Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

Huang, Hung‐Lung; Smith, William L.; Li, Jun; Antonelli, Paolo; Wu, Xiangqian; Knuteson, Robert O.; Huang, Bormin; Osborne, Brian

doi:10.1175/2090.1

Cited by 26 publications

(22 citation statements)

References 13 publications

(13 reference statements)

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“…In order to allow for derivation of surface emissivity and cloud properties, 20 additional window channels and channels on weak absorption lines in the window are added. The use of weak absorption lines in the derivation of surface and cloud emissivity is discussed in (Huang et al, 2004).…”

Section: Channel Selection Based On Information Contentmentioning

confidence: 99%

Selection of IASI channels for use in numerical weather prediction

Collard

2007

Quart J Royal Meteoro Soc

124

137

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ABSTRACT:The Infrared Atmospheric Sounding Interferometer (IASI) is an infrared Fourier-transform spectrometer that has been flying on the MetOp satellite series since October 2006. It measures the radiance emitted from the Earth in 8461 channels, covering the spectral interval 645-2760 cm −1 at a resolution of 0.5 cm −1 (apodized). The high volume of data resulting from IASI presents many challenges, particularly in the areas of data transmission, storage and assimilation.The simplest methods for reducing the data volume are spatial sampling and channel selection. In this paper, the issue of channel selection is discussed, and a selection of 300 channels suitable for transmission to numerical-weather-prediction centres is examined; the primary aim is the improvement of the temperature, humidity, ozone and carbon-dioxide state vectors. The channel-selection method takes account of redundancy, the effects of interfering species, and robustness against the choice of assumed background-error covariance and atmospheric state.

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Section: Channel Selection Based On Information Contentmentioning

confidence: 99%

Selection of IASI channels for use in numerical weather prediction

Collard

2007

Quart J Royal Meteoro Soc

124

137

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“…The derivation is similar to but differs slightly in symbols and development from those of Mahesh et al (2001), Holz et al (2006), and Huang et al (2004) to illustrate some key points. Next, we describe MLEV, followed by the CO 2 slicing method as applied by Mahesh et al (2001) and the CO 2 slicing/sorting method of Holz et al (2006).…”

Section: Cloud-height Retrieval Methodsmentioning

confidence: 99%

“…The "local" emissivity variance (LEV) is then calculated according to Eq. (5) of Huang et al (2004):…”

Section: Mlevmentioning

confidence: 99%

“…where this is defined as the local emissivity variance because the mean of is not taken over the entire spectral range ν 1 to 3646 P. M. Rowe et al: Surface-based cloud-height retrievals ν 2 but rather over a small wavenumber region ( ν) about ν: Huang et al (2004) use ν 1 = 750 and ν 2 = 950 cm −1 in Eq. (7) and use an interval of ν = 5 cm −1 in Eq.…”

Section: Mlevmentioning

confidence: 99%

“…This allows uncertainties associated with retrieval methodology and instrument characteristics to be isolated. Two established methods for retrieving cloud height using spectrally resolved infrared instruments, the minimum local emissivity variance (MLEV) technique (Huang et al, 2004) and the CO 2 slicing (e.g., Menzel et al, 1983;Mahesh et al, 2001) and sorting (Holz et al, 2006) technique, are further developed and intercompared here. Although these have been compared for satellite-based retrievals from upwelling radiances (Holz et al, 2006), they have not been compared for retrievals from downwelling radiances or with consideration of variability in noise characteristics and spectral sampling between different types of spectrometers, which are key engineering barriers to developing an autonomous surface-based system.…”

Section: Introductionmentioning

confidence: 99%

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Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

2016

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Abstract. Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO 2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than ∼ 0.3, cloud-height retrievals from simulated spectra using CO 2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5 cm −1 , mean biases are found to be ∼ 0.2 km for clouds with bases below 2 and −0.2 km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO 2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2 mW/(m 2 sr cm −1 ), at a resolution of 4 cm −1 , average retrieval errors are found to be less than ∼ 0.5 km for cloud bases within 1 km of the surface, increasing to ∼ 1.5 km at 4 km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

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Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements

Pavolonis

Heidinger

Sieglaff³

2013

JGR Atmospheres

127

202

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A fully automated, globally applicable algorithm to retrieve ash and dust cloud properties from infrared satellite measurements is presented. The algorithm, which will serve as the official operational algorithm of the next generation Geostationary Operational Environmental Satellite (GOES-R), utilizes an optimal estimation framework that allows uncertainties in the measurements and forward model to be taken into account and uncertainty estimates for each of the retrieved parameters to be determined. The retrieval approach is globally applicable because background atmospheric water vapor, surface temperature, and surface emissivity are explicitly accounted for on a pixel-by-pixel basis. The retrieval is demonstrated using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the Second Generation Meteosat. Ash clouds from the 2010 eruption of Eyjafjallajökull in Iceland and the 2010 eruption of Soufriere Hills in the eastern Caribbean and a Saharan dust cloud were analyzed, and the accuracy of the retrieval was evaluated using spaceborne lidar measurements. The validation analysis shows that the retrieved ash/ dust cloud height, cloud emissivity, and effective particle radius generally agrees well with lidar measurements, especially when volcanic ash clouds are assumed to be composed of andesite and dust clouds composed of kaolinite.

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Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

Cited by 26 publications

References 13 publications

Selection of IASI channels for use in numerical weather prediction

Selection of IASI channels for use in numerical weather prediction

Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements

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