 - 91 percent
fraction
sifrc stratus (ice) cloud 0 - 50 percent
low-level fraction
altocumulus
aclfrc (liquid)mid-level cloud 0 - 38 percent
fraction
altostratus
aslfrc (liquid)mid-level cloud 0 - 62 percent
fraction
nimbostratus
nslfrc (liquid)mid-level cloud 0 - 75 percent
fraction
acifrc altocumulus (ice)mid-level 0 - 95 percent
cloud fraction
asifrc altostratus (ice)mid-level 0 - percent
cloud fraction 100
mean nimbostratus
nsifrc (ice)mid-level cloud 0 - 50 percent
fraction
crfrc mean cirrus high-level 0 - percent
cloud fraction 100
crsfrc mean cirrostratus 0 - 77 percent
high-level cloud fraction
dcfrc mean deep convective 0 - 62 percent
high-level cloud fraction
iscvr mean ice/snow cover 0 - percent
100
tvprs TOVS mean surface pressure 475 - millibars
1000
tvtmp TOVS mean near-surface air 216 - kelvin
temperature 322
tvprw1 TOVS mean precipitable 0 - 5 cm
water for 1000 - 680 mb
tvprw6 TOVS mean precipitable 0.03 - cm
water for 680 - 310 mb 2.0
* Temporal Coverage: 1/86-1/87;1/89-12/93 are available in
1988)
* Temporal Resolution: Gridded monthly means
* Spatial Coverage: Global
* Spatial Resolution: 1 degree x 1 degree equal angle grid
(re-gridded from equal area grid)
Source
ISCCP was established in 1982 as part of the World Climate
Research Programme (WCRP) to collect and analyze satellite
radiance measurements to infer the global distribution of clouds,
their properties, and their diurnal, seasonal, and interannual
variations. Data collection began on 1 July 1983 and is currently
planned to continue through 30 June 2000.
The first phase of the project produced the gridded, 3-hourly
(stage C1) and monthly (stage C2) datasets (Rossow, and Schiffer
1991, Rossow et al. 1989). A subset of the monthly mean C2 data,
consisting of six parameters and covering the period of July 1983
to June 1991, is available as part of the Interdescipline Dataset
Collection. In the second phase of the project new versions of
products (D-series) have been produced, with the addition of a 30
km research product. The processing of the D-series data is
on-going.
There are four principle product levels:
* Reduced Resolution Radiance Data(B3)
These data are a reduced resolution version (in both time and
space) of the original visible and infrared images from all
of the operational weather satellites. The data consist of
radiances (4-7 km pixels) spaced at 30 km interval, every 3
hours, from the individual satellites. The calibration and
navigation data are also appended (Rossow et al., 1987). When
the D algorithms were introduced, a new (BT) calibration
table product was also added.
* Pixel Level Cloud Product(DX)
These products consist of calibrated radiances and viewing
geometry, cloud detection results, cloud and surface
properties from radiative analysis for individual satellite
at a 30 km resolution.
* Gridded Cloud Product(D1)
These are spatial averages of the 3-hourly DX quantities and
statistical summaries on a global equal area grid (
approximate 280 km by 280 km cell size). These products are
obtained by merging the results from all satellites. The
atmosphere and surface properties from TOVS are appended as
well.
* Climatological Summary Product(D2)
The D2 products are monthly averages of D1 quantities and
statistics including the data sets corresponding to eight
3-hourly monthly means( periods centered on, 0, 3, 6, 9, 12,
15, 18, and 21 hours UTC) for each product at equal area
grid. There are a total of 130 parameters for each map grid
cells in the original D2 data set. We have chosen 6 of these
and only the monthly means (average of the eight 3-hour
monthly mean sets) to include in the Interdisciplinary Data
Collection.
The resulting datasets and analysis products are being used to
improve understanding and modeling of the role of clouds in
climate, with the primary focus being the elucidation of the
effects of clouds on the radiation balance. These data can also
used to support many other cloud studies, including understanding
of the hydrological cycle.
Data are collected from the suite of weather satellites operated
by several nations and processed by several groups in government
agencies, laboratories, and universities. For each operational
satellite, a Satellite Processing Center (SPC) collects the raw
satellite data and sends it to the Global Processing Center (GPC).
