scholarly journals Implementation of a semi-Lagrangian fully-implicit time integration of the unified soundproof system of equations for numerical weather prediction

2021 ◽  
Author(s):  
Abdessamad Qaddouri ◽  
Claude Girard ◽  
Syed Zahid Husain ◽  
Rabah Aider
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Time Integration ◽  
Implicit Time ◽  
System Of Equations ◽  
Dynamical Core ◽  
Vertical Coordinate ◽  
Numerical Weather ◽  
Fully Implicit ◽  
Computational Performance

AbstractAn alternate dynamical core that employs the unified equations of A. Arakawa and C.S. Konor (2009) has been developed within Environment and Climate change Canada’s GEM (Global Environmental Multiscale) atmospheric model. As in the operational GEM dynamical core, the novel core utilizes the same fully-implicit two-time-level semi-Lagrangian scheme for time discretization while the log-pressure-based terrain-following vertical coordinate has been slightly adapted. Overall, the new dynamical core implementation required only minor changes to the existing informatics code of the GEM model and from a computational performance perspective, the new core does not incur any significant additional cost. A broad range of tests – that include both two-dimensional idealized theoretical cases and three-dimensional deterministic forecasts covering both hydrostatic and non-hydrostatic scales–have been carried out to evaluate the performance of the new dynamical core. For all the tested cases, when compared to the operational GEM model, the new dynamical core based on the unified equations has been found to produce statistically equivalent results. These results imply that the unified equations can be adopted for operational numerical weather prediction that would employ a single soundproof system of equations to produce reliable forecasts for all meteorological scales of interest with negligible changes for the computational overhead.

scholarly journals Semi-implicit semi-Lagrangian modelling of the atmosphere: a Met Office perspective

2016 ◽  
Vol 7 (3) ◽  
pp. 4-25 ◽  
Author(s):  
Tommaso Benacchio ◽  
Nigel Wood
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Time Integration ◽  
Implicit Time ◽  
Good Representation ◽  
Solution Approach ◽  
Numerical Weather ◽  
Geostrophic Balance ◽  
The Impact

Abstract The semi-Lagrangian numerical method, in conjunction with semi-implicit time integration, provides numerical weather prediction models with numerical stability for large time steps, accurate modes of interest, and good representation of hydrostatic and geostrophic balance. Drawing on the legacy of dynamical cores at the Met Office, the use of the semi-implicit semi-Lagrangian method in an operational numerical weather prediction context is surveyed, together with details of the solution approach and associated issues and challenges. The numerical properties and performance of the current operational version of the Met Office’s numerical model are then investigated in a simplified setting along with the impact of different modelling choices.

scholarly journals FVM 1.0: a nonhydrostatic finite-volume dynamical core for the IFS

2019 ◽  
Vol 12 (2) ◽  
pp. 651-676 ◽  
Author(s):  
Christian Kühnlein ◽  
Willem Deconinck ◽  
Rupert Klein ◽  
Sylvie Malardel ◽  
Zbigniew P. Piotrowski ◽  
...  
Keyword(s):  
Finite Volume ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Implicit Time ◽  
Integration Scheme ◽  
Advective Transport ◽  
Numerical Weather ◽  
Time Stepping ◽  
Spectral Transform ◽  
Volume Integration

