scholarly journals Conceptualizing the Impact of Dust Contaminated Infrared Radiances on Data Assimilation for Numerical Weather Prediction

2020 ◽  
Author(s):  
Jared W. Marquis ◽  
Mayra I. Oyola ◽  
James R. Campbell ◽  
Benjamin C. Ruston ◽  
Carmen Córdoba-Jabonero ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Dust Aerosol ◽  
Dew Point ◽  
Numerical Weather ◽  
Radiance Assimilation ◽  
Infrared Radiances ◽  
The Impact ◽  
Assimilation System

AbstractNumerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction - Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dew point, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

Assessment of the Impact of Observations on Analyses Derived from Observing System Experiments

2012 ◽  
Vol 140 (1) ◽  
pp. 245-257 ◽  
Author(s):  
Cristina Lupu ◽  
Pierre Gauthier ◽  
Stéphane Laroche
Keyword(s):  
North America ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Prediction System ◽  
Numerical Weather ◽  
Amount Of Information ◽  
The North ◽  
The Impact ◽  
Assimilation System

Abstract Observing system experiments (OSEs) are commonly used to quantify the impact of different observation types on forecasts produced by a specific numerical weather prediction system. Recently, methods based on degree of freedom for signal (DFS) have been implemented to diagnose the impact of observations on the analyses. In this paper, the DFS is used as a diagnostic to estimate the amount of information brought by subsets of observations in the context of OSEs. This study is interested in the evaluation of the North American observing networks applied to OSEs performed at the Meteorological Service of Canada for the period of January and February 2007. The relative values of the main observing networks over North America derived from DFS calculations are compared with those from OSEs in which aircraft or radiosonde data have been removed. The results show that removing some observation types from the assimilation system influences the effective weight of the remaining assimilated observations, which may have an increased impact to compensate for the removal of other observations. The response of the remaining observations when a given set of observations is denied is illustrated comparing DFS calculations with the observations’ impact estimated from OSEs.

scholarly journals Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

2019 ◽  
Vol 11 (8) ◽  
pp. 981 ◽  
Author(s):  
Roger Randriamampianina ◽  
Harald Schyberg ◽  
Máté Mile
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The Arctic ◽  
Medium Range Weather Forecast ◽  
Numerical Weather ◽  
Radiance Data ◽  
The Impact ◽  
Assimilation System

In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low.

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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.

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