scholarly journals The ALADIN System and its Canonical Model Configurations AROME CY41T1 and ALARO CY40T1

2017 ◽  
Author(s):  
Piet Termonia ◽  
Claude Fischer ◽  
Eric Bazile ◽  
François Bouyssel ◽  
Radmila Brožková ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Forecast Model ◽  
Canonical Model ◽  
Limited Area ◽  
Prediction System ◽  
Climate Simulations ◽  
Process Studies

Abstract. The ALADIN System is a numerical weather prediction system (NWP) developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 Partner Institutes of this consortium. These configurations are called the ALADIN Canonical Model Configurations (CMCs). There are currently three CMCs: the ALADIN baseline-CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs and to document their most recent versions, and (iii) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the Partner Institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

scholarly journals The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1

2018 ◽  
Vol 11 (1) ◽  
pp. 257-281 ◽  
Author(s):  
Piet Termonia ◽  
Claude Fischer ◽  
Eric Bazile ◽  
François Bouyssel ◽  
Radmila Brožková ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Forecast Model ◽  
Canonical Model ◽  
Limited Area ◽  
Climate Simulations ◽  
Process Studies

Abstract. The ALADIN System is a numerical weather prediction (NWP) system developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the ALADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

scholarly journals Assessment of Doppler Radar Radial Wind Observation Quality from Different Echo Sources for Assimilation during the Sydney 2014 Forecast Demonstration Project

2018 ◽  
Vol 35 (8) ◽  
pp. 1605-1620 ◽  
Author(s):  
Susan Rennie ◽  
Peter Steinle ◽  
Alan Seed ◽  
Mark Curtis ◽  
Yi Xiao
Keyword(s):  
Quality Control ◽  
Control System ◽  
Statistical Analysis ◽  
High Resolution ◽  
Radial Velocity ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Demonstration Project ◽  
Prediction System

AbstractA new quality control system, primarily using a naïve Bayesian classifier, has been developed to enable the assimilation of radial velocity observations from Doppler radar. The ultimate assessment of this system is the assimilation of observations in a pseudo-operational numerical weather prediction system during the Sydney 2014 Forecast Demonstration Project. A statistical analysis of the observations assimilated during this period provides an assessment of the data quality. This will influence how observations will be assimilated in the future, and what quality control and errors are applicable. This study compares observation-minus-background statistics for radial velocities from precipitation and insect echoes. The results show that with the applied level of quality control, these echo types have comparable biases. With the latest quality control, the clear air observations of wind are apparently of similar quality to those from precipitation and are therefore suitable for use in high-resolution NWP assimilation systems.

scholarly journals Sensitivity of Radiative Fluxes to Aerosols in the ALADIN-HIRLAM Numerical Weather Prediction System

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 205
Author(s):  
Laura Rontu ◽  
Emily Gleeson ◽  
Daniel Martin Perez ◽  
Kristian Pagh Nielsen ◽  
Velle Toll
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Heating Rates ◽  
Radiative Fluxes ◽  
Numerical Weather ◽  
The Impact

The direct radiative effect of aerosols is taken into account in many limited-area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We studied the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single-column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA simple broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud–radiation–aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and the vertical distribution of the aerosol species. In such cases, regional weather models should use external real-time aerosol data for radiation parametrizations. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust.

scholarly journals Sensitivity of Radiative Fluxes to Aerosols in the ALADIN-HIRLAM Numerical Weather Prediction System

2020 ◽  
Author(s):  
Laura Rontu ◽  
Emily Gleeson ◽  
Daniel Martin Perez ◽  
Kristian Pagh Nielsen ◽  
Velle Toll
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Heating Rates ◽  
Radiative Fluxes ◽  
Numerical Weather ◽  
The Impact

The direct radiative effect of aerosols is taken into account in many limited area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We study the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud-radiation-aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and vertical distribution of the aerosol species. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust.

scholarly journals An Evaluation of MÉRA, a High-Resolution Mesoscale Regional Reanalysis

2018 ◽  
Vol 57 (9) ◽  
pp. 2179-2196 ◽  
Author(s):  
Eoin Whelan ◽  
Emily Gleeson ◽  
John Hanley
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Climate ◽  
Prediction System ◽  
High Quality ◽  
Regional Reanalysis ◽  
Horizontal Grid ◽  
Very High

