scholarly journals Retrieval of Ice Cloud Optical Thickness and Effective Particle Size Using a Fast Infrared Radiative Transfer Model

2011 ◽  
Vol 50 (11) ◽  
pp. 2283-2297 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Bryan A. Baum ◽  
Steven Platnick ◽  
Andrew K. Heidinger ◽  
...  
Keyword(s):  
Particle Size ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Distribution Method ◽  
Ice Cloud ◽  
Effective Particle Size ◽  
Effective Particle ◽  
Cloud Optical Thickness

AbstractA computationally efficient radiative transfer model (RTM) is developed for the inference of ice cloud optical thickness and effective particle size from satellite-based infrared (IR) measurements and is aimed at potential use in operational cloud-property retrievals from multispectral satellite imagery. The RTM employs precomputed lookup tables to simulate the top-of-the-atmosphere (TOA) radiances (or brightness temperatures) at 8.5-, 11-, and 12-μm bands. For the clear-sky atmosphere, the optical thickness of each atmospheric layer resulting from gaseous absorption is derived from the correlated-k-distribution method. The cloud reflectance, transmittance, emissivity, and effective temperature are precomputed using the Discrete Ordinate Radiative Transfer model (DISORT). For an atmosphere containing a semitransparent ice cloud layer with a visible optical thickness τ smaller than 5, the TOA brightness temperature differences (BTDs) between the fast model and the more rigorous DISORT results are less than 0.1 K, whereas the BTDs are less than 0.01 K if τ is larger than 10. With the proposed RTM, the cloud optical and microphysical properties are retrieved from collocated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) in conjunction with the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data. Comparisons between the retrieved ice cloud properties (optical thickness and effective particle size) based on the present IR fast model and those from the Aqua/MODIS operational collection-5 cloud products indicate that the IR retrievals are smaller. A comparison between the IR-retrieved ice water path (IWP) and CALIOP-retrieved IWP shows robust agreement over most of the IWP range.

scholarly journals Influence of Cloud-Top Height and Geometric Thickness on a MODIS Infrared-Based Ice Cloud Retrieval

2009 ◽  
Vol 48 (4) ◽  
pp. 818-832 ◽  
Author(s):  
Kevin J. Garrett ◽  
Ping Yang ◽  
Shaima L. Nasiri ◽  
Christopher R. Yost ◽  
Bryan A. Baum
Keyword(s):  
Particle Size ◽  
Optical Thickness ◽  
Lookup Table ◽  
Infrared Observation ◽  
Ice Cloud ◽  
Brightness Temperatures ◽  
Effective Particle Size ◽  
Effective Particle ◽  
Level 2 ◽  
Geometric Thickness

Abstract The retrieval of ice cloud microphysical and optical properties from satellite-based infrared observation remains a challenging research topic, partly because of the sensitivity of observed infrared radiances to many surface and atmospheric parameters that vary on fine spatial and temporal scales. In this study, the sensitivity of an infrared-based ice cloud retrieval to effective cloud temperature is investigated, with a focus on the effects of cloud-top height and geometric thickness. To illustrate the sensitivity, the authors first simulate brightness temperatures at 8.5 and 11.0 μm using the discrete ordinates radiative transfer (DISORT) model for five cloud-top heights ranging from 8 to 16 km and for varying cloud geometric thicknesses of 1, 2, 3, and 5 km. The simulations are performed across a range of visible optical thicknesses from 0.1 to 10 and ice cloud effective diameters from 30 to 100 μm. Furthermore, the effective particle size and optical thickness of ice clouds are retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements on the basis of a lookup-table approach. Specifically, the infrared brightness temperatures are simulated from the collocated Atmospheric Infrared Sounder (AIRS) level-2 product at 28 atmospheric levels and prescribed ice cloud parameters. Variations of the retrieved effective particle size and optical thickness versus cloud-top height and geometric thickness are investigated. Results show that retrievals based on the 8.5- and 11.0-μm bispectral approach are most valid for cloud-top temperatures of less than 224 K with visible optical thickness values between 2 and 5. The present retrievals are also compared with the collection-5 MODIS level-2 ice cloud product.

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).

Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance

2021 ◽  
Author(s):  
Marta Luffarelli ◽  
Yves Govaerts
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Solution Space ◽  
Single Scattering ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Earth System ◽  
System Components

<p>The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.</p><p> </p><p>Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.</p><p> </p><p>The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.  </p><p> </p><p>Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.</p>

scholarly journals Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements

2013 ◽  
Vol 6 (1) ◽  
pp. 105-119 ◽  
Author(s):  
J. T. Wiensz ◽  
D. A. Degenstein ◽  
N. D. Lloyd ◽  
A. E. Bourassa
Keyword(s):  
Particle Size ◽  
Optical Thickness ◽  
Imaging System ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Reconstruction Technique ◽  
Cirrus Cloud ◽  
Multiplicative Algebraic Reconstruction Technique ◽  
Cloud Particle

Abstract. We present a technique for estimating the optical thickness of subvisual cirrus clouds detected by OSIRIS (Optical Spectrograph and Infrared Imaging System), a limb-viewing satellite instrument that measures scattered radiances from the UV to the near-IR. The measurement set is composed of a ratio of limb radiance profiles at two wavelengths that indicates the presence of cloud-scattering regions. Cross-sections and phase functions from an in situ database are used to simulate scattering by cloud-particles. With appropriate configurations discussed in this paper, the SASKTRAN successive-orders of scatter radiative transfer model is able to simulate accurately the in-cloud radiances from OSIRIS. Configured in this way, the model is used with a multiplicative algebraic reconstruction technique (MART) to retrieve the cloud extinction profile for an assumed effective cloud particle size. The sensitivity of these retrievals to key auxiliary model parameters is shown, and it is shown that the retrieved extinction profile, for an assumed effective cloud particle size, models well the measured in-cloud radiances from OSIRIS. The greatest sensitivity of the retrieved optical thickness is to the effective cloud particle size. Since OSIRIS has an 11-yr record of subvisual cirrus cloud detections, the work described in this manuscript provides a very useful method for providing a long-term global record of the properties of these clouds.

scholarly journals Cloud sensitivity studies for stratospheric and lower mesospheric ozone profile retrievals from measurements of limb-scattered solar radiation

2009 ◽  
Vol 2 (2) ◽  
pp. 653-678 ◽  
Author(s):  
T. Sonkaew ◽  
V. V. Rozanov ◽  
C. von Savigny ◽  
A. Rozanov ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Relative Error ◽  
Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Absorption Bands ◽  
Ozone Profile ◽  
Mesospheric Ozone ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. Clouds in the atmosphere play an important role in reflection, absorption and transmission of solar radiation and thus affect trace gas retrievals. The main goal of this paper is to examine the sensitivity of stratospheric and lower mesospheric ozone retrievals from limb-scattered radiance measurements to clouds using the SCIATRAN radiative transfer model and retrieval package. The retrieval approach employed is optimal estimation, and the considered clouds are vertically and horizontally homogeneous. Assuming an aerosol-free atmosphere and Mie phase functions for cloud particles, we compute the relative error of ozone profile retrievals in a cloudy atmosphere if clouds are neglected in the retrieval. To access altitudes from the lower stratosphere up to the lower mesosphere, we combine the retrievals in the Chappuis and Hartley ozone absorption bands. We find significant cloud sensitivity of the limb ozone retrievals in the Chappuis bands at lower stratospheric altitudes. The relative error in the retrieved ozone concentrations gradually decreases with increasing altitude and becomes negligible above approximately 40 km. The parameters with the largest impact on the ozone retrievals are cloud optical thickness, ground albedo and solar zenith angle. Clouds with different geometrical thicknesses or different cloud altitudes have a similar impact on the ozone retrievals for a given cloud optical thickness value, if the clouds are outside the field of view of the instrument. The effective radius of water droplets has a small influence on the error, i.e., less than 0.5% at altitudes above the cloud top height. Furthermore, the impact of clouds on the ozone profile retrievals was found to have a rather small dependence on the solar azimuth angle (less than 1% for all possible azimuth angles). For the most frequent cloud types, the total error is below 6% above 15 km altitude, if clouds are completely neglected in the retrieval. Neglecting clouds in the ozone profile retrievals generally leads to a low bias for a low ground albedo and to a high bias for a high ground albedo, assuming that the ground albedo is well known.

scholarly journals Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

Sign in / Sign up

Export Citation Format

Share Document