scholarly journals Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

2019 ◽  
Vol 32 (3) ◽  
pp. 957-972 ◽  
Author(s):  
Takeshi Doi ◽  
Swadhin K. Behera ◽  
Toshio Yamagata
Keyword(s):  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Economic Losses ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Operational Systems ◽  
The Indian Ocean ◽  
Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System

1998 ◽  
Vol 124 (550) ◽  
pp. 1935-1960 ◽  
Author(s):  
R. Buizza ◽  
T. Petroliagis ◽  
T. Palmer ◽  
J. Barkmeijer ◽  
M. Hamrud ◽  
...  
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Model Resolution ◽  
Ensemble Size ◽  
Ensemble Prediction System

A large ensemble decadal prediction system with MPI-ESM

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

<p>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.</p>

scholarly journals Representation of model error in a convective-scale ensemble prediction system

2014 ◽  
Vol 21 (1) ◽  
pp. 19-39 ◽  
Author(s):  
L. H. Baker ◽  
A. C. Rudd ◽  
S. Migliorini ◽  
R. N. Bannister
Keyword(s):  
Model Error ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Skill Scores ◽  
The Uk ◽  
Parameter Values ◽  
The Impact

Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

Bayesian Approach to Decision Making Using Ensemble Weather Forecasts

2006 ◽  
Vol 21 (2) ◽  
pp. 220-231 ◽  
Author(s):  
Richard W. Katz ◽  
Martin Ehrendorfer
Keyword(s):  
Decision Making ◽  
Prior Distribution ◽  
Economic Value ◽  
Bayesian Estimator ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
The Face ◽  
The Cost

Abstract The economic value of ensemble-based weather or climate forecasts is generally assessed by taking the ensembles at “face value.” That is, the forecast probability is estimated as the relative frequency of occurrence of an event among a limited number of ensemble members. Despite the economic value of probability forecasts being based on the concept of decision making under uncertainty, in effect, the decision maker is assumed to ignore the uncertainty in estimating this probability. Nevertheless, many users are certainly aware of the uncertainty inherent in a limited ensemble size. Bayesian prediction is used instead in this paper, incorporating such additional forecast uncertainty into the decision process. The face-value forecast probability estimator would correspond to a Bayesian analysis, with a prior distribution on the actual forecast probability only being appropriate if it were believed that the ensemble prediction system produces perfect forecasts. For the cost–loss decision-making model, the economic value of the face-value estimator can be negative for small ensemble sizes from a prediction system with a level of skill that is not sufficiently high. Further, this economic value has the counterintuitive property of sometimes decreasing as the ensemble size increases. For a more plausible form of prior distribution on the actual forecast probability, which could be viewed as a “recalibration” of face-value forecasts, the Bayesian estimator does not exhibit this unexpected behavior. Moreover, it is established that the effects of ensemble size on the reliability, skill, and economic value have been exaggerated by using the face-value, instead of the Bayesian, estimator.

scholarly journals An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

2011 ◽  
Vol 15 (7) ◽  
pp. 2327-2347 ◽  
Author(s):  
N. Addor ◽  
S. Jaun ◽  
F. Fundel ◽  
M. Zappa
Keyword(s):  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Probability Forecasts ◽  
The City ◽  
Model Chain

Abstract. The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

scholarly journals GEPS-Based Monthly Prediction at the Canadian Meteorological Centre

2016 ◽  
Vol 144 (12) ◽  
pp. 4867-4883 ◽  
Author(s):  
Hai Lin ◽  
Normand Gagnon ◽  
Stephane Beauregard ◽  
Ryan Muncaster ◽  
Marko Markovic ◽  
...  
Keyword(s):  
North America ◽  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The Past ◽  
The Pacific ◽  
Summer Half ◽  
Forecasting System ◽  
Set Up