The Correlative Data Center coordinates the delivery of other
satellite and conventional weather data to the GPC. The Satellite
Calibration Center (SCC) normalizes the calibration of the
geostationary satellites with respect to a polar orbiter satellite
standard. All ISCCP data products are archived at the NASA Langley
Research Center, Distributed Active Archive Center.
The satellites involved in the D-version products are listed in
the table below:
Temporal and Regional Coverage
Satellite Sensor start end Longitudes
NOAA-7 AVHRR 07/83 01/85 global
NOAA-8 AVHRR 10/83 06/84 global
NOAA-9 AVHRR 02/85 10/88 global
NOAA-10 AVHRR 12/86 08/91 global
NOAA-11 AVHRR 11/88 09/94 global
NOAA-12 AVHRR 09/91 ----- global
NOAA-14 AVHRR 02/95 ----- global
GOES-5 VISSR 07/83 07/84 112W-50W
GOES-6 VISSR 07/83 01/89 135W-98W
04/87 04/92 135W-98W
GOES-7 VISSR
05-92 ----- 112W-50W
GOES-8 I-M Imager 03/95 ---- 112W-50W
GOES-9 I-M Imager ----- ---- 135W-98W
METEOSAT-2 MIR 07/83 07/88 60W-60E
08/88 06/89 60W-60E
METEOSAT-3 MIR 02/90 04/90 60W-60E
05/92 04/95 112W-50W
07/89 01/90 60W-60E
METEOSAT-4 MIR
05/90 01/94 60W-60E
METEOSAT-5 MIR 06/95 ----- 60W-60E
GMS-1 VISSR 02/84 05/84 140E
07/83 01/84 140E
GMS-2 VISSR
07/84 09/84 140E
GMS-3 VISSR 10/84 11/89 160W-80E
GMS-4 VISSR 12/89 08/91 160W-80E
GMS-5 VISSR 09/91 ---- 160W-80E
INSAT-1 VHRR 04/88 03/89 74.5E
Notes: NOAA-7, 9, 11 & 14 were/are afternoon polar orbiting
satellites, while NOAA 8, 10 12 were/are morning polar orbiting
satellites. NOAA-14 was launched as a afternoon satellite, because
NOAA-13 launched failed. METEOSAT-3 was re-positioned, in May
1992, to a GOES-East position.
The Files
Compressed:
The data files have been compressed using Lempel-Ziv coding. Files
with a .gz ending are compressed versions of the .bin file. When
decompressing the files use the -N option so that the original
.bin file name ending is restored. For additional information on
decompression see aareadme file in the directory:
software/decompression/
Uncompressed:
The ISCCP subset presented here consists of 3132 datafiles (=87
monthly mean data filess per parameter x 36 parameters). Though
size of single data file is only .259 MB but if whole 4 years
worth of data is downloaded it would require ~476 MB of disk
storage.
Format
* File Size: 259200 bytes, 64800 data values
* Data Format: IEEE floating point notation
* Headers, trailers, and delimiters: none
* Fill value: -999.99
* Continent mask: none (data valid over land and water)
* 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 monthly files is
isccp_d2.pppppp.1nmegg.[yymm].ddd
where
isccp_d2 = data product designator
pppppp = parameter (cldfrc,cldtmp,cldprs,cldtau,...)
1 = number of levels(=1)
n = pressure levels for vertical coordinate, (n=not
applicable)
m = temporal period, (m = monthly)
e = horizontal grid resolution, (e = 1 x 1 degree)
gg = spatial coverage, gg = global (land and ocean)
yy = year
mm = month
ddd = file type designation, (gz=compressed, bin=binary,
ctl=GrADS control file)
NOTE: When decompressing the data files be sure to use the -N
option. This will restore the original .bin filename. For
additional information on decompression see the format section of
this readme and the aareadme file in the directory:
software/decompression/
Directory Path:
/data/inter_disc/radiation_clouds/isccp_d2/pppppp/yyyy/
where pppppp is the parameter and 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
The ISCCP cloud analysis has two main components, cloud detection
and radiative analysis, respectively. The cloud detection
component has four major steps, the first two were part of the
C-series cloud product (Rossow and Garder 1993a), and the last two
are new to the D-series. The first three steps, of the cloud
detection component, produce the refined clear-sky radiances,
while the fourth step is a final threshold test using the refined
clear-sky radiances. A brief synopsis of the four steps are as
follows:
1. The first estimate of the clear sky radiance values is
derived by performing a series of test (Rossow et al. 1989)
using space-time variations of the IR and VIS radiances. The
VIS radiance are normalized and are expressed as fractional
values of the sensor measurement when looking directly at the
Sun. These unitless radiances go to zero as the solar zenith
angle approaches 90 degrees; reflectance, on the other hand
normally increase.
