MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu, Xiangwen; Wu, Tongwen; Yang, Song; Li, Tim; Wei, Jie; Zhang, Li; Wang, Zaizhi; Liang, Xiaoyan; Li, Qiaoping; Cheng, Yang; Ren, Hong‐Li; Fang, Yongjie; Nie, Suping

doi:10.1007/s00382-016-3264-7

Cited by 79 publications

(90 citation statements)

References 72 publications

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“…The processes of extracting MJO signals from observation, simulation and prediction runs are completely in the same way as those in Liu et al (2017), in which several procedures were adopted based on the techniques in Wheeler and Hendon (2004), Lin et al (2008a) and Gottschalck et al (2010). The preprocess for simulation outputs is different from that for prediction outputs.…”

Section: Validation Data and Methodsmentioning

confidence: 99%

“…We use the original hindcasts for the S2S project (EXP0_S2S) and the improved hindcasts with new initialization schemes (EXP1_Ini) by BCC S2S forecast model. Details of these two sets of hindcast can be found in Liu et al (2017). Compared to EXP0_S2S, EXP1_Ini use different atmospheric reanalysis to generate atmosphere initial condition, and also introduce observational daily sea surface temperature data to upgrade ocean initial condition.…”

Section: Prediction Experiments Using Optimized Parametersmentioning

confidence: 99%

“…The multi-model ensemble results can further increase MJO prediction skill by about 1 week, as shown by the Intraseasonal Variability Hindcast Experiment (ISVHE) Project . Even with the above-mentioned gratifying progress, MJO prediction is still considerably challenging due to its significant sensitivity to initial conditions (e.g., Vitart et al 2007;Fu et al 2011;Liu et al 2017), air-sea coupling (e.g., Vitart et al 2007;Fu et al 2013), differences among models (e.g., Fu et al 2013;Zhang et al 2013;Neena et al 2014), and the characteristics of MJO event itself (e.g., Lin et al 2008a;Rashid et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…It has provided comprehensive reforecasts for every initial date during the past 20 years and real-time forecast products up to now. Liu et al (2017) is a detailed documentation on the progress of BCC's participation in the S2S project, including the model version used to build a forecast system, initialization and ensemble forecast schemes, performance and deficiency, and further improvement on MJO prediction. It pointed out that the BCC_CSM has apparent biases in spectrum, structure and propagation of MJO, which may cause the limited skill of MJO prediction to some extent.…”

Section: Introductionmentioning

confidence: 99%

“…It pointed out that the BCC_CSM has apparent biases in spectrum, structure and propagation of MJO, which may cause the limited skill of MJO prediction to some extent. Even with these model deficiencies, the MJO prediction skill was still enhanced from 15-16 to 21-22 days because of the improvement of initial conditions (Liu et al 2017). In this context, it is necessary to explore whether this skill can be further enhanced by optimizing the model itself.…”

Section: Introductionmentioning

confidence: 99%

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Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

Self Cite

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Using the sub-seasonal to seasonal forecast model of Beijing Climate Center, several key physical parameters are perturbed by the Latin hypercube sampling method to find a better configuration for representation of Madden-Julian oscillation (MJO) in the free-run simulation. We find that although model simulation is especially sensitive to some parameters, there are overall no significant linear relationships between model skill and any one of the parameters, and the optimum performance can be obtained by combined perturbations of multiple parameters. By optimization, MJO's spectrum, intensity, spatial structure and propagation, as well as the mean state and variance, are all improved to some extent, suggesting the correspondence and interrelation of model's performances in simulating different characteristics of MJO. Further, several sets of initialized hindcasts using the optimized parameters are conducted, and their results are compared with the hindcasts using only improved initial conditions. We show that with an optimized model, the forecast of MJO beyond 3-week lead time is not improved, and the maximum useful skill is only slightly increased, implying that a decrease of model error does not always translate into an increase of forecast skill at all lead time. However, the skill is obviously enhanced during lead times of 2-3 weeks for forecasts in most seasons and initial phases except for a few cases. Particularly, the deficiency in forecasting MJO's propagation from the Indian Ocean to the Pacific is relieved, further highlighting the positive contribution of reducing model error compared to previous work that only reduced initial condition error. In this study, we also show benefits of multi-scheme ensemble strategy in describing uncertainties of model error and initial condition error and thus improving MJO forecast.

