ISCCP indexed Documentation

















1.1 Parameter/Measurement.

There are 132 quantities reported for the ISCCP-C1 product for each 280 X 280 km map cell. These quantities are arranged into two groups: the first 74 values are integers representing various counts, while the last 58 values are real numbers representing physical quantities. These quantities are listed in Item 14 at the end of this catalog. There are 72 quantities reported for the ISCCP-C2 product for each 280 X 280 km map cell, consisting of averages of the hour-monthly mean values obtained separately for each of the 00, 03, 06, 09, 12, 15, 18, and 21 Greenwich Mean Time (GMT) time periods. In addition to mean cloud properties, the frequency of occurrence and average properties of ten cloud types are reported. These quantities are listed in Item 14 at the end of this catalog.

Count values on the tape for ISCCP-C1 and ISCCP-C2 range from 0 to 255. Values are coded with counts from 1 to 253, with count 0 reserved for underflow and count 254 reserved for overflow. Overflow occurs at lower values for pressures and cloud thicknesses. The count 255 is reserved to signify NO DATA. If a count value <1 or >253 is converted to a physical value, using the current version of the conversion tables described, the table returns a value of -100.0 for a count of 0, -200.0 for a count of 254, and -1000.0 for a count of 255. The physical quantities for these data are IR radiance, temperature, pressure, VIS radiance, reflectance, optical thickness, precipitable water amount, and ozone column abundance.

1.2 Unit of Measurement.

The units defined for the physical quantities in the C1 and C2 data are:
Parameter Units
------------------------ ------------------------
Infrared (IR) Radiance Degrees Kelvin
Temperature Degrees Kelvin
Precipitable Water Centimeters
Pressure Millibars
Visible (VIS) Radiance Unitless
Ozone Column Abundance Dobson Units
Reflectance Unitless
Cloud Optical Thickness Unitless

NOTE: VIS radiances and actual reflectances are both coded using the same table. However, the radiances are not reflectances. Their value represents the measured intensity as a fraction of the effective solar constant of the radiometer. This quantity divided by the cosine of the solar zenith angle is equal to a reflectance. Both brightness temperatures (representing IR radiances) and physical temperatures are also coded using the same table.

The count value to physical relationship is not always linear. Since the radiometers measure radiances (generally with a linear response), the sensitivity and accuracy of the physical quantities derived from these radiances may not be the same over their whole range. For example, warm temperatures are measured more precisely by most radiometers than cold temperatures. It is misleading to present the data with the same "apparent" precision over the whole range. Hence, the temperature and optical thickness conversion tables are non-linear in a way that approximates the linear response of the AVHRR instrument: a difference of a single count represents a larger temperature difference for cold temperatures than for warm temperatures or a larger optical thickness difference for larger values than for smaller values. This means that a linear average of the count values representing temperature or optical thickness produces an energy-weighted result. If a different weighting is desired, then the conversion tables should be applied before averaging. All other conversion tables are linear.

1.3 Data Source.

The primary data sets used to infer the cloud properties are reduced resolution narrowband radiance (0.6 and 11 um) measurements made by the imaging radiometers on operational weather satellites (Schiffer and Rossow, 1985). These data, called Stage B3 data, have a nominal spatial resolution of 30 km and temporal resolution of 3 hours produced by sampling the full resolution imaging data. Global coverage is provided by up to five geostationary satellites (METEOSAT, INSAT, GMS, GOES-East and GOES-West) and up to two polar orbiting NOAA satellite (see Rossow et al., 1985, revised August 1987, for complete details). In the analysis of these data, two correlative data sets are utilized: data from the TIROS Operational Vertical Sounder (TOVS) operation system on the NOAA polar orbiting satellites and data from NOAA and US NAVY operational analyses of several satellite and surface measurements. The former provides daily, global atmospheric temperature and humidity profiles, plus ozone column abundances, while the latter provides weekly snow and sea ice coverage.

1.4 Data Set Identification.

The cloud analysis products of the International Satellite Cloud Climatology Project (ISCCP), called Stage C1 and C2 data, are constructed from the original B3 radiances, the results of the three parts of a cloud algorithm and the correlative data used in the analysis. Stage C1 data represent the global, merged results reported every 3 hours with a spatial resolution of 280 km; Stage C2 data are the monthly averages and other summary statistics of the Stage C1 quantities.


2.1 Spatial Coverage.

Global coverage is provided by the set of satellites employed in ISCCP. The approximate coverage of each sensor is listed below in degrees of longitude and latitude:

/tr> /tr>
Satellite Sensor Longitude range Latitude range  
------------ ------ --------------------- --------------------  
NOAA-7,8,9,10, AVHRR Global Global (1)
GOES-5(E) VISSR 15 W to 135 W 60 N to 60 S  
GOES-6(W) VISSR 75 W to 165 E 60 N to 60 S (2)
GOES-7(E) VISSR 15 W to 135 W 60 N to 60 S (3)
METEOSAT-2,3,4,5 MIR 60 W to 60 E 60 N to 60 S (4)
GMS-1 VISSR 160 W to 80 E 60 N to 30 S  
GMS-2,3,4 VISSR 160 W to 80 E 60 N to 60 S  
INSAT-1A,1B,1C,1D VISSR 20 E to 120 E 60 N to 60 S  
------------ ------ --------------------- -------------------- 

(1)  Global coverage over a period of approximately 24 hours.
(2)  For better coverage of seasonal weather events, GOES-6 was moved to 98 W on August 30, 1984, to 108 W on November 22, 1984, back to 98 W on July 28, 1986,and to 135 W from April 1987 until its failure on 1/21/89.
(3)  For better coverage of seasonal weather events, GOES-7 is positioned between 98W and 108W, with a position of 98W during the tropical convective season. This regular change in positions will occur until GOES-I becomes operational, approximately late 1993.
(4)  METEOSAT-3 was positioned to 50 deg W. in early August 1991 to supplement the coverage of GOES-7. This move was necessary in order to provide coverage of Atlantic weather events during the absence of a GOES East Satellite.

2.2 Spatial Resolution.

The spatial resolution for the C1 and C2 data set is 280 km x 280 km on an equal area grid (an equal angle grid of 2.5 deg x 2.5 deg is also provided). For the spatial resolutions of the sensors before and after processing, see the specific descriptions for each sensor in Item 4.3 of this catalog entry.


3.1 Temporal coverage.

The ISCCP data are planned to cover the twelve year period from July 1983 to 1995. The overall coverage of each of the satellites and sensors participating in the ISCCP are listed below:

Satellite Sensor ISCCP-C1 Start Date* ISCCP-C1 End Date*
--------- ------ -------------------- ------------------
NOAA-7 AVHRR 07/01/83 01/31/85
NOAA-8 AVHRR 10/01/83 06/24/84
NOAA-9 AVHRR 02/01/85 11/08/88
NOAA-10 AVHRR 11/17/86 08/30/91
NOAA-11 AVHRR 10/18/88 ongoing
NOAA-12 AVHRR 09/01/91 ongoing
GOES-5 VISSR 07/01/83 07/30/84
GOES-6 VISSR 07/01/83 01/21/89
GOES-7 VISSR 04/26/87 ongoing
METEOSAT-2 MIR 07/01/83 08/11/88
METEOSAT-3 MIR 08/11/88 01/25/91
METEOSAT-4 MIR 06/19/89 ongoing
METEOSAT-5 MIR ---- (pending) ----
GMS-1 VISSR 01/21/84 06/30/84
GMS-2 VISSR 07/01/83 09/27/84
GMS-3 VISSR 09/27/84 12/04/89
GMS-4 VISSR 12/04/89 ongoing
INSAT-1A VISSR 04/82 09/82
INSAT-1B VISSR 04/01/84 ongoing
INSAT-1C VISSR ---- problems
INSAT-1D VISSR 06/01/90 (proposed) ----
--------- ------ ---------- --------

* Note: Some gaps exist between start and end dates.
** NOAA-D was launched on May 14, 1991. NOAA-D became NOAA-12 and replaced NOAA-10 as the morning satellite.

3.2 Temporal Resolution.

Stage C1 data represent the global, merged results reported every 3 hours. Values retrieved on a daily or weekly basis appear repeatedly at 3 hourly intervals. Stage C2 data contain monthly summaries of the Stage C1 data. Some of the variables depend on the availability of the visible channels and are therefore not available at all time periods.


4.1 Mission Objectives.

ISCCP focuses on the study of the distribution and variation of cloud radiative properties. Scientific objectives are as follows:

  1. To collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations.
  2. To improve the understanding and modeling of the effects of clouds on climate.
  3. To elucidate the role of clouds in the radiation balance (top of the atmosphere and surface) and also improve the understanding of the long-term global hydrologic cycle.