2. The first threshold test is performed by determining which
radiance measurements deviate from the first clear sky values
by an amount greater than the uncertainty in the estimated
clear radiances.
3. Additional test are performed to remove some infrequent
errors in the clear sky radiances that occur under certain
circumstances. These test are performed based on the results
of the first two test. Estimates of the daytime clear solar
reflectance for the polar orbiting satellites NIR channel,
are also acquired.
4. The final step involves repeating a threshold test using the
final refined clear sky radiances, produced from the first
three step, with three changes:
o the IR thresholds for land surfaces are reduced by 2.0
K,
o the VIS threshold test is changed to a test of
reflectance values instead of radiance values, and
o NIR threshold test is performed for polar orbiting data
over ice and snow-covered surfaces only
The first threshold test is done only on the IR and VIS, comparing
them to the clear sky radiance derived in the first step. Any
observed radiance which varies from the corresponding clear sky
radiance by more than the threshold values is regarded as cloudy
All remaining values are called clear. The threshold values vary
(see table below) depending on surface type.
First Cloud Threshold Values (Rossow et al., 1996)
SURFACE TYPES
RADIANCE
1 2 3 4
IR (K) 2.5 4.0 6.0 8.0
VIS (fraction of Sun looking
measurement) 0.03 0.03 0.06 0.06
IR SURFACE TYPES:
Type 1 = "low variability" water - all open water except Type
2,
Type 2 = "high variability" water - water within 75 km of a
coastline, water within 50 km of sea ice, or sea ice-covered
water,
Type 3 = "low variability" land - all open land including land
within 50 km of a coastline or snow-covered land except Type
4,
Type 4 = "high variability" land - high topography pixels
(height > 1750 m), all pixels within 300 km regions that are
rough topography (standard deviation of heights > 1000 m) or
that are high topography (mean height > 2500 m), or permanently
ice-covered locations (Iceland, Greenland and Anarctica).
VIS SURFACE TYPES:
The VIS types are basically divided into two groups. The first
group consists of all open water including near-coast and sea
ice-covered water. This group uses the threshold value 0.03.
The second group consist of all land type include snow and ice
covered land. This group uses the threshold value 0.06.
In the fourth step the threshold test is repeated on the final
clear sky radiance using the threshold values, for IR, VIS, NIR
and TNIR (NIR brightness temperature) listed below.
Final Cloud Detection Threshold Interval Values
(Rossow et al., 1996)
SURFACE TYPES
WAVELENGTH
1 2 3 4
IR (K) 2.5 3.5 4.0 6.0
VIS Reflectance 0.030 0.030 0.060 0.090
VIS Radiance Limit 0.025 0.025 0.040 0.040
TNIR (K) 8.0 8.0 8.0 8.0
NIR Reflectance 0.045 0.045 0.055 0.055
Surface types for the IR test:
Type 1 = open water,
Type 2 = near-coastal water, sea ice margin and sea ice,
Type 3 = open land, and
Type 4 = near-coastal land, high topography, snow margin, and snow
and ice-covered land.
Surface types for the VIS & NIR test:
The same four surface types are used for the VIS and NIR tests,
except the sea ice margin and sea ice are changed to Type 3.
In the radiation analysis component of the ISCCP cloud analysis,
surface properties are retrieved from the final clear sky
radiance, and used with the atmospheric data to do further
analyses of individual pixel radiances. From this analysis surface
properties and cloud properties are deduced for each individual
pixel, based on whether the threshold tests indicate clear or
cloudy conditions.
