Ice Cloud Optical Thickness, Effective Radius, And Ice Water Path Inferred From Fused MISR and MODIS Measurements Based on a Pixel‐Level Optimal Ice Particle Roughness Model

2019 ◽  
Vol 124 (22) ◽  
pp. 12126-12140
Author(s):  
Yi Wang ◽  
Ping Yang ◽  
Souichiro Hioki ◽  
Michael D. King ◽  
Bryan A. Baum ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Effective Radius ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Roughness Model ◽  
Cloud Optical Thickness ◽  
Ice Water

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

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

2017 ◽  
Vol 10 (11) ◽  
pp. 4317-4339 ◽  
Author(s):  
Johan Strandgren ◽  
Jennifer Fricker ◽  
Luca Bugliaro
Keyword(s):  
Remote Sensing ◽  
Optical Thickness ◽  
Cirrus Clouds ◽  
Cirrus Cloud ◽  
Retrieval Error ◽  
Water Path ◽  
Retrieval Accuracy ◽  
Ice Water Path ◽  
Retrieval Errors ◽  
Ice Water

Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale 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 SEVIRI) 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.

scholarly journals A spectral method for discriminating thermodynamic phase and retrieving cloud optical thickness and effective radius using transmitted solar radiance spectra

2015 ◽  
Vol 8 (3) ◽  
pp. 1361-1383 ◽  
Author(s):  
S. E. LeBlanc ◽  
P. Pilewskie ◽  
K. S. Schmidt ◽  
O. Coddington
Keyword(s):  
Root Mean Square ◽  
Optical Thickness ◽  
Liquid Water ◽  
Effective Radius ◽  
Least Square ◽  
Mean Square ◽  
Ice Cloud ◽  
Cloud Optical Thickness ◽  
Water Cloud ◽  
Thermodynamic Phase

Abstract. A new retrieval scheme for cloud optical thickness, effective radius, and thermodynamic phase was developed for ground-based measurements of cloud shortwave solar spectral transmittance. Fifteen parameters were derived to quantify spectral variations in shortwave transmittance due to absorption and scattering of liquid water and ice clouds, manifested by shifts in spectral slopes, curvatures, maxima, and minima. To retrieve cloud optical thickness and effective particle radius, a weighted least square fit that matched the modeled parameters was applied. The measurements for this analysis were made with the ground-based Solar Spectral Flux Radiometer in Boulder, Colorado, between May 2012 and January 2013. We compared the cloud optical thickness and effective radius from the new retrieval to two other retrieval methods. By using multiple spectral features, we find a closer fit (with a root mean square difference over the entire spectra of 3.1% for a liquid water cloud and 5.9% for an ice cloud) between measured and modeled spectra compared to two other retrieval methods which diverge by a root mean square of up to 6.4% for a liquid water cloud and 22.5% for an ice cloud. The new retrieval introduced here has an average uncertainty in effective radius (± 1.2 μm) smaller by factor of at least 2.5 than two other methods when applied to an ice cloud.

scholarly journals Large-Scale Analysis of Relationships between Mineral Dust, Ice Cloud Properties, and Precipitation from Satellite Observations Using a Bayesian Approach: Theoretical Basis and First Results for the Tropical Atlantic Ocean

2017 ◽  
Vol 2017 ◽  
pp. 1-18
Author(s):  
Lars Klüser ◽  
Thomas Popp
Keyword(s):  
Atlantic Ocean ◽  
Bayesian Approach ◽  
Mineral Dust ◽  
Tropical Atlantic ◽  
Ice Crystal ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water ◽  
Dust Load

Mineral dust and ice cloud observations from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the relationships between desert dust aerosols and ice clouds over the tropical Atlantic Ocean during the hurricane season 2008. Cloud property histograms are first adjusted for varying cloud top temperature or ice water path distributions with a Bayesian approach to account for meteorological constraints on the cloud variables. Then, histogram differences between dust load classes are used to describe the impact of dust load on cloud property statistics. The analysis of the histogram differences shows that ice crystal sizes are reduced with increasing aerosol load and ice cloud optical depth and ice water path are increased. The distributions of all three variables broaden and get less skewed in dusty environments. For ice crystal size the significant bimodality is reduced and the order of peaks is reversed. Moreover, it is shown that not only are distributions of ice cloud variables simply shifted linearly but also variance, skewness, and complexity of the cloud variable distributions are significantly affected. This implies that the whole cloud variable distributions have to be considered for indirect aerosol effects in any application for climate modelling.

scholarly journals CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

2013 ◽  
Vol 6 (5) ◽  
pp. 8187-8233 ◽  
Author(s):  
J. Gong ◽  
D. L. Wu
Keyword(s):  
Climate Models ◽  
Cloud Microphysics ◽  
Water Path ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Ice Water Path ◽  
Satellite Sensors ◽  
Key Variables ◽  
Cloud Ice ◽  
Ice Water

Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables to determine cloud radiative and thermodynamical properties in the climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the forward models between collocated-and-coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up-table (LUT) of Tcir–IWP relationships as a function of ht and frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of normalized probability density function (PDF). Compared to the empirical model, current radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains as an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

scholarly journals Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

2017 ◽  
Vol 10 (9) ◽  
pp. 3547-3573 ◽  
Author(s):  
Johan Strandgren ◽  
Luca Bugliaro ◽  
Frank Sehnke ◽  
Leon Schröder
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Optical Thickness ◽  
Cirrus Clouds ◽  
Percentage Error ◽  
Cirrus Cloud ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water

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 SEVIRI 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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