4.2 Key Satellite Flight Parameters.

The NOAA spacecraft are a series of satellites in 870 km (nominal) circular, near-polar, sun-synchronous orbits with an inclination angle of approximately 99 degrees (retrograde) to the equator. They cross the equator during local morning and afternoon (and corresponding night times), with an orbital period of approximately 102 minutes. Each sequential orbit covers adjacent longitudes near the equator and overlapping longitudes near the poles. The Advanced Very High Resolution Radiometer (AVHRR) on-board this series is composed of up to five spectral channels with a nadir resolution of 1.1 km. The AVHRR 1.1 km data is not available globally. The AVHRR Global Area Coverage (GAC) data, with a resolution of 4.4 km, does provide global coverage. In addition, temperature sounding and ozone observations are made by the TIROS (Television and Infrared Operational Satellite) Operational Vertical Sounder (TOVS) and are used in the ISCCP analysis of B3 data.

The Geostationary Operational Environmental Satellite (GOES) series consist of spin-stabilized spacecraft in geostationary circular orbit located over 75 degrees west longitude for GOES-East (5 & 7), and 135 degrees west longitude for GOES-West (6). The GOES-6 satellites was routinely moved to provide better coverage of seasonal weather events until its failure on 1/21/89. The GOES-7 satellite is now utilized in this manner. Data are collected in the visible and infrared bands by the Visible Infrared Spin-Scan Radiometer (VISSR). The visible channel detector consists of eight identical photo-multiplier tubes that scan the Earth in parallel, producing a visible channel resolution of 0.9 km. The IR detector produces a resolution of 7 km.

METEOSAT is in geostationary circular orbit over the equator centered at the Greenwich Meridian (0 degrees E longitude), with the exception of METEOSAT-3. METEOSAT-3 is now centered at 50 degrees west and is functioning as a replacement for GOES-7 which was moved westward as in response to the failure of GOES-6. The Multispectral Imaging Radiometer (MIR) on METEOSAT-2, -3, -4, and -5 collects data over the Earth in three spectral regions, one in the visible and two in the infrared. The satellite scans the Earth from east to west and, if the water vapor channel (6.7 um) is turned off, is capable of producing a visible resolution of 2.5 km. The infrared resolution is 5.0 km for both IR channels.

The Geostationary Meteorological Satellites (GMS) are a series of satellites placed in geostationary circular orbit over the equator centered at 140 degrees east longitude. The VISSR onboard the GMS satellite collects data with four identical detectors operating in parallel, producing a visible resolution of 1.25 km and an infrared resolution of 5.0 km.

The INSAT satellites are a series of spacecraft placed in geostationary circular orbit over the equator centered at 74 degrees E longitude. This series carries a VISSR similar to those on the GMS and GOES satellites, yielding a visible resolution of 1.25 km and an infrared resolution of 11 km.

4.3 Principles of Operation.

The AVHRR is a four or five channel scanning radiometer that operates in the visible, near-infrared, and far-infrared regions. The fifth channel was added on the AVHRR/2 instrument flown on NOAA-7, -9, -11 and -12. Scanning is provided by an elliptical beryllium mirror rotating at 360 rpm about an axis parallel to the Earth. A two-stage radiant cooler is designed to provide a basic temperature of 95 degrees K for the IR detectors. The telescope is an 8-inch afocal, all-reflective system, with polarization of less than 10 percent. Instrument operation is controlled by 26 commands and monitored by 20 analog housekeeping parameters.

The VISSR instrument operates in the visible region of 0.55 to 0.75 micrometers and in the infrared region of 10.5 to 12.6 micrometers. Each of the eight photo-multiplier tubes on the visible detector is 0.025 X 0.021 mrads, with a dynamic range of 3-100% albedo. The infrared portion of the instrument consists of two detectors cooled to 95 degrees K, with an instantaneous field-of-view (IFOV) of 192 X 192 microradians. The VISSR telescope has an aperture of 40 cm and a focal length of 291 cm, and routes the IR wavelengths to separate detectors. The video analog output of all detectors is transmitted to the VISSR Digital Multiplexer (VDM) where it is sequentially sampled every 2 microseconds by the visible channel and every 8 microseconds by the IR channel.

The Multispectral Imaging Radiometer (MIR) sensor on METEOSAT is a scanning radiometer which provides images in the visible and thermal IR regions of the spectrum. The instrument produces images of the full Earth disc viewed from a geostationary orbit. A reduced image format, corresponding to a limited band across the Earth's disc, may be selected by telecommand. The optical reflector system of the radiometer includes a movable Ritchey-Chretien telescope with primary and secondary mirrors. This includes a mirror located in the center of the primary mirror inclined at 45 degrees to the optical axis, four folding mirrors, and a separation mirror for diverting light to the visible sensor.

The optically-collected visible and IR signals are converted into analog electric signals by five detectors. These are divided into two subsets, two visible and three IR. The detectors are distributed across the focal plane of the radiometer and as a result of the relative displacement of the detectors in this plane, their respective fields-of-view (FOV) do not coincide but are displaced relative to each other.

The two visible detectors are positioned in the focal plane of the primary telescope. Their instantaneous FOV at the Earth's surface (2.5 square km) is determined by their physical size (250 X 250 micrometers sensitive area) and the telescope's focal length (3650 millimeters). While the visible detectors function properly at ambient temperatures, the three IR detectors must be cooled to less than 95 degrees K.

Each IR detector is 70 square micrometers and generates an instantaneous 5 km square FOV at the subsatellite point. One visible channel time shares with the water vapor channel so that the resolution of the visible image changes depending on the choice of channels.

The GMS Visible and IR Spin-Scan Radiometer (VISSR) is very similar to the scanning radiometers carried on Synchronous Meteorological Satellite (SMS) and GOES (1 through 3) satellites except for some modifications to stepping gears and detector portions. The number of steps in each scan is 2500 for the IR detector on GMS versus 1821 for GOES.

The INSAT VISSR is also a scanning radiometer with a visible channel cover- ing 0.55 to 0.75 micrometers and an IR channel covering the 10.5 to 12.5 micrometer spectral regions. The full disc can be scanned every half-hour (23 minute scan plus 7 minute housekeeping), processing from north to south. Sector scanning of the 1/4 disc (full east to west, 1/4 north to south) is possible every 6 minutes.

4.4 Instrument Measurement Geometry.

The following table lists the measuring geometry characteristics for the satellites employed by the ISCCP program:
--------- ----------- -------------- --------------
NOAA Cross-track Moving south to 55.4 degrees
scan mirror north, scanning  
west to east    
--------- ----------- -------------- --------------
GOES Spacecraft spin Stepping north to 20 X 20 degrees
motion plus south, scan west  
scan mirror to east  
--------- ----------- -------------- --------------
METEOSAT Spacecraft spin Stepping south to 18 X 18 degrees
motion plus north, scan east  
scan mirror west  
--------- ----------- -------------- --------------
GMS Spacecraft spin Stepping north to 18 X 18 degrees
motion plus south, scan west  
scan mirror to east  
--------- ----------- -------------- --------------
INSAT Spacecraft spin Stepping north to Not available
motion plus south, scan east  
scan mirror to west  
--------- ----------- -------------- --------------


5.1 Processing Steps and Data Sets.

The ISCCP C1 data are comprised of two Level I databases archived at the ISCCP Central Archive (ICA). The data have been reduced from the original resolutions for each of the satellites. At the B1 stage of processing all data sets have a nominal 10 km resolution except for data sets from the polar orbiters (NOAA series), which are retained at the original 4 km resolution. At the B3 stage all data sets have a nominal 30 km resolution. Radiance values at the 30 kilometer resolution for each of the sensors are normalized to the polar orbiter radiometer response. The cloud analysis products of ISCCP, called Stage C1 and C2 data, are constructed from the original B3 radiances, the results of the three parts of the cloud algorithms, and the correlative data used in the analysis. Stage C1 data represent the global, merged results reported every 3 hours with a spatial resolution of 280 km; Stage C2 data are the monthly averages and the other summary statistics of the Stage C1 quantities. Additional information about the processing steps can be found within Item 5.2.

5.2 Derivation Techniques/Algorithms.

The cloud analysis algorithm for ISCCP-C1 was developed from a three year pilot study that compared the performance of nine different algorithms applied to the same data (Rossow et al., 1985; Rossow, W.B. and R.A. Schiffer, 1991). This algorithm has three fundamental parts: cloud detection, radiative transfer model analysis, and statistical analysis.

