Modeling water emission induced by the Shoemaker-Levy 9/Jupiter catastrophic impact

2001 ◽  
Vol 79 (2-3) ◽  
pp. 123-129
Author(s):  
C Cecchi-Pestellini ◽  
F Scappini
Keyword(s):  
Radiative Transfer ◽  
Thermal Emission ◽  
Statistical Equilibrium ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Input Parameters ◽  
Maser Effect ◽  
Catastrophic Impact

We reconsider the water emission observed at 22.2 GHz by Cosmovici et al. (1996) during the Shoemaker–Levy 9/Jupiter impact. In contrast to the maser effect proposed by the authors, we discuss the detection in terms of thermal emission. A statistical equilibrium and radiative transfer model is constructed and the results that can be obtained depending on different input parameters are evaluated in the light of literature data. PACS No.: 96.30K

scholarly journals Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

2014 ◽  
Vol 18 (2) ◽  
pp. 5-9 ◽  
Author(s):  
Anna M. Jarocińska
Keyword(s):  
Radiative Transfer ◽  
Field Measurements ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Biophysical Parameters ◽  
Input Parameters ◽  
Vegetation Characteristics ◽  
Field Reflectance ◽  
Lower Water Content

Abstract Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

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

2018 ◽  
Vol 11 (7) ◽  
pp. 2763-2788 ◽  
Author(s):  
Ghislain Picard ◽  
Melody Sandells ◽  
Henning Löwe
Keyword(s):  
Radiative Transfer ◽  
Thermal Emission ◽  
Electromagnetic Properties ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Hard Sphere Model ◽  
Snow Layer ◽  
Mathematical Basis ◽  
Snow Properties ◽  
Snow Microstructure

Abstract. 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.

scholarly journals Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask

2021 ◽  
Vol 13 (2) ◽  
pp. 222
Author(s):  
Xingming Liang ◽  
Quanhua Liu
Keyword(s):  
Radiative Transfer ◽  
High Efficiency ◽  
Infrared Imaging ◽  
Thermal Emission ◽  
Model Performance ◽  
Global Ocean ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Stability ◽  
Clear Sky

A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M). The objectives were to (1) enhance FCDN_CSM performance to include daytime analysis, and improve model stability, accuracy, and efficiency; and (2) further understand the model performance based on a combination of the statistics and physical interpretation. According to the analyses of the F-score, the prediction result showed ~96% and ~97% accuracy for day and night, respectively. The type Cloud was the most accurate, followed by Clear-Sky. The O-M mean biases are comparable to the ACSPO CSM for all bands, both day and night. The standard deviations (STD) were slightly degraded in long wave IRs (M14, M15, and M16), mainly due to contamination by a 3% misclassification of the type Cloud, which may require the model to be further fine-tuned to improve prediction accuracy in the future. However, the consistent O-M means and STDs persist throughout the prediction period, suggesting that FCDN_CSM version 2 is robust and does not have significant overfitting. Given its high F-scores, spatial and long-term stability for both day and night, high efficiency, and acceptable O-M means and STDs, FCDN_CSM version 2 is deemed to be ready for use in the FCDN_CRTM.

