Dynamical seasonal prediction of Southern African summer precipitation

Chaoxia Yuan; Tomoki Tozuka; Willem A. Landman; Toshio Yamagata

doi:10.1007/s00382-013-1923-5

Development of Dynamical Seasonal Prediction System for Northern Winter using the Cryospheric Condition of Late Autumn

Atmosphere ◽

10.14191/atmos.2013.23.1.073 ◽

2013 ◽

Vol 23 (1) ◽

pp. 73-83 ◽

Cited By ~ 2

Author(s):

Taehyoun Shim ◽

Jee-Hoon Jeong ◽

Baek-Min Kim ◽

Seong-Joong Kim ◽

Hyun-Kyung Kim

Keyword(s):

Seasonal Prediction ◽

Prediction System ◽

Late Autumn ◽

Northern Winter ◽

Dynamical Seasonal Prediction

Machine learning-based dynamical seasonal prediction of summer rainfall in China

10.5194/egusphere-egu2020-12271 ◽

2020 ◽

Author(s):

Jialin Wang ◽

Jing Yang ◽

Hongli Ren ◽

Jinxiao Li ◽

Qing Bao ◽

...

Keyword(s):

Machine Learning ◽

Prediction Method ◽

Summer Rainfall ◽

Seasonal Prediction ◽

Prediction Skill ◽

Gradient Boosting ◽

Disaster Reduction ◽

Best Fitting ◽

Application Prospects ◽

Dynamical Seasonal Prediction

<p>The seasonal prediction of summer rainfall is crucial for regional disaster reduction but currently has a low prediction skill. This study developed a machine learning (ML)-based dynamical (MLD) seasonal prediction method for summer rainfall in China based on suitable circulation fields from an operational dynamical prediction model CAS FGOALS-f2. Through choosing optimum hyperparameters for three ML methods to reach the best fitting and the least overfitting, gradient boosting regression trees eventually exhibit the highest prediction skill, obtaining averaged values of 0.33 in the reference training period (1981-2010) and 0.19 in eight individual years (2011-2018) of independent prediction, which significantly improves the previous dynamical prediction skill by more than 300%. Further study suggests that both reducing overfitting and using the best dynamical prediction are imperative in MLD application prospects, which warrants further investigation.</p>

A New Scheme for Improving the Seasonal Prediction of Summer Precipitation Anomalies

Weather and Forecasting ◽

10.1175/2008waf2222171.1 ◽

2009 ◽

Vol 24 (2) ◽

pp. 548-554 ◽

Cited By ~ 54

Author(s):

Huijun Wang ◽

Ke Fan

Keyword(s):

Summer Precipitation ◽

Seasonal Prediction ◽

Western Pacific ◽

Prediction Skill ◽

Skill Levels ◽

Pattern Correlation ◽

Model Predictions ◽

Original Scheme ◽

Precipitation Anomalies ◽

The Western Pacific

Abstract A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.

U.S. Summer Precipitation and Temperature Patterns Following the Peak Phase of El Niño

Journal of Climate ◽

10.1175/jcli-d-11-00660.1 ◽

2012 ◽

Vol 25 (20) ◽

pp. 7204-7215 ◽

Cited By ~ 20

Author(s):

Hui Wang ◽

Arun Kumar ◽

Wanqiu Wang ◽

Bhaskar Jha

Keyword(s):

Surface Temperature ◽

El Niño ◽

El Nino ◽

Surface Air Temperature ◽

Summer Precipitation ◽

Seasonal Prediction ◽

Atmospheric Models ◽

Temperature Anomalies ◽

Peak Phase ◽

Precipitation And Temperature

Abstract Evidence for spatially coherent, but different, U.S. summer precipitation and surface air temperature anomalies during the evolving phase and during the summers following the peak phase of the winter El Niño is presented. The spatial patterns during the decaying phase of El Niño are distinctive from patterns in the preceding summer when El Niño is in its evolving phase, that is, the traditional “simultaneous” composite patterns associated with El Niño. The analysis of a multimodel ensemble of global atmospheric models forced by observed sea surface temperature further confirms that the differences in the U.S. summer precipitation and surface temperature anomalies between the developing and decaying phases of El Niño are a result of the atmospheric response to tropical warm SST anomalies that are shifted eastward and are confined east of 120°W during the decaying phase of El Niño. Given the distinctive pattern, and relatively large amplitude of these anomalies during the decaying phase of El Niño, the results may have implications for the seasonal prediction of U.S. summer precipitation and temperature following winter El Niños.

On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon

MAUSAM ◽

10.54302/mausam.v62i3.313 ◽

2021 ◽

Vol 62 (3) ◽

pp. 339-360

Author(s):

D.R. SIKKA ◽

SATYABANBISHOYI RATNA

Keyword(s):

Boundary Condition ◽

High Resolution ◽

Summer Monsoon ◽

General Circulation ◽

Atmospheric General Circulation Model ◽

Monsoon Season ◽

Circulation Model ◽

Seasonal Prediction ◽

Atmospheric General Circulation ◽

Dynamical Seasonal Prediction

The paper is devoted to examine the ability of a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 Atmospheric General Circulation Model (AGCM), for exploring its utility for long-range dynamical prediction of seasonal Indian summer monsoon rainfall (ISMR) based on 5-members ensemble for the hindcast mode 20-year (1985-2004) period with observed global sea surface temperatures (SSTs) as boundary condition and 6-year (2005-2010) period in the forecast-mode with NCEP Coupled Forecast System (CFS) SSTs as boundary condition. ISMR simulations are examined on five day (pentad) rainfall average basis. It is shown that the model simulated ISMR, based on 5-members ensemble average basis had limited skill in simulating extreme ISMR seasons (drought/excess ISMR). However, if the ensemble averaging is restricted to similar ensemble members either in the overall run of pentad-wise below (B) and above (A) normal rainfall events, as determined by the departure for thethreshold value given by coefficient of variability (CV) for the respective pentads based on IMD observed climatology, or during the season as a whole on the basis of percentage anomaly of ISMR from the seasonal climatology, the foreshadowing of drought/excess monsoon seasons improved considerably. Our strategy of improving dynamical seasonal prediction of ISMR was based on the premise that the intra-seasonal variability (ISV) and intra-annual variability (IAV) are intimately connected and characterized by large scale perturbations westward moving (10-20 day) and northward moving (30-60 day) modes of monsoon ISV during the summer monsoon season. As such the cumulative excess of B events in the simulated season would correspond to drought season and vice-versa. The paper also examines El Niño-Monsoon connections of the simulated ISMR series and they appear to have improved considerably in the proposed methodology. This strategy was particularly found to improve for foreshadowing of droughts. Based on results of the study a strategy is proposed for using the matched signal for simulated ISMR based on excess B over A events and vice-versa for drought or excess ISMR category. The probability distribution for the forecast seasonal ISMR on category basis is also proposed to be based on the relative ratio of similar ensemble members and total ensembles on percentage basis. The paper also discusses that extreme monsoon season are produced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modes in a combined manner and hence stresses to improve prediction of IOD mode in ocean-atmosphere coupled model just as it has happened for the prediction ENSO mode six to nine months in advance.