A. Cloud Detection.
The cloud detection step analyzes the radiance data twice: first to determine an estimate for the radiance values that represent clear conditions and second, to determine which radiance measurements deviate from these clear sky values. Cloudy conditions are defined to be those that exhibit radiance values that are sufficiently different from the clear values.

To avoid spurious diurnal variations of cloudiness caused by changes in methodology associated with the presence or absence of VIS data, the clear sky composite procedure relies primarily on IR radiance tests to obtain both the VIS and IR clear radiances. However, since the daytime results can be improved by use of the VIS channel measurements, these results are incorporated so that the IR-only results can be reconstructed.

  1. Clear Sky Composites The cloud detection part of the analysis must separate the radiance data set into two parts: the radiance values representing clear scenes and those representing cloudy scenes (in this case a "scene" is one image pixel with a field-of-view size ranging from 4 to 8 km, depending on satellite). Many studies have suggested that the radiances associated with clear scenes are generally, though not always, less variable in space and time than those associated with cloudy scenes (Rossow et al., 1985, Seze and Rossow, 1991, and references therein, see Item 11.2). Thus, the cloud detection process starts by first testing all the radiances for low spatial and temporal variability to determine a best estimate of the clear scene radiance value at each location and time. The distribution of these clear radiance values is referred to as the clear sky composite.

    The clear sky composite values are obtained as the result of two tests and the accumulation of three kinds of statistics over two time periods. The two tests for low spatial and temporal variability are meant to isolate many, but not all, of the clear image pixels from the images. The statistics are used to check whether these radiance values have the proper characteristics thought to represent clear conditions. This latter aspect of the algorithm is necessary because the magnitude of the radiance variability associated with different surface types and different cloud types is highly changeable. In some climate regimes the surface properties are much more variable; in some cases, the cloud properties are not highly variable. Thus, no single aspect of the cloud detection algorithm is successful everywhere. The final step of the process is an intercomparison of the results of all the preliminary steps to determine the best value of the clear radiances.

    The first step tests the spatial variability of the IR radiances within small regions (about 100 km on land and 300 km over ocean). All pixels determined to be much colder (by 3.5 K over ocean and 6.5 K over land) than the warmest pixel are labelled "cloudy"; all others (including the warmest) are labelled "undecided". The second step tests the time variability of the IR radiances over three days at the same GMT. All pixels determined to be much colder (by 3.5 K over ocean and 8.0 K over land) than the values at the same location either yesterday or tomorrow are labelled "cloudy"; all pixels found to be the same temperature (to within 1.1 K over ocean and 2.5 K over land) as they were either yesterday or tomorrow are labelled "clear". (Performing comparisons at the same local time each day avoids the large diurnal variation of land surface temperature.) The remaining pixels with intermediate variability are labelled "undecided".

    Three statistics are collected: the differences of the extremum radiances (maximum IR and minimum VIS) for consecutive 5 day periods, the number and average values of the pixels labelled "clear" for 5 day periods, and the number and average values of the pixels labelled "clear" by the time test and "undecided" by the space test. Large differences in the extremum radiances generally indicate remaining cloud contamination; low populations of "clear" pixels indicate persistent cloudiness. If these statistics suggest little cloud contamination, then the clear sky composite is formed from the 5-day-average radiances of "clear" pixels. If the statistics indicate contamination, then the clear sky composite is formed from the 30-day-average. If the 30-day statistic (number of "clear" pixels) is too small, then the 30-day extremum radiance is used.

    Each pixel is labelled by the combined results of the two tests and the statistics. The two tests produce four categories, depending on whether the tests agree or disagree. The combinations (space-time of cloudy-cloudy, cloudy-undecided, and undecided-cloudy are labelled 'cloudy'; the combination of undecided-clear is labelled 'clear'. The combination undecided-undecided is labelled 'undecided', while the combination cloudy-clear is labelled 'mixed'. The statistics indicate whether the short-term or long-term values were employed in the clear sky composite, (i.e., whether the clear radiance value is more or less accurate). All of these labels are used to evaluate the success of the analysis by checking their consistency.

    The version of the algorithm used to produce this C1 data does not use any correlative data to construct the clear sky composite, except a classification data set that indicates whether a particular location is land, water, or coast. The revised algorithm will use an augmented classification that includes topography and snow/ice cover.

  2. Threshold Decision The final decision on the status of an image pixel is made by the bispectral threshold test (IR-only threshold at night). Using the clear sky radiances derived for each location and time, all image pixels with radiance values that are sufficiently different from clear sky conditions are declared to be cloudy. To improve the detection of cirrus and low-level clouds, single channel detections are allowed. Thus, if the VIS radiance or the IR radiance is different from the clear sky values, the pixel is called a cloud. The magnitude of the difference required is set by the estimate of the uncertainty in the clear radiance values. In these results, the VIS thresholds are 3.5 % to 6.0 % (land) and the IR thresholds are 3.0 deg K (ocean) and 8.0 deg K (land). (VIS radiances are represented as a percent of the instrument response obtained when measuring the full solar flux; IR radiances are represented as brightness temperatures in deg K.) Thus, if a pixel has a VIS (IR) radiance greater than (less than) the clear value by more than the threshold amount, it is considered to be cloudy.

    This labelling of pixels as cloudy or clear is performed without regard to the previous labels derived from the clear sky composite analysis; however, the success of the cloud detection is indicated by whether these labels generally agree or disagree. The clear sky composite analysis is meant to be more conservative than the threshold test because the low variability of the surface properties means that not all measurements need to be included to get a good estimate of the clear radiance values. Thus, the image pixels are generally equally divided among the 'cloudy', 'clear', and 'undecided' categories obtained from the clear sky composite analysis; the 'mixed' category is usually very small except when the algorithm is having difficulty distinguishing the clouds. The threshold decision is made for all pixels; most pixels labelled as 'cloudy' or 'clear' in the clear sky composite analysis are similarly labelled by the threshold decision, confirming the success of the method. The 'undecided' category contains radiances that are too ambiguous to make a reliable decision in the composite analysis. These pixels are usually divided into cloudy and clear in rough proportion to the number of pixels already labelled as cloudy or clear. This two stage decision process provides an "error" check from the internal consistency of the result.

    The actual relationship between the pixel radiances and the clear radiances is recorded for each pixel by a two-part code: each part records a value from 0 to 5 representing the VIS and IR results separately. This preserves the ability to reconstruct the IR-only results in the final C1 data.

    VIS code - 0 no decision (no data)
    VIS code - 1 less than clear VIS by more than threshold
    VIS code - 2 less that clear VIS by less than threshold
    VIS code - 3 greater than clear VIS by less than threshold
    VIS code - 4 greater than clear VIS by more than threshold
    VIS code - 5 greater than clear VIS by more than 2 X threshold

    IR code - 0 no decision (no data)
    IR code - 1 greater than clear IR by more than threshold
    IR code - 2 greater than clear IR by less than threshold
    IR code - 3 less than clear IR by less than threshold
    IR code - 4 less than clear IR by more than threshold
    IR code - 5 less than clear IR by more than 2 X threshold

    In the C1 data various additional combinations of the two codes (VIS-IR) are used as follows to diagnose the performance of the algorithm. Classes 2, 3, 4, and 5 are also counted in class 1, while classes 7 and 8 are also counted in class 6.

    0 = bad (no data)          5 = bad-cloudy
    1 = cloudy          6 = clear
    2 = VIS-only-cloudy          7 = uncertain-clear
    3 = IR-only-cloudy          8 = bad-clear
    4 = uncertain-cloudy

    Uncertain-cloudy (class 4) pixels are pixels with radiance values that are between the threshold and two times the threshold amount. Because these radiances are often caused by broken clouds or occur when the scene is ambiguous, a count of the pixels lying near the threshold indicates how much the result would change if a different threshold value were used. This count provides a dynamic sensitivity monitor or uncertainty estimate. VIS-only-cloudy (class 2) and IR-only-cloudy (class 3) pixels are those that were determined to be cloudy by a single channel decision; that is, VIS-only-cloudy pixels have a VIS radiance that is greater than the clear sky VIS value by more than the threshold amount but have an IR radiance that is similar to the clear IR value. (At night there are no VIS-only-cloudy pixels and all cloudy pixels are IR-only-cloudy pixels.) Uncertain-clear (class 7) pixels are those which have a radiance value similar to the clear value in one channel but deviate from clear conditions too much in the other channel. For example, the VIS (IR) radiance may be similar to the clear value, but the IR (VIS) radiance is much warmer (darker) than the clear value. Bad-cloudy (class 5) pixels are similar to uncertain-clear pixels except that one channel has a radiance value similar to cloudy radiances. Bad-clear (class 8) pixels have VIS and IR radiances that are much too dark and warm compared to the clear radiance values. Bad (class 0) pixels are daytime pixels with no VIS decision or any pixels with no IR decision (usually due to lost data).

