Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China

Ma, Feng; Ye, Aizhong; Deng, Xiaoya; Zhou, Zheng; Liu, Xiaojie; Duan, Qingyun; Miao, Chiyuan; Di, Zhenhua; Gong, Wei

doi:10.1002/joc.4333

Cited by 62 publications

(58 citation statements)

References 63 publications

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“…Because of the large volumes of data that are produced within the NMME (Table 1), global-scale studies have focused on the evaluation of model skill at specific lead times Mo and Lettenmaier, 2014), or for specific seasons (Wang, 2014), models (Jia et al, 2015;Saha et al, 2014), or climate quantities (Barnston and Lyon, 2016;Mo and Lyon, 2015). Regional evaluations of NMME forecast skill have focused principally on North America (Infanti and Kirtman, 2016), the United States (Misra and Li, 2014;Roundy et al, 2015;Slater et al, 2017), the southeastern United States , but also China (Ma et al, 2015a(Ma et al, , 2015b, Iran (Shirvani and Landman, 2016) and South Asia (Sikder et al, 2015). Thus, most of the effort of the NMME model skill evaluation has been over the USA, and far less attention has been paid to Europe, with some exceptions, such as Thober et al (2015), who used NMME forecasts as input for the mesoscale hydrologic model (mHM).…”

Section: Introductionmentioning

confidence: 99%

“…The predictive skill of these equally weighted multi-models tends to be greater than or equal to the skill of the best model within the ensemble Hagedorn et al, 2005;Ma et al, 2015a;Slater et al, 2017;Thober et al, 2015;Wood et al, 2015). Generally, multi-model ensembles can outperform single-model ensembles when the individual models are overconfident, so the multi-model widens the ensemble spread and reduces the average ensemble-mean error (Weigel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Weighting of NMME temperature and precipitation forecasts across Europe

Slater

Villarini

Bradley

2017

Journal of Hydrology

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Highlights• NMME precipitation and temperature forecast skill are assessed across Europe• The forecasting skill of five weighted multi-models is compared• Equal model weighting preserves forecast skill, but with considerable biases• Bayesian updating reduces conditional biases and homogenizes the skill PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weighting of NMME temperature and precipitation forecasts across Europe

Slater

Villarini

Bradley

2017

Journal of Hydrology

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“…NMME data have been widely used for ensemble analysis and hydroclimate forecasting Wood, 2012, 2013;Becker et al, 2014;Delsole et al, 2014;Mo and Lettenmaier, 2014;Tian et al, 2014;Ma et al, 2015]. In this study, six models, which are being implemented to produce real-time seasonal forecasts, are used for drought analysis.…”

Section: Model Forecasts and Observationsmentioning

confidence: 99%

Seasonal drought predictability and forecast skill over China

Yuan

2015

JGR Atmospheres

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Under a changing environment, seasonal droughts have been exacerbated with devastating impacts. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple climate models, the predictability and forecast skill for drought over China are investigated. The 3 month standardized precipitation index is used as the drought index, and the predictability is quantified by using a perfect model assumption. Ensemble hindcasts from multiple climate models are assessed individually, and the grand multimodel ensemble is also evaluated. Drought forecast skill for model ensemble mean is higher than individual ensemble members, and North American Multimodel Ensemble grand ensemble performs the best. Predictability is higher than forecast skill, indicating the room for improving drought forecast. Drought predictability and forecast skill are positively correlated in general, but they vary depending on seasons, regions, and forecast leads. Higher drought predictability and forecast skill are found over regimes where ENSO has significant impact. For the ENSO-affected regimes, both drought predictability and forecast skill in ENSO years are higher than that in neutral years. This study suggests that predictability not only provides a measure for selecting climate models for ensemble drought forecast in ENSO-affected regimes but also serves as an indicator for forecast skill especially when in situ and/or remote sensing measurements for the hindcast verifications are considered unreliable.

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“…The anomaly correlation, which measures how well large (small) values of forecast indicate large (small) values of observation, has been widely used to represent the skill of GCM forecasts (e.g., Jia et al, ; Tian et al, ; Yuan, ; van den Dool et al, ; Zhao, Bennett, et al, ). While GCM forecasts are informative, their anomaly correlation exhibits spatial and temporal variation (Kirtman et al, ; Ma et al, ; Saha et al, ). That is, the anomaly correlation varies by latitude, longitude, and initialization time.…”

Section: Introductionmentioning

confidence: 99%

Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China

et al. 2018

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The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three‐dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL‐CM2p1‐aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North‐American Multi‐Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA‐RSMAS‐CCSM3), 45% for COLA‐RSMAS‐CCSM4, 59% for GFDL‐CM2p1, 67% for GFDL‐CM2p1‐aer04, 65% for GFDL‐CM2p5‐FLOR‐A06, 57% for GFDL‐CM2p5‐FLOR‐B01, 48% for NCAR‐CESM1, and 60% for NCEP‐CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts.

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Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China

Cited by 62 publications

References 63 publications

Weighting of NMME temperature and precipitation forecasts across Europe

Weighting of NMME temperature and precipitation forecasts across Europe

Seasonal drought predictability and forecast skill over China

Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China

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