AbstractMet Éireann, the Irish Meteorological Service, has generated a very high resolution (2.5-km horizontal grid) regional climate reanalysis for Ireland called the Met Éireann Reanalysis (MÉRA). MÉRA spans the period from 1981 to 2015 and was produced using the shared ALADIN–HIRLAM numerical weather prediction system. This article includes comparisons with the ERA-Interim and Uncertainties in Ensembles of Regional Reanalyses (UERRA) datasets, analysis of data assimilation outputs, precipitation comparisons, and a focus on extremes of wind and rainfall. The comparisons with the reanalysis datasets show that MÉRA provides a high-quality reconstruction of recent Irish climate and benefits from the use of a very high resolution grid, in particular in relation to wind and precipitation extremes.

scholarly journals The Pan-Canadian High Resolution (2.5 km) Deterministic Prediction System

2016 ◽  
Vol 31 (6) ◽  
pp. 1791-1816 ◽  
Author(s):  
Jason A. Milbrandt ◽  
Stéphane Bélair ◽  
Manon Faucher ◽  
Marcel Vallée ◽  
Marco L. Carrera ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Forecast Model ◽  
Added Value ◽  
Precipitation Forecast ◽  
Initial Surface ◽  
Prediction System ◽  
Global Environmental ◽  
Fractional Cloud ◽  
Land Data Assimilation System

Abstract Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

scholarly journals Increasing horizontal resolution in numerical weather prediction and climate simulations: illusion or panacea?

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130289 ◽  
Author(s):  
Nils P. Wedi
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Space Versus ◽  
Physical Space ◽  
Horizontal Resolution ◽  
Spectral Space ◽  
Numerical Weather ◽  
Climate Simulations ◽  
Forecast Efficiency

The steady path of doubling the global horizontal resolution approximately every 8 years in numerical weather prediction (NWP) at the European Centre for Medium Range Weather Forecasts may be substan- tially altered with emerging novel computing architectures. It coincides with the need to appropriately address and determine forecast uncertainty with increasing resolution, in particular, when convective-scale motions start to be resolved. Blunt increases in the model resolution will quickly become unaffordable and may not lead to improved NWP forecasts. Consequently, there is a need to accordingly adjust proven numerical techniques. An informed decision on the modelling strategy for harnessing exascale, massively parallel computing power thus also requires a deeper understanding of the sensitivity to uncertainty—for each part of the model—and ultimately a deeper understanding of multi-scale interactions in the atmosphere and their numerical realization in ultra-high-resolution NWP and climate simulations. This paper explores opportunities for substantial increases in the forecast efficiency by judicious adjustment of the formal accuracy or relative resolution in the spectral and physical space. One path is to reduce the formal accuracy by which the spectral transforms are computed. The other pathway explores the importance of the ratio used for the horizontal resolution in gridpoint space versus wavenumbers in spectral space. This is relevant for both high-resolution simulations as well as ensemble-based uncertainty estimation.

scholarly journals HARMONIE-AROME single-column tools and experiments

2020 ◽  
Vol 17 ◽  
pp. 255-267
Author(s):  
Emily Gleeson ◽  
Stephen Outten ◽  
Bjørg Jenny Kokkvoll Engdahl ◽  
Eoin Whelan ◽  
Ulf Andrae ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Canonical Model ◽  
Prediction System ◽  
Numerical Weather ◽  
Model Configuration ◽  
Single Column ◽  
Transfer Study

Abstract. The single-column version of the shared ALADIN-HIRLAM numerical weather prediction system, called MUSC, was developed by Météo-France in the 2000s and has a growing user-base in both HIRLAM and ALADIN countries. Tools to derive the required input, to run the experiments and to handle outputs of experiments carried out using MUSC have been developed within the HARMONIE-AROME canonical model configuration of the ALADIN-HIRLAM system are described within and constitute a large portion of this paper. The paper also illustrates the usefulness of the single-column approach for testing and developing HARMONIE-AROME physical parametrizations related to cloud microphysics and radiative transfer. Study cases concerning these physical parametrizations have been included for illustration purposes.

scholarly journals Impact of Doppler Radar Wind Observations on Australian High-Resolution Numerical Weather Prediction

2020 ◽  
Vol 35 (2) ◽  
pp. 309-324
Author(s):  
Susan Rennie ◽  
Lawrence Rikus ◽  
Nathan Eizenberg ◽  
Peter Steinle ◽  
Monika Krysta
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Error ◽  
Numerical Weather ◽  
Skill Scores ◽  
The Impact

Abstract The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

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