Visualizing Model Data Using a Fast Approximation of a Radiative Transfer Model

Abstract Visualizing model forecasts using simulated satellite imagery has proven very useful because the depiction of forecasts using cloud imagery can provide inferences about meteorological scenarios and physical processes that are not characterized well by depictions of those forecasts using radar reflectivity. A forward radiative transfer model is capable of providing such a visible-channel depiction of numerical weather prediction model output, but present-day forward models are too slow to run routinely on operational model forecasts. It is demonstrated that it is possible to approximate the radiative transfer model using a universal approximator whose parameters can be determined by fitting the output of the forward model to inputs derived from the raw output from the prediction model. The resulting approximation is very close to the result derived from the complex radiative transfer model and has the advantage that it can be computed in a small fraction of the time required by the forward model. This approximation is carried out on model forecasts to demonstrate its utility as a visualization and forecasting tool.

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SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Sensors ◽

10.3390/s19071532 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1532 ◽

Cited By ~ 1

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.

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Evaluation of shortwave radiation fluxes in the multi-layer SPARTACUS-Urban scheme using DART

10.5194/egusphere-egu21-16076 ◽

2021 ◽

Author(s):

Megan Stretton ◽

William Morrison ◽

Robin Hogan ◽

Sue Grimmond

Keyword(s):

Radiative Transfer ◽

Urban Form ◽

Climate Models ◽

Weather Prediction ◽

Computational Cost ◽

Three Dimensional ◽

Shortwave Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Urban Structure

<p>The heterogenous structure of cities impacts radiative exchanges (e.g. albedo and heat storage). Numerical weather prediction (NWP) models often characterise the urban structure with an infinite street canyon &#8211; but this does not capture the three-dimensional urban form. SPARTACUS-Urban (SU) - a fast, multi-layer radiative transfer model designed for NWP - is evaluated using the explicit Discrete Anisotropic Radiative Transfer (DART) model for shortwave fluxes across several model domains &#8211; from a regular array of cubes to real cities .</p><p>SU agrees with DART (errors < 5.5% for all variables) when the SU assumptions of building distribution are fulfilled (e.g. randomly distribution). For real-world areas with pitched roofs, SU underestimates the albedo (< 10%) and shortwave transmission to the surface (< 15%), and overestimates wall-plus-roof absorption (9-27%), with errors increasing with solar zenith angle. SU should be beneficial to weather and climate models, as it allows more realistic urban form (cf. most schemes) without large increases in computational cost.</p>

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Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

Remote Sensing ◽

10.3390/rs11202338 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2338 ◽

Cited By ~ 2

Author(s):

Liu ◽

Chu ◽

Yin ◽

Liu

Keyword(s):

Radiative Transfer ◽

Brightness Temperature ◽

Weather Prediction ◽

Order Relation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Radar Network ◽

Intense Precipitation ◽

Standard Deviations ◽

Light Precipitation

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

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RTTOV-gb – Adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

10.5194/gmd-2016-65 ◽

2016 ◽

Cited By ~ 1

Author(s):

Francesco De Angelis ◽

Domenico Cimini ◽

James Hocking ◽

Pauline Martinet ◽

Stefan Kneifel

Keyword(s):

Radiative Transfer ◽

Liquid Water ◽

Weather Prediction ◽

Microwave Radiometer ◽

Software Tool ◽

Radiative Transfer Model ◽

Transfer Model ◽

60 Ghz ◽

Variational Assimilation ◽

Temperature And Humidity

Abstract. Ground-based microwave radiometers (MWR) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward looking microwave sensors. In addition, the Tangent Linear, Adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e. the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (~ 0.5 K) at all channels used in this analysis. Brightness temperatures (TB) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and colocated ground-based MWR observations. Differences between simulated and measured TB are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a 1-Dimensional Variational (1D-Var) software tool in an Observing-System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to NWP model in the first few kilometers from the ground.

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A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

Atmospheric Measurement Techniques ◽

10.5194/amt-13-3235-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 3235-3261

Author(s):

Steven Albers ◽

Stephen M. Saleeby ◽

Sonia Kreidenweis ◽

Qijing Bian ◽

Peng Xian ◽

...

Keyword(s):

Solar Radiation ◽

Radiative Transfer ◽

Numerical Weather Prediction ◽

Land Surface ◽

Weather Prediction ◽

Lower Boundary ◽

Radiative Transfer Model ◽

Transfer Model ◽

Numerical Weather ◽

Visible Wavelength

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

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SHDOMPPDA: A Radiative Transfer Model for Cloudy Sky Data Assimilation

Journal of the Atmospheric Sciences ◽

10.1175/2006jas2047.1 ◽

2007 ◽

Vol 64 (11) ◽

pp. 3854-3864 ◽

Cited By ~ 34

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.

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Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1413.1 ◽

2010 ◽

Vol 27 (10) ◽

pp. 1609-1623 ◽

Cited By ~ 29

Author(s):

B. Petrenko ◽

A. Ignatov ◽

Y. Kihai ◽

A. Heidinger

Keyword(s):

High Resolution ◽

Radiative Transfer ◽

Weather Prediction ◽

Radiative Transfer Model ◽

Transfer Model ◽

Clear Sky ◽

Brightness Temperatures ◽

Spatial Uniformity ◽

Uniformity Test ◽

Cross Platform

Abstract The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

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Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0091.1 ◽

2018 ◽

Vol 57 (3) ◽

pp. 493-515 ◽

Cited By ~ 7

Author(s):

S. K. Mukkavilli ◽

A. A. Prasad ◽

R. A. Taylor ◽

A. Troccoli ◽

M. J. Kay

Keyword(s):

Radiative Transfer ◽

Climate Models ◽

Global Climate ◽

Weather Prediction ◽

Wrf Model ◽

Global Climate Models ◽

Radiative Transfer Model ◽

Transfer Model ◽

Dust Event ◽

Direct Normal Irradiance

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.

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