scholarly journals Numerical Experiments of an Advanced Radiative Transfer Model in the U.S. Navy Operational Global Atmospheric Prediction System

2012 ◽  
Vol 51 (3) ◽  
pp. 554-570 ◽  
Author(s):  
Ming Liu ◽  
Young-Joon Kim ◽  
Qingyun Zhao
Keyword(s):  
Radiative Transfer ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Forecast Errors ◽  
Prediction System ◽  
Surface Data ◽  
Ice Water ◽  
The Impact ◽  
Mean Square Errors

AbstractA high-order accurate radiative transfer (RT) model developed by Fu and Liou has been implemented into the Navy Operational Global Atmospheric Prediction System (NOGAPS) to improve the energy budget and forecast skill. The Fu–Liou RT model is a four-stream algorithm (with a two-stream option) integrating over 6 shortwave bands and 12 longwave bands. The experimental 10-day forecasts and analyses from data assimilation cycles are compared with the operational output, which uses a two-stream RT model of three shortwave and five longwave bands, for both winter and summer periods. The verifications against observations of radiosonde and surface data show that the new RT model increases temperature accuracy in both forecasts and analyses by reducing mean bias and root-mean-square errors globally. In addition, the forecast errors also grow more slowly in time than those of the operational NOGAPS because of accumulated effects of more accurate cloud–radiation interactions. The impact of parameterized cloud effective radius in estimating liquid and ice water optical properties is also investigated through a sensitivity test by comparing with the cases using constant cloud effective radius to examine the temperature changes in response to cloud scattering and absorption. The parameterization approach is demonstrated to outperform that of constant radius by showing smaller errors and better matches to observations. This suggests the superiority of the new RT model relative to its operational counterpart, which does not use cloud effective radius. An effort has also been made to improve the computational efficiency of the new RT model for operational applications.

scholarly journals Implementation of Slant-Path Radiative Transfer in Environment Canada’s Global Deterministic Weather Prediction System

2020 ◽  
Vol 148 (10) ◽  
pp. 4231-4245
Author(s):  
Maziar Bani Shahabadi ◽  
Mark Buehner ◽  
Josep Aparicio ◽  
Louis Garand
Keyword(s):  
Standard Deviation ◽  
Radiative Transfer ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Prediction System ◽  
Horizontal Position ◽  
Slant Path ◽  
The Impact ◽  
Background State

AbstractThe standard approach for assimilating satellite radiance observations is to interpolate all vertical levels of the background state and analysis increment to the same horizontal location for input to the radiative transfer model. This can add significant error for observations with large zenith angle. The impact of accounting for the true slanted satellite-viewing geometry was tested by modifying the horizontal interpolation routines in Environment and Climate Change Canada’s Global Deterministic Weather Prediction System. Consequently, model variables are interpolated to a different horizontal position at each model level, for either just the innovation or both the innovation and increment calculation. When this slant-path operator is used for simulation of radiances, reductions in innovation standard deviation, up to 4.5%, for upper-tropospheric and stratospheric temperature and humidity channels of ATMS, AMSU-A, MHS, and CrIS instruments have similar magnitudes as reported in previous studies. In data assimilation experiments, statistically significant reductions in innovation standard deviation (up to 0.3%) for global GPSRO observations are obtained, due to an improved background state. Verification of short- and medium-range forecasts against ERA5, and our own analyses over the region poleward of 60°S show statistically significant reductions of error standard deviation by 2%–3% for wind and temperature in the upper troposphere and lower stratosphere. These positive impacts are mostly due to performing slant-path interpolation on the background state, while also using slant path on the analysis increment has little additional impact. This is expected since the analysis increment in this global configuration has lower spatial resolution, with grid spacing comparable with the maximum horizontal position error from not using the slant-path operator.

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 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.

Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model

2020 ◽  
Author(s):  
Dominic Fawcett ◽  
Jonathan Bennie ◽  
Karen Anderson
Keyword(s):  
Radiative Transfer ◽  
Vegetation Index ◽  
Structural Parameters ◽  
Point Clouds ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Vegetation Height ◽  
Plant Area Index ◽  
The Impact

<p>The light environment within vegetated landscapes is a key driver of microclimate, creating varied habitats over small spatial extents and controls the distribution of understory plant species. Modelling spatial variations of light at these scales requires finely resolved (< 1 m) information on topography and canopy properties. We demonstrate an approach to modelling spatial distributions and temporal progression of understory photosynthetically active radiation (PAR) utilising a three dimensional radiative transfer model (discrete anisotropic radiative transfer model: DART) where the scene is parameterised by drone-based data.</p><p>The study site, located in west Cornwall, UK, includes a small mixed woodland as well as isolated free-standing trees. Data were acquired from March to August 2019. Vegetation height and distribution were derived from point clouds generated from drone image data using structure-from-motion (SfM) photogrammetry. These data were supplemented by multi-temporal multispectral imagery (Parrot Sequoia camera) which were used to generate an empirical model by relating a vegetation index to plant area index derived from hemispherical photography taken over the same time period. Simulations of the 3D radiative budget were performed for the PAR wavelength interval (400 – 700 nm) using DART.</p><p>Besides maps of instantaneous above and below canopy irradiance, we provide models of daily light integrals (DLI) which are assessed against field validation measurements with PAR quantum sensors. We find relatively good agreement for simulated PAR in the woodland. The impact of simplifying assumptions regarding leaf angular distributions and optical properties are discussed. Finally, further opportunities which fine-grained drone data can provide in a radiative transfer context are highlighted.</p>

Impact of 3D cloud structures on tropospheric NO2 column measurements from UV-VIS sounders

2020 ◽  
Author(s):  
Huan Yu ◽  
Arve Kylling ◽  
Claudia Emde ◽  
Bernhard Mayer ◽  
Kerstin Stebel ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Mitigation Strategies ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Vertical Column ◽  
Ice Clouds ◽  
Cloud Data ◽  
One Step ◽  
The Impact

<p>Operational retrievals of tropospheric trace gases from space-borne spectrometers are made using 1D radiative transfer models. To minimize cloud effects generally only partially cloudy pixels are analysed using simplified cloud contamination treatments based on radiometric cloud fraction estimates and photon path length corrections based on oxygen collision pair (O<sub>2</sub>-O<sub>2</sub>) or O<sub>2</sub>A-absorption band measurements. In reality, however, the impact of clouds can be much more complex, involving scattering of clouds in neighbouring pixels and cloud shadow effects. Therefore, to go one step further, other correction methods may be envisaged that use sub-pixel cloud information from co-located imagers. Such methods require an understanding of the impact of clouds on the real 3D radiative transfer. We quantify this impact using the MYSTIC 3D radiative transfer model. The generation of realistic 3D input cloud fields, needed by MYSTIC (or any other 3D radiative transfer model), is non-trivial. We use cloud data generated by the ICOsahedral Non-hydrostatic (ICON) atmosphere model for a region including Germany, the Netherlands and parts of other surrounding countries. The model simulates realistic liquid and ice clouds with a horizontal spatial resolution of 156 m and it has been validated against ground-based and satellite-based observational data.</p><p>As a trace gas example, we study NO<sub>2</sub>, a key tropospheric trace gas measured by the atmospheric Sentinels. The MYSTIC 3D model simulates visible spectra, which are ingested in standard DOAS retrieval algorithms to retrieve the NO<sub>2</sub> column amount. Spectra are simulated for a number of realistic cloud scenarios, snow free surface albedos, and solar and satellite geometries typical of low-earth and geostationary orbits. The retrieved NO<sub>2</sub> vertical column densities (VCD) are compared with the true values to identify conditions where 3D cloud effects lead to significant biases on the NO<sub>2</sub> VCDs. A variety of possible mitigation strategies for such pixels are then explored.</p>

scholarly journals Physical behaviour of anthropogenic light propagation into the nocturnal environment