Information on cloud types are derived based on ranges of values
(see table below) from the Cloud Top Pressure (cldprs) and Cloud
Optical Thickness (cldtau).
Cloud Types
(Rossow et al., 1996)
NAME CLDPRS RANGE CLDTAU TYPE
(mb) RANGE
LOW
Cumulus > 680 <= 3.55 liquid, ice
Stratocumulus > 680 3.55 - liquid, ice
22.63
Stratus > 680 > 22.63 liquid, ice
MIDDLE
Altocumulus 440 - 680 <= 3.55 liquid, ice
Altostratus 440 - 680 3.55 - liquid, ice
22.63
Nimbostratus 440 - 680 > 22.64 liquid, ice
HIGH
Cirrus <= 440 <= 3.55 ice
Cirrostratus <= 440 3.55 - ice
22.63
Deep Convection <= 440 > 22.63 ice
The ISCCP products include the infrared estimates for both day and
night observations but the combined VIS/IR/NIR products, cloud
optical thickness estimate and surface reflectance are available
only during the day. The combined VIS/IR/NIR products are more
accurate. Therefore a correction is made to the infrared only
nighttime cloud products when the mean diurnal total cloud
fraction is calculated. The correction is determined from a
comparison of daytime VIS/IR/NIR and IR only cloud fractions.
Processing Sequence and Algorithms
The ISCCP project collects visible (~0.6 micrometers), near
infrared (~3.7 micrometers) and thermal infrared (~11 micrometers)
data from several geostationary weather satellites and from the
National Oceanic and Atmospheric Administration (NOAA) operational
meteorological satellites which are in Sun-synchronous near polar
orbits. A subset of the monthly mean (D2) products was
incorporated into the Interdiscipline Data Collection. A hierarchy
of satellite data, which indicated a preference for data from one
satellite over another, was used if data were available from more
than one satellite for one location during a single time step.
Geostationary satellite data were given higher preference over the
NOAA polar orbiting data, for latitudes of 55 degrees and below.
Data with the smallest satellite zenith angle are preferred in
cases where geostationary data overlaps. NOAA polar orbiting data
are used above 55 degrees latitude, due to the high satellite
zenith angle of geostationary data above that latitude. The
geostationary satellites involved are the GOES 5-9, METEOSAT 2-5
and the GMS 1-5, and INSAT-1. Due to numerous problems 3-hourly
coverage from 55 N to 55 S is not always available in some
regions. This is particularly true around India. The NOAA 7-14
satellites, which observe all regions on the Earth at least twice
a day, were used to fill data gaps in these regions.
Atmospheric effects on the satellite radiances were taken into
account, in the ISCCP D series processing, by using atmospheric
temperature and humidity profile data and ozone column abundance
data in the radiative model. The TIROS Operational Vertical
Sounder (TOVS) atmospheric datasets and the NOAA GFDL
temperature/humidity and NIMBUS 4 BUV ozone climatology datasets
were used for this purpose. In our interdisciplinary data
collection, we have included four parameters of the TOVS datasets
that are used in the ISCCP algorithm and are part of the ISCCP-D
dataset.
A merged ice/snow cover dataset was develop by ISCCP to help
differentiate between clear and cloudy scenes in high latitude and
higher elevation regions. The input datasets used in this merged
ice/snow cover product are the U.S. Navy weekly sea ice analyses
(from paper maps) through 1991, sea ice derived from daily
analysis of the SSM/I microwave measurements after 1991, and
NOAA's Synoptic Analysis Branch northern hemisphere weekly snow
and ice cover charts. Three other datasets along with the ice/snow
cover were used to specify different surface types as a function
of latitude/longitude:
1. Masaki (1972) land/water/coast classification
2. U.S. Navy topographic height dataset from NCAR
3. Matthews (1983) land vegetation types
The current version of the ISCCP C2-series was released 1991.
Since then the C-series data has gone through an extensive review
and a number of improvements have been recommended. The D-series
data was developed as a result of this process. A synopses of the
changes between the C-series and D-series datasets is listed
below.
