Nimbus-7 Global Cloud Climatology. Part II: First Year Results

Stowe, Larry L.; Yeh, H.-Y. M.; Eck, T. F.; Wellemeyer, Charlie G.; Kyle, H. Lee; Team, The Nimbus-7 Cloud Data Processing

doi:10.1175/1520-0442(1989)002<0671:ngccpi>2.0.co;2

Cited by 83 publications

(22 citation statements)

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“…The maximum fraction of POAs in the tropics follows the motion of the intertropical convergence zone, while the minimum occurrence in subtropical areas is located immediately north (south) of the tropical peak in the Northern (Southern) Hemisphere. The above zonal feature and the seasonal variation of POAs are consistent with cloud occurrence in these regions (Stowe et al, 1989;Wang et al, 1996). There is much less variation for NOAs except in the Southern Hemisphere 50 • S-60 • S. NOAs in this region peak in the late austral winter and early austral spring when the area is frequently covered with sea ice.…”

Section: Causes Of Ozone Anomaly Occurrence Over Cloudy Areassupporting

confidence: 70%

“…8a and c), along with the monthly variation of fraction and slope over the Indian Ocean and South America for comparison. The fractions of POAs for these four regions are slightly different and basically agree with the occurrence of high-reflectivity convective clouds or marine stratocumulus clouds (Stowe et al, 1989;Thompson et al, 1993). However, the two marine stratocumulus regions and the two convective cloudy areas show obviously different slope variations.…”

Section: Marine Stratocumulus Clouds and Enhanced Tropospheric Ozonesupporting

confidence: 68%

“…Each thin curve represents the frequency distribution for one day, and the daily curves are very similar for each day. In both hemispheres, these correlation coefficients peak around zero in the winter (In this paper, a season refers to both boreal and austral ones unless specified such as austral winter) but shift to the right in the summer mostly due to the summer increase in cloud occurrence (Stowe et al, 1989). There is a significant fraction of large negative or positive correlation coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…These are regions of frequent marine stratocumulus (Rossow and Schiffer, 1991;Thompson et al, 1993). Other regions with POAs of fractions > 40% include the United States, central China, southern Australia, and tropical convective cloudy areas (Ramanathan et al, 1989;Stowe et al, 1989;Wang et al, 1996) such as central America, central Africa, and the western Pacific Ocean. Mid-latitude regions are extensively associated with high dense POAs with a fraction of 15-40%.…”

Section: Occurrence Of Ozone Anomaliesmentioning

confidence: 99%

“…We use Temperature Humidity Infrared Radiometer (THIR) cloud measurements on board the N7 satellite to analyze errors in assumed monthly mean CTPs from ISCCP cloud data. THIR CTPs are derived from radiances measured at 11.5 µm along with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) 2.5 • ×2.5 • 6-h grids of temperature profiles (Stowe et al, 1989). The six-year (1979)(1980)(1981)(1982)(1983)(1984) THIR CTPs are collocated with TOMS measurements and archived in TOMS data products.…”

Section: Causes Of Ozone Anomaly Occurrence Over Cloudy Areasmentioning

confidence: 99%

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Occurrence of ozone anomalies over cloudy areas in TOMS version-7 level-2 data

2003

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Abstract. This study investigates anomalous ozone distributions over cloudy areas in Nimbus-7 (N7) and Earth-Probe (EP) TOMS version-7 data and analyzes the causes for ozone anomaly formation. A 5 • -longitude by 5 • -latitude region is defined to contain a Positive Ozone Anomaly (POA) or Negative Ozone Anomaly (NOA) if the correlation coefficient between total ozone and reflectivity is ≥ 0.5 or ≤ −0.5. The average fractions of ozone anomalies among all cloud fields are 31.8 ± 7.7% and 35.8 ± 7.7% in the N7 and EP TOMS data, respectively. Some ozone anomalies are caused by ozone retrieval errors, and others are caused by actual geophysical phenomena. Large cloud-height errors are found in the TOMS version-7 algorithm in comparison to the Temperature Humidity Infrared Radiometer (THIR) cloud data. On average, cloud-top pressures are overestimated by ∼200 hPa (THIR cloud-top pressure ≤ 200 hPa) for high-altitude clouds and underestimated by ∼150 hPa for low-altitude clouds (THIR cloud-top pressure ≥ 750 hPa). Most tropical NOAs result from negative errors induced by large cloud-height errors, and most tropical POAs are caused by positive errors due to intra-cloud ozone absorption enhancement. However, positive and negative errors offset each other, reducing the ozone anomaly occurrence in TOMS data. Large ozone/reflectivity slopes for mid-latitude POAs show seasonal variation consistent with total ozone fluctuation, indicating that they result mainly from synoptic and planetary wave disturbances. POAs with an occurrence fraction of 30-60% occur in regions of marine stratocumulus off the west coast of South Africa and off the west coast of South America. Both fractions and ozone/reflectivity slopes of these POAs show seasonal variations consistent with that in the tropospheric ozone. About half the ozone/reflectivity slope can be explained by ozone retrieval errors over clear and cloudy areas. The remaining slope may result from there being more ozone production because of rich ozone precurCorrespondence to: X. Liu (xliu@cfa.harvard.edu) sors and higher photolysis rates over high-frequency, lowaltitude clouds than in clear areas. Ozone anomalies due to ozone retrieval errors have important implications in TOMS applications such as tropospheric ozone derivation and analysis of ozone seasonal variation.

