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Ron McTaggart-Cowan
,
Leo Separovic
,
Rabah Aider
,
Martin Charron
,
Michel Desgagné
,
Pieter L. Houtekamer
,
Danahé Paquin-Ricard
,
Paul A. Vaillancourt
, and
Ayrton Zadra

Abstract

Accurately representing model-based sources of uncertainty is essential for the development of reliable ensemble prediction systems for NWP applications. Uncertainties in discretizations, algorithmic approximations, and diabatic and unresolved processes combine to influence forecast skill in a flow-dependent way. An emerging approach designed to provide a process-level representation of these potential error sources, stochastically perturbed parameterizations (SPP), is introduced into the Canadian operational Global Ensemble Prediction System. This implementation extends the SPP technique beyond its typical application to free parameters in the physics suite by sampling uncertainty both within the dynamical core and at the formulation level using “error models” when multiple physical closures are available. Because SPP perturbs components within the model, internal consistency is ensured and conservation properties are not affected. The full SPP scheme is shown to increase ensemble spread to keep pace with error growth on a global scale. The sensitivity of the ensemble to each independently perturbed “element” is then assessed, with those responsible for the bulk of the response analyzed in more detail. Perturbations to surface exchange coefficients and the turbulent mixing length have a leading impact on near-surface statistics. Aloft, a tropically focused error model representing uncertainty in the advection scheme is found to initiate growing perturbations on the subtropical jet that lead to forecast improvements at higher latitudes. The results of Part I suggest that SPP has the potential to serve as a reliable representation of model uncertainty for ensemble NWP applications.

Significance Statement

Ensemble systems account for the negative impact that uncertainties in prediction models have on forecasts. Here, uncertain model parameters and algorithms are subjected to perturbations representing impact on forecast errors. By initiating error growth within the model calculations, the equally skillful members of the ensemble remain physically realistic and self-consistent, which is not guaranteed by other depictions of model error. This “stochastically perturbed parameterization” technique (SPP) comprises many small error sources, each analyzed in isolation. Each source is related to a limited set of processes, making it possible to determine how the individual perturbations affect the forecast. We conclude that SPP in the Canadian Global Ensemble Forecasting System produces realistic estimates of the impact of model uncertainties on forecast skill.

Open access
Claude Girard
,
André Plante
,
Michel Desgagné
,
Ron McTaggart-Cowan
,
Jean Côté
,
Martin Charron
,
Sylvie Gravel
,
Vivian Lee
,
Alain Patoine
,
Abdessamad Qaddouri
,
Michel Roch
,
Lubos Spacek
,
Monique Tanguay
,
Paul A. Vaillancourt
, and
Ayrton Zadra

Abstract

The Global Environmental Multiscale (GEM) model is the Canadian atmospheric model used for meteorological forecasting at all scales. A limited-area version now also exists. It is a gridpoint model with an implicit semi-Lagrangian iterative space–time integration scheme. In the “horizontal,” the equations are written in spherical coordinates with the traditional shallow atmosphere approximations and are discretized on an Arakawa C grid. In the “vertical,” the equations were originally defined using a hydrostatic-pressure coordinate and discretized on a regular (unstaggered) grid, a configuration found to be particularly susceptible to noise. Among the possible alternatives, the Charney–Phillips grid, with its unique characteristics, and, as the vertical coordinate, log-hydrostatic pressure are adopted. In this paper, an attempt is made to justify these two choices on theoretical grounds. The resulting equations and their vertical discretization are described and the solution method of what is forming the new dynamical core of GEM is presented, focusing on these two aspects.

Full access
Yihong Duan
,
Jiandong Gong
,
Jun Du
,
Martin Charron
,
Jing Chen
,
Guo Deng
,
Geoff DiMego
,
Masahiro Hara
,
Masaru Kunii
,
Xiaoli Li
,
Yinglin Li
,
Kazuo Saito
,
Hiromu Seko
,
Yong Wang
, and
Christoph Wittmann

The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.

The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.

The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).

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Hai Lin
,
William J. Merryfield
,
Ryan Muncaster
,
Gregory C. Smith
,
Marko Markovic
,
Frédéric Dupont
,
François Roy
,
Jean-François Lemieux
,
Arlan Dirkson
,
Viatcheslav V. Kharin
,
Woo-Sung Lee
,
Martin Charron
, and
Amin Erfani

Abstract

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Open access
Martin Charron
,
Saroja Polavarapu
,
Mark Buehner
,
P. A. Vaillancourt
,
Cécilien Charette
,
Michel Roch
,
Josée Morneau
,
Louis Garand
,
Josep M. Aparicio
,
Stephen MacPherson
,
Simon Pellerin
,
Judy St-James
, and
Sylvain Heilliette

Abstract

A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.

Full access
Om P. Tripathi
,
Mark Baldwin
,
Andrew Charlton-Perez
,
Martin Charron
,
Jacob C. H. Cheung
,
Stephen D. Eckermann
,
Edwin Gerber
,
David R. Jackson
,
Yuhji Kuroda
,
Andrea Lang
,
Justin McLay
,
Ryo Mizuta
,
Carolyn Reynolds
,
Greg Roff
,
Michael Sigmond
,
Seok-Woo Son
, and
Tim Stockdale

Abstract

The first multimodel study to estimate the predictability of a boreal sudden stratospheric warming (SSW) is performed using five NWP systems. During the 2012/13 boreal winter, anomalous upward propagating planetary wave activity was observed toward the end of December, which was followed by a rapid deceleration of the westerly circulation around 2 January 2013, and on 7 January 2013 the zonal-mean zonal wind at 60°N and 10 hPa reversed to easterly. This stratospheric dynamical activity was followed by an equatorward shift of the tropospheric jet stream and by a high pressure anomaly over the North Atlantic, which resulted in severe cold conditions in the United Kingdom and northern Europe. In most of the five models, the SSW event was predicted 10 days in advance. However, only some ensemble members in most of the models predicted weakening of westerly wind when the models were initialized 15 days in advance of the SSW. Further dynamical analysis of the SSW shows that this event was characterized by the anomalous planetary wavenumber-1 amplification followed by the anomalous wavenumber-2 amplification in the stratosphere, which resulted in a split vortex occurring between 6 and 8 January 2013. The models have some success in reproducing wavenumber-1 activity when initialized 15 days in advance, but they generally failed to produce the wavenumber-2 activity during the final days of the event. Detailed analysis shows that models have reasonably good skill in forecasting tropospheric blocking features that stimulate wavenumber-2 amplification in the troposphere, but they have limited skill in reproducing wavenumber-2 amplification in the stratosphere.

Full access