Retrieval of radiative and microphysical properties of clouds from multispectral infrared measurements

Iwabuchi, Hironobu; Saito, Masanori; Tokoro, Yuka; Putri, Nurfiena Sagita; Sekiguchi, M

doi:10.1186/s40645-016-0108-3

Cited by 54 publications

(45 citation statements)

References 56 publications

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“…The lowest retrieval errors are observed at high altitudes (CTH CALIOP ∈ [15, 17] km), where the CTH CiPS can be retrieved with a small error also for sub-visual cirrus. Similar features are observed using an optimal estimation method in Iwabuchi et al (2016). For thin to sub-visual cirrus clouds, CiPS is more likely to overestimate the CTH (positive MPE).…”

Section: The Cips Retrieval Errors As a Function Of Optical Thicknesssupporting

confidence: 57%

“…Please also note that a 800 % MAPE observed at IOT CALIOP = 0.01 translates into a small absolute error (0.08). Similar optical thickness retrieval errors are shown for the optimal estimation retrieval by Iwabuchi et al (2016), demonstrating that the large errors are not an artefact of the ANN but rather due to physical constraints discussed above. There are approx.…”

Section: The Cips Retrieval Errors As a Function Of Optical Thicknesssupporting

confidence: 56%

“…Similar to physically based retrieval methods (e.g. Iwabuchi et al, 2016), CiPS struggles for low and optically thin cirrus clouds, where the radiative contrast between the cirrus clouds and the underlying surface is weaker. This implies that although CiPS (and ANNs in general) lacks the explicit implementation of physical principles, the CiPS cirrus cloud retrievals have similar limitations as physically based retrievals where the physical principles are explicitly implemented.…”

Section: Discussionmentioning

confidence: 99%

“…This reflects the importance of these channels in physically based retrievals (e.g. Ewald et al, 2013;Heidinger et al, 2015;Iwabuchi et al, 2016). For the CTH CiPS retrieval, the latitude is the dominant input variable, followed by the water vapour channels.…”

Section: Relative Importance Of the Cips Input Datamentioning

confidence: 99%

“…Although ANNs are a powerful alternative to physically based cloud retrievals (e.g. Platnick et al, 2003;Bugliaro et al, 2011;Minnis et al, 2011;Stengel et al, 2014;Heidinger et al, 2015;Wang et al, 2016;Iwabuchi et al, 2016), they are trained to learn patterns and model relationships, and physical principles are not imposed for the scenes being investigated. Consequently, it is difficult to predict how sensitive the retrievals are to different land surface types and the wealth of natural atmospheric situations and if there are physical conditions where the ANNs are incapable of retrieving meaningful results.…”

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confidence: 99%

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Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate largescale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary.In this paper the CiPS (Cirrus Properties from SE-VIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m −2 . For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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Section: The Cips Retrieval Errors As a Function Of Optical Thicknesssupporting

confidence: 57%

Section: The Cips Retrieval Errors As a Function Of Optical Thicknesssupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Relative Importance Of the Cips Input Datamentioning

confidence: 99%

mentioning

confidence: 99%

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Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Ice particle morphology and microphysical properties of cirrus clouds inferred from combined CALIOP‐IIR measurements

et al. 2017

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Ice particle morphology and microphysical properties of cirrus clouds are essential for assessing radiative forcing associated with these clouds. We develop an optimal estimation‐based algorithm to infer cirrus cloud optical thickness (COT), cloud effective radius (CER), plate fraction including quasi‐horizontally oriented plates (HOPs), and the degree of surface roughness from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Infrared Imaging Radiometer (IIR) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) platform. A simple but realistic ice particle model is used, and the relevant bulk optical properties are computed using state‐of‐the‐art light‐scattering computational capabilities. Rigorous estimation of uncertainties related to surface properties, atmospheric gases, and cloud heterogeneity is performed. The results based on the present method show that COTs are quite consistent with other satellite products and CERs essentially agree with the other counterparts. A 1 month global analysis for April 2007, in which CALIPSO off‐nadir angle is 0.3°, shows that the HOP has significant temperature‐dependence and is critical to the lidar ratio when cloud temperature is warmer than −40°C. The lidar ratio is calculated from the bulk optical properties based on the inferred parameters, showing robust temperature dependence. The median lidar ratio of cirrus clouds is 27–31 sr over the globe.

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Added value of far‐infrared radiometry for remote sensing of ice clouds

Libois

Blanchet

2017

JGR Atmospheres

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Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far‐infrared (FIR, λ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection‐based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

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Retrieval of radiative and microphysical properties of clouds from multispectral infrared measurements

Cited by 54 publications

References 56 publications

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Ice particle morphology and microphysical properties of cirrus clouds inferred from combined CALIOP‐IIR measurements

Added value of far‐infrared radiometry for remote sensing of ice clouds

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