    A summary of the pixel-by-pixel threshold information is provided for each region (nominal size is 280 X 280 km) in the C1 data set. Cloud amount is defined in the ISCCP data as the number of cloudy pixels within the region. Although this approach is thought to overestimate cloud coverage when using "low" resolution satellite data, no technique is yet available that determines fractional cover of individual pixels for all cases. Hence, the meaning of the cloud amount reported in C1 data is defined by this procedure: the cloud amount obtained from this analysis is an "effective value," which indicates the variation of actual cloud amount on a scale >5-10 km. In C1 the number of cloudy pixels is reported together with the total number of pixels; cloud fraction is the ratio of these numbers.

    The uncertainty estimate is provided by the number of pixels classified as uncertain-cloudy by the threshold and by the number of pixels labelled as undecided in the composite step; however, several other counts are summarized which indicate in different ways that the algorithm is or is not performing as expected. In particular, when the scene is ambiguous or the clear sky composite value is contaminated, then the number of mixed, uncertain-clear, bad-clear, and bad-cloudy pixels grows. Generally, the number of bad-clear pixels should be small. Further investigation of the behaviour of these "error" counts will improve their interpretation.

B. Radiative Transfer Model Analysis
Once pixels are classified as cloudy or clear, the radiances are compared to radiative transfer model calculations designed to simulate the measurements of the AVHRR channels (to which all the radiometers have been normalized). These comparisons are used to isolate the surface reflectances and temperatures from the clear radiances and the cloud optical thicknesses and cloud top temperatures from the cloudy radiances. Atmospheric properties that affect the satellite measured radiances are specified from the correlative data.

Analysis proceeds in five steps.

  1. Retrieval of surface temperatures from the clear pixel IR radiances (if present) and the clear sky composite IR radiances. The surface is assumed to be a blackbody. The effects of atmospheric water vapor absorption are calculated.

  2. Retrieval of surface reflectances from the clear pixel VIS radiances (if present) and the clear sky composite VIS radiances. The surface is assumed to be an isotropic reflector. The effects of Rayleigh scattering and ozone absorption are calculated. No retrieval is performed at night.

  3. Retrieval of cloud top temperature and pressure from the cloudy pixel IR radiance (if present) utilizing the surface temperature from the clear sky composite and assuming clouds to be an opaque single layer.

  4. Retrieval of cloud optical thickness from the cloudy pixel VIS radiances (if present) utilizing the surface reflectance and assuming the cloud is a single, conservative Mie scattering layer. Over oceans a model of surface reflectance is employed in place of the retrieved surface reflectance. No retrieval is performed at night.

  5. If the optical thickness of the cloud is small, the cloud top temperature is recalculated to account for transmission of radiation from the surface using the retrieved optical thickness. The revised cloud top pressure is then used to recalculate the optical thickness. No adjustment is performed at night.

A number of consistency checks are made to determine if the radiative analysis is performing as expected. These checks generally detect problems with the data or errors in the cloud decision. Among the most interesting results so far is the check on the altitude adjustment. This procedure encounters difficulties with the optically thinner clouds because the VIS radiance measurement accuracy and the uncertainties in the calculation of the clear radiances from the retrieved surface reflectance prevent a meaningful measurement of the optical thickness. In other words, even though the cloud may be "obvious" in the IR image, its VIS radiance effect may be negligible. The presence of this condition is tested by solving the radiative transfer equation of the minimum cloud optical thickness consistent with the coldest possible cloud top temperature (cloud top at the tropopause), the surface temperature and the observed brightness temperature. If the retrieved optical thickness is less than this value (often the retrieved value is zero because there is no measurable difference between the observed radiance and the clear radiance), then the cloud top temperature is set to the coldest possible value and the optical thickness is set to its minimum value. The success of this technique appears good in some preliminary tests, but a refinement of this approach is possible.

Interesting results also include the check on the surface temperature retrieval when low cloud contamination of the clear sky radiances is present. This refers to the effect of undetected clouds on the surface temperature retrieval because the retrieval overestimates the atmospheric emission over the higher, but mislabelled, cloud tops. This causes the retrieved temperature to be colder than the observed brightness temperature, the opposite of the expected relationship. Further tests are underway to develop useful error flags.

C. Statistical Analysis The average and variance of each cloud and surface parameter are provided for the 280 km region. The average value is reported directly,along with the total number of pixels used to calculate it and the root mean square value. The variance is calculated from the number of pixels, the average value, and the root mean square value. Both the average and the root mean square values are decoded in the same way.

The reported cloud parameters represent averages over all cloudy pixels in each region at that time. These average values do not indicate the structure of the clouds present, however. Original ISCCP plans called for reporting the properties of five cloud types: low, middle, high, cirrus, and deep convective clouds. The latter two types were qualitatively defined to be optically thin and thick high clouds, respectively. Consideration of how best to define these types precisely, as well as studies of the adequacy of this classification scheme to represent the actual cloud structures, was part of a pilot study on the uses of ISCCP data. Recommendations from that study were to make the definitions more flexible and to preserve greater resolution in cloud top location and cloud optical thickness. Thus, cloud type information is presented in the C1 data by counting the number of cloudy pixels with optical thicknesses and top pressures in each of 25 categories. (Only the five cloud top pressure categories exist at night. These results are reported in the first optical thickness class within each pressure category.)

Several features of this method of classification are:

  1. Definitions of low clouds, for example, can be formulated by combining or interpolating among the five cloud top pressure categories. In particular the height categories can be adjusted to the levels in a climate model.

  2. Different definitions that depend on the optical thickness, such as cirrus (i.e., "thin", "high" clouds) can be adopted. Users can construct their own cloud classes.

  3. The mean values of optical thickness and top pressure are assumed to be equal to the central value within each class.

  4. The cloud top temperature can be obtained by interpolation of the reported atmospheric temperature profile at the cloud top pressure.

  5. To allow for the diurnal studies, the number of VIS-only cloudy pixels contributing to each cloud top pressure class is reported separately. For diurnal comparisons, these numbers should be subtracted from the totals reported in each pressure class.

A single C1 data file represents the merging of analysis results from all available satellites within the three hour time period; however, in one map cell the values from only one satellite are reported. Each location has an established hierarchy of satellite observations based on the variations of viewing geometry and time coverage characteristic of each satellite. For low latitudes, observations from the nearest geostationary satellite are preferred, while the polar regions (poleward of 55 degrees latitude) are covered only by the polar orbiter. If data from the primary geostationary satellites are not available, then a secondary geostationary satellite may be used if the viewing geometry is not too extreme. If no geostationary data are available at low latitudes, then polar orbiter data are used, if available. Since the time period of each data set is 3 hours long, anywhere from zero to two polar orbiter observations may be reported within this time period. The satellite that contributes the specific results is identified with a code number which is defined in the Volume ID file.

The basic objective of the ISCCP-C2 analysis is to summarize the cloud analysis results (Stage C1 data) on a monthly time scale. To preserve information about diurnal variability, the results are first averaged over the calendar month, separately for 00, 03, 06, 09, 12, 15, 18, and 21 GMT. These eight time periods are referred to as the hour-monthly means. The number of days of observations contributing to the average values is recorded as the sixth parameter in each map grid cell. Then, the hour-monthly mean values are averaged to obtain the monthly mean values. Hour-monthly mean values which consist of less than three daily observations are excluded from the monthly mean. Before averaging over the eight hour-monthly mean time periods a number of adjustments are made.

Averaging the quantities from Stage C1 data to produce the Stage C2 data can be done in two ways, depending on the purpose. Some quantities, such as cloud optical thickness or cloud top temperature, are related to the effect of clouds on radiation in a non-linear way. Thus, an average value meant to be indicative of the average radiative effect of clouds must give equal weight to these values proportional to their effect. Since these quantities were retrieved from radiation measurements, this weighting is also related to the variation of relative measurement precision over the range of the parameters. All quantities in Stage C2 data are averaged in this way, except for parameter 20, called PATH. For most parameters, this weighting procedure produces an average value that is not much different than that given by a simple linear average. This is not the case for cloud optical thickness, where a simple linear average produces a global monthly mean value that is about 60% larger than that produced by an energy-weighted average. Parameter 17, TAU, gives the value which represents the average radiative effect of the clouds. Since cloud optical thickness is proportional to cloud water content, parameter 20, PATH, records the result of a simple linear average of optical thickness values. For a constant cloud particle size distribution (as assumed in the retrieval of optical thicknesses), cloud water path, WP, is given by

WP = [40/3]*[r~ * PATH]/Q       kg/m**2

where r~ is the average particle radius in cm, and Q is the normalized Mie extinction efficiency at 0.6 micrometers wavelength. For the cloud particle size distribution used, with r~ approximately equal to -.001 cm,

WP = 6.292 PATH       g/m**2

5.3 Special Corrections/Adjustments.

The method used to reconstruct the variances utilizes the mean and root mean square (rms) values of each quantity; however, this approach does not retain enough precision to report small variances accurately. In this version of C1 data, the variances are only accurate to the nearest 10% of the mean value. Since most variances are smaller than this amount, most variances are reported to be zero.

