The MOGREPS short-range ensemble prediction system

2008 ◽  
Vol 134 (632) ◽  
pp. 703-722 ◽  
Author(s):  
Neill E. Bowler ◽  
Alberto Arribas ◽  
Kenneth R. Mylne ◽  
Kelvyn B. Robertson ◽  
Sarah E. Beare
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system

2009 ◽  
Vol 135 (640) ◽  
pp. 767-776 ◽  
Author(s):  
Neill E. Bowler ◽  
Alberto Arribas ◽  
Sarah E. Beare ◽  
Kenneth R. Mylne ◽  
Glenn J. Shutts
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

scholarly journals On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

scholarly journals Short-Range (0–12 h) PQPFs from Time-Lagged Multimodel Ensembles Using LAPS

2012 ◽  
Vol 140 (5) ◽  
pp. 1496-1516 ◽  
Author(s):  
Hui-Ling Chang ◽  
Huiling Yuan ◽  
Pay-Liam Lin
Keyword(s):  
Short Range ◽  
Calibration Procedure ◽  
Local Analysis ◽  
Ensemble Prediction ◽  
Observation Data ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Training Samples ◽  
Time Lagged ◽  
Multimodel Ensembles

This study pioneers the development of short-range (0–12 h) probabilistic quantitative precipitation forecasts (PQPFs) in Taiwan and aims to produce the PQPFs from time-lagged multimodel ensembles using the Local Analysis and Prediction System (LAPS). By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. Since LAPS adopts diabatic data assimilation, it is utilized to mitigate the “spinup” problem and produce more accurate precipitation forecasts during the early prediction stage (0–6 h). The LAPS ensemble prediction system (EPS) has a good spread–skill relationship and good discriminating ability. Therefore, though it is obviously wet biased, the forecast biases can be corrected to improve the skill of PQPFs through a linear regression (LR) calibration procedure. Sensitivity experiments for two important factors affecting calibration results are also conducted: the experiments on different training samples and the experiments on the accuracy of observation data. The first point reveals that the calibration results vary with training samples. Based on the statistical viewpoint, there should be enough samples for an effective calibration. Nevertheless, adopting more training samples does not necessarily produce better calibration results. It is essential to adopt training samples with similar forecast biases as validation samples to achieve better calibration results. The second factor indicates that as a result of the inconsistency of observation data accuracy in the sea and land areas, only separate calibration for these two areas can ensure better calibration results of the PQPFs.

PEARP, the Météo-France short-range ensemble prediction system

2014 ◽  
Vol 141 (690) ◽  
pp. 1671-1685 ◽  
Author(s):  
L. Descamps ◽  
C. Labadie ◽  
A. Joly ◽  
E. Bazile ◽  
P. Arbogast ◽  
...  
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals HarmonEPS—The HARMONIE Ensemble Prediction System

2019 ◽  
Vol 34 (6) ◽  
pp. 1909-1937 ◽  
Author(s):  
Inger-Lise Frogner ◽  
Ulf Andrae ◽  
Jelena Bojarova ◽  
Alfons Callado ◽  
Pau Escribà ◽  
...  
Keyword(s):  
Short Range ◽  
Initial Conditions ◽  
Forecast Model ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Lateral Boundary Conditions ◽  
Further Development

Abstract HarmonEPS is the limited-area, short-range, convection-permitting ensemble prediction system developed and maintained by the HIRLAM consortium as part of the shared ALADIN–HIRLAM system. HarmonEPS is the ensemble realization of HARMONIE–AROME, used for operational short-range forecasting in HIRLAM countries. HarmonEPS contains a range of perturbation methodologies to account for uncertainties in the initial conditions, forecast model, surface, and lateral boundary conditions. This paper describes the state of the system at the version labeled cycle 40 and highlights some directions for further development. The different perturbation methods available are evaluated and compared where appropriate. Several institutes have operational or preoperational implementations of HarmonEPS, such as MEPS (Finland, Norway, and Sweden), COMEPS (Denmark), IREPS (Ireland), KEPS (the Netherlands), AEMET-γSREPS (Spain), and RMI-EPS (Belgium), and these systems are briefly described and compared with the ensemble prediction system (IFS ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF).

scholarly journals Verification of a short-range ensemble precipitation prediction system over Iberia

2010 ◽  
Vol 25 ◽  
pp. 55-63 ◽  
Author(s):  
D. Santos-Muñoz ◽  
M. L. Martin ◽  
A. Morata ◽  
F. Valero ◽  
A. Pascual
Keyword(s):  
Short Range ◽  
Initial Conditions ◽  
Ensemble Prediction ◽  
Positive Contribution ◽  
Prediction System ◽  
Good Reliability ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
The Mean

Abstract. The purpose of this paper is the verification of a short-range ensemble prediction system (SREPS) built with five different model physical process parameterization schemes and two different initial conditions from global models, allowing to construct several versions of the non-hydrostatic mesoscale MM5 model for a 1-month period of October 2006. From the SREPS, flow-dependent probabilistic forecasts are provided by means of predictive probability distributions over the Iberian Peninsula down to 10-km grid spacing. In order to carry out the verification, 25 km grid of observational precipitation records over Spain from the Spanish Climatic Network has been used to evaluate the ensemble accuracy together with the mean model performance and forecast variability by means of comparisons between such records and the ensemble forecasts. This verification has been carried out upscaling the 10 km probabilistic forecast to the observational data grid. Temporal evolution of precipitation forecasts for spatial averaged ensemble members and the ensemble mean is shown, illustrating the consistency of the SREPS. Such evolutions summarize the SREPS information, showing each of the members as well as the ensemble mean evolutions. The Talagrand diagram derived from the SREPS results shows underdispersion which indicates some bias behaviour. The Relative Operating Characteristic (ROC) curve shows a very outstanding area, indicating potential usefulness of the forecasting system. The forecast probability and the mean observed frequency present good agreement with the SREPS results close to the no-skill line. Because the probability has a good reliability and a positive contribution to the brier skill score, a positive value of this skill is obtained. Moreover, the probabilistic meteogram of the spatial daily mean precipitation values shows the range of forecast values, providing discrete probability information in different quantile intervals. The epsgram shows different daily distributions, indicating the predictability of each day.

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