Community radiative transfer model for radiance assimilation and applications

2012 ◽  
Author(s):  
Quanhua Liu ◽  
Paul van Delst ◽  
Yong Chen ◽  
David Groff ◽  
Yong Han ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radiance Assimilation

scholarly journals Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS

2020 ◽  
Vol 148 (7) ◽  
pp. 2971-2995
Author(s):  
Mingjing Tong ◽  
Yanqiu Zhu ◽  
Linjiong Zhou ◽  
Emily Liu ◽  
Ming Chen ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Positive Impact ◽  
Control Variable ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Water Control ◽  
Microphysics Scheme ◽  
Temperature Forecast ◽  
Radiance Assimilation

Abstract Motivated by the use of the GFDL microphysics scheme in the Finite-Volume Cubed-Sphere Dynamical Core Global Forecast System (FV3GFS), the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow, and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Subgrid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500-hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the tropics.

scholarly journals Development of an OMI AI data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

2020 ◽  
Author(s):  
Jianglong Zhang ◽  
Robert J. D. Spurr ◽  
Jeffrey S. Reid ◽  
Peng Xian ◽  
Peter R. Colarco ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Uv Spectrum ◽  
Model Simulations ◽  
Monitoring Instrument ◽  
Radiance Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract. Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests both RMSE and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation, when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for improving the accuracy of aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not out-perform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully-multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly-developed modeling system contains the necessary ingredients for assimilation of radiances in the ultra-violet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from TROPOspheric Monitoring Instrument (TROPOMI), Ozone Mapping and Profiler Suite (OMPS) and eventually with the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

scholarly journals An update on the RTTOV fast radiative transfer model (currently at version 12)

2018 ◽  
Author(s):  
Roger Saunders ◽  
James Hocking ◽  
Emma Turner ◽  
Peter Rayer ◽  
David Rundle ◽  
...  
Keyword(s):  
User Interface ◽  
Radiative Transfer ◽  
Data Assimilation ◽  
Graphical User Interface ◽  
Jacobian Matrix ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Satellite Retrieval ◽  
Radiance Assimilation ◽  
Atmospheric State

Abstract. This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top of atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments and for training or teaching with a graphical user interface. An overview of the RTTOV model is given highlighting the updates and increased capability of the latest versions and gives some examples of its current performance when compared with more accurate line by line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

scholarly journals Improvements to Cloud-Top Brightness Temperatures Computed from the CRTM at 3.9 μm

2018 ◽  
Vol 146 (11) ◽  
pp. 3927-3944 ◽  
Author(s):  
Lewis Grasso ◽  
Daniel T. Lindsey ◽  
Yoo-Jeong Noh ◽  
Christopher O’Dell ◽  
Ting-Chi Wu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
The United States ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Brightness Temperatures ◽  
Radiance Assimilation ◽  
Weather Events ◽  
Environmental Prediction ◽  
Cloud Tops ◽  
Operational Data

ABSTRACT In preparation for all-sky satellite radiance assimilation, the Community Radiative Transfer Model (CRTM), version 2.1.3, was used to produce Geostationary Operational Environmental Satellite-12/13 (GOES-12/13) imagery near 3.9 μm. For the current study, model output simulated from different models, microphysics, and weather events was used by the CRTM to generate imagery over and near the United States. A direct comparison of observed and CRTM GOES-12/13 imagery near 3.9 μm revealed that CRTM brightness temperatures of solid-water cold cloud tops were approximately 30 K less than observed values. Two CRTM errors were identified and resolved: 1) a coding error that was found by the CRTM team and 2) incorrect optical properties of ice, resulting in improved values of brightness temperatures. Further, changes in microphysics also contributed to improvements, save for one case. The coding error solution appeared in the publicly released CRTM, version 2.3.0, on 27 November 2017, while the inclusion of the optical property solution is undetermined. Since the CRTM is the radiative transfer model within the operational data assimilation system at the National Centers for Environmental Prediction (NCEP), improvements to both the CRTM and model microphysics will be beneficial for future all-sky radiance assimilation activities.

scholarly journals An update on the RTTOV fast radiative transfer model (currently at version 12)

2018 ◽  
Vol 11 (7) ◽  
pp. 2717-2737 ◽  
Author(s):  
Roger Saunders ◽  
James Hocking ◽  
Emma Turner ◽  
Peter Rayer ◽  
David Rundle ◽  
...  
Keyword(s):  
User Interface ◽  
Radiative Transfer ◽  
Data Assimilation ◽  
Graphical User Interface ◽  
Jacobian Matrix ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Satellite Retrieval ◽  
Radiance Assimilation ◽  
Atmospheric State

Abstract. This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

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

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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