Highlights of differences between the C-series and
D-series cloud data
(Rossow et al., 1996)
Revised VIS and IR calibrations to
eliminate spurious changes between
different reference polar orbits.
(Brest et al. 1996)
Radiance Calibrations
Revised normalizations of
geostationary satellite
calibrations to eliminate
occasional short-term deviations.
(Brest et al. 1996)
Improved cirrus detection over
land by lowering IR threshold from
6 K to 4 K
Improved polar cloud detection
over ice and snow surfaces by
Cloud Detection lowering VIS threshold from 0.12
to 0.06 and by using threshold
test on 3.7 micrometer radiances
Improved detection of low clouds
at high latitudes by changing to
VIS reflectance threshold test
Improved treatment of cold (top
temperature < 260 K) clouds by
using ice polycrystal scattering
phase function to retrieve optical
thickness and top temperature
Improved retrieval of cloud
optical thickness over ice and
snow surfaces using 3.7 micrometer
Radiation Model radiances
Improved retrieval of cloud top
temperatures by including effects
of IR scattering
Improved retrieval of surface and
cloud top temperatures by adopting
new treatment of water vapor
continuum absorption in IR
Better resolved variations of
optically thicker clouds by adding
6th optical thickness category
Added correct cloud water path
parameter
Reported actual average values of
cloud top temperature, pressure,
optical thickness and water path
for each of nine cloud types
defined by cloud top pressure and
Gridded Product optical thickness in the 3-hourly
Contents dataset
Reported separate cloud properties
for liquid and ice forms of low
and middle-level clouds
Provided conversion of cloud top
pressures to cloud top heights
above mean sea level based on
atmospheric temperature profile
added cloud amount frequency
distribution to monthly dataset
Archived pixel-level cloud
Increase Resolution products with resolution of 30 km
and 3 hr
The ISCCP D2 data sets described in this readme have been
reformatted for a subset of parameters, so that they are
consistent with other CIDC data sets.
Rergridding from an equal area world grid to 1 x 1 degree equal
angle grid
For consistency with the other data sets in the Goddard DAAC's
Interdisciplinary Data Collection, the original data in HDF format
was extracted from the long term data archive at Langley Research
Center (LaRC) DAAC and converted from 8-bit quantities (scaled
integer values) into 32-bit floating point quantities (unscaled
values) and regridded to 1 x 1 degree equal angle grid from their
original approximate 280 km x 280 km equal area grid maps.
In the regridding process the original data at equal area grid
with constant 2.5 degree latitudinal increments and variable
longitudinal increments, ranging from 2.5 degree at the equator to
120 degree at the pole were replicated as many time as needed to
produce a 1 by 1 degree latitude-longitude equal angle product. A
weighted average was used in cases where two equal area grid cells
overlapped a single one degree grid cell. This weighting was based
on the percent area each original equal area grid cells covered in
the 1 degree area. Changes in grid area due to changing latitude
were taken into consideration in this procedure. This regridding
method is different from the one used by ISCCP to convert their
data from approximate 280km xx 280 km equal area to 2.5 by 2.5
degree equal angle. Their method did not use a weighted average in
cases where grid cells overlapped, but instead chose one of the
grid cell values over another. As a result the values from the
statistical files in the original dataset can not be related to
all of the grid values in this regridded dataset.
Also, the south to north orientation was reversed, and for each
latitude zone, data along the longitude was made to start from 180
west going towards east,again for conformity to the 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
The monthly mean summary of the data set given here can be used
for many types of climate studies including:
* Study of global, seasonal, and interannual cloud variability
(Rossow et al., 1993)
* Correlation with other climate parameters (Kyle et al., 1995)
* Investigation of global energy transport (Sohn and Smith,
1992).
Validation of Data
The ISCCP D-series product was produced in part as a result of
intensive research done on the C-series data, in which over 200
research articles have been written. Similar research is
continuing with the D-series product and includes comparison of
observations from an on-going series of field experiments.
Rossow et al. (1996) discuss the differences between the ISCCP C
series and D series algorithms, as well as on-going validation
efforts being made on the D-series product. The ISCCP project has
documented known and fixed data errors in their dataset.