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Section: Validation Data and Methodsmentioning

confidence: 99%

Section: Prediction Experiments Using Optimized Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

Self Cite

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MJO modulation and its ability to predict boreal summer tropical cyclone genesis over the northwest Pacific in Met Office Hadley Centre and Beijing Climate Center seasonal prediction systems

Zhao

Ren

Eade

et al. 2019

Quart J Royal Meteoro Soc

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Many of the current seasonal prediction systems show useful skill in Madden–Julian Oscillation (MJO) prediction but limited skill in forecasting tropical cyclones (TC). Quantifying the influences of MJO on TC in an operational prediction system is therefore useful for prediction purposes. The results in this study show that forecast skill of boreal summer TC genesis potential index (GPI) anomaly by operational seasonal prediction systems in Met Office Hadley Centre (MOHC) and Beijing Climate Center (BCC) are significantly higher than persistence out to four weeks. In the prediction system of MOHC, stronger MJO initial conditions are conducive to better GPI anomaly forecast, and MJO phases 4–7 make for higher GPI anomaly skill at lead 11–20 days. However, the MJO influence on TC forecasting skill is not so significant in BCC's prediction system. Both the prediction systems could reproduce the relationship that there are larger positive GPI anomalies in MJO phases 4–5 and negative ones in MJO phases 8–1 over the South China Sea (SCS) and northwest Pacific (WNP). However, the intensity of MJO effect on GPI is generally underestimated in BCC_CSM1.2, which may be due to the response of inadequate ascending motion and negative outgoing long‐wave radiation accompanied by weak MJO over SCS and WNP. In GloSea5, the absolute vorticity, relative humidity, vertical velocity and wind shear terms are relatively closer to ERA‐Interim during lead 1–10 days. Considering the intrinsic predictability of the MJO of more than 2 weeks, the forecasted MJO phase information has significant implications for TC genesis prediction on intraseasonal time‐scales.

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Configuration and hindcast quality assessment of a Brazilian global sub‐seasonal prediction system

Guimarães

Coelho

Woolnough

et al. 2020

Quart J Royal Meteoro Soc

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This article presents the Centre for Weather Forecast and Climate Studies (CPTEC) developments for configuring a global sub-seasonal prediction system and assessing its ability in producing retrospective predictions (hindcasts) for meteorological conditions of the following 4 weeks. Six Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) configurations were tested in terms of vertical resolution, deep convection and boundary-layer parametrizations, as well as soil moisture initialization. The aim was to identify the configuration with best performance when predicting weekly accumulated precipitation, weekly mean 2 m temperature (T2M) and the Madden-Julian Oscillation (MJO) daily evolution. Hindcasts assessment was performed for 12 extended austral summers (November-March, 1999/2000) with two start dates for each month for the six configurations and two ensemble approaches. The first approach, referred to as Multiple Configurations Ensemble (MCEN), was formed of one ensemble member from each of the six configurations. The second, referred to as Initial Condition Ensemble (ICEN), was composed of six ensemble members produced with the chosen configuration as the best using an empirical orthogonal function (EOF) perturbation methodology. The chosen configuration presented high correlation and low root-mean-squared error (RMSE) for precipitation and T2M anomaly predictions at the first week and these indices degraded as lead time increased, maintaining moderate performance up to week-4 over the tropical Pacific and northern South America. For MJO predictions, this configuration crossed the 0.5 bivariate correlation threshold in 18 days. The ensemble approaches improved the correlation and RMSE of precipitation and T2M anomalies. ICEN improved precipitation and T2M predictions performance over eastern South America at week-3 and over northern South America at week-4. Improvements were also noticed for MJO predictions. The time to cross the above-mentioned threshold increased to 21 days for MCEN and to 20 days for ICEN. K E Y W O R D Sforecast verification, intraseasonal variability, MJO Q J R Meteorol Soc. 2020;146:1067-1084.wileyonlinelibrary.com/journal/qj

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MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Cited by 79 publications

References 72 publications

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

MJO modulation and its ability to predict boreal summer tropical cyclone genesis over the northwest Pacific in Met Office Hadley Centre and Beijing Climate Center seasonal prediction systems

Configuration and hindcast quality assessment of a Brazilian global sub‐seasonal prediction system

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