The AROME-France Convective-Scale Operational Model

2011 ◽  
Vol 139 (3) ◽  
pp. 976-991 ◽  
Author(s):  
Y. Seity ◽  
P. Brousseau ◽  
S. Malardel ◽  
G. Hello ◽  
P. Bénard ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Three Dimensional ◽  
Forecast Model ◽  
Scale Model ◽  
Prediction System ◽  
Operational Model ◽  
Technical Developments ◽  
Convective Scale ◽  
Scientific Technical ◽  
Physical Parameterizations

Abstract After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Météo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the associated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.

scholarly journals SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

2019 ◽  
Vol 36 (3) ◽  
Author(s):  
Xiang-Yu Huang ◽  
Dale Barker ◽  
Stuart Webster ◽  
Anurag Dipankar ◽  
Adrian Lock ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Prediction System ◽  
Local Data ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
The United Kingdom ◽  
Convective Scale ◽  
The Impact

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

scholarly journals Assimilating AIRS Temperature and Mixing Ratio Profiles Using an Ensemble Kalman Filter Approach for Convective-Scale Forecasts

2012 ◽  
Vol 27 (3) ◽  
pp. 541-564 ◽  
Author(s):  
Thomas A. Jones ◽  
David J. Stensrud
Keyword(s):  
Kalman Filter ◽  
Weather Prediction ◽  
Forecast Model ◽  
Mixing Ratio ◽  
Ensemble Forecasts ◽  
Southern Plains ◽  
Ensemble Mean ◽  
Convective Scale ◽  
Filter Approach ◽  
The Impact

Abstract One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

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 Satellite Data Assimilation in Numerical Weather Prediction Models. Part II: Uses of Rain-Affected Radiances from Microwave Observations for Hurricane Vortex Analysis

2007 ◽  
Vol 64 (11) ◽  
pp. 3910-3925 ◽  
Author(s):  
Fuzhong Weng ◽  
Tong Zhu ◽  
Banghua Yan
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Forecast Model ◽  
Background Information ◽  
Radiative Transfer Model ◽  
Asymmetric Structure ◽  
Transfer Model ◽  
Surface Parameters

Abstract A hybrid variational scheme (HVAR) is developed to produce the vortex analysis associated with tropical storms. This scheme allows for direct assimilation of rain-affected radiances from satellite microwave instruments. In the HVAR, the atmospheric temperature and surface parameters in the storms are derived from a one-dimension variational data assimilation (1DVAR) scheme, which minimizes the cost function of both background information and satellite measurements. In the minimization process, a radiative transfer model including scattering and emission is used for radiance simulation (see Part I of this study). Through the use of 4DVAR, atmospheric temperatures from the Advanced Microwave Sounding Unit (AMSU) and surface parameters from the Advanced Microwave Scanning Radiometer (AMSR-E) are assimilated into global forecast model outputs to produce an improved analysis. This new scheme is generally applicable for variable stages of storms. In the 2005 hurricane season, the HVAR was applied for two hurricane cases, resulting in improved analyses of three-dimensional structures of temperature and wind fields as compared with operational model analysis fields. It is found that HVAR reproduces detailed structures for the hurricane warm core at the upper troposphere. Both lower-level wind speed and upper-level divergence are enhanced with reasonable asymmetric structure.

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 Scientific Challenges of Convective-Scale Numerical Weather Prediction

2018 ◽  
Vol 99 (4) ◽  
pp. 699-710 ◽  
Author(s):  
Jun-Ichi Yano ◽  
Michał Z. Ziemiański ◽  
Mike Cullen ◽  
Piet Termonia ◽  
Jeanette Onvlee ◽  
...  
Keyword(s):  
Turbulent Flows ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Equation System ◽  
Bayesian Probability ◽  
Ensemble Prediction ◽  
Synoptic Scale ◽  
Probabilistic Forecasts ◽  
Convective Scale ◽  
Prediction Systems

AbstractAfter extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days.Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows.The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

scholarly journals The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness

2018 ◽  
Vol 11 (6) ◽  
pp. 2333-2351 ◽  
Author(s):  
Partha Sarathi Bhattacharjee ◽  
Jun Wang ◽  
Cheng-Hsuan Lu ◽  
Vijay Tallapragada
Keyword(s):  
Optical Thickness ◽  
Forest Fires ◽  
Positive Impact ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Forecast Model ◽  
Environmental Modeling ◽  
Aerosol Optical Thickness ◽  
Aerosol Component ◽  
Mean Square Errors