Abstract Dynamical monthly prediction at the Canadian Meteorological Centre (CMC) was produced as part of the seasonal forecasting system over the past two decades. A new monthly forecasting system, which has been in operation since July 2015, is set up based on the operational Global Ensemble Prediction System (GEPS). This monthly forecasting system is composed of two components: 1) the real-time forecast, where the GEPS is extended to 32 days every Thursday; and 2) a 4-member hindcast over the past 20 years, which is used to obtain the model climatology to calibrate the monthly forecast. Compared to the seasonal prediction system, the GEPS-based monthly forecasting system takes advantage of the increased model resolution and improved initialization. Forecasts of the past 2-yr period (2014 and 2015) are verified. Analysis is performed separately for the winter half-year (November–April), and the summer half-year (May–October). Weekly averages of 2-m air temperature (T2m) and 500-hPa geopotential height (Z500) are assessed. For Z500 in the Northern Hemisphere, limited skill can be found beyond week 2 (days 12–18) in summer, while in winter some skill exists over the Pacific and North American region beyond week 2. For T2m in North America, significant skill is found over a large part of the continent all the way to week 4 (days 26–32). The distribution of the wintertime T2m skill in North America is consistent with the influence of the Madden–Julian oscillation, indicating that a significant part of predictability likely comes from the tropics.

scholarly journals Global Sea Surface Temperature Prediction Using a Multimodel Ensemble

2007 ◽  
Vol 135 (9) ◽  
pp. 3239-3247 ◽  
Author(s):  
Jong-Seong Kug ◽  
June-Yi Lee ◽  
In-Sik Kang
Keyword(s):  
Lead Time ◽  
Ensemble Methods ◽  
Seasonal Prediction ◽  
Ensemble Method ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Predictive Skill ◽  
Forecast Lead Time ◽  
Global Oceans

Abstract In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be predicted as a boundary condition to the AGCM. In this study, a global SST prediction system has been developed as a part of the tier-two seasonal prediction system. This system uses predictions from four models—one dynamic, two statistical, and persistence—and a simple composite ensemble method is applied to these models. The simple composite ensemble prediction system has predictive skill over most of the global oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared with other more sophisticated ensemble methods. The simple composite method has forecast skill comparable to the other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.

Fog Prediction from a Multimodel Mesoscale Ensemble Prediction System

2010 ◽  
Vol 25 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Binbin Zhou ◽  
Jun Du
Keyword(s):  
Eastern China ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Ensemble ◽  
Ensemble Size ◽  
Detection Scheme ◽  
Ensemble Prediction System ◽  
Fog Forecasting ◽  
Probabilistic Forecasts

Abstract A new multivariable-based diagnostic fog-forecasting method has been developed at NCEP. The selection of these variables, their thresholds, and the influences on fog forecasting are discussed. With the inclusion of the algorithm in the model postprocessor, the fog forecast can now be provided centrally as direct NWP model guidance. The method can be easily adapted to other NWP models. Currently, knowledge of how well fog forecasts based on operational NWP models perform is lacking. To verify the new method and assess fog forecast skill, as well as to account for forecast uncertainty, this fog-forecasting algorithm is applied to a multimodel-based Mesoscale Ensemble Prediction System (MEPS). MEPS consists of 10 members using two regional models [the NCEP Nonhydrostatic Mesoscale Model (NMM) version of the Weather Research and Forecasting (WRF) model and the NCAR Advanced Research version of WRF (ARW)] with 15-km horizontal resolution. Each model has five members (one control and four perturbed members) using the breeding technique to perturb the initial conditions and was run once per day out to 36 h over eastern China for seven months (February–September 2008). Both deterministic and probabilistic forecasts were produced based on individual members, a one-model ensemble, and two-model ensembles. A case study and statistical verification, using both deterministic and probabilistic measuring scores, were performed against fog observations from 13 cities in eastern China. The verification was focused on the 12- and 36-h forecasts. By applying the various approaches, including the new fog detection scheme, ensemble technique, multimodel approach, and the increase in ensemble size, the fog forecast accuracy was steadily and dramatically improved in each of the approaches: from basically no skill at all [equitable threat score (ETS) = 0.063] to a skill level equivalent to that of warm-season precipitation forecasts of the current NWP models (0.334). Specifically, 1) the multivariable-based fog diagnostic method has a much higher detection capability than the liquid water content (LWC)-only based approach. Reasons why the multivariable approach works better than the LWC-only method were also illustrated. 2) The ensemble-based forecasts are, in general, superior to a single control forecast measured both deterministically and probabilistically. The case study also demonstrates that the ensemble approach could provide more societal value than a single forecast to end users, especially for low-probability significant events like fog. Deterministically, a forecast close to the ensemble median is particularly helpful. 3) The reliability of probabilistic forecasts can be effectively improved by using a multimodel ensemble instead of a single-model ensemble. For a small ensemble such as the one in this study, the increase in ensemble size is also important in improving probabilistic forecasts, although this effect is expected to decrease with the increase in ensemble size.

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