The mean cloud properties reported in the C1 product are the final values from the radiative analysis. This means that the daytime values of cloud top temperature (TC) and cloud top pressure (PC) have been altered by the effects of the VIS channel measurements. Since the same adjustment is not performed at night, direct comparison of the day and night values of cloud top temperature and pressure must be interpreted with caution. However, the vertical distribution of clouds can be reconstructed from cloud classes and the mean IR radiance values. The visible only (VIS-ONLY) numbers can be subtracted from the total number of pixels at each pressure level, while the IR radiances can be used to estimate the cloud top temperature and pressure without TAU corrections.

Producing the ISCCP-C2 product involved performing a number of adjustments on the ISCCP-C1 data before determining the monthly averages. The adjustments necessary included VIS adjustments during daytime, VIS adjustments during nighttime, calibration adjustments, standard adjustments, special METEOSAT adjustments, and diurnal adjustments

VIS adjustments during daytime (Adj1):

In the Stage C1 data, two different versions of cloud amount and cloud top temperature/pressure are reported for daytime conditions. One version of cloud amount is obtained from the IR radiances alone, as must be done for nighttime conditions; the other version combines cloud detections from both the VIS and IR radiances. IR radiances are insensitive to low-level clouds, especially broken ones, the VIS radiances analysis detects more low-level cloudiness than the IR analysis. Likewise, one version of the cloud top temperature/pressure is obtained directly from the IR radiances as is done for nighttime conditions and the other version adjusts the values consistent with the cloud optical thickness value retrieved from the VIS radiances. This adjustment is significant only for optically thin clouds, which transmit IR radiation from below the cloud and, consequently, appear to have a higher temperature/pressure than they actually do. Thus, the VIS/IR version is superior to the IR-only version. Stage C2 data contain the VIS/IR versions of cloud amount, cloud top temperature and cloud top pressure.

VIS adjustments during nighttime (Adj2):

The mean differences between the VIS/IR and IR-only results during daytime conditions are used to adjust the nighttime results in the hour-monthly mean data. Daytime differences between VIS/IR and IR-only values of total cloud amount, mean cloud top pressure and cloud top temperature are linearly interpolated over the nighttime periods between the dusk and dawn values. This interpolated difference is then added to the IR-only value during this time period. The magnitude of these corrections is generally small. The smaller (<= 5%) cloud amount adjustments are distributed nearly uniformly over the globe with values slightly higher over ocean than over land. The larger adjustments occur in near coastal regions, land and ocean, in low latitudes primarily associated with tropical rain forests and marine stratus regimes. The unadjusted cloud amount is reported as the last parameter in each map grid cell. The cloud top pressure correction is positive where low clouds predominate, primarily in marine stratus regimes over oceans, and negative where high, thin clouds predominate, primarily over land, especially in desert areas.

Interpolation to fill during nighttime (Adj3)

Values of the cloud optical thickness (both TAU and PATH) are interpolated over the nighttime period between the dusk and dawn values.

Standard adjustment (Adj4):

To produce Stage C1 data, results from several satellites are merged into a single global dataset. In regions where more than one satellite provides results, the merger process selects the preferred satellite according to a specified hierarchy that favours data continuity and observations made closer to nadir view. Frequency histograms of the differences in the overlapping measurements between all pairs of satellites are collected and the modal value estimated from the average of the mode value and the three nearest values above and below the mode value. These estimated differences for each satellite, when compared to the reference polar orbiter, are applied to adjust for small residual radiance calibration differences. The corrected quantities in the hour-monthly mean are: cloud top and surface temperature, cloud optical thickness and water path, and surface reflectance. Magnitudes of these corrections are illustrated in the table below. Actual calibration adjustments for each month are reported in the record prefixes for each parameter for each satellite.

The magnitude of the calibration adjustments applied to Stage C2 data to remove small residual calibration differences are shown here as the standard deviation and range of all corrections applied to each satellite over the period July 1983 - February 1987.

Parameter Std Dev Range
----------------------------- ------- --------
Cloud Top Temperature 0.74 K +- 2.5 K
Surface Temperature 1.10 K +- 3.0 K
Cloud Optical Thickness
and Water Path 0.02 +- 0.08
Surface Visible Reflectance 2% +- 8%

Special METEOSAT adjustment (Adj5):

The spectral response of the METEOSAT "visible" channel is wider than that of the other radiometers used in the ISCCP analysis; normalization of METEOSAT radiances is done using spectrally uniform targets (clouds and clear ocean areas). The spectral response difference means that surface reflectances calculated for vegetated land areas from METEOSAT are larger than for the other satellites. This difference in surface reflectance is removed in the hour-monthly mean dataset by using regression relations that are obtained by comparing METEOSAT and NOAA measurements as a function of vegetation type and season. A single relationship that varies with season was found to represent differences as a function of vegetation. Adjustment factors are applied for each season and are given in the table below. Unadjusted values can be recovered from Stage C2 data by multiplying by the slopes (given in the table below) and adding the intercept values.

Adjustment factors applied to METEOSAT land surface reflectances to reduce them to values measured at an approximate wavelength of 0.6 +- 0.1 micrometers are shown in the table below. Seasons are the standard three-month periods in the northern hemisphere.

Adjustment: Adjusted Value = (Original Value - Intercept)/Slope

Season Slope Intercept
------ ----- ---------
Winter 0.893 0.1154
Spring 0.786 0.1135
Summer 0.752 0.1290
Fall 0.820 0.1362

Diurnal adjustment (Adj6):

Before the hour-monthly means are combined into a monthly mean, small corrections are made to account for incomplete sampling of the diurnal variations of cloud and surface properties. An incomplete sample is less than 8 hour-monthly observations at low and middle latitudes. These adjustments are determined using the zonally averaged variations of the quantities in local time at all locations with eight hour-monthly mean values available. The diurnal average is calculated for the number of samples actually available and compared with the average of eight samples to determine the effect of sub-sampling on the diurnal average. The calculations are performed within each latitude interval, separately for land and water areas. The quantities that are adjusted are the total cloud amount, cloud top temperature and pressure, cloud optical thickness and water path, and the surface temperature. These adjustments affect only the monthly mean values and are not applied to the individual hour-monthly means.

5.4 Processing Changes.

A. Cloud Detection

  1. Clear Sky Composites
    Since diurnal-uniform quality is desired, algorithm design has emphasized improvements in the accuracy of the IR tests. A number of very good techniques for improving the accuracy of the clear VIS radiance composite (cf., Rossow et al., 1985b) already exist. In addition, accurate models of the clear VIS radiances over ocean are available (Minnis and Harrison, 1984). Relatively good models of the land surface reflectance are also being constructed (see Matthews and Rossow, 1987, and references therein). Thus, to preserve the uniformity of the IR dependent results, while improving the daylight analysis results, the final clear-sky composite will be modified using the ocean model and the stable statistics of the land surface reflectances (Matthews and Rossow, 1987). All results dependent on VIS tests will be held separately to facilitate diurnal studies. A comparison of the IR-only results with the full daytime results will also permit estimates of the errors derived from the nighttime analysis. Early tests show that removing cloud contamination only changes the clear-sky IR radiance by 1-3 degrees K.

    Spatial variations of radiances can also be caused by changes in surface properties. Thus, the clear-sky composite will also be modified to avoid comparisons where snow or sea ice cover has changed.

    An improved version of the statistics for the clear-sky condition will also be implemented. The primary purpose of the revision is to avoid tests that depend on single data values. For example, the image from GOES-East for 19 July at 12 GMT was affected by a spurious gain change in the IR channel. This event caused 50 degree K colder than normal brightness temperatures, and caused the algorithm to classify the image as cloudy. Although the B3 data are undergoing quality inspection that should remove this "large" error, small errors of this type will be detected using statistical intercomparisons of the clear radiances over small regions. Thus, the alternative method will allow for identification and elimination of spurious data values.