2015 ◽  
Vol 370 (1667) ◽  
pp. 20140117 ◽  
Author(s):  
Martin Aubé
Keyword(s):  
Radiative Transfer ◽  
Natural Environment ◽  
Spectral Power ◽  
Radiative Transfer Model ◽  
City Centre ◽  
Light Pollution ◽  
Transfer Model ◽  
Light Sources ◽  
Night Time ◽  
The Impact

Propagation of artificial light at night (ALAN) in the environment is now known to have non negligible consequences on fauna, flora and human health. These consequences depend on light levels and their spectral power distributions, which in turn rely on the efficiency of various physical processes involved in the radiative transfer of this light into the atmosphere and its interactions with the built and natural environment. ALAN can affect the living organisms by direct lighting and indirect lighting (scattered by the sky and clouds and/or reflected by local surfaces). This paper mainly focuses on the behaviour of the indirect light scattered under clear sky conditions. Various interaction processes between anthropogenic light sources and the natural environment are discussed. This work mostly relies on a sensitivity analysis conducted with the light pollution radiative transfer model, Illumina (Aubé et al . 2005 Light pollution modelling and detection in a heterogeneous environment: toward a night-time aerosol optical depth retrieval method. In Proc. SPIE 2005, vol. 5890, San Diego, California, USA). More specifically, the impact of (i) the molecular and aerosol scattering and absorption, (ii) the second order of scattering, (iii) the topography and obstacle blocking, (iv) the ground reflectance and (v) the spectrum of light devices and their angular emission functions are examined. This analysis considers different behaviour as a function of the distance from the city centre, along with different zenith viewing angles in the principal plane.

scholarly journals Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies

2013 ◽  
Vol 13 (14) ◽  
pp. 6687-6711 ◽  
Author(s):  
M. J. Alvarado ◽  
V. H. Payne ◽  
E. J. Mlawer ◽  
G. Uymin ◽  
M. W. Shephard ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Fixed Ratio ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Profiles ◽  
A Posteriori ◽  
The Impact ◽  
Temperature Water

Abstract. Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H2O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus important to improve the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Interferometer (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H2O profiles. We show that the spectroscopy in the CO2 ν2 and ν3 bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to mean changes of ~0.5 K in the retrieved vertical temperature profiles between the surface and 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO2 bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H2O spectroscopy in LBLRTM v12.1 substantially improves the a posteriori residuals in the P-branch of the H2O ν2 band, while the improvements in the R-branch are more modest. The H2O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H2O spectroscopy. We also find that the use of a fixed ratio of HDO to H2O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch relative to the R-branch of the H2O ν2 band. There were no changes to O3 spectroscopy between the two model versions, and so both versions give positive a posteriori residuals of ~ 0.3 K in the R-branch of the O3 ν3 band. While the updates to the H2O self-continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO2 ν3 band head, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic a posteriori residuals remain in the ν4 band of CH4, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in LBLRTM v12.1, suggesting that the updated spectroscopy could improve retrievals of CH4 from satellite observations.

scholarly journals Three-dimensional effects in polarization signatures as observed from precipitating clouds by low frequency ground-based microwave radiometers

2006 ◽  
Vol 6 (3) ◽  
pp. 5427-5456
Author(s):  
A. Battaglia ◽  
C. Simmer ◽  
H. Czekala
Keyword(s):  
Radiative Transfer ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
Low Frequency ◽  
Radiative Transfer Model ◽  
Observation Angle ◽  
Transfer Model ◽  
One Dimensional ◽  
Dimensional Effects ◽  
The Impact

