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

2018 ◽  
Vol 57 (3) ◽  
pp. 493-515 ◽  
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.

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

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 – 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 – 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>

Optimizing Weather Model Radiative Transfer Physics for Intel’s Many Integrated Core (MIC) Architecture

2016 ◽  
Vol 26 (04) ◽  
pp. 1650019 ◽  
Author(s):  
John Michalakes ◽  
Michael J. Iacono ◽  
Elizabeth R. Jessup
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Wrf Model ◽  
Multiscale Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Weather Model ◽  
Hardware Performance Counters ◽  
S Peak ◽  
Many Integrated Core

Large numerical weather prediction (NWP) codes such as the Weather Research and Forecast (WRF) model and the NOAA Nonhydrostatic Multiscale Model (NMM-B) port easily to Intel's Many Integrated Core (MIC) architecture. But for NWP to significantly realize MIC’s one- to two-TFLOP/s peak computational power, we must expose and exploit thread and fine-grained (vector) parallelism while overcoming memory system bottlenecks that starve oating-point performance. We report on our work to improve the Rapid Radiative Transfer Model (RRTMG), responsible for 10-20 percent of total NMM-B run time. We isolated a standalone RRTMG benchmark code and workload from NMM-B and then analyzed performance using hardware performance counters and scaling studies. We restructured the code to improve vectorization, thread parallelism, locality, and thread contention. The restructured code ran three times faster than the original on MIC and, also importantly, 1.3x faster than the original on the host Xeon Sandybridge.

Introducing cloud horizontal overlap at NWP scales (1-10 km) in a fast 3D radiative transfer model

2021 ◽  
Author(s):  
Mihail Manev ◽  
Bernhard Mayer ◽  
Claudia Emde ◽  
Aiko Voigt
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Scale Effects ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Subgrid Scale ◽  
Computationally Efficient ◽  
Realistic Description

<p><span>Interactions between radiation and clouds are a source of significant uncertainty in both numerical weather prediction </span><span>(NWP) </span><span>and climate models. </span><span>Future models need to both incorporate more realistic description of physical processes and be computationally efficient. </span><span>With the </span><span>steadi</span><span>ly</span> <span>increasing</span><span> resolution of NWP models, </span><span>previously neglected effects like the horizontal propagation of radiation become more important. </span></p><p><span>Here we present a </span><span>hybrid</span><span> radiative transfer model that combines </span><span>a traditional twostream maximum random overlap </span><span>(twomaxrnd)</span><span> radiative solver </span><span>(</span><span>Č</span><span>rnivec and Mayer, 20</span><span>19</span><span>)</span><span> with a Neighbouring Column Approximation </span><span>(NCA)</span> <span>model </span><span>(Klinger and Mayer, 20</span><span>19</span><span>)</span><span>, which parametrizes horizontal photon transport between adjacent grid-cells. </span><span>Thereby </span><span>the hybrid</span> <span>includes</span><span> both subgrid-scale effects and grid-scale horizontal transport. </span><span>In addition</span><span> we introduced </span><span>a</span><span> horizontal </span><span>cloud </span><span>overlap scheme </span><span>to the hybrid model</span><span>. In order to </span><span>differentiate between different overlap concepts and deduce optimal overlap coefficients we used</span><span> high resolution radiative </span><span>transfer </span><span>simulations </span><span>of LES cloud fields </span><span>(horizontal resolution of 100-300 m)</span> <span>deploying </span><span>a very accurate Monte Carlo </span><span>(MYSTIC)</span><span> model </span><span>(</span><span>Mayer, 2009</span><span>)</span><span>.</span></p><p><span>Further we assess the performance of the </span><span>hybrid</span><span> model </span><span>at the NWP scale (1-10 km)</span><span> for various </span><span>realistic </span><span>cloud configurations </span><span>using</span><span> results from the benchmark MYSTIC model</span> <span>and determine the differences compared to other solvers that only consider either grid-scale or subgrid-scale effects, twomaxrnd, Tripleclouds and NCA.</span></p><p> </p>

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

2019 ◽  
Vol 11 (20) ◽  
pp. 2338 ◽  
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).

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

2016 ◽  
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.