Contacts
Points of Contact
For information about or assistance in using any DAAC data,
contact
EOS Goddard Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
e-mail: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
For questions about ISCCP science, contact
Dr. William B. Rossow
NASA Goddard Institute for Space Studies
2880 Broadway
New York, NY 10025 USA
e-mail: clwbr@giss.nasa.gov
(212) 678-5567
The long term archives for the ISCCP data products are at:
(Not including the Stage DX 30 km resolution data)
ISCCP Central Archives
National Climatic Data Center
Federal Building 151 Patton Ave.
Asheville, NC 28801-5001
email: satorder@ncdc.noaa.gov
(704) 271-4800 (option #5) (voice)
(704) 271-4876
(Including the Stage DX 30 km resolution data)
Langley DAAC
Mail Stop 157B
NASA Langley Research Center
Hampton, VA 23681-0001
e-mail: userserv@eosdis.larc.nasa.gov
telnet eosdis.larc.nasa.gov
(804) 864-8656 (voice)
(804) 864-9807 (fax)
References
Brest, C.L., and W.B. Rossow, 1992. Radiometric calibration and
monitoring of NOAA AVHRR data for ISCCP. Int. J. Remote Sens.,
13:235-273.
Brest, C.L., W.B. Rossow, and M.D. Roiter, 1996. Update on ISCCP
calibration for visible and infrared radiances. J. Atmos. Ocean.
Tech., (submitted).
Kyle, H. L., M. Weiss, and P. Ardanuy, 1995. Cloud, surface
temperature, and outgoing longwave radiation for the period from
1979 to 1990, J. Climate, 8:2644-2658.
Masaki, G.T., 1972 (rev., 1976). The Wolf Plotting and Contouring
Package. GSFC Computer Program Lib. #A00227, Computer Sciences
Corporation, Goddard Space Flight Center, Greenbelt, MD, 187 pp.
Matthews, E., 1983. Global vegetation and land use: New
high-resolution data bases for climate studies. J. Clim. Appl.
Meteor., 26:170-202.
Rossow, W. B., and R. A. Schiffer, 1991. ISCCP cloud data
products, Bull. Amer. Meteor. Soc., 72:2-20.
Rossow, W. B., and L. C. Garder, 1993a. Cloud detection using
satellite measurements of infrared and visible radiances for
ISCCP, J. Climate, 6: 2341-2369.
Rossow, W. B., and L. C. Garder, 1993b. Validation of ISCCP cloud
detection, J. Climate, 6: 2370-2393.
Rossow, W. B., and Y.-C Zhang, 1995. Calculation of surface and
top of atmosphere radiative fluxes from physical quantities based
on ISCCP data sets: 2. Validation and first results, J. Geophys.
Res., 100:1167-1197.
Rossow, W. B., E. Kinsella, A. Wolf, and L. Garder, 1987.
International satellite Cloud Climatology Project (ISCCP)
Description of Reduced Resolution Radiance Data. In, WMO/TD No.
58, (eds), World Meteorological Organization, Geneva, 143 pp.
Rossow, W.B., L.C. Garder, and A.A. Lacis, 1989. Global seasonal
cloud variations from satellite radiance measurements. Part I:
Sensitivity of Analysis. J. Climate, 2:419-458.
Rossow, W. B., A. W. Walker, and L. C. Garder, 1993. Comparison of
ISCCP and other cloud amounts, J. Climate, 6:2394-2418.
Rossow, W. B., A. W. Walker, D. E. Beuschel, and M. D. Roiter,
1996. International Satellite Cloud Climatology Project (ISCCP):
documentation of new cloud datasets, 115 pages, available on
internet at : http://isccp.giss.nasa.gov/documents.html
Sohn, B. J., and E. A. Smith, 1992. Global energy transports and
the influence of cloud on transport requirements: A satellite
analyses, J. Climate, 5:717-734.
------------------------------------------------------------------------
[NASA] [GSFC] [Goddard DAAC] [cidc site]
NASA Goddard GDAAC CIDC
Last update:Mon Aug 18 17:10:29 EDT 1997
Page Author: Page Author: James McManus -- mcmanus@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov