ISCCP Cloud Algorithm Intercomparison

Rossow, William B.; Mosher, Frederick R.; Kinsella, E.; Arking, Albert; Des̄bois, M.; Harrison, Edwin F.; Minnis, Patrick; Ruprecht, Eberhard; Sèze, Geneviève; Simmer, Clemens; Smith, Eric A.

doi:10.1175/1520-0450(1985)024<0887:icai>2.0.co;2

Cited by 175 publications

(94 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rossow et al, 1993;Jin et al, 1996;Thomas et al, 2004). Discrepancies exist not only from different measurement characteristics and sampling strategies, but perhaps as significantly, from retrieval algorithm differences and a priori assumptions (Rossow et al, 1985;Wielicki and Parker, 1992;Kahn et al, 2007b). CloudSat and CALIOP generally provide more direct and easily interpreted observations of cloud detection and vertical cloud structure than passive methods.…”

Section: Introductionmentioning

confidence: 99%

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

et al. 2008

View full text Add to dashboard Cite

Abstract. The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2-3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6-3.0±1.2-3.6 km (−5.8 to −0.2±0.5-2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Correspondence to: B. H. Kahn (brian.h.kahn@jpl.nasa.gov) Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

et al. 2008

View full text Add to dashboard Cite

show abstract

“…However, the present knowledge of the cloud distribution accompanies many uncertainties and inaccuracies, especially in the polar regions (Hughes, 1984;Crane and Barry, 1984). Satellite data have become a strong tool to obtain the global cloud climatology and many kinds of algorithm have been proposed to obtain clouds from the satellite imagery (Rossow et al, 1985). However, using these methods, it is difficult to detect cloud cover in the polar regions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Clouds in Antarctica from Infrared Multispectral Data of AVHRR

Yamanouchi

Suzuki

Kawaguchi

1987

Journal of the Meteorological Society of Japan

108

View full text Add to dashboard Cite

A method to detect cloud cover in the Antarctic using only the infrared channels of AVHRR is discussed. From the data of NOAA-7 received at Syowa Station, the difference in the brightness temperature of each channel appeared to be useful for the identification of clouds. The brightness temperature of the channels 3 (3.7*m) and 4 (11*m) shows the positive difference when the thickness of clouds are in some particular range, and then tends to show negative difference for the thick cloud. Thin clouds have the difference in the brightness temperature between channels 4 and 5 (12 * m). These tendencies are explained by the radiative properties of model clouds theoretically calculated. On the graph of these temperature difference against the channel 4 brightness temperature, pixels of the same cloud distribute on the particular arch starting from the clear pixel. From the arch, clouds can be distinguished from the ground surface. The particle size, temperature and thickness of the cloud can also be inferred. At the low temperature over the inland snow surface, many troubles arise. The channel 3 brightness temperature accompanies poor resolution and large noise at the low temperature. The brightness temperature difference between channels 4 and 5 shows strong dependence on temperature and viewing angle at the low temperature due to the nonlinearity error and variation of snow surface emissivity. An empirical correction is applied to the low temperature data for the automatic cloud detection.

show abstract

“…Second, these observations are available from geostationary satellites, making it possible to track cloud developments with frequent measurements of the same locations. For instance, visible and thermal infrared observations from polar orbiters and geostationary meteorological satellites have been combined by the International Satellite Cloud Climatology Project (ISCCP) to provide global cloud information dataset since 1983, every 3 h, with an ;30-km spatial resolution (Rossow and Schiffer 1999). To obtain global uniformity in the ISCCP climatology, which is built from observations from several instruments, the number of channels has been limited to one visible (VIS) and one infrared (IR) window.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain global uniformity in the ISCCP climatology, which is built from observations from several instruments, the number of channels has been limited to one visible (VIS) and one infrared (IR) window. A serial threshold technique is used to detect clouds; furthermore, a radiative transfer code and ancillary data such as temperature and humidity profiles are used to retrieve the cloud-top pressure and cloud optical thickness (Rossow et al 1985).…”

Section: Introductionmentioning

confidence: 99%

A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Aires¹,

Marquisseau²,

Prigent³

et al. 2011

Monthly Weather Review

View full text Add to dashboard Cite

A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations. Clear sky and low, medium, and opaque-high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

show abstract

ISCCP Cloud Algorithm Intercomparison

Cited by 175 publications

References 15 publications

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

Detection of Clouds in Antarctica from Infrared Multispectral Data of AVHRR

A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Contact Info

Product

Resources

About