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 SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1532 ◽  
Author(s):  
Guido Masiello ◽  
Carmine Serio ◽  
Sara Venafra ◽  
Laurent Poutier ◽  
Frank-M. Göttsche
Keyword(s):  
Radiative Transfer ◽  
Real Time ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Real Time Processing ◽  
In Situ Observations ◽  
Infrared Radiances

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

scholarly journals SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

2019 ◽  
Author(s):  
Guido Masiello ◽  
Carmine Serio ◽  
Sara Venafra ◽  
Laurent Poutier ◽  
Frank-M. Göttsche
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Real Time ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Real Time Processing ◽  
In Situ Observations

Timely processing of observations from hyper-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances for the physical retrieval of surface temperature and emissivity. The new radiative transfer model improves computational time by a factor of ≈ 7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the simultaneous retrieval of surface temperature and emissivity in three infrared channels (8.7, 10.8, 12 μm). The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e. average differences are generally well below 0.01.

Accuracy Enhancement of the Two-Stream Radiative Transfer Model for Computing Absorption Bands at the Presence of Aerosols

2021 ◽  
pp. 79-86
Author(s):  
Ana del Águila ◽  
Dmitry S. Efremenko
Keyword(s):  
Radiative Transfer ◽  
Performance Enhancement ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Discrete Ordinate Method ◽  
Discrete Ordinate ◽  
Absorption Bands ◽  
Accuracy Enhancement ◽  
Gaseous Absorption ◽  
Stream Model

The two-stream model is the fastest radiative transfer model among those based on the discrete ordinate method. Although its accuracy is not high enough to be used in applications, the two-stream model gets more attention in computationally demanding tasks such as line-by-line simulations in the gaseous absorption bands. For this reason, we designed the cluster low-streams regression (CLSR) technique, in which a spectrum computed with a two-stream model, is refined by using statistical dependencies between two- and multistream radiative transfer models. In this paper, we examine the efficiency of this approach for computing Hartley-Huggins, O2 A-, water vapour and CO2 bands at the presence of aerosols. The numerical results evidence that the errors of the CLSR method is not biased and around 0.05 %, while the performance enhancement is two orders of magnitude.

Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept

2021 ◽  
pp. 56-62
Author(s):  
Dmitry S. Efremenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Discrete Ordinate ◽  
The Neural Network ◽  
Computationally Expensive ◽  
Eigenvalue Solver

Artificial neural networks are attracting increasing attention in various applications. They can be used as ‘universal approximations’, which substitute computationally expensive algorithms by relatively simple sequences of functions, which simulate a reaction of a set of neurons to the incoming signal. In particular, neural networks have proved to be efficient for parameterization of the computationally expensive radiative transfer models (RTMs) in atmospheric remote sensing. Although a direct substitution of RTMs by neural networks can lead to the multiple performance enhancements, such an approach has certain drawbacks, such as loss of generality, robustness issues, etc. In this regard, the neural network is usually trained for a specific application, predefined atmospheric scenarios and a given spectrometer. In this paper a new concept of neural-network based RTMs is examined, in which the neural network substitutes not the whole RTM but rather a part of it (the eigenvalue solver), thereby reducing the computational time while maintaining its generality. The explicit dependencies on geometry of observation and optical thickness of the medium are excluded from training. It is shown that although the speedup factor due to this approach is modest (around 3 times against 103 speed up factor of other approaches reported in recent papers), the resulting neural network is flexible and easy to train. It can be used for arbitrary number of atmospheric layers. Moreover, this approach can be used in conjunction with any RTMs based on the discrete ordinate method. The neural network is applied for simulations of the radiances at the top of the atmosphere in the Huggins band.

scholarly journals A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere

2021 ◽  
Vol 13 (2) ◽  
pp. 270
Author(s):  
Adrian Doicu ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Trace Gases ◽  
Three Dimensional ◽  
Principal Component ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Proof Of Concept ◽  
Discrete Ordinate ◽  
Distribution Method ◽  
Total Column

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.

scholarly journals Improvement of the Simulation of Cloud Longwave Scattering in Broadband Radiative Transfer Models

