scholarly journals Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System

2019 ◽  
Vol 11 (3) ◽  
pp. 234 ◽  
Author(s):  
Philip Browne ◽  
Patricia de Rosnay ◽  
Hao Zuo ◽  
Andrew Bennett ◽  
Andrew Dawson
Keyword(s):  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Analysis ◽  
Forecast Errors ◽  
Numerical Weather ◽  
Weakly Coupled ◽  
Spatial Coverage ◽  
Ocean Atmosphere

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling.

scholarly journals Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

2016 ◽  
Vol 31 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Michaël Zamo ◽  
Liliane Bel ◽  
Olivier Mestre ◽  
Joël Stein
Keyword(s):  
Wind Speed ◽  
Random Forests ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Near Surface ◽  
Numerical Weather ◽  
Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Impact of Consistent Semi-Lagrangian Trajectory Calculations on Numerical Weather Prediction Performance

2017 ◽  
Vol 145 (10) ◽  
pp. 4127-4150 ◽  
Author(s):  
Syed Zahid Husain ◽  
Claude Girard
Keyword(s):  
Numerical Weather Prediction ◽  
Numerical Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Multiscale Model ◽  
Dynamical Equations ◽  
Numerical Weather ◽  
Trajectory Calculation ◽  
Trajectory Calculations

Inconsistencies may arise in numerical weather prediction models—that are based on semi-Lagrangian advection—when the governing dynamical and the kinematic trajectory equations are discretized in a dissimilar manner. This study presents consistent trajectory calculation approaches, both in the presence and absence of off-centering in the discretized dynamical equations. Both uniform and differential off-centering in the discretized dynamical equations have been considered. The proposed consistent trajectory calculations are evaluated using numerical experiments involving a nonhydrostatic two-dimensional theoretical mountain case and hydrostatic global forecasts. The experiments are carried out using the Global Environmental Multiscale model. Both the choice of the averaging method for approximating the velocity integral in the discretized trajectory equations and the interpolation scheme for calculating the departure positions are found to be important for consistent trajectory calculations. Results from the numerical experiments confirm that the proposed consistent trajectory calculation approaches not only improve numerical consistency, but also improve forecast accuracy.

scholarly journals EMPOL 1.0: a new parameterization of pollen emission in numerical weather prediction models

2013 ◽  
Vol 6 (6) ◽  
pp. 1961-1975 ◽  
Author(s):  
K. Zink ◽  
A. Pauling ◽  
M. W. Rotach ◽  
H. Vogel ◽  
P. Kaufmann ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Birch Pollen ◽  
Small Scale ◽  
Pollen Emission ◽  
Numerical Weather ◽  
Pollen Concentrations ◽  
Scale Modelling ◽  
Reactive Trace Gases

Abstract. Simulating pollen concentrations with numerical weather prediction (NWP) systems requires a parameterization for pollen emission. We have developed a parameterization that is adaptable for different plant species. Both biological and physical processes of pollen emission are taken into account by parameterizing emission as a two-step process: (1) the release of the pollen from the flowers, and (2) their entrainment into the atmosphere. Key factors influencing emission are temperature, relative humidity, the turbulent kinetic energy and precipitation. We have simulated the birch pollen season of 2012 using the NWP system COSMO-ART (Consortium for Small-scale Modelling – Aerosols and Reactive Trace Gases), both with a parameterization already present in the model and with our new parameterization EMPOL. The statistical results show that the performance of the model can be enhanced by using EMPOL.

Forecasting wave height probabilities with numerical weather prediction models

2005 ◽  
Vol 32 (14-15) ◽  
pp. 1841-1863 ◽  
Author(s):  
Mark S. Roulston ◽  
Jerome Ellepola ◽  
Jost von Hardenberg ◽  
Leonard A. Smith
Keyword(s):  
Wave Height ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

scholarly journals Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

2018 ◽  
Vol 11 (8) ◽  
pp. 3347-3368 ◽  
Author(s):  
Yurii Batrak ◽  
Ekaterina Kourzeneva ◽  
Mariken Homleid
Keyword(s):  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Mean Absolute Error ◽  
Weather Prediction ◽  
Absolute Error ◽  
The Arctic ◽  
Polar Regions ◽  
Form Drag ◽  
Numerical Weather ◽  
Ice Surface Temperature

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.

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