Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012

Su, Xiang; Yuan, Huiling; Zhu, Yuejian; Luo, Yan; Wang, Yuan

doi:10.1002/2014jd021733

Cited by 50 publications

(28 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Observing System Research and Predictability Experiment (THORPEX; http://www.wmo.int/pages/prog/arep/wwrp/new/thorpex_new.html) of the World Meteorological Organization established a data archive of operational ensemble predictions named THORPEX International Grand Global Ensemble (TIGGE) [Bougeault et al, 2010], with the aim of improving the accuracy of 1 day to 2 week high-impact weather forecasts and advancing the development of ensemble forecasting techniques [Richardson et al, 2005]. The performance of the EPSs has been assessed and compared with a focus on the prediction of tropical cyclones [Taraphdar et al, 2016;Froude et al, 2007;Froude, 2009Froude, , 2010Froude, , 2011, rainfall events [Hamill et al, 2008;Wiegand et al, 2011;Hamill, 2012;Su et al, 2014;Ye et al, 2013], and associated synoptic-scale features [e.g., Buizza et al, 2004;Park et al, 2008;Zhi et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

Huang

Luo

2017

JGR Atmospheres

View full text Add to dashboard Cite

Based on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium‐Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April–June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013–2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light‐to‐heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm‐moist air into south China by the low‐level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30–36 h to 18–24 h to 6–12 h for the cases with large‐scale forcing, but not for the locally produced cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

Huang

Luo

2017

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…Traditionally, the spread‐skill relationship is used to depict the agreement between the ensemble spread (standard deviation between ensemble members that measures the uncertainty of ensemble) and the expected forecast error of ensemble‐mean or control forecast (Grimit and Mass, ). Many studies use the traditional spread‐skill relationship (or spread‐error relationship) to evaluate ensemble precipitation forecasts (Ebert, ; Martin et al , ; Bouttier et al , ; Su et al , ). However, the traditional metrics of forecast error, such as root‐mean‐square error (RMSE) and mean absolute error (MAE), are not suitable for the convection‐allowing EPSs due to the serious double penalty (Mittermaier et al , ).…”

Section: Introductionmentioning

confidence: 99%

Spatial spread‐skill relationship in terms of agreement scales for precipitation forecasts in a convection‐allowing ensemble

Chen

Yuan

Xue

2017

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

Verification of precipitation is one of the major issues in evaluating numerical weather prediction. In this study, a recently developed neighbourhood‐based method in terms of agreement scales is applied to characterize the scale‐dependent spatial spread‐skill relationship of precipitation forecasts in a 3 km convection‐allowing ensemble prediction system (EPS) over the Yangtze‐Huaihe river basin of China. Thirty cases during the Meiyu season of 2013 are classified into two weather regimes, large coverage (LC) and small coverage (SC), based on the precipitation fractional coverage. Overall, precipitation distributions for these two weather regimes are reasonably forecast by the EPS. The results show that the spatial spread‐skill relationship depends highly on the weather regime. The spatial spread‐skill relationship under SC is poorer and shows more diurnal variation compared to that under LC. In addition, this article extends the neighbourhood‐based method to investigate the relative influence of precipitation intensity and placement on the spatial spread‐skill relationship. With increasing precipitation threshold, the relative impact of precipitation intensity on the relationship gradually decreases, and the influence of precipitation placement becomes dominant.

show abstract

“…With the rapid development of computer technology and meteorological science, the meteorological ensemble prediction is superior to the traditional one in terms of forecast accuracy and period, and it has been widely accepted by many national meteorological departments [9]. Moreover, The Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the China Meteorological Administration (CMA) have all realized the operation of numerical ensemble prediction [10]. At present, the Global Ensemble Forecast System (GEFS) [11], Climate Forecast System (CFS) [12], and THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) [13] are the most representative products in the world.…”

Section: Introductionmentioning

confidence: 99%

Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Chen

Huang

et al. 2020

Water

View full text Add to dashboard Cite

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

show abstract

Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012

Cited by 50 publications

References 34 publications

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

Spatial spread‐skill relationship in terms of agreement scales for precipitation forecasts in a convection‐allowing ensemble

Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Contact Info

Product

Resources

About