scholarly journals Interpretation of AIRS Data in Thin Cirrus Atmospheres Based on a Fast Radiative Transfer Model

2007 ◽  
Vol 64 (11) ◽  
pp. 3827-3842 ◽  
Author(s):  
Qing Yue ◽  
K. N. Liou ◽  
S. C. Ou ◽  
B. H. Kahn ◽  
P. Yang ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Optical Depth ◽  
Crystal Size ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cirrus Cloud ◽  
Ice Crystal ◽  
Ice Crystal Size

Abstract A thin cirrus cloud thermal infrared radiative transfer model has been developed for application to cloudy satellite data assimilation. This radiation model was constructed by combining the Optical Path Transmittance (OPTRAN) model, developed for the speedy calculation of transmittances in clear atmospheres, and a thin cirrus cloud parameterization using a number of observed ice crystal size and shape distributions. Numerical simulations show that cirrus cloudy radiances in the 800–1130-cm−1 thermal infrared window are sufficiently sensitive to variations in cirrus optical depth and ice crystal size as well as in ice crystal shape if appropriate habit distribution models are selected a priori for analysis. The parameterization model has been applied to the Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite to interpret clear and thin cirrus spectra observed in the thermal infrared window. Five clear and 29 thin cirrus cases at nighttime over and near the Atmospheric Radiation Measurement program (ARM) tropical western Pacific (TWP) Manus Island and Nauru Island sites have been chosen for this study. A χ2-minimization program was employed to infer the cirrus optical depth and ice crystal size and shape from the observed AIRS spectra. Independent validation shows that the AIRS-inferred cloud parameters are consistent with those determined from collocated ground-based millimeter-wave cloud radar measurements. The coupled thin cirrus radiative transfer parameterization and OPTRAN, if combined with a reliable thin cirrus detection scheme, can be effectively used to enhance the AIRS data volume for data assimilation in numerical weather prediction models.

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 Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres

2020 ◽  
Vol 28 (18) ◽  
pp. 25730
Author(s):  
Wenwen Li ◽  
Feng Zhang ◽  
Yi-Ning Shi ◽  
Hironobu Iwabuchi ◽  
Mingwei Zhu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Brightness

Volcanic ash detection and retrievals using SLSTR. Test case: 2019 Raikoke  eruption

2021 ◽  
Author(s):  
Ilaria Petracca ◽  
Davide De Santis ◽  
Stefano Corradini ◽  
Lorenzo Guerrieri ◽  
Matteo Picchiani ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Volcanic Ash ◽  
Thermal Infrared ◽  
Integrated Approach ◽  
Effective Radius ◽  
Volcanic Plume ◽  
Transfer Model ◽  
Volcanic Cloud

<p>When an eruption event occurs it is necessary to accurately and rapidly determine the position and evolution during time of the volcanic cloud and its parameters (such as Aerosol Optical Depth-AOD, effective radius-Re and mass-Ma of the ash particles), in order to ensure the aviation security and the prompt management of the emergencies.</p><p>Here we present different procedures for volcanic ash cloud detection and retrieval using S3 SLSTR (Sentinel-3 Sea and Land Surface Temperature Radiometer) data collected the 22 June at 00:07 UTC by the Sentinel-3A platform during the Raikoke (Kuril Islands) 2019 eruption.</p><p>The volcanic ash detection is realized by applying an innovative machine learning based algorithm, which uses a MultiLayer Perceptron Neural Network (NN) to classify a SLSTR image in eight different surfaces/objects, distinguishing volcanic and weather clouds, and the underlying surfaces. The results obtained with the NN procedure have been compared with two consolidated approaches based on an RGB channels combination in the visible (VIS) spectral range and the Brightness Temperature Difference (BTD) procedure that exploits the thermal infrared (TIR) channels centred at 11 and 12 microns (S8 and S9 SLSTR channels respectively). The ash volcanic cloud is correctly identified by all the models and the results indicate a good agreement between the NN classification approach, the VIS-RGB and BTD procedures.</p><p>The ash retrieval parameters (AOD, Re and Ma) are obtained by applying three different algorithms, all exploiting the volcanic cloud “mask” obtained from the NN detection approach. The first method is the Look Up Table (LUT<sub>p</sub>) procedure, which uses a Radiative Transfer Model (RTM) to simulate the Top Of Atmosphere (TOA) radiances in the SLSTR thermal infrared channels (S8, S9), by varying the aerosol optical depth and the effective radius. The second algorithm is the Volcanic Plume Retrieval (VPR), based on a linearization of the radiative transfer equation capable to retrieve, from multispectral satellite images, the abovementioned parameters. The third approach is a NN model, which is built on a training set composed by the inputs-outputs pairs TOA radiances vs. ash parameters. The results of the three retrieval methods have been compared, considering as reference the LUT<sub>p</sub> procedure, since that it is the most consolidated approach. The comparison shown promising agreement between the different methods, leading to the development of an integrated approach for the monitoring of volcanic ash clouds using SLSTR.</p><p>The results presented in this work have been obtained in the sphere of the VISTA (Volcanic monItoring using SenTinel sensors by an integrated Approach) project, funded by ESA and developed within the EO Science for Society framework [https://eo4society.esa.int/projects/vista/].</p>

scholarly journals Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands

2020 ◽  
Vol 12 (18) ◽  
pp. 2939
Author(s):  
Chang-Hwan Park ◽  
Thomas Jagdhuber ◽  
Andreas Colliander ◽  
Johan Lee ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Microwave Remote Sensing ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Time Varying ◽  
Vegetation Fraction ◽  
Omega Model