2018 ◽  
Vol 75 (7) ◽  
pp. 2217-2233 ◽  
Author(s):  
Guanglin Tang ◽  
Ping Yang ◽  
George W. Kattawar ◽  
Xianglei Huang ◽  
Eli J. Mlawer ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Multiple Scattering ◽  
General Circulation ◽  
General Circulation Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Discrete Ordinate ◽  
Wide Range ◽  
Scaling Methods ◽  
Computational Errors

Abstract Cloud longwave scattering is generally neglected in general circulation models (GCMs), but it plays a significant and highly uncertain role in the atmospheric energy budget as demonstrated in recent studies. To reduce the errors caused by neglecting cloud longwave scattering, two new radiance adjustment methods are developed that retain the computational efficiency of broadband radiative transfer simulations. In particular, two existing scaling methods and the two new adjustment methods are implemented in the Rapid Radiative Transfer Model (RRTM). The results are then compared with those based on the Discrete Ordinate Radiative Transfer model (DISORT) that explicitly accounts for multiple scattering by clouds. The two scaling methods are shown to improve the accuracy of radiative transfer simulations for optically thin clouds but not effectively for optically thick clouds. However, the adjustment methods reduce computational errors over a wide range, from optically thin to thick clouds. With the adjustment methods, the errors resulting from neglecting cloud longwave scattering are reduced to less than 2 W m−2 for the upward irradiance at the top of the atmosphere and less than 0.5 W m−2 for the surface downward irradiance. The adjustment schemes prove to be more accurate and efficient than a four-stream approximation that explicitly accounts for multiple scattering. The neglect of cloud longwave scattering results in an underestimate of the surface downward irradiance (cooling effect), but the errors are almost eliminated by the adjustment methods (warming effect).

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 Analytic Green’s Function for Radiative Transfer in Plane-Parallel Atmospheres

2005 ◽  
Vol 62 (8) ◽  
pp. 2910-2924 ◽  
Author(s):  
Yi Qin ◽  
Michael A. Box
Keyword(s):  
Radiative Transfer ◽  
Green’S Function ◽  
Green's Function ◽  
Discrete Ordinate Method ◽  
Bidirectional Reflectance Distribution Function ◽  
Discrete Ordinate ◽  
Bidirectional Reflectance ◽  
Plane Parallel ◽  
Computing Complexity ◽  
Bidirectional Reflectance Distribution

Abstract Green’s function is a widely used approach for boundary value problems. In problems related to radiative transfer, Green’s function has been found to be useful in land, ocean, and atmosphere remote sensing. It is also a key element in higher order perturbation theory. This paper presents an explicit expression of the Green’s function, in terms of the source and radiation field variables, for a plane-parallel atmosphere with either vacuum boundaries or a reflecting [atmosphere–bidirectional reflectance distribution function (BRDF)] surface. A FORTRAN 95 code, Green’s function and discrete ordinate method (GDOM), has been developed to efficiently compute the Green’s function. This code also integrates with an implementation of the discrete ordinate method with several extensions and improvements. Computing complexity of the Green’s function algorithm is analyzed, and validation of the code is discussed.

scholarly journals Advanced Doubling–Adding Method for Radiative Transfer in Planetary Atmospheres

2006 ◽  
Vol 63 (12) ◽  
pp. 3459-3465 ◽  
Author(s):  
Quanhua Liu ◽  
Fuzhong Weng
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Satellite Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Thermal Source ◽  
New Development ◽  
Joint Center ◽  
Computational Resources ◽  
Computation Codes

The doubling–adding method (DA) is one of the most accurate tools for detailed multiple-scattering calculations. The principle of the method goes back to the nineteenth century in a problem dealing with reflection and transmission by glass plates. Since then the doubling–adding method has been widely used as a reference tool for other radiative transfer models. The method has never been used in operational applications owing to tremendous demand on computational resources from the model. This study derives an analytical expression replacing the most complicated thermal source terms in the doubling–adding method. The new development is called the advanced doubling–adding (ADA) method. Thanks also to the efficiency of matrix and vector manipulations in FORTRAN 90/95, the advanced doubling–adding method is about 60 times faster than the doubling–adding method. The radiance (i.e., forward) computation code of ADA is easily translated into tangent linear and adjoint codes for radiance gradient calculations. The simplicity in forward and Jacobian computation codes is very useful for operational applications and for the consistency between the forward and adjoint calculations in satellite data assimilation. ADA is implemented into the Community Radiative Transfer Model (CRTM) developed at the U.S. Joint Center for Satellite Data Assimilation.

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