Abstract. Consistent negative polarization differences (i.e. differences between the vertical and the horizontal brightness temperature) are observed when looking at precipitating systems by ground-based radiometers at slant angles. These signatures can be partially explained by one-dimensional radiative transfer computations that include oriented non-spherical raindrops. However some cases are characterized by polarization values that exceed differences expected from one-dimensional radiative transfer. A three-dimensional fully polarized Monte Carlo model has been used to evaluate the impact of the horizontal finiteness of rain shafts with different rain rates at 10, 19, and 30 GHz. The results show that because of the reduced slant optical thickness in finite clouds, the polarization signal can strongly differ from its one-dimensional counterpart. At the higher frequencies and when the radiometer is positioned underneath the cloud, significantly higher negative values for the polarization are found which are also consistent with some observations. When the observation point is located outside of the precipitating cloud, typical polarization patterns (with troughs and peaks) as a function of the observation angle are predicted. An approximate 1-D slant path radiative transfer model is considered as well and results are compared with the full 3-D simulations to investigate whether or not three-dimensional effects can be explained by geometry effects alone. The study has strong relevance for low-frequency passive microwave polarimetric studies.

scholarly journals Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic

2021 ◽  
Vol 14 (1) ◽  
pp. 369-389
Author(s):  
Soheila Jafariserajehlou ◽  
Vladimir V. Rozanov ◽  
Marco Vountas ◽  
Charles K. Gatebe ◽  
John P. Burrows
Keyword(s):  
Radiative Transfer ◽  
Grain Morphology ◽  
Effective Radius ◽  
Ice Crystals ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Snow Layer ◽  
Airborne Measurements ◽  
Size And Shape ◽  
Atmosphere System

Abstract. Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne observations. In this paper, we simulate the reflectance in a snow–atmosphere system, using the phenomenological radiative transfer model SCIATRAN, and compare the results with that of airborne measurements. To minimize the differences between measurements and simulation, we determine and employ the key atmospheric and surface parameters, such as snow grain morphologies (or habits). First, we report on a sensitivity study. This addresses the requirement for adequate a priori knowledge about snow models and ancillary information about the atmosphere. For this aim, we use the well-validated phenomenological radiative transfer model, SCIATRAN. Second, we present and apply a two-stage snow grain morphology (i.e., size and shape of ice crystals in the snow) retrieval algorithm. We then describe the use of this new retrieval for estimating the most representative snow model, using different types of snow morphologies, for the airborne observation conditions performed by NASA's Cloud Absorption Radiometer (CAR). Third, we present a comprehensive comparison of the simulated reflectance (using retrieved snow grain size and shape and independent atmospheric data) with that from airborne CAR measurements in the visible (0.670 µm) and near infrared (NIR; 0.870 and 1.6 µm) wavelength range. The results of this comparison are used to assess the quality and accuracy of the radiative transfer model in the simulation of the reflectance in a coupled snow–atmosphere system. Assuming that the snow layer consists of ice crystals with aggregates of eight column ice habit and having an effective radius of ∼99 µm, we find that, for a surface covered by old snow, the Pearson correlation coefficient, R, between measurements and simulations is 0.98 (R2∼0.96). For freshly fallen snow, assuming that the snow layer consists of the aggregate of five plates ice habit with an effective radius of ∼83 µm and having surface inhomogeneity, the correlation is ∼0.97 (R2∼0.94) in the infrared and 0.88 (R2∼0.77) in the visible wavelengths. The largest differences between simulated and measured values are observed in the glint area (i.e., in the angular regions of specular and near-specular reflection), with relative azimuth angles <±40∘ in the forward-scattering direction. The absolute difference between the modeled results and measurements in off-glint regions, with a viewing zenith angle of less than 50∘, is generally small ∼±0.025 and does not exceed ±0.05. These results will help to improve the calculation of snow surface reflectance and relevant assumptions in the snow–atmosphere system algorithms (e.g., aerosol optical thickness retrieval algorithms in the polar regions).

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