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

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.

scholarly journals Evaluation of Cirrus Parameterizations for Radiative Flux Computations in Climate Models Using TOVS–ScaRaB Satellite Observations

2007 ◽  
Vol 20 (17) ◽  
pp. 4459-4475 ◽  
Author(s):  
C. J. Stubenrauch ◽  
F. Eddounia ◽  
J. M. Edwards ◽  
A. Macke
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Climate Models ◽  
Radiative Flux ◽  
Satellite Observations ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ice Crystal ◽  
Infrared Observation

Abstract Combined simultaneous satellite observations are used to evaluate the performance of parameterizations of the microphysical and optical properties of cirrus clouds used for radiative flux computations in climate models. Atmospheric and cirrus properties retrieved from Television and Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS) observations are given as input to the radiative transfer model developed for the Met Office climate model to simulate radiative fluxes at the top of the atmosphere (TOA). Simulated cirrus shortwave (SW) albedos are then compared to those retrieved from collocated Scanner for Radiation Budget (ScaRaB) observations. For the retrieval, special care has been given to angular direction models. Three parameterizations of cirrus ice crystal optical properties are represented in the Met Office radiative transfer model. These parameterizations are based on different physical approximations and different hypotheses on crystal habit. One parameterization assumes pristine ice crystals and two ice crystal aggregates. By relating the cirrus ice water path (IWP) retrieved from the effective infrared emissivity to the cirrus SW albedo, differences between the parameterizations are amplified. This study shows that pristine crystals seem to be plausible only for cirrus with IWP less than 30 g m−2. For larger IWP, ice crystal aggregates lead to cirrus SW albedos in better agreement with the observations. The data also indicate that climate models should allow the cirrus effective ice crystal diameter (De) to increase with IWP, especially in the range up to 30 g m−2. For cirrus with IWP less than 20 g m−2, this would lead to SW albedos that are about 0.02 higher than the ones of a constant De of 55 μm.

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

2010 ◽  
Vol 27 (10) ◽  
pp. 1609-1623 ◽  
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.

Northern Hemisphere Atmospheric Blocking Representation in Global Climate Models: Twenty Years of Improvements?

2016 ◽  
Vol 29 (24) ◽  
pp. 8823-8840 ◽  
Author(s):  
Paolo Davini ◽  
Fabio D’Andrea
Keyword(s):  
Northern Hemisphere ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Weather Prediction ◽  
Global Climate Models ◽  
Main Concern ◽  
Atmospheric Blocking ◽  
Atlantic Sector

Abstract The correct simulation of midlatitude atmospheric blocking has always been a main concern since the earliest days of numerical modeling of Earth’s atmosphere. To this day blocking represents a considerable source of error for general circulation models from both a numerical weather prediction and a climate perspective. In the present work, 20 years of global climate model (GCM) developments are analyzed from the special point of view of Northern Hemisphere atmospheric blocking simulation. Making use of a series of equivalent metrics, three generations of GCMs are compared. This encompasses a total of 95 climate models, many of which are different—successive—versions of the same model. Results from model intercomparison projects AMIP1 (1992), CMIP3 (2007), and CMIP5 (2012) are taken into consideration. Although large improvements are seen over the Pacific Ocean, only minor advancements have been achieved over the Euro-Atlantic sector. Some of the most recent GCMs still exhibit the same negative bias as 20 years ago in this region, associated with large geopotential height systematic errors. Some individual models, nevertheless, have improved and do show good performances in both sectors. Negligible differences emerge among ocean-coupled or atmosphere-only simulations, suggesting weak relevance of sea surface temperature biases. Conversely, increased horizontal resolution seems to be able to alleviate the Euro-Atlantic blocking bias.

scholarly journals Coupling sky images with three-dimensional radiative transfer models: a new method to estimate cloud optical depth

2015 ◽  
Vol 8 (10) ◽  
pp. 11285-11321 ◽  
Author(s):  
F. A. Mejia ◽  
B. Kurtz ◽  
K. Murray ◽  
L. M. Hinkelman ◽  
M. Sengupta ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Zenith Angle ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Direct Normal Irradiance

Abstract. A method for retrieving cloud optical depth (τc) using a ground-based sky imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a 3-D Radiative Transfer Model (3DRTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλ(τc, θ0, ϑs, ϑz) and the red-blue ratio, RBR(τc, θ0, ϑs, ϑz) are retrieved from the 3DRTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeas(ϑs, ϑz), in addition to RBRmeas(ϑs, ϑz) to obtain a unique solution for τc. The RRBR method is applied to images taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and validated against measurements from a microwave radiometer (MWR); output from the Min method for overcast skies, and τc retrieved by Beer's law from direct normal irradiance (DNI) measurements. A τc RMSE of 5.6 between the Min method and the USI are observed. The MWR and USI have an RMSE of 2.3 which is well within the uncertainty of the MWR. An RMSE of 0.95 between the USI and DNI retrieved τc is observed. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

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