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William B. Rossow and Leonid C. Garder

Abstract

The International Satellite Cloud Climatology Project (ISCCP) began in 1983 to collect and analyze weather satellite radiance datasets to produce a new global cloud climatology as part of the World Climate Research Programme. This paper, the first of three, describes the cloud detection part of the ISCCP analysis. Key features of the cloud detection algorithm are 1) use of space and time radiance variation tests over several different space and time domains to account for the global variety of cloudy and clear characteristics, 2) estimation of clear radiance values for every time and place, and 3) use of radiance thresholds that vary with type of surface and climate regime. Design of the detection algorithm was supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes. A summary of these statistical results is presented to illustrate how the cloud detection method works in a variety of circumstances. The sensitivity of the results to changing test parameter values is determined to provide a first estimate of the uncertainty of ISCCP cloud amounts. These test results (which exclude polar regions) suggest detection uncertainties of about 10% with possible negative biases of 5% (especially at night).

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William B. Rossow and Leonid C. Garder

Abstract

The International Satellite Cloud Climatology Project (ISCCP) began in 1983 to collect and analyze weather satellite datasets to produce a new global cloud climatology as part of the World Climate Research Programme. The first step of the analysis is detection of the presence of clouds at each location and time by a series of tests on the space / time variations of infrared and visible radiances. This paper describes the validation of the ISCCP cloud detections by verifying the accuracy of the inferred clear-sky radiance. Comparison of retrieved surface temperatures to other measurements shows that bias errors are <2 K and random errors are about 2 K for sea surface (monthly means at 280-km scales) and that bias errors are <2 K and random errors are about 4 K for land surfaces (3 hourly at 280-km scale). Bias errors over a few persistently cloudy locations are sometimes −(2–4) K and over winter sea ice may be about +2 K. Surface reflectances are confirmed to be within 3% of other measurements and models for ocean, except for sun glint geometries, and to be within 3%–5% for land surfaces. Sufficiently accurate validation data are not available for visible reflectances of sea ice and snow-covered land, but some tests of specific cases suggest that errors are ∼10%. These errors in clear-sky radiances suggest uncertainties in the ISCCP cloud detections of about 10% with a small (3%–6%) negative bias over land. Some specific regions exhibit both larger rms uncertainties and somewhat larger biases in cloud amount approaching 10%. ISCCP cloud detections are more in error over the polar regions than anywhere else. Based on comparisons with an analysis of radiances measured at other wavelengths, the ISCCP analysis appears to miss 15%–25% of the clouds in summer but only 5%–10% of the winter clouds.

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William B. Rossow, Christopher L. Brest, and Leonid C. Garder

Abstract

Global, daily, visible, and infrared radiance measurements from the NOAA-5 Scanning Radiometer (SR) are analyzed for the months of January, April, July, and October 1977 to infer surface radiative properties A radiative transfer model that simulates the spectral and angular characteristics of the NOAA-5 SR measurements is used to retrieve monthly mean surface visible reflectances and temperature at 25 km resolution. These surface properties were found sufficiently accurate for simulation of clear sky radiances to determine global, seasonal variations in cloudiness. Further comparisons of these results with other data highlight the analysis difficulties and radiative model shortcomings that must be overcome to monitor regional and seasonal variations of earth's surface. These preliminary results also provide an estimate of the magnitude of these variations.

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William B. Rossow, Leonid C. Garder, and Andrew A. Lacis

Abstract

Global, daily, visible and infrared radiance measurements from the NOAA-5 Scanning Radiometer (SR) are analyzed for the months of January, April, July and October 1977 to infer cloud and surface radiative properties. In this first paper in a three part series, the data and analysis method are described. A unique feature of the method is that it utilizes radiative transfer models that simulate the SR measurements using explicit parameters representing the properties of the surface, atmosphere, and clouds. The simulations also account for variations that depend on viewing geometry. The analysis combines several datasets so that the cloud contributions to the SR measurements can be isolated. The accuracy of all the results depends primarily on the proper separation of the total radiance distribution into those parts representing clear and cloudy scenes. Comparison of the surface properties retrieved from the clear scene radiances [see also Rossow et al. (1989)], sensitivity tests of the cloud detection algorithm, and comparisons of the resulting cloud amounts (see also Part II) provide an assessment of the accuracy of the method.

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William B. Rossow, Alison W. Walker, and Leonid C. Garder

Abstract

A new 8-year global cloud climatology has been produced by the International Satellite Cloud Climatology Project (ISCCP) that provides information every 3 h at 280-km spatial resolution covering the period from July 1983 through June 1991. If cloud detection errors and differences in area sampling are neglected, individual ISCCP cloud amounts agree with individual surface observations to within 15% rms with biases of only a few percent. When measurements of small-scale, broken clouds are isolated in the comparison, the rms differences between satellite and surface cloud amounts are about 25%, similar to the rms difference between ISCCP and Landsat determinations of cloud amount. For broken clouds, the average ISCCP cloud amounts are about 5% smaller than estimated by surface observers (difference between earth cover and sky cover), but about 5% larger than estimated from very high spatial resolution satellite observations (overestimate due to low spatial resolution offset by underestimate due to finite radiance thresholds). Detection errors, caused by errors in the ISCCP clear-sky radiances or incorrect radiance threshold magnitude are the dominant source of error in monthly average cloud amounts. The ISCCP cloud amounts appear to he too low over land by about 10%, somewhat less in summer and somewhat more in winter, and about right (maybe slightly low) over oceans. In polar regions, ISCCP cloud amounts are probably too low by about 15%–25% in summer and 5%–10% in winter. Comparison of the ISCCP climatology to three other cloud climatologies shows excellent agreement in the geographic distribution and seasonal variation of cloud amounts; there is little agreement about day/night contrasts in cloud amount. Notable results from ISCCP are that the global annual mean cloud amount is about 63%, being about 23% higher over oceans than over land, that it varies by <1% rms from month to month, and that it has varied by about 4% on a time wale ≈2–4 years. The magnitude of interannual variations of local (280-km scale) monthly mean cloud amounts is about 7%–9%. Longitudinal contrasts in cloud amount are just as large as latitudinal contrasts. The largest seasonal variation of cloud amount occurs in the tropics, being larger in summer than in winter; the seasonal variation in middle latitudes has the opposite phase. Polar regions may have little seasonal variability in cloud amount. The ISCCP results show slightly more nighttime than daytime cloud amount over oceans and more daytime than nighttime cloud amount over land.

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