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.

scholarly journals Comparison of PARASOL Observations with Polarized Reflectances Simulated Using Different Ice Habit Mixtures

2013 ◽  
Vol 52 (1) ◽  
pp. 186-196 ◽  
Author(s):  
Benjamin H. Cole ◽  
Ping Yang ◽  
Bryan A. Baum ◽  
Jerome Riedi ◽  
Laurent C.-Labonnote ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ice Crystal ◽  
Ice Clouds ◽  
Atmospheric Sciences ◽  
Reflection Data ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Polarized Reflectance

AbstractInsufficient knowledge of the habit distribution and the degree of surface roughness of ice crystals within ice clouds is a source of uncertainty in the forward light scattering and radiative transfer simulations of ice clouds used in downstream applications. The Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 ice microphysical model presumes a mixture of various ice crystal shapes with smooth facets, except for the compact aggregate of columns for which a severely rough condition is assumed. When compared with Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) polarized reflection data, simulations of polarized reflectance using smooth particles show a poor fit to the measurements, whereas very rough-faceted particles provide an improved fit to the polarized reflectance. In this study a new microphysical model based on a mixture of nine different ice crystal habits with severely roughened facets is developed. Simulated polarized reflectance using the new ice habit distribution is calculated using a vector adding–doubling radiative transfer model, and the simulations closely agree with the polarized reflectance observed by PARASOL. The new general habit mixture is also tested using a spherical albedo differences analysis, and surface roughening is found to improve the consistency of multiangular observations. These results are consistent with previous studies that have used polarized reflection data. It is suggested that an ice model incorporating an ensemble of different habits with severely roughened surfaces would potentially be an adequate choice for global ice cloud retrievals.

SHDOMPPDA: A Radiative Transfer Model for Cloudy Sky Data Assimilation

2007 ◽  
Vol 64 (11) ◽  
pp. 3854-3864 ◽  
Author(s):  
K. Franklin Evans
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Spherical Harmonics ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Discrete Ordinate Method ◽  
Discrete Ordinate ◽  
Plane Parallel

Abstract The spherical harmonics discrete ordinate method for plane-parallel data assimilation (SHDOMPPDA) model is an unpolarized plane-parallel radiative transfer forward model, with corresponding tangent linear and adjoint models, suitable for use in assimilating cloudy sky visible and infrared radiances. It is derived from the spherical harmonics discrete ordinate method plane-parallel (SHDOMPP, also described in this article) version of the spherical harmonics discrete ordinate method (SHDOM) model for three-dimensional atmospheric radiative transfer. The inputs to the SHDOMPPDA forward model are profiles of pressure, temperature, water vapor, and mass mixing ratio and number concentration for a number of hydrometeor species. Hydrometeor optical properties, including detailed phase functions, are determined from lookup tables as a function of mass mean radius. The SHDOMPP and SHDOMPPDA algorithms and construction of the tangent-linear and adjoint models are described. The SHDOMPPDA forward model is validated against the Discrete Ordinate Radiative Transfer Model (DISORT) by comparing upwelling radiances in multiple directions from 100 cloud model columns at visible and midinfrared wavelengths. For this test in optically thick clouds the computational time for SHDOMPPDA is comparable to DISORT for visible reflection, and roughly 5 times faster for thermal emission. The tangent linear and adjoint models are validated by comparison to finite differencing of the forward model.

scholarly journals Evaluation of Cirrus Parameterizations for Radiative Flux Computations in Climate Models Using TOVS–ScaRaB Satellite Observations

2007 ◽  
Vol 20 (17) ◽  
pp. 4459-4475 ◽  
Author(s):  
C. J. Stubenrauch ◽  
F. Eddounia ◽  
J. M. Edwards ◽  
A. Macke
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Climate Models ◽  
Radiative Flux ◽  
Satellite Observations ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ice Crystal ◽  
Infrared Observation

Abstract Combined simultaneous satellite observations are used to evaluate the performance of parameterizations of the microphysical and optical properties of cirrus clouds used for radiative flux computations in climate models. Atmospheric and cirrus properties retrieved from Television and Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS) observations are given as input to the radiative transfer model developed for the Met Office climate model to simulate radiative fluxes at the top of the atmosphere (TOA). Simulated cirrus shortwave (SW) albedos are then compared to those retrieved from collocated Scanner for Radiation Budget (ScaRaB) observations. For the retrieval, special care has been given to angular direction models. Three parameterizations of cirrus ice crystal optical properties are represented in the Met Office radiative transfer model. These parameterizations are based on different physical approximations and different hypotheses on crystal habit. One parameterization assumes pristine ice crystals and two ice crystal aggregates. By relating the cirrus ice water path (IWP) retrieved from the effective infrared emissivity to the cirrus SW albedo, differences between the parameterizations are amplified. This study shows that pristine crystals seem to be plausible only for cirrus with IWP less than 30 g m−2. For larger IWP, ice crystal aggregates lead to cirrus SW albedos in better agreement with the observations. The data also indicate that climate models should allow the cirrus effective ice crystal diameter (De) to increase with IWP, especially in the range up to 30 g m−2. For cirrus with IWP less than 20 g m−2, this would lead to SW albedos that are about 0.02 higher than the ones of a constant De of 55 μm.

scholarly journals Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

2013 ◽  
Vol 52 (3) ◽  
pp. 710-726 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Steven Platnick ◽  
Andrew K. Heidinger ◽  
Bryan A. Baum ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Spectral Response ◽  
Thermal Infrared ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Retrieval Method ◽  
Ice Cloud ◽  
Cloud Properties

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

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