Deriving cloud information from all-sky images for intra-hour PV nowcasting

2021 ◽  
Author(s):  
Philipp Gregor ◽  
Tobias Zinner ◽  
Bernhard Mayer ◽  
Josef Schreder
Keyword(s):  
Power Plant ◽  
Radiative Transfer ◽  
Power Plants ◽  
Reference Data ◽  
Weather Prediction ◽  
Energy Output ◽  
Cloud Base ◽  
Transfer Model ◽  
Weather Situation ◽  
Cloud Base Height

<p>Energy output from photovoltaics (PV) strongly depends on the respective weather situation. To ensure continuous energy availability in power grids with large PV contribution, flexibly manageable power plants have to compensate for variations in PV power production. Within the project NETFLEX, an intra-hour irradiance now-casting algorithm is developed as a basis for a PV power forecast used for management of a combined PV / biogas power plant.<!-- a combined PV / biogas power plant? --></p><p>The now-casting algorithm is designed around a cloud representation in a simplistic 2D advection model, which is updated with currently measured data and which projects cloud situations up to 15 minutes into the future. Main input to the model are images captured by two CMS Schreder all-sky imagers (ASI) installed at the PV plant in locations separated by about 530m. Captured images are processed to extract cloud masks, cloud base heights and cloud movement. To obtain cloud masks, ratios of red and blue channels as well as saturation and brightness are compared to reference data from a clearsky library.<!-- Kommt mir hier etwas zu ausführlich vor. Lieber etwas wie?: To obtain a cloud mask, ratios of red and blue channels as well as saturation and brightness are compared to reference data from a clearsky library. --><!-- --> This library is composed from synthetic clearsky data computed by the radiative transfer model <em>libRadtran</em> (Mayer and Kylling, 2005), which are processed to resemble imager geometry and optics. The creation of synthetic references allows for any desired sun position and aerosol condition. Simultaneously captured images of both cameras are evaluated and corresponding pixels are matched. Exact calibration of the imager geometry then allows for cloud base height derivation using the method of miss-pointing vectors (Kölling et al., 2019). Consecutive images are evaluated <!-- “Per imager”  “for each camera” -->for each ASI to estimate horizontal cloud motion by matching corresponding pixels. All cloud information computed from ASI images is assimilated into the 2D model as a base for cloud field predictions with information about cloud position, base height and velocity. The model-centered approach allows for flexible integration of additional data sources, e.g. satellite imagery and numerical weather prediction data.</p><p>Validation of image evaluation methods and now-casting model is done using synthetic all-sky images of LES cloud fields<!-- Könnte hier noch etwas von “promising results“ sagen. Aber das ist vermutlich klar und wahrscheinlich auch auf der Konferenz deutlicher aufzuführen. -->. Additionally, cloud base height from a ceilometer as well as global and direct integrated solar irradiance were measured on site of the PV power plant. This also allows for validation on real world cases.<br><br></p><p><em>Literature:</em></p><p>Kölling, T., Zinner, T., and Mayer, B.: <em>Aircraft-based stereographic reconstruction of 3-D cloud geometry</em>, Atmos. Meas. Tech., 12, 1155–1166, 2019.</p><p>Mayer, B. and Kylling, A.: <em>Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use</em>, Atmos. Chem. Phys., 5, 1855–1877, 2005.</p>

Development of a Fast Three-Dimensional Dynamic Radiative Transfer Solver for Numerical Weather Prediction Models


2021 ◽  
Author(s):  
Richard Maier ◽  
Bernhard Mayer ◽  
Claudia Emde ◽  
Aiko Voigt
Keyword(s):  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Transfer Model ◽  
Heating Rates ◽  
Time Step ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

