Intercomparison of Deep Convective Cloud Fractions From Passive Infrared and Microwave Radiance Measurements

Hong, Gang; Heygster, Georg; Künzi, K.

doi:10.1109/lgrs.2004.838405

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…MSPPS average IWP for the tropical continental region is nearly a factor five higher than its total average. This is likely an indication of the deep convection which is generally more intense and more frequent over land than over ocean (Hong et al, 2005). The ocean-only IWP noPrecip dataset has in general a smaller fraction of IWP in the tropics and a larger fraction in the westerlies, than the other datasets.…”

Section: Iwp Seasonal Cyclementioning

confidence: 89%

Assessing observed and modelled spatial distributions of ice water path using satellite data

et al. 2011

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Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible.ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP Correspondence to: S. Eliasson (s.eliasson@ltu.se) variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations.

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Section: Iwp Seasonal Cyclementioning

confidence: 89%

Assessing observed and modelled spatial distributions of ice water path using satellite data

et al. 2011

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“…This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1-2], atmosphere [3][4][5][6], oceans [7][8][9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11][12][13][14], urban areas [15][16][17], and vegetated areas.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Jacob¹,

Schmugge

Olioso

et al. 2008

Advances in Land Remote Sensing

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U n c o r r e c t e d P r o o f 244 F. Jacob et al.Abstract Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. IntroductionUsing TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1-2], atmosphere [3][4][5][6], oceans [7][8][9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11][12][13][14], urban areas [15][16][17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil-vegetation-atmosphere interface [18][19][20][21][22][23][24]; (2) interactions between land surface and atmospheric boundary la...

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“…They only observe cloud top temperatures which frequently are similar for deep convective clouds and cirrus clouds [ Liu et al , 1995; Sui et al , 1997; Hall and Vonder Haar , 1999]. Hong et al [2005a] found that the deep convective cloud fraction (cloud/rain fraction in units of percent is defined as the ratio of total of cloud/rain flagged pixels over the total number of samples) detected with infrared methods by the brightness temperature threshold of 215 K, is over three times larger than that detected by the method of Hong et al [2005b] from passive microwave sensors sensitive to frozen hydrometeors in the interior of deep convective clouds. The authors explained this discrepancy by the fact that infrared threshold methods cannot distinguish between deep convective clouds and thick high cirrus clouds.…”

Section: Introductionmentioning

confidence: 99%

“…The dominant cloud type characteristic at each stage is cumulus, cumulonimbus, and anvil cloud (cirrus) [ Inoue , 2003], respectively. These cirrus clouds, tightly connected with deep convective clouds [ Garreaud and Wallace , 1997; Chou and Neelin , 1999; Dessler and Yang , 2003; Comstock and Jakob , 2004; Hong et al , 2005a], can extend and persist for some hours after the deep convective clouds themselves dissipate [ Gray and Jacobson , 1977; Weickmann et al , 1977]. It has been speculated that the extension and persistence of cirrus clouds may lead to some hours time lag for the maximum of the deep convective cloud fraction detected by infrared satellite observations [ Slingo et al , 2004; Hong et al , 2005a].…”

Section: Introductionmentioning

confidence: 99%

Effect of cirrus clouds on the diurnal cycle of tropical deep convective clouds

2006

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[1] The cirrus clouds tightly connected with tropical deep convective clouds can extend and persist for some hours after the deep convective clouds themselves dissipate. This can result in time lags of the diurnal cycle of deep convective clouds detected from infrared satellite measurements with different brightness temperature thresholds because different amounts of cirrus clouds contaminate the measurement. The diurnal cycles of rain from the Tropical Ocean-Global Atmosphere (TOGA) radar during the Tropical Rainfall Measuring Mission (TRMM) Wet Season Atmospheric Mesoscale Campaign (WETAMC) Large-Scale Biosphere Atmosphere (LBA) Experiment in Amazonia and the diurnal cycles of deep convective clouds and high cold clouds from the Precipitation Radar (PR), Visible and Infrared Scanner (VIRS) onboard the TRMM satellite over the tropics (30°S-30°N) from November 1998 to April 1999 are investigated to study the influence of cirrus clouds on the observed diurnal cycle of tropical deep convective clouds. A 2-hour time lag of the diurnal cycle of deep convective clouds from the VIRS with respect to that from the PR is found over land. Over ocean the cirrus clouds generated by deep convective clouds enhance the diurnal cycle of the deep convective clouds from the VIRS, and a time lag similar to that over land also occurs. The influence of cirrus clouds leads the diurnal cycle of the deep convective clouds from the VIRS to depend strongly on the selected IR threshold and to be very different from that of the PR over the maritime continent. Moreover, over ocean and the maritime continent, from late afternoon to midnight the strong increase of the deep convective clouds from the VIRS is mainly due to the developing cirrus clouds near and above the tropical tropopause layer.

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Intercomparison of Deep Convective Cloud Fractions From Passive Infrared and Microwave Radiance Measurements

Cited by 9 publications

References 32 publications

Assessing observed and modelled spatial distributions of ice water path using satellite data

Assessing observed and modelled spatial distributions of ice water path using satellite data

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Effect of cirrus clouds on the diurnal cycle of tropical deep convective clouds

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