  2. Threshold Decision
    The only planned refinements in the threshold step are the possible addition of two extra values for snow and ice surfaces and the implementation of thresholds that are linear in radiance. Many algorithms apply thresholds in terms of physical quantities. However, not all the radiometers made measurements that are linear in these quantities. To properly account for the radiometer performance and the changing sensitivity to clouds under different circumstances, the thresholds will be made linear in measured radiances. This actually introduces no change for the VIS channel; however, the IR radiance data are currently handled as brightness temperatures rather than linear counts. This change is most important in the polar regions where the radiometers are less sensitive to clouds because of the very low temperatures. This approach is more consistent with the interpretation of the threshold magnitude as an estimate of the clear sky radiance uncertainty. In addition, some refinement of the uncertainty and error counts discussed above may occur after study of the distribution and behaviour of these quantities.

B. Radiative Transfer Model Analysis The planned refinements for the radiative transfer model analysis are intended to improve the reporting of the error checks thereby providing better documentation of algorithm performance. In particular, those "error" conditions that indicate cloud contamination of the clear radiances or improper labelling of the pixels will be added to the summary statistics to provide internal error estimates.

There have been some minor changes to the radiative analysis to prevent unnecessary data losses. The most noticeable problem in these results is that small uncertainties in the retrieval process can cause very low clear radiances to produce surface reflectances that are slightly less than zero. The current code discards these data causing a loss of data near the terminator in the images. This result suggests that the daytime analysis should be ended at a somewhat larger solar zenith angle (the cutoff is currently 81 degrees). However, these small negative values are also valid, but "inaccurate" measurements of a small value; hence, processing can proceed by setting small negative values to zero. Another problem is that the B3 data contain no geostationary VIS images for the three time periods near local midnight, even when small portions of the image actually have solar zenith angles larger than the cut-off value. The current analysis code interprets this situation as a loss of data; hence, portions of these images were unnecessarily discarded. Changes in the software logic will avoid these problems.

C. Statistical Analysis Based on further accuracy tests and studies of the information content of the statistics, there may be some refinements or changes in definitions. Suggested changes include adding more pressure categories in the cloud classification, adding an alternative VIS cloud parameter equivalent to the surface reflectance, and improving the height adjustment information to indicate how many pixels actually changed height categories.


6.1 Data Validation by Producer.

Comparisons of parameters retrieved from the ISCCP analysis to other measurements of the same or related parameters shows that the cloud amounts are generally accurate to within 5-10% random and <5% bias, except in polar regions (where the ISCCP cloud amounts are lower limits), over land areas in winter and at night (where ISCCP values are biased low by about 5-7%). Cloud top temperatures are accurate to within 2 K, with the exception of optical thin cirrus clouds, where the error is about 5-8K. Cloud optical thicknesses are accurate to about 10%, except for optically thin cirrus, where errors are about 25-40%. Surface temperatures are accurate to about 2 K over oceans and about 4 K over land. Surface visible reflectances are accurate to about 2-4%.

6.2 Confidence Level/Accuracy Judgment.

The overlapping portions of the first C1 data provide an opportunity to compare the normalized calibrations of the geostationary satellites directly. This comparison, though still being studied, does not show any significant discrepancies for July 1983. The comparison results suggest an uncertainty in the normalizations of no more than 1-3 % in visible radiances and 1-3 degrees K in IR radiances. This cross-check procedure will be incorporated into the operational C1 data production to provide further monitoring of the radiance calibrations.

6.3 Usage Guidance.

The C1 data products are intended to meet two objectives: 1) to report the "results" of the cloud analysis and 2) to aid in the identification of statistics which are meaningful to cloud climatology studies. The latter objective will be met by providing fewer statistics and more detail. This detail will be useful for later validation studies, algorithm improvements and radiative model development efforts.


            Dr. William B. Rossow (or Alison Walker)
            ISCCP Global Processing Center
            NASA Goddard Institute for Space Studies
            2880 Broadway
            New York, NY 10025
            Commercial and FTS: (212) 678-5567


8.1 Tape Products

    8.1.1 Stage C1 (ISCCP-C1)

Medium/Specification: C1 data are written in "IBM Standard Label" format on 9-track, 6250-bpi magnetic tapes.

Format and Content: Each actual data file is accompanied by additional, very short header and trailer files. Thus, users whose computer systems do not use these additional files should start reading with the second actual file on the tape and read every third file thereafter. All C1 data files are coded entirely as 1-byte binary values. Each data file is composed of 67 data records, 13,200 bytes in length. There is no header record in a data file. Instead, each record begins with a 132-byte prefix that identifies the contents of that record. The prefix is followed by 99 map cells of data, each represented by 132 bytes. The 132 quantities are listed in Item 14 of this catalog.

Data Quantity/Rate: 2 tapes/month.

Status: The tapes covering the period 07/01/83 through 08/30/90 are available. The coverage of the C1 products includes the FIRE (CIRRUS and MARINE STRATOCUMULUS) regions and time periods, 10/01/86 - 11/30/86 and 06/01/87 - 07/31/87.

Plans/Schedule: NCDS will continue to support and archive these data.

    8.1.2 Stage C2 (ISCCP-C2)

Medium/Specification: C2 data are written in "IBM Standard Label" format on 9-track, 6250-bpi magnetic tapes

Format and Content: Each actual data file is accompanied by additional, very short header and trailer files. Thus, users whose computer systems do not use these additional files should start reading with the second actual file on the tape and read every third file thereafter. All C1 data files are coded entirely as 1-byte binary values. Each data file is composed of 67 data records, 7,200 bytes in length. There is no header record in a data file. Instead, each record begins with a 72-byte prefix that identifies the contents of that record. The prefix is followed by 99 map cells of data, each represented by 72 bytes.

Data Quantity/Rate: 1 tape/year.

Status: Tapes covering the period July 1, 1983 through December 31, 1990 are available.

Plans/Schedule: NCDS will continue to support and archive these data.

8.2 Film Products.

A poster photograph exhibiting six of the ISCCP parameters for July 1983 is available from the Goddard Institute of Space Studies.

8.3 Other Products.

    8.3.1 Stage C2 (ISCCP-C2) Monthly Averages and Summary Statistics for ISCCP-C2 in Common Data Format (CDF).

Medium/Specification: Magnetic disk

Format and Content: The data are stored in a special data-set-independent format, designated the Common Data Format (CDF). CDF was developed as a uniform method of storing and retrieving data on magnetic disk. The CDF contains data and descriptions about the data. A standard software package called the "CDF Library" allows a user within an NCDS session to create and access these data and descriptions. These data contain the monthly averages and summary statistics for the ISCCP-C1 data at the same spatial resolution, including mean diurnal variations, and can be accessed within the Data Applications subsystem of NCDS.

Data Quantity/Rate: Approximately 330,000 512-byte blocks in 72 files.

Status: Available online within NCDS.

Plans/Schedule: NCDS will continue to support this data set online, and update as additional data are received.

    8.3.2 The Greenhouse Effect Detection Experiment (GEDEX) CD-ROM

Medium/Specifications: CD-ROM in ISO 9660 standard

Format and Content: The data on this disk will be stored in a special data-set-independent format, designated as the Common Data Format (CDF). CDF was developed as a uniform method of storing and retrieving data on disk. The CDF contains data and descriptions about the data. A standard software package called the "CDF Library" will be included with this CD-ROM allowing a user to create and access these data and descriptions. The CD-ROM will consist of many different data sets related to the Greenhouse Effect, including the ISCCP-C2 data set, and the accompanying NCDS data set documentation. Some of the parameters that compose this data set have been rederived in a manner different than was previously done. These changes are presented on the GEDEX CD-ROM. The low cloud amounts are derived from a combination of cumulus and stratus measurements. The amount of middle clouds was derived from a combination of altostratus and nimbostratus measurements. The amount of high clouds was derived from measurements of cirrus, cirrostratus, and deep convective clouds. Although this change in the derivation of these three parameters introduces a small diurnal effect, the representation of the vertical cloud distribution is improved.

Data Quantity/Rate: One CD-ROM disk set

Status: Release date for this product is scheduled for early 1992.

Plans/Schedule: Users interested in this product should contact the NCDS/Goddard DAAC User Support Office for more details.

    8.3.3 The ISCCP-C2 CD-ROM

Medium/Specifications: CD-ROM in ISO 9660 standard

Format and Content: The data on this disk consist of the ISCCP-C2 data, read routines and associated documentation. Documentation for ISCCP Level C1, ISCCP TOVS, and the ISCCP Ice/Snow data is also included.

Data Quantity/Rate: One CD-ROM disk

Status: Release date for this product is scheduled for late spring 1992.

Plans/Schedule: Users interested in this product should contact the NCDS/Goddard DAAC User Support Office for more details.


