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

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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A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

Weather and Forecasting ◽

10.1175/waf-d-19-0032.1 ◽

2019 ◽

Vol 34 (6) ◽

pp. 1675-1691 ◽

Cited By ~ 4

Author(s):

Yu Xia ◽

Jing Chen ◽

Jun Du ◽

Xiefei Zhi ◽

Jingzhuo Wang ◽

...

Keyword(s):

Bias Correction ◽

Weather Prediction ◽

Systematic Errors ◽

Ensemble Prediction ◽

Ensemble Model ◽

Prediction System ◽

Ensemble Prediction System ◽

Probabilistic Forecasts ◽

Ensemble Mean ◽

Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

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HarmonEPS—The HARMONIE Ensemble Prediction System

Weather and Forecasting ◽

10.1175/waf-d-19-0030.1 ◽

2019 ◽

Vol 34 (6) ◽

pp. 1909-1937 ◽

Cited By ~ 7

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).

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MM5 Ensemble Mean Forecasts in the Taiwan Area for the 2003 Mei-Yu Season

Weather and Forecasting ◽

10.1175/waf960.1 ◽

2006 ◽

Vol 21 (6) ◽

pp. 1006-1023 ◽

Cited By ~ 12

Author(s):

Fang-Ching Chien ◽

Yi-Chin Liu ◽

Ben Jong-Dao Jou

Keyword(s):

Initial Conditions ◽

Mixed Phase ◽

Rainfall Simulation ◽

Ensemble Prediction ◽

Forecast System ◽

Ensemble Prediction System ◽

First Choice ◽

Ensemble Mean ◽

Better Than ◽

Taiwan Area

Abstract This paper presents an evaluation study of a real-time fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) mesoscale ensemble prediction system in the Taiwan area during the 2003 mei-yu season. The ensemble system consists of 16 members that used the same nested domains of 45- and 15-km resolutions, but different model settings of the initial conditions (ICs), the cumulus parameterization scheme (CPS), and the microphysics scheme (MS). Verification of geopotential height, temperature, relative humidity, and winds in the 15-km grid shows that the members using the Kain–Fritsch CPS performed better than those using the Grell CPS, and those using the Central Weather Bureau (CWB) Nonhydrostatic Forecast System (NFS) ICs fared better than those using the CWB Global Forecast System (GFS) ICs. The members applying the mixed-phase MS generally exhibited the smallest errors among the four MSs. Precipitation verification shows that the members using the Grell CPS, in general, had higher equitable threat scores (ETSs) than those using the Kain–Fritsch CPS, that the members with the GFS ICs performed better than with the NFS ICs, and that the mixed-phase and Goddard MSs gave relatively high ETSs in the rainfall simulation. The bias scores show that, overall, all 16 members underforecasted rainfall. Comparisons of the ensemble means show that, on average, an ensemble mean, no matter how many members it contains, can produce better forecasts than an individual member. Among the three possible elements (IC, CPS, and MS) that can be varied to compose an ensemble, the ensemble that contains members with all three elements varying performed the best, while that with two elements varying was second best, and that with only one varying was the worst. Furthermore, the first choice for composing an ensemble is to use perturbed ICs, followed by the perturbed CPS, and then the perturbed MS.

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Sensitivity of Global Ensemble Forecasts to the Initial Ensemble Mean and Perturbations: Comparison of EnKF, Singular Vector, and 4D-Var Approaches

Monthly Weather Review ◽

10.1175/2010mwr3296.1 ◽

2010 ◽

Vol 138 (10) ◽

pp. 3886-3904 ◽

Cited By ~ 4

Author(s):

Mark Buehner ◽

Ahmed Mahidjiba

Keyword(s):

Ensemble Prediction ◽

Boreal Summer ◽

Prediction System ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Var Analysis ◽

Initial Ensemble ◽

Ensemble Mean ◽

The Tropics ◽

The Impact

Abstract This study examines the sensitivity of global ensemble forecasts to the use of different approaches for specifying both the initial ensemble mean and perturbations. The current operational ensemble prediction system of the Meteorological Service of Canada uses the ensemble Kalman filter (EnKF) to define both the ensemble mean and perturbations. To evaluate the impact of different approaches for obtaining the initial ensemble perturbations, the operational EnKF approach is compared with using either no initial perturbations or perturbations obtained using singular vectors (SVs). The SVs are computed using the (dry) total-energy norm with a 48-h optimization time interval. Random linear combinations of 60 SVs are computed for each of three regions. Next, the impact of replacing the initial ensemble mean, currently the EnKF ensemble mean analysis, with the higher-resolution operational four-dimensional variational data assimilation (4D-Var) analysis is evaluated. For this comparison, perturbations are provided by the EnKF. All experiments are performed over two-month periods during both the boreal summer and winter using a system very similar to the global ensemble prediction system that became operational on 10 July 2007. Relative to the operational configuration that relies on the EnKF, the use of SVs to compute initial perturbations produces small, but statistically significant differences in probabilistic forecast scores in favor of the EnKF both in the tropics and, for a limited set of forecast lead times, in the summer hemisphere extratropics, whereas the results are very similar in the winter hemisphere extratropics. Both approaches lead to significantly better ensemble forecasts than with no initial perturbations, though results are quite similar in the tropics when using SVs and no perturbations. The use of an initial-time norm that does not include information on analysis uncertainty and the lack of linearized moist processes in the calculation of the SVs are two factors that limit the quality of the resulting SV-based ensemble forecasts. Relative to the operational configuration, use of the 4D-Var analysis to specify the initial ensemble mean results in improved probabilistic forecast scores during the boreal summer period in the southern extratropics and tropics, but a near-neutral impact otherwise.

