Dependence on initial conditions versus model formulations for medium‐range forecast error variations

Magnusson, Linus; Chen, Jan‐Huey; Lin, Shian‐Jiann; Zhou, Linjiong; Chen, Xi

doi:10.1002/qj.3545

Cited by 16 publications

(16 citation statements)

References 47 publications

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“…It shows that all models in this study are capable of producing high-quality forecasts given a high-quality analysis, and that the initial shock seems to not cause a significant harm to the forecast quality. This result is in line with Magnusson et al (2019) where a similar improvement was seen for the GFDL/FVGFS model (an earlier version of the SHiELD model), by using the ECMWF analyses.…”

Section: Discussionsupporting

confidence: 90%

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Skill of Medium-Range Forecast Models Using the Same Initial Conditions

Magnusson

Ackerley

Bouteloup

et al. 2022

Bulletin of the American Meteorological Society

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In the DIMOSIC (DIfferent MOdels, Same Initial Conditions) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are: (1) evaluate the forecast skill and how it depends on model formulation, (2) assess systematic differences and errors at short lead times, (3) compare multi-model ensemble spread to model uncertainty schemes, and (4) identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multi-model ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations skill are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.

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Section: Discussionsupporting

confidence: 90%

“…In a collaboration between GFDL and ECMWF, the global FV3 model was initialized from both NCEP and ECMWF initial conditions. Valuable results emerged from this intercomparison, both in terms of mid-latitude forecast errors (Magnusson et al 2019) and tropical cyclone forecast errors (Chen et al 2019).…”

mentioning

confidence: 84%

Skill of Medium-Range Forecast Models Using the Same Initial Conditions

Magnusson

Ackerley

Bouteloup

et al. 2022

Bulletin of the American Meteorological Society

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“…In addition to the sensitivity to the cumulus convection parameterizations, here we also compare the relative impact of the initial conditions. A similar comparison between the relative impact of model physics and initial state within the NOAA FV3 GFS framework was recently made in Magnusson et al (2019) and Chen et al (2019), where a more general investigation of the midlatitude medium range forecast error (Magnusson et al 2019) and tropical cyclone (Chen et al 2019) forecast performance was conducted. Here we focus specifically on tropical variability, and the relative impact of the initial state versus the cumulus parameterizations on convectively coupled equatorial waves.…”

Section: Initial Conditions and Replay Methodologymentioning

confidence: 87%

Convectively Coupled Equatorial Wave Simulations Using the ECMWF IFS and the NOAA GFS Cumulus Convection Schemes in the NOAA GFS Model

Bengtsson

Dias

Gehne

et al. 2019

Monthly Weather Review

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There is a longstanding challenge in numerical weather and climate prediction to accurately model tropical wave variability, including convectively coupled equatorial waves (CCEWs) and the Madden–Julian oscillation. For subseasonal prediction, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has been shown to be superior to the NOAA Global Forecast System (GFS) in simulating tropical variability, suggesting that the ECMWF model is better at simulating the interaction between cumulus convection and the large-scale tropical circulation. In this study, we experiment with the cumulus convection scheme of the ECMWF IFS in a research version of the GFS to understand which aspects of the IFS cumulus convection scheme outperform those of the GFS convection scheme in the tropics. We show that the IFS cumulus convection scheme produces significantly different tropical moisture and temperature tendency profiles from those simulated by the GFS convection scheme when it is coupled with other physics schemes in the GFS physics package. We show that a consistent treatment of the interaction between parameterized convective plumes in the GFS planetary boundary layer (PBL) and the IFS convection scheme is required for the GFS to replicate the tropical temperature and moisture profiles simulated by the IFS model. The GFS model with the IFS convection scheme, and the consistent treatment between the convection and PBL schemes, produces much more organized convection in the tropics, and generates tropical waves that propagate more coherently than the GFS in its default configuration due to better simulated interaction between low-level convergence and precipitation.

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“…While our analysis utilizing the error amplitude and error in the wave activity flux suggests that one area of investigation is the treatment of convection, errors in the initial conditions may also play a significant role in forecast busts [4,[68][69][70]. These errors in the initial conditions could play a role in the difficulty in representing MCS, although the difficulty in accurately representing the persistence of both the rapidly propagating MCS and the general eastward progression of the envelop of deep convection across the continent east of the Rockies [49] also appear to be important areas for future work.…”

Section: Discussionmentioning

confidence: 99%

The Role of Continental Mesoscale Convective Systems in Forecast Busts within Global Weather Prediction Systems

et al. 2019

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Despite significant, steady improvements in the skill of medium-range weather predictionsystems over the past several decades, the accuracy of these forecasts are occasionally very poor.These forecast failures are referred to as “busts” or “dropouts”. The lack of a clear explanationfor bust events limits the development and implementation of strategies designed to reduce theiroccurrence. This study seeks to explore a flow regime where forecast busts occur over Europe inassociation with mesoscale convective systems over North America east of the Rocky Mountains.Our investigation focuses on error growth in the European Centre for Medium-Range WeatherForecasting’s (ECMWF’s) global model during the summer 2015 PECAN (Plains Elevated Convectionat Night) experiment. Observations suggest that a close, but varied interrelationship can occurbetween long-lived, propagating, mesoscale convection systems over the Great Plains and Rossbywave packets. Aloft, the initial error occurs in the ridge of the wave and then propagates downstreamas an amplifying Rossby wave packet producing poor forecasts in middle latitudes and, in somecases, the Arctic. Our results suggest the importance of improving the representation of organizeddeep convection in numerical models, particularly for long-lived mesoscale convective systems thatproduce severe weather and propagate near the jet stream.

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Dependence on initial conditions versus model formulations for medium‐range forecast error variations

Cited by 16 publications

References 47 publications

Skill of Medium-Range Forecast Models Using the Same Initial Conditions

Skill of Medium-Range Forecast Models Using the Same Initial Conditions

Convectively Coupled Equatorial Wave Simulations Using the ECMWF IFS and the NOAA GFS Cumulus Convection Schemes in the NOAA GFS Model

The Role of Continental Mesoscale Convective Systems in Forecast Busts within Global Weather Prediction Systems

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