Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

doi:10.1002/2015jd024526

Cited by 43 publications

(48 citation statements)

References 72 publications

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“…The same properties can be retrieved for optically thin cirrus clouds during night as well using only thermal observations (e.g. Prabhakara et al, 1988;Ackerman et al, 1990;Yue and Liou, 2009;Minnis et al, 2011;Heidinger et al, 2015;Wang et al, 2016), but with a limited accuracy due to the low sensitivity to large ice crystal sizes and large optical thicknesses.…”

Section: Introductionmentioning

confidence: 95%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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Section: Introductionmentioning

confidence: 95%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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“…Kahn et al 2008;Posselt et al, 2008), systematic uncertainties and approximations taken in the forward model (e.g. Wang et al, 2016;Irion et al, 2018), and instrument calibration drift (e.g. Pagano et al, 2012;Yue et al, 2017a;Manning and Aumann, 2017).…”

Section: Introductionmentioning

confidence: 99%

Ice cloud microphysical trends observed by the Atmospheric Infrared Sounder

et al. 2018

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Abstract. We use the Atmospheric Infrared Sounder (AIRS) version 6 ice cloud property and thermodynamic phase retrievals to quantify variability and 14-year trends in ice cloud frequency, ice cloud top temperature (T ci ), ice optical thickness (τ i ) and ice effective radius (r ei ). The trends in ice cloud properties are shown to be independent of trends in information content and χ 2 . Statistically significant decreases in ice frequency, τ i , and ice water path (IWP) are found in the SH and NH extratropics, but trends are of much smaller magnitude and statistically insignificant in the tropics. However, statistically significant increases in r ei are found in all three latitude bands. Perturbation experiments consistent with estimates of AIRS radiometric stability fall significantly short of explaining the observed trends in ice properties, averaging kernels, and χ 2 trends. Values of r ei are larger at the tops of opaque clouds and exhibit dependence on surface wind speed, column water vapour (CWV) and surface temperature (T sfc ) with changes up to 4-5 µm but are only 1.9 % of all ice clouds. Non-opaque clouds exhibit a much smaller change in r ei with respect to CWV and T sfc . Comparisons between DARDAR and AIRS suggest that r ei is smallest for singlelayer cirrus, larger for cirrus above weak convection, and largest for cirrus above strong convection at the same cloud top temperature. This behaviour is consistent with enhanced particle growth from radiative cooling above convection or large particle lofting from strong convection.

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“…For example the split-window technique (Inoue, 1985) applied to the Advanced Very-High-Resolution Radiometer (AVHRR; Parol et al, 1991) and the Imaging Infrared Radiometer (IIR) onboard CALIPSO (Garnier et al, 2012(Garnier et al, , 2013) is used to retrieve CED and COT from the brightness temperature difference of two different channels in the infrared atmospheric windows where gaseous absorption is small. Based on the same spectral information, an optimal estimation method (OEM; Rodgers, 2000) is used for the Atmospheric Infrared Sounder V6 (AIRS, Kahn et al, 2014Kahn et al, , 2015) and in the research-level code of Wang et al (2016b, a) for MODIS. Another advantage of the TIR is that measurements can also be obtained in nighttime conditions, which gives a distinct benefit compared to solarreflectance channels for developing ice cloud climatologies.…”

Section: Introductionmentioning

confidence: 99%

Scale dependence of cirrus horizontal heterogeneity effects on TOA measurements – Part I: MODIS brightness temperatures in the thermal infrared

et al. 2017

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Abstract. This paper presents a study on the impact of cirrus cloud heterogeneities on MODIS simulated thermal infrared (TIR) brightness temperatures (BTs) at the top of the atmosphere (TOA) as a function of spatial resolution from 50 m to 10 km. A realistic 3-D cirrus field is generated by the 3DCLOUD model (average optical thickness of 1.4, cloudtop and base altitudes at 10 and 12 km, respectively, consisting of aggregate column crystals of D eff = 20 µm), and 3-D thermal infrared radiative transfer (RT) is simulated with the 3DMCPOL code. According to previous studies, differences between 3-D BT computed from a heterogenous pixel and 1-D RT computed from a homogeneous pixel are considered dependent at nadir on two effects: (i) the optical thickness horizontal heterogeneity leading to the plane-parallel homogeneous bias (PPHB) and the (ii) horizontal radiative transport (HRT) leading to the independent pixel approximation error (IPAE). A single but realistic cirrus case is simulated and, as expected, the PPHB mainly impacts the low-spatialresolution results (above ∼ 250 m) with averaged values of up to 5-7 K, while the IPAE mainly impacts the high-spatialresolution results (below ∼ 250 m) with average values of up to 1-2 K. A sensitivity study has been performed in order to extend these results to various cirrus optical thicknesses and heterogeneities by sampling the cirrus in several ranges of parameters. For four optical thickness classes and four optical heterogeneity classes, we have found that, for nadir observations, the spatial resolution at which the combination of PPHB and HRT effects is the smallest, falls between 100 and 250 m. These spatial resolutions thus appear to be the best choice to retrieve cirrus optical properties with the smallest cloud heterogeneity-related total bias in the thermal infrared. For off-nadir observations, the average total effect is increased and the minimum is shifted to coarser spatial resolutions.

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Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

Cited by 43 publications

References 72 publications

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Ice cloud microphysical trends observed by the Atmospheric Infrared Sounder

Scale dependence of cirrus horizontal heterogeneity effects on TOA measurements – Part I: MODIS brightness temperatures in the thermal infrared

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