Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models

2009 ◽  
Vol 135 (639) ◽  
pp. 457-468 ◽  
Author(s):  
J. Manners ◽  
J.-C. Thelen ◽  
J. Petch ◽  
P. Hill ◽  
J.M. Edwards
Keyword(s):  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Temporal Sampling

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>

scholarly journals An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

2020 ◽  
Vol 12 (17) ◽  
pp. 2758
Author(s):  
Stuart Fox
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Full Range ◽  
Numerical Weather Prediction Model ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Nwp Model ◽  
Cloud Ice

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

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 The Plant–Craig Stochastic Convection Scheme in ICON and Its Scale Adaptivity

2014 ◽  
Vol 71 (9) ◽  
pp. 3404-3415 ◽  
Author(s):  
Richard J. Keane ◽  
George C. Craig ◽  
Christian Keil ◽  
Günther Zängl
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Precipitation Variability ◽  
Convection Scheme ◽  
Numerical Weather ◽  
Multiple Resolutions ◽  
Input Variables

Abstract The emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.

scholarly journals EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian M. Grams
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Sets ◽  
Forecast Uncertainty ◽  
Numerical Weather ◽  
Process Oriented ◽  
Air Streams

Abstract. Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending air streams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within two days. This requires numerical data with high spatial and temporal resolution which is often not available from standard output and requires expensive computations. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models' skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and of their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective.

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

scholarly journals Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 489 ◽  
Author(s):  
Jürgen Helmert ◽  
Aynur Şensoy Şorman ◽  
Rodolfo Alvarado Montero ◽  
Carlo De Michele ◽  
Patricia de Rosnay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Availability ◽  
Observation Data ◽  
Climate Change Scenarios ◽  
Numerical Weather ◽  
The Future

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

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