scholarly journals Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment

2020 ◽  
Vol 12 (22) ◽  
pp. 3825
Author(s):  
Xingming Liang ◽  
Quanhua Liu
Keyword(s):  
Radiative Transfer ◽  
Infrared Imaging ◽  
Thermal Emission ◽  
Model Simulation ◽  
Iterative Refinement ◽  
Sensor Data ◽  
Global Ocean ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model was trained and tested in the nighttime global ocean clear-sky domain, in which the VIIRS observation minus CRTM (O-M) biases have been well validated in recent years. The atmosphere profile from the European Centre for Medium-Range Weather Forecasts (ECMWF) and sea surface temperature (SST) from the Canadian Meteorology Centre (CMC) were used as FCDN_CRTM input, and the CRTM-simulated brightness temperatures (BTs) were defined as labels. Six dispersion days’ data from 2019 to 2020 were selected to train the FCDN_CRTM, and the clear-sky pixels were identified by an enhanced FCDN clear-sky mask (FCDN_CSM) model, which was demonstrated in Part 1. The trained model was then employed to predict CRTM BTs, which were further validated with the CRTM BTs and the VIIRS sensor data record (SDR) for both efficiency and accuracy. With iterative refinement of the model design and careful treatment of the input data, the agreement between the FCDN_CRTM and the CRTM was generally good, including the satellite zenith angle and column water vapor dependencies. The mean biases of the FCDN_CRTM minus CRTM (F-C) were typically ~0.01 K for all five bands, and the high accuracy persisted during the whole analysis period. Moreover, the standard deviations (STDs) were generally less than 0.1 K and were consistent for approximately half a year, before they significantly degraded. The validation with VIIRS SDR data revealed that both the predicted mean biases and the STD of the VIIRS observation minus FCDN_CRTM (V-F) were comparable with the VIIRS minus direct CRTM simulation (V-C). Meanwhile, both V-F and V-C exhibited consistent global geophysical and statistical distribution, as well as stable long-term performance. Furthermore, the FCDN_CRTM processing time was more than 40 times faster than CRTM simulation. The highly efficient, accurate, and stable performances indicate that the FCDN_CRTM is a potential solution for global and real-time monitoring of sensor observation minus model simulation, particularly for high-resolution sensors.

scholarly journals SMRT: An active / passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

2018 ◽  
Author(s):  
Ghislain Picard ◽  
Melody Sandells ◽  
Henning Löwe
Keyword(s):  
Radiative Transfer ◽  
Thermal Emission ◽  
Electromagnetic Properties ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Hard Sphere Model ◽  
Snow Layer ◽  
Mathematical Basis ◽  
Snow Properties ◽  
Snow Microstructure

Abstract. 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 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.

scholarly journals Infrared Continental Surface Emissivity Spectra Retrieved from AIRS Hyperspectral Sensor

2008 ◽  
Vol 47 (6) ◽  
pp. 1619-1633 ◽  
Author(s):  
E. Péquignot ◽  
A. Chédin ◽  
N. A. Scott
Keyword(s):  
Radiative Transfer ◽  
Skin Temperature ◽  
Land Surface ◽  
Thermal Emission ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Emissivity ◽  
Spectral Window ◽  
Emissivity Spectra

Abstract Atmospheric Infrared Sounder (AIRS; NASA Aqua platform) observations over land are interpreted in terms of monthly mean surface emissivity spectra at a resolution of 0.05 μm and skin temperature. For each AIRS observation, an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the thermodynamic initial guess retrieval (TIGR) climatological library of about 2300 representative clear-sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas (4A) line-by-line radiative transfer model. Then, surface temperature is evaluated by using a single AIRS channel (centered at 12.183 μm) chosen for its almost constant emissivity with respect to soil type. Emissivity is then calculated for a set of 40 atmospheric windows (transmittance greater than 0.5) distributed over the AIRS spectrum. The overall infrared emissivity spectrum at 0.05-μm resolution is finally derived from a combination of high-spectral-resolution laboratory measurements of various materials carefully selected within the Moderate-Resolution Imaging Spectroradiometer/University of California, Santa Barbara (MODIS/UCSB) and Advanced Spaceborne Thermal Emission and Reflection Radiometer/Jet Propulsion Laboratory (ASTER/JPL) emissivity libraries. It is shown from simulations that the accuracy of the method developed in this paper, the multispectral method (MSM), varies from about 3% around 4 μm to considerably less than 1% in the 10–12-μm spectral window. Three years of AIRS observations (from April 2003 to March 2006) between 30°S and 30°N have been processed and interpreted in terms of monthly mean surface skin temperature and emissivity spectra from 3.7 to 14.0 μm at a spatial resolution of 1° × 1°. AIRS retrievals are compared with the MODIS (also flying aboard the NASA/Aqua platform) monthly mean L3 products and with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies baseline-fit method (UW/CIMSS BF) global infrared land surface emissivity database.

Probing clouds in planets with a simple radiative transfer model: the Jupiter case

2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model
Sign in / Sign up

Export Citation Format

Share Document