Abstract. We present a nonhydrostatic finite-volume global atmospheric model formulation for numerical weather prediction with the Integrated Forecasting System (IFS) at ECMWF and compare it to the established operational spectral-transform formulation. The novel Finite-Volume Module of the IFS (henceforth IFS-FVM) integrates the fully compressible equations using semi-implicit time stepping and non-oscillatory forward-in-time (NFT) Eulerian advection, whereas the spectral-transform IFS solves the hydrostatic primitive equations (optionally the fully compressible equations) using a semi-implicit semi-Lagrangian scheme. The IFS-FVM complements the spectral-transform counterpart by means of the finite-volume discretization with a local low-volume communication footprint, fully conservative and monotone advective transport, all-scale deep-atmosphere fully compressible equations in a generalized height-based vertical coordinate, and flexible horizontal meshes. Nevertheless, both the finite-volume and spectral-transform formulations can share the same quasi-uniform horizontal grid with co-located arrangement of variables, geospherical longitude–latitude coordinates, and physics parameterizations, thereby facilitating their comparison, coexistence, and combination in the IFS. We highlight the advanced semi-implicit NFT finite-volume integration of the fully compressible equations of IFS-FVM considering comprehensive moist-precipitating dynamics with coupling to the IFS cloud parameterization by means of a generic interface. These developments – including a new horizontal–vertical split NFT MPDATA advective transport scheme, variable time stepping, effective preconditioning of the elliptic Helmholtz solver in the semi-implicit scheme, and a computationally efficient implementation of the median-dual finite-volume approach – provide a basis for the efficacy of IFS-FVM and its application in global numerical weather prediction. Here, numerical experiments focus on relevant dry and moist-precipitating baroclinic instability at various resolutions. We show that the presented semi-implicit NFT finite-volume integration scheme on co-located meshes of IFS-FVM can provide highly competitive solution quality and computational performance to the proven semi-implicit semi-Lagrangian integration scheme of the spectral-transform IFS.

The Common Community Physics Package (CCPP): bridging the gap between research and operations to improve U.S. numerical weather prediction capabilities

2020 ◽  
Author(s):  
Dom Heinzeller ◽  
Grant Firl ◽  
Ligia Bernardet ◽  
Laurie Carson ◽  
Man Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Modeling Framework ◽  
Initial Development ◽  
Numerical Weather ◽  
Computational Performance ◽  
The Common ◽  
The U.S ◽  
Physical Parameterizations

<p>Improving numerical weather prediction systems depends critically on the ability to transition innovations from research to operations (R2O) and to provide feedback from operations to research (O2R). This R2O2R cycle, sometimes referred to as "crossing the valley of death", has long been identified as a major challenge for the U.S. weather enterprise.</p><p>As part of a broader effort to bridge this gap and advance U.S. weather prediction capabilities, the Developmental Testbed Center (DTC) with staff at NOAA and NCAR has developed the Common Community Physics Package (CCPP) for application in NOAA's Unified Forecasting System (UFS). The CCPP consists of a library of physical parameterizations and a framework, which interfaces the physics with atmospheric models based on metadata information and standardized interfaces. The CCPP physics library contains physical parameterizations from the current operational U.S. global, mesoscale and high-resolution models, future implementation candidates, and additional physics from NOAA, NCAR and other organizations. The range of physics options in the CCPP physics library enables the application of the UFS - as well as every other model using the CCPP - across scales, from now-casting to seasonal and from high-resolution regional to global ensembles.</p><p>While the initial development of the CCPP was centered around the FV3 (Finite-Volume Cubed-Sphere) dynamical core of the UFS, its focus has since widened. The CCPP is also used by the DTC Single Column Model to support a hierarchical testing strategy, and by the next generation NEPTUNE (Navy Environmental Prediction sysTem Utilizing the Numa corE) model of the Naval Research Laboratory. Further, and most importantly, NOAA and NCAR recently signed an agreement to jointly develop the CCPP framework as a single, standardized way to interface physics with their models of the atmosphere (and other compartments of the Earth system). This places the CCPP in the heart of several of the U.S. flagship models and opens the door for bringing innovations from a large research community into operations.</p><p>In this contribution, we will present a brief overview of the concept of the CCPP, its technical design and the requirements for parameterizations to be considered as CCPP-compliant. We will describe the integration of CCPP in the UFS and touch upon the challenges in creating a flexible modeling framework while maintaining high computational performance. We will also provide information on how to obtain, use and contribute to the CCPP, as well as on the future development of the CCPP framework and upcoming additions to the CCPP physics library.</p>

L'évolution opérationnelle du modèle Arpège et de ses paramétrisations physiques

2021 ◽  
pp. 047
Author(s):  
François Bouyssel ◽  
Marta Janisková ◽  
Éric Bazile ◽  
Yves Bouteloup ◽  
Jean-Marcel Piriou
Keyword(s):  
Research And Development ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Post Processing ◽  
Dynamical Core ◽  
Var Analysis ◽  
Numerical Weather ◽  
Physical Parameterizations