9.1 Archive Identification


            ISCCP Central Archive
            Satellite Data Service Division (SDSD)
            Princeton Executive Square, Suite 100
            Washington, DC 20233
            Commercial and FTS: (301) 763-8400
            Telex: RCA 248376 OBSWUR or TRT 197683 KWBC
            Commercial and FTS Telefax: (301) 763-8443


            Goddard DAAC User Support Office
            Code 935
            NASA Goddard Space Flight Center
            Greenbelt, Maryland 20771
            Commercial and FTS: (301) 286-3209
            NSI DECnet: NCF::NCDSUSO

9.2 Procedures for Obtaining Data.

Users can obtain the ISCCP-C1 and ISCCP-C2 through NASA's Climate Data System (NCDS). To use NCDS, you must be able to access the National Space Science Data Center (NSSDC) Computer Facility (NCF) and obtain an NCF/VAX user account. Enquiries for ISCCP-C1 and ISCCP-C2 data from NCDS should be directed to:

            Goddard DAAC User Support Office
            Code 935
            NASA Goddard Space Flight Center
            Greenbelt, Maryland 20771
            Commercial and FTS: (301) 286-3209
            NSI DECnet: NCF::NCDSUSO

9.3 NCDS Status/Plans.

The NASA Climate Data System (NCDS) supports the ISCCP-C2 monthly data set online in Common Data Format (CDF). The Common Data Format is data-set independent, which allows the application of a single set of analysis tools regardless of the original data format. To access these data, specify the following CDF within the Data Application subsystem of the NCDS:


The NCDS provides access to the ISCCP-C1 and the ISCCP-C2 HOUR-MONTHLY data through the Data Access subsystem. To access these data, specify the data set "ISCCP-C1" or "ISCCP-C2" within the search criteria screen of the NCDS Data Access subsystem.


            User Services Branch
            Satellite Data Services Division (SDSD)
            Princeton Executive Square, Suite 100
            Washington, DC 20233
            Commercial and FTS: (301) 763-8400
            Telex: RCA 248376 OBSWUR or TRT 197683 KWBC
            Commercial and FTS Telefax: (301) 763-8443

For information on access to NCDS contact:

            Goddard DAAC User Support Office
            Code 935
            NASA Goddard Space Flight Center
            Greenbelt, Maryland 20771
            Commercial and FTS: (301) 286-3209
            NSI DECnet: NCF::NCDSUSO


11.1 Satellite/Instrument/Data Processing Documentation

a.  Rossow, W.B., L.C. Garder, P-J. Lu and A.W. Walker, 1991. "International Satellite Cloud Climatology Project (ISCCP) Documentation of Cloud Data." WMO/TD No. 266 (revised). World Meteorological Organization, Geneva, 76 pp. plus three appendices.

b.  Rossow, W.B., E. Kinsella, A. Wolf, L. Garder, July 1985, revised August 1987. "International Satellite Cloud Climatology Project Description of Reduced Resolution Radiance Data." WMO TD-No. 58, World Meteorological Organization/ International Council of Scientific Unions.

c.  World Climate Research Programme, November, 1982. "The International Satellite Cloud Climatology Project Preliminary Implementation Plan." World Meteorological Organization. WCP-35.

11.2 Journal Articles and Study Reports

a.  Hirai, M. et al., 1975. "Development of Geostationary Meteorological Satellite (GMS) of Japan." Proc. of the Eleventh International Symposium of Space Technology and Science, Tokyo, Japan, 461-465.

b.  Matthews, E., and W.B. Rossow, 1987. "Regional and Seasonal Variations of Surface Reflectance from Satellites Observations at 0.6 um. J. Climate Appl. Meteor., 26: 170-202.

c.  Minnis, P., and E.F. Harrison, 1984. "Diurnal Variability of Regional Cloud and Clear Sky Radiative Parameters Derived from GOES Data. Part I: Analysis Method." J. Climate Appl. Meteor., 23: 993-1011.

d.  Raschke, E., W. Rossow and R. Schiffer, 1987. "The International Satellite Cloud Climatology Project - Preliminary Results and its Potential Aspects." Advanced Space Research, 7: (3)137-(3)145.

e.  Rossow, W.B., and L. Garder, 1984. "Selection of Map Grid for Data Analysis and Archival." J. Climate Appl. Meteor., 23: 1253-1257.

f.  Rossow, W.B., F. Mosher, E. Kinsella, A. Arking, M. Desbois, E. Harrison, P. Minnis, E. Ruprecht, G. Seze, C. Simmer and E. Smith, 1985. "ISCCP Cloud Algorithm Intercomparison." J. Climate Appl. Meteor., 24: 877-903.

g.  Rossow, W.B., 1989. "Measuring Cloud Properties from Space: A Review." J. of Climate, 2: 201-213.

h.  Rossow, W.B., L.C. Garder, and L.C. Lacis, 1989. "Global, Seasonal Cloud Variations from Satellite Radiance Measurements, Part I: Sensitivity of Analysis." J. of Climate, 2: 419-458.

i.  Rossow, W.B., C.L. Brest, and L.C. Garder, 1989. "Global, Seasonal Surface Variations from Satellite Radiance Measurements." J. of Climate, 2: 214-247.

j.  Rossow, W.B., and R.A. Schiffer, 1991. "ISCCP Cloud Data Products." Bull. Amer. Meteor. Soc., 72: 2-20.

k.  Schiffer, R.A., and W.B. Rossow, 1983. "The International Satellite Cloud Climatology Project (ISCCP) -- The First Project of the World Climate Research Program." Bull. Amer. Meteor. Soc., 64: 779-784.

l.  Schiffer, R.A., and W.B. Rossow, 1985. "ISCCP Global Radiance Data Set. A New Resource for Climate Research." Bull. Amer. Meteor. Soc., 66: 1498-1505.

m.  Seze, G., and M. Desbois, 1987. "Cloud Cover Analysis from Satellite Imagery using Spatial and Temporal Characteristics of the Data." J. Climate Appl. Meteor., 26: 287-303.

n.  Seze, G., and W.B. Rossow, 1987. "Time-cumulated Visible and Infrared Histograms used as Descriptor of Cloud Cover." Advanced Space Research, 7: (3)155-(3)158.

o.  Seze, G., and W.B. Rossow, 1991. "Time-cumulated Visible and Infrared Radiance Histograms Used as Descriptors of Surface and Cloud Variations." Int. J. Remote Sensing, 12: 877-920.

p.  Seze, G., and W.B. Rossow, 1991. "Effects of Satellite Data Resolution on Measuring the Space/Time Variations of Surfaces and Clouds." Int. J. Remote Sensing, 12: 921-952.

11.3 Archive/DBMS Usage Documentation

a.  Olsen, L.M, J.W. Closs, and F.E. Corprew, November 1991. "NASA's Climate Data System Primer: Version 4.0." EOS DAAC, NASA Goddard Space Flight Center, Greenbelt, Maryland.

b.  Rossow, W.B., L.C. Garder, P-J. Lu and A.W. Walker, 1991. "International Satellite Cloud Climatology Project (ISCCP) Documentation of Cloud Data." WMO/TD No. 266 (revised). World Meteorological Organization, Geneva, 76 pp. plus three appendices.


The Cloud Product from Nimbus-7 THIR is available through the NASA Climate Data System (NCDS). Specify the data set "CMATRIX" in the search criteria screen of the Data Access subsystem of NCDS. The NCDS also supports tape copies at the file level for the ISCCP-B3 product.


Sample Common Data Format (CDF) data sets are available through NCDS. The CDF is a special data-set independent format that converts data to a format that allows storage and retrieval regardless of the scaling or precision of the original data. The CDF data sets are accessible via the CDF Browse, CDF List, and Graphics options within the Data Applications subsystem of the NCDS. To view these sample data sets specify one of the following:


A sample plot is available for the ISCCP-C1 and ISCCP-C2 data sets. They are accessible via the Graphics option of the Data Manipulation subsystem of the NCDS. To view this sample plot, specify the plot files:



14.1 NCDS has divided the ISCCP-C1 data set into two datatypes: CLD_SFC and CLDPIXEL. CLD_SFC holds the cloud and surface properties and mean radiances data, and CLDPIXEL holds the pixel data. NCDS has divided the ISCCP-C2 data set into two datatypes: MONTHLY and HOUR-MONTHLY. MONTHLY contains the monthly means, and HOUR-MONTHLY contains the 3 hour monthly means for 00, 03, 06, 09, 12, 15, 18, 21 GMT.

The ice/snow and atmospheric profiles are supported as separate data sets: ISCCP_IS and ISCCP-TOVS, respectively.