Abstract. An accurate representation of aerosols in global numerical weather prediction (NWP) models is important to predict major air pollution events and to also understand aerosol effects on short-term weather forecasts. Recently the global aerosol forecast model at NOAA, the NOAA Environmental Modeling System (NEMS) GFS Aerosol Component (NGAC), was upgraded from its dust-only version 1 to include five species of aerosols (black carbon, organic carbon, sulfate, sea salt and dust). This latest upgrade, now called NGACv2, is an in-line aerosol forecast system providing three-dimensional aerosol mixing ratios along with aerosol optical properties, including aerosol optical thickness (AOT), every 3 h up to 5 days at global 1∘×1∘ resolution. In this paper, we evaluated nearly 1.5 years of model AOT at 550 nm with available satellite retrievals, multi-model ensembles and surface observations over different aerosol regimes. Evaluation results show that NGACv2 has high correlations and low root mean square errors associated with African dust and also accurately represented the seasonal shift in aerosol plumes from Africa. Also, the model represented southern African and Canadian forest fires, dust from Asia, and AOT within the US with some degree of success. We have identified model underestimation for some of the aerosol regimes (particularly over Asia) and will investigate this further to improve the model forecast. The addition of a data assimilation capability to NGAC in the near future is expected to provide a positive impact in aerosol forecast by the model.

scholarly journals A Scalable Version of the Navy Operational Global Atmospheric Prediction System Spectral Forecast Model

2000 ◽  
Vol 8 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Thomas E. Rosmond
Keyword(s):  
Shared Memory ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Distributed Shared Memory ◽  
Forecast Model ◽  
Naval Research ◽  
Prediction System ◽  
Single Node ◽  
Numerical Weather ◽  
Wide Range

The Navy Operational Global Atmospheric Prediction System (NOGAPS) includes a state-of-the-art spectral forecast model similar to models run at several major operational numerical weather prediction (NWP) centers around the world. The model, developed by the Naval Research Laboratory (NRL) in Monterey, California, has run operational at the Fleet Numerical Meteorological and Oceanographic Center (FNMOC) since 1982, and most recently is being run on a Cray C90 in a multi-tasked configuration. Typically the multi-tasked code runs on 10 to 15 processors with overall parallel efficiency of about 90%. resolution is T159L30, but other operational and research applications run at significantly lower resolutions. A scalable NOGAPS forecast model has been developed by NRL in anticipation of a FNMOC C90 replacement in about 2001, as well as for current NOGAPS research requirements to run on DOD High-Performance Computing (HPC) scalable systems. The model is designed to run with message passing (MPI). Model design criteria include bit reproducibility for different processor numbers and reasonably efficient performance on fully shared memory, distributed memory, and distributed shared memory systems for a wide range of model resolutions. Results for a wide range of processor numbers, model resolutions, and different vendor architectures are presented. Single node performance has been disappointing on RISC based systems, at least compared to vector processor performance. This is a common complaint, and will require careful re-examination of traditional numerical weather prediction (NWP) model software design and data organization to fully exploit future scalable architectures.

scholarly journals Optimization of the Assimilation of Radar Data at the Convective Scale Using Specific Background Error Covariances in Precipitation

2012 ◽  
Vol 140 (11) ◽  
pp. 3495-3506 ◽  
Author(s):  
Thibaut Montmerle
Keyword(s):  
Forecast Error ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Radar Data ◽  
Background Error ◽  
Low Level ◽  
Convective Scale ◽  
Scale Structures ◽  
High Level ◽  
The Impact

Abstract This study focuses on the impact of using specific background error covariances in precipitating areas in the Application of Research to Operations at Mesoscale (AROME-France) numerical weather prediction (NWP) system that considers reflectivities and radial velocities in its assimilation system. Such error covariances are deduced from the application of geographical masks on forecast differences generated from an ensemble assimilation of various precipitating cases. The retrieved forecast error covariances are then applied in an incremental three-dimensional variational data assimilation (3D-Var) specifically in rainy areas, in addition to the operational climatological background error covariances that are used elsewhere. Such heterogeneous formulation gives better balanced and more realistic analysis increments, as retrieved from the assimilation of radar data. For instance, midlevel humidification allows for the reinforcement of the low-level cooling and convergence, the warming in clouds, and high-level divergence. Smaller forecast error horizontal lengths explain the smaller-scale structures of the increments and render possible the increase of data densities in rainy areas. Larger error variances for the dynamical variables give more weight to wind observations such as radial winds. A reduction of the spinup is also shown and is positively correlated to the size of the area where rainy forecast error covariances are applied. Positive forecast scores on cumulated rain and on low-level temperature and humidity are finally displayed.

Sign in / Sign up

Export Citation Format

Share Document