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

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.394 ◽

2009 ◽

Vol 135 (640) ◽

pp. 767-776 ◽

Cited By ~ 92

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

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An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-4891-2009 ◽

2009 ◽

Vol 6 (4) ◽

pp. 4891-4917

Author(s):

J. A. Velázquez ◽

T. Petit ◽

A. Lavoie ◽

M.-A. Boucher ◽

R. Turcotte ◽

...

Keyword(s):

Initial Conditions ◽

Ensemble Prediction ◽

Prediction System ◽

Forecast Horizon ◽

Time Step ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Hydrological Forecasting ◽

Updating Procedure ◽

First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

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A large ensemble decadal prediction system with MPI-ESM

10.5194/egusphere-egu21-9263 ◽

2021 ◽

Author(s):

Sebastian Brune ◽

Vimal Koul ◽

David Marcolino Nielsen ◽

Laura Hövel ◽

Holger Pohlmann ◽

...

Keyword(s):

North Atlantic ◽

Prediction Skill ◽

Ensemble Prediction ◽

Prediction System ◽

Decadal Prediction ◽

Ensemble Size ◽

Large Ensemble ◽

Ensemble Prediction System ◽

Atlantic Sector ◽

Ensemble Mean

Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.

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The WRF-Solar Ensemble Prediction System To Provide Solar Irradiance Probabilistic Forecasts

10.1109/pvsc43889.2021.9518554 ◽

2021 ◽

Author(s):

Ju-Hye Kim ◽

Pedro A. Jimenez ◽

Manajit Sengupta ◽

Jaemo Yang ◽

Jimy Dudhia ◽

...

Keyword(s):

Solar Irradiance ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System ◽

Probabilistic Forecasts

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

Monthly Weather Review ◽

10.1175/mwr2911.1 ◽

2005 ◽

Vol 133 (7) ◽

pp. 1825-1839 ◽

Cited By ~ 36

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.

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Preliminary study on terrain uncertainty and its perturbing scheme

10.5194/egusphere-egu2020-1856 ◽

2020 ◽

Author(s):

Li jun ◽

Du jun ◽

Liu yu ◽

Xu jianyu

Keyword(s):

Model Uncertainty ◽

Heavy Rain ◽

Ensemble Prediction ◽

Rain Event ◽

Positive Contribution ◽

Perturbation Scheme ◽

Perturbation Techniques ◽

Ensemble Prediction System ◽

Parameterization Schemes ◽

Ensemble Mean

1.IntroductionA key issue in developing the ensemble prediction technique is the recognition of uncertain factors in numerical forecasting and how to use appropriate perturbation techniques to reflect these uncertain processes and improve ensemble prediction levels.Plenty of corresponding perturbation techniques have been developed. Such as Initial uncertainty and model uncertainty,In addition to the influence of IC and model uncertainty ,precipitation is closely related to terrain.The influence of terrain on the heavy rain includes the following three aspects:(1) The terrain has significant effect on the climatic distribution of precipitation.(2)The windward slope and leeward slope and other dynamic effects generated by terrain impact the triggering and intensity of precipitation.(3)The thermal effect is triggered by the heating of land surface of terrain at different height and latent heat release when airflow rises ,and this thermal action makes mountain precipitation closely related to terrain distribution .What are the terrain uncertainties in the model?(1)Different vertical coordinate systems lead to significant differences in terrain treatment(2)The conversion from real terrain to model terrain is closely associated with the resolution of the model and different terrain interpolation schemes, and it affects the simulation results of precipitation .(3)Measuring error of real terrain, etc.In this report, A terrain perturbation scheme (ter) has been firstly incorporated into an ensemble prediction system (EPS) and preliminarily tested in the simulation of the extremely heavy rain event occurred on 21 July, 2012 in Beijing, along with other three perturbation schemes.2.Case,data and schemes(1)Case: Based on the extremely heavy rain case in Beijing on July 21,2012, maximum precipitation center more than 400mm.(2)Data: GEPS of NCEP were used as initial background fields and lateral boundary condition , surface and upper-level observation of GTS,Rain gauge etc.(3)Model: WRFv4.3, 9km horizontal resolution ,511*511 grid point, 51 vertical layers,KF Eta,WSM6,etc(4)Experiments schemes: Four different perturbation schemes were used in the experiments and six members in each experiment. Sch_1(IC) considered the IC uncertainty ,the parameterization schemes were same but IC/LBC came from different GEPS members. Sch_2(phy) considered the Phy uncertainty ,the IC/LBC were same but PHY schemes were comprised of different parameterization schemes. Sch_3-4(ter and icter) considered the terrain uncertainty ,the second aspect of terrain uncertainty was considered in this study. Two different model terrain smoothing schemes and 3 terrain interpolation schemes were used to reflect the forecast error caused by terrain height. Icter is the mixed scheme of ter and ic.3.Preliminary test and results(1)Precipitation is closely related to terrain, terrain uncertainties have significant effect on the intensity and falling area of precipitation.(2) Only a simple terrain perturbation can produce a significant forecast spread , and its ensemble mean forecast is also improved compared with control forecast. for this case, it has a slightly positive contribution to the spread and probability forecast of precipitation on the basis of not impacting the quality of ensemble mean forecast.(3) In this case, the magnitude of spread generated by the terrain perturbation scheme is significantly smaller than that generated by the initial perturbation and physics process perturbation schemes.

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