. The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.

/pdf/smrt-an-active-passive-microwave-radiative-transfer-model-4wrdho6fm0.pdf

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Energy yield (EY) modelling is an indispensable tool to minimize payback time of emerging perovskite-based multi-junction photovoltaics (PV) but it relies on many assumptions about device architecture and environmental conditions. Here, we propose a comprehensive framework that enables rapid simulation of complex architectures of perovskite-based multi-junction PV and detailed calculation of their power output under realistic irradiation conditions in various climatic zones. Applying the framework to perovskite/silicon multi-junction solar modules, we showcase the impact of tracking on energy losses arising from spectral variations. Moreover, we demonstrate the strong dependency of the EY of bifacial multi-junction solar modules on the albedo.

/pdf/methodology-of-energy-yield-modelling-of-perovskite-based-fmgm1b3boc.pdf

Methodology of energy yield modelling of perovskite-based multi-junction photovoltaics

Data from hyperspectral infrared sounders are routinely ingested worldwide by National Weather Centers (NWCs). The cloud-free fraction of this data is used for initializing forecasts which include profiles of temperature, water vapor, water cloud and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in NWC models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature and water vapor from ECMWF (European Center for Medium-range Weather Forecasting) were used as input for the RTMs. For non-frozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm(exp -1)have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2616 cm(exp -1) at night are reasonably consistent with results at 900 cm(exp -1). Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2616 cm(exp -1) are inferior to those at 900 cm(exp -1) for daytime calculations.

/pdf/evaluation-of-radiative-transfer-models-with-clouds-532wri9csz.pdf

Evaluation of Radiative Transfer Models With Clouds

A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTM-SOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cm−1 resolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10−3 mW/cm2/sr/cm−1 and the relative error is typically less than 0.2%.

Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region.

Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals

A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.

/pdf/a-principal-component-based-radiative-transfer-forward-model-md591y545z.pdf

A principal component-based radiative transfer forward model (PCRTM) for vertically inhomogeneous cloud

An ultra-fast principal component based physical retrieval algorithm has been developed at NASA Langley research center. Works are under way to maximize the application potential of the algorithm in order to generate reliable products from hyper-spectral sensor data for climate studies. The algorithm has been tested using synthetic data for various infrared sensors. This paper describes in detail about retrieval sensitivity study carried out for several hyper-spectral infrared sensors using this physical algorithm. The retrieval accuracy obtained using the algorithm for the atmospheric parameters including trace gases of interests is discussed. Its dependence on the sensor system noise and spectral resolution is illustrated by comparing the retrieval performance achieved for different sensors. PCRTM has been demonstrated to be a reliable tool for end-to-end sensor performance simulations and has great potential for real-time trace gas retrieval applications.

Application study of principal component based physical retrieval algorithm for hyperspectral infrared sensors

Bimolecular nucleophilic substitution (SN2) plays a vital role in organic synthesis. Compared with nucleophiles with one reactive center, ambident nucleophiles can form isomer products. Determining the isomer branching ratios through experiments is difficult, and research on related dynamics characteristics is limited. This study uses dynamics trajectory simulations to explore the dynamics characteristics of the SN2 reaction of ambident nucleophiles CN- and CH3I. The calculated rate constants reproduce the experimental results at room temperature. The dynamics simulations reveal the mechanism of the competition between isomer products CH3CN and CH3NC with a ratio of 0.93 : 0.07. This mechanism is attributed to the height of the central barrier, which strongly stabilizes the transition state of the CH3CN product channel of the formed C-C bond. The product internal energy partitionings and the velocity scattering angle distributions are calculated based on the trajectory simulations, and are in almost agreement with the experimental results obtained at a low collision energy. The dynamics of the title reaction with the ambident nucleophile CN- are also compared with the SN2 dynamics of one reactive center F- and the substrate CH3Y (Y = Cl, I) reactions. This intensive review shows the competition of isomer products for the SN2 reaction of the ambident nucleophile CN- in the current study. This work provides unique insights into reaction selectivity for organic synthesis.

Dynamics of nucleophilic substitution on ambident nucleophiles CN- and iodomethane: insights into the competition mechanism with neutral isomeric products.

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Papers

Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region.

A principal component-based radiative transfer forward model (PCRTM) for vertically inhomogeneous cloud

Application study of principal component based physical retrieval algorithm for hyperspectral infrared sensors

Dynamics of nucleophilic substitution on ambident nucleophiles CN- and iodomethane: insights into the competition mechanism with neutral isomeric products.