These are the variables included for the ISCCP-C1 Stage C1 tape:

Byte #       Contents

Location information:

1                 Latitude index (equal-area)
2                 Longitude index (equal-area)
3                 Lower longitude index (2.5 deg lat/lon)
4                 Upper longitude index (2.5 deg lat/lon)

Cloud amount:

5                 Total number of pixels
6                 Number of cloudy pixels
7                 Number of IR-cloudy pixels
8                 Number of marginal IR-cloudy pixels
9 (d)            Number of marginal VIS/IR-cloudy pixels
10 (d)          Number of IR-only-cloudy pixels

Identification, snow/ice and viewing geometry:

11               Day/Night/Land/Water/Coast code
12               Satellite identification
13               Snow/ice cover percent
14 (d)         Cosine of solar zenith angle (MUO = 0-100)
15               Cosine of satellite zenith angle (MUE = 0-100)
16 (d)          Relative azimuth angle (PHI = 0 - 180 degrees)

Clear sky quality information:

17               Number of pixels with long term statistics
18 (d)          Number of pixels from cloud contaminated region (VIS)
19               Number of pixels cooler than clear IR
20               Number of pixels warmer than clear IR
21               Number of pixels much warmer than clear IR
22 (d)          Number of clear IR pixels brighter than clear VIS
23 (d)          Number of clear IR pixels darker than clear VIS
24 (d)          Number of clear IR pixels much darker than clear VIS
25               Number of clear pixels showing IR cloud contamination in RAD ANAL

PC distribution (UNADJUSTED PC)

26              Number of pixels   5 < PC < 180 mb
27              Number of pixels   180 < PC < 310 mb
28              Number of pixels   310 < PC < 440 mb
29              Number of pixels   440 < PC < 560 mb
30              Number of pixels   560 < PC < 680 mb
31              Number of pixels   680 < PC < 800 mb
32              Number of pixels   800 < PC < 1000 mb


33(d)          Number of pixels   5 < PC < 180 mb
34(d)          Number of pixels   180 < PC < 310 mb
35(d)          Number of pixels   310 < PC < 440 mb
36(d)          Number of pixels   440 < PC < 560 mb
37(d)          Number of pixels   560 < PC < 680 mb
38(d)          Number of pixels   680 < PC < 800 mb
39(d)          Number of pixels   800 < PC < 1000 mb

PC/TAU distribution (ADJUSTED PC)

40(d)          Number of pixels   5 < PC < 180 mb      0.02 < TAU < 1.27
41(d)          Number of pixels   5 < PC < 180 mb      1.27 < TAU < 3.55
42(d)          Number of pixels   5 < PC < 180 mb      3.55 < TAU < 9.38
43(d)          Number of pixels   5 < PC < 180 mb      9.38 < TAU < 22.63
44(d)          Number of pixels   5 < PC < 180 mb      22.63 < TAU < 119.59
45(d)          Number of pixels   180 < PC < 310 mb      0.02 < TAU < 1.27
46(d)          Number of pixels   180 < PC < 310 mb      1.27 < TAU < 3.55
47(d)          Number of pixels   180 < PC < 310 mb      3.55 < TAU < 9.38
48(d)          Number of pixels   180 < PC < 310 mb      9.38 < TAU < 22.63
49(d)          Number of pixels   180 < PC < 310 mb      22.63 < TAU < 119.59
50(d)          Number of pixels   310 < PC < 440 mb      0.02 < TAU < 1.27
51(d)          Number of pixels   310 < PC < 440 mb      1.27 < TAU < 3.55
52(d)          Number of pixels   310 < PC < 440 mb      3.55 < TAU < 9.38
53(d)          Number of pixels   310 < PC < 440 mb      9.38 < TAU < 22.63
54(d)          Number of pixels   310 < PC < 440 mb      22.63 < TAU < 119.59
55(d)          Number of pixels   440 < PC < 560 mb      0.02 < TAU < 1.27
56(d)          Number of pixels   440 < PC < 560 mb      1.27 < TAU < 3.55
57(d)          Number of pixels   440 < PC < 560 mb      3.55 < TAU < 9.38
58(d)          Number of pixels   440 < PC < 560 mb      9.38 < TAU < 22.63
59(d)          Number of pixels   440 < PC < 560 mb      22.63 < TAU < 119.59
60(d)          Number of pixels   560 < PC < 680 mb      0.02 < TAU < 1.27
61(d)          Number of pixels   560 < PC < 680 mb      1.27 < TAU < 3.55
62(d)          Number of pixels   560 < PC < 680 mb      3.55 < TAU < 9.38
63(d)          Number of pixels   560 < PC < 680 mb      9.38 < TAU < 22.63
64(d)          Number of pixels   560 < PC < 680 mb      22.63 < TAU < 119.59
65(d)          Number of pixels   680 < PC < 800 mb      0.02 < TAU < 1.27
66(d)          Number of pixels   680 < PC < 800 mb      1.27 < TAU < 3.55
67(d)          Number of pixels   680 < PC < 800 mb      3.55 < TAU < 9.38
68(d)          Number of pixels   680 < PC < 800 mb      9.38 < TAU < 22.63
69(d)          Number of pixels   680 < PC < 800 mb      22.63 < TAU < 119.59
70(d)          Number of pixels   800 < PC < 1000 mb      0.02 < TAU < 1.27
71(d)          Number of pixels   800 < PC < 1000 mb      1.27 < TAU < 3.55
72(d)          Number of pixels   800 < PC < 1000 mb      3.55 < TAU < 9.38
73(d)          Number of pixels   800 < PC < 1000 mb      9.38 < TAU < 22.63
74(d)          Number of pixels   800 < PC < 1000 mb      22.63 < TAU < 119.59

Mean cloud properties:

75              Mean PC for IR-cloudy pixels
76              Sigma-PC for IR-cloudy pixels
77              Mean PC for marginal IR-cloudy pixels
78(d)          Mean PC for VIS/IR-cloudy pixels
79(d)          Mean PC for marginal VIS/IR-cloudy pixels
80              Mean TC for IR-cloudy pixels
81              Sigma-TC for IR-cloudy pixels
82              Mean TC for marginal IR-cloudy pixels
83(d)          Mean TC for VIS/IR-cloudy pixels
84(d)          Mean TC for marginal VIS/IR-cloudy pixels
85(d)          Mean TAU for VIS/IR-cloudy pixels
86(d)          Sigma-TAU for VIS/IR-cloudy pixels
87(d)          Mean TAU for marginal IR-cloudy pixels
88(d)          Mean TAU for marginal VIS/IR-cloudy pixels

Mean surface properties:

89              Mean TS from clear sky composite
90              Mean TS for IR-clear pixels
91              Sigma-TS for IR-clear pixels
92(d)          Mean TS for VIS/IR-clear pixels
93(d)          Mean RS from clear sky composite
94(d)          Mean RS for VIS/IR-clear pixels
95(d)          Sigma-RS for VIS/IR-clear pixels
96(d)          Mean RS for IR-clear pixels

Mean radiances:

97              Mean IR for IR-cloudy pixels
98              Sigma-IR for IR-cloudy pixels
99(d)          Mean IR for VIS/IR-cloudy pixels
100            Mean IR for IR-clear pixels
101            Sigma-IR for IR-clear pixels
102(d)        Mean IR for VIS/IR-clear pixels
103            Mean IR from clear sky composite
104(d)        Mean VIS for VIS/IR-cloudy pixels
105(d)        Sigma-VIS for VIS/IR-cloudy pixels
106(d)        Mean VIS for IR-cloudy pixels
107(d)        Mean VIS for VIS/IR-clear pixels
108(d)        Sigma-VIS for VIS/IR-clear pixels
109(d)        Mean VIS for IR-clear pixels
110(d)        Mean VIS from clear sky composite

Coincident cloud amount differences:

111            Difference in mean MUE
112            Difference in mean cloud frequency

The following variables are available on the ISCCP-C1 tape, but are offered as a separate data set called ISCCP-TOVS through NCDS.

Atmospheric properties:

113             Atmospheric origin code
114             PS, surface pressure
115             TS, surface temperature
116             T, temperature 900 mb
117             T, temperature 740 mb
118             T, temperature 620 mb
119             T, temperature 500 mb
120             T, temperature 375 mb
121             T, temperature 245 mb
122             T, temperature 115 mb
123             PT, tropopause pressure
124             TT, tropopause temperature
125             ST, stratosphere temperature at 50 mb
126             ST, stratosphere temperature at 15 mb
127             PW, precipitable water at 900 mb
128             PW, precipitable water at 740 mb
129             PW, precipitable water at 620 mb
130             PW, precipitable water at 500 mb
131             PW, precipitable water at 375 mb
132             O3, ozone abundance

The variables included for the ISCCP Stage C2 data are also listed.