<div> <div> <div> <div> <p>The increasing resolution of numerical weather prediction models makes 3D radiative effects more and more important. These effects are usually neglected by the simple 1D independent column approximations used in most of the current models. On top of that, these 1D radiative transfer solvers are also called far less often than the model’s dynamical core.</p> <p>To address these issues, we present a new „dynamic“ approach of solving 3D radiative transfer. Building upon the existing TenStream solver (Jakub and Mayer, 2015), radiation in this 3D model is not solved completely in each radiation time step, but is rather only transported to adjacent grid boxes. For every grid box, outgoing fluxes are then calculated from the incoming fluxes from the neighboring grid cells of the previous time step. This allows to reduce the computational cost of 3D radiative transfer models to that of current 1D solvers.</p> <p>Here, we show first results obtained with this new solver with a special emphasis on heating rates. Furthermore, we demonstrate issues related to the dynamical treatment of radiation as well as possible solutions to these problems.</p> <p>In the future, the speed of this newly developed 3D dynamic TenStream solver will be further increased by reducing the number of spectral bands used in the radiative transfer calculations with the aim to get a 3D solver that can be called even more frequently than the 1D two-stream solvers used nowadays.</p> <p>Reference:<br><span>Jakub, F. and Mayer, B. (2015), A three-dimensional parallel radiative transfer model for atmospheric heating rates for use in cloud resolving models—The TenStream solver, Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 163, 2015, Pages 63-71, ISSN 0022-4073, . </span></p> </div> </div> </div> </div>

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>

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 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.

scholarly journals Thermodynamic and Radiative Structure of Stratocumulus-Topped Boundary Layers*

2015 ◽  
Vol 72 (1) ◽  
pp. 430-451 ◽  
Author(s):  
Virendra P. Ghate ◽  
Mark A. Miller ◽  
Bruce A. Albrecht ◽  
Christopher W. Fairall
Keyword(s):  
Radiative Transfer ◽  
Boundary Layers ◽  
Great Plains ◽  
Potential Temperature ◽  
Radiative Properties ◽  
Radiative Transfer Model ◽  
Cloud Base ◽  
Transfer Model ◽  
Flux Divergence ◽  
Liquid Water Path

Abstract Stratocumulus-topped boundary layers (STBLs) observed in three different regions are described in the context of their thermodynamic and radiative properties. The primary dataset consists of 131 soundings from the southeastern Pacific (SEP), 90 soundings from the island of Graciosa (GRW) in the North Atlantic, and 83 soundings from the U.S. Southern Great Plains (SGP). A new technique that makes an attempt to preserve the depths of the sublayers within an STBL is proposed for averaging the profiles of thermodynamic and radiative variables. A one-dimensional radiative transfer model known as the Rapid Radiative Transfer Model was used to compute the radiative fluxes within the STBL. The SEP STBLs were characterized by a stronger and deeper inversion, together with thicker clouds, lower free-tropospheric moisture, and higher radiative flux divergence across the cloud layer, as compared to the GRW STBLs. Compared to the STBLs over the marine locations, the STBLs over SGP had higher wind shear and a negligible (−0.41 g kg−1) jump in mixing ratio across the inversion. Despite the differences in many of the STBL thermodynamic parameters, the differences in liquid water path at the three locations were statistically insignificant. The soundings were further classified as well mixed or decoupled based on the difference between the surface and cloud-base virtual potential temperature. The decoupled STBLs were deeper than the well-mixed STBLs at all three locations. Statistically insignificant differences in surface latent heat flux (LHF) between well-mixed and decoupled STBLs suggest that parameters other than LHF are responsible for producing decoupling.

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.

scholarly journals Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

2011 ◽  
Vol 4 (1) ◽  
pp. 789-813
Author(s):  
B. Calpini ◽  
D. Ruffieux ◽  
J.-M. Bettems ◽  
C. Hug ◽  
P. Huguenin ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Local Dispersion ◽  
Swiss Plateau ◽  
Nwp Model

Abstract. The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP) model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers). This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS). The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every 3 h). This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plant) and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer) and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release) the best picture of the 24-h evolution of air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations) required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an emergency response exercise performed in winter 2009.

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

2012 ◽  
Vol 29 (5) ◽  
pp. 745-754 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Robert Rabin ◽  
Jason Otkin ◽  
John S. Kain ◽  
Scott Dembek
Keyword(s):  
Radiative Transfer ◽  
Prediction Model ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Physical Processes ◽  
Operational Model ◽  
Time Required

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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