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Section: Causes Of Ozone Anomaly Occurrence Over Cloudy Areassupporting

confidence: 70%

Section: Marine Stratocumulus Clouds and Enhanced Tropospheric Ozonesupporting

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Section: Occurrence Of Ozone Anomaliesmentioning

confidence: 99%

Section: Causes Of Ozone Anomaly Occurrence Over Cloudy Areasmentioning

confidence: 99%

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Occurrence of ozone anomalies over cloudy areas in TOMS version-7 level-2 data

2003

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An interactive hybrid expert system for polar cloud and surface classification

1992

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An interactive hybrid expert system is developed to classify polar scenes using AVHRR LAC imagery. A total of 183 spectral and textural signatures are generated from which the 20 "best" are chosen using the Sequential Forward Selection procedure. These 20 features are used to populate the working memory of the expert system. A probabilistic neural network is used as the inference engine to make probabilistic estimates of class membership. As part of the inference engine, a sophisticated outlier test is performed to provide a measure of classification confidence.During a session, the user is provided with an extensive set of on-screen aids to assist in labelling. The user may modify the knowledge base by adding new samples to existing classes or by including new classes. The expert system provides confidence measures and a distance measure from the proposed class cluster centre. The interactive environment allows the user to test the impact of class labelling upon the knowledge base before new data is entered. For users working with very large datasets and very complex scenes, the integrity of the knowledge base is the primary concern.A bootstrap method is used to validate classification accuracy. On the basis of 100 bootstrap samples, an overall classification accuracy of 87% is achieved, with a standard deviation of 1%. The result is that much more

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Monthly mean upper tropospheric humidities in cloud‐free areas from meteosat observations

Berg

Pyomjamsri

Schmetz

1991

Intl Journal of Climatology

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The purpose of this paper is to study the annual variation of the upper tropospheric relative humidity fields extracted from radiance measurements in the water vapour channel (in the thermal infra-red at wavelengths around 6 3 pm) aboard the geostationary satellite METEOSAT. Monthly mean fields for April, July and October 1988 and January 1989 have been created from the operational Upper Tropospheric Humidity (hereafter UTH) product derived routinely at the European Space Operations Centre (ESOC). The present product retrieval IS confined to cloud-free areas in the upper troposphere, thus the monthly means represent an average over cloud-free cases.Principally the patterns of the monthly mean UTH fields reflect the large-scale circulation of the atmosphere. In the low latitudes, belts with relatively high and low UTH values are located over the ascending and descending branches of the 'Hadley cell', respectively. The distinct patterns of monthly mean UTH fields can be used to characterize the geographical structure of the Hadley cell.The UTH fields complement existing data on atmospheric humidity for use in numerical weather prediction and climatological research. The monthly mean fields presented in this paper may be useful for validating upper tropospheric water vapour fields in climate models.

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Nimbus-7 Global Cloud Climatology. Part II: First Year Results

Cited by 83 publications

References 0 publications

Occurrence of ozone anomalies over cloudy areas in TOMS version-7 level-2 data

Occurrence of ozone anomalies over cloudy areas in TOMS version-7 level-2 data

An interactive hybrid expert system for polar cloud and surface classification

Monthly mean upper tropospheric humidities in cloud‐free areas from meteosat observations

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