Au cours de ses années à la direction du Groupe de modélisation et d'assimilation pour la prévision (Gmap), Jean-François Geleyn dirigea avec enthousiasme, dynamisme, efficacité et une grande expertise la préparation de toutes les évolutions des systèmes opérationnels de prévision numérique du temps Arpège et Aladin. Plusieurs évolutions majeures furent implémentées en opérationnel au cours de cette période. La contribution de Jean-François Geleyn fut notamment remarquable sur le noyau dynamique, le post-traitement des prévisions, leur évaluation et tout particulièrement les paramétrisations physiques des modèles de prévision. Il coordonna en effet tous les travaux de recherche et développement sur les paramétrisations physiques des modèles Arpège et Aladin et sur les paramétrisations physiques linéarisées développées pour l'analyse 4D-Var dans son groupe et chez les partenaires Aladin. Over the years when Jean-François Geleyn was the head of GMAP he led with great enthusiasm, dynamism, efficiency and expertise the preparation of all the evolutions of operational numerical weather prediction (NWP) systems of Arpège and Aladin. Several major changes were implemented operationally during this period. Jean-François Geleyn's contribution was remarkable in several topics such as the dynamical core, the post-processing of weather predictions, their validation, and especially the physical parameterizations of the numerical weather predictions models. In principle, he coordinated all the research and development activities on physical parameterizations for Arpège and Aladin NWP models and on the linearized physical parameterizations developed for the 4D-Var analysis in his group and among the Aladin partners.

A Hybrid Differential-Ensemble Linear Forecast Model for 4D-Var

2021 ◽  
Vol 149 (1) ◽  
pp. 3-19
Author(s):  
T. J. Payne
Keyword(s):  
Data Assimilation ◽  
Linear Model ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Forecast Model ◽  
Regression Technique ◽  
Time Step ◽  
Dynamical Core ◽  
Numerical Weather ◽  
Better Than

AbstractA key component of the 4D-Var data assimilation method used widely for numerical weather prediction is the linear forecast model, which is approximately tangent linear to the forecast model. Traditionally this has been based on differentiating the forecast model, though recently some authors have experimented with an ensemble regression technique, the localized ensemble tangent linear model (LETLM). We propose a hybrid of the two, in which a simplified conventional tangent-linear model (e.g., just the dynamical core) is used together with an LETLM-like adjustment every time step to account for the remaining processes (in this example, the parameterized physics). This is much cheaper than the LETLM, and in tests using the Met Office’s linear model performs considerably better than either a pure LETLM (with a very large ensemble) or the existing linear model.

A new vertical coordinate formulation to improve forecasts of fog and low stratus in high-resolution numerical weather prediction models

2021 ◽  
Author(s):  
Stephanie Westerhuis ◽  
Oliver Fuhrer
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Vertical Coordinate ◽  
Electrical Grid ◽  
Numerical Weather ◽  
Terrain Following ◽  
Numerical Weather Prediction Models

<p>Fog and low stratus pose a major challenge for numerical weather prediction (NWP) models. Despite high resolution in the horizontal (~1 km) and vertical (~20 m), operational NWP models often fail to accurately predict fog and low stratus. This is a major issue at airports which require visibility predictions, or for energy agencies estimating day-ahead input into the electrical grid from photovoltaic power.</p><p>Most studies dedicated to fog and low stratus forecasts have focused on the physical parameterisations or grid resolutions. We illustrate how horizontal advection at the cloud top of fog and low stratus in a grid with sloping vertical coordinates leads to spurious numerical diffusion and subsequent erroneous dissipation of the clouds. This cannot be prevented by employing a higher-order advection scheme. After all, the formulation of the terrain-following vertical coordinate plays a crucial role in regions which do not exhibit perfectly flat orography. We suggest a new vertical coordinate formulation which allows for a faster decay of the orographic signal with altitude and present its positive impact on fog and low stratus forecasts. Our experiments indicate that smoothing of the vertical coordinates at low altitudes is a crucial measure to prevent premature dissipation of fog and low stratus in high-resolution NWP models.</p>

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