Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP Climate Forecast System

2013 ◽  
Vol 34 (1) ◽  
pp. 98-113 ◽  
Author(s):  
S. Abhilash ◽  
A. K. Sahai ◽  
S. Pattnaik ◽  
B. N. Goswami ◽  
Arun Kumar
Keyword(s):  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ensemble Prediction System ◽  
Extended Range ◽  
Ncep Climate Forecast System

scholarly journals Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

2009 ◽  
Vol 137 (4) ◽  
pp. 1480-1492 ◽  
Author(s):  
Frédéric Vitart ◽  
Franco Molteni
Keyword(s):  
Lead Time ◽  
Monsoon Rainfall ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Sensitivity Experiments ◽  
Extended Range ◽  
Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

scholarly journals Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan

2015 ◽  
Vol 143 (5) ◽  
pp. 1833-1848 ◽  
Author(s):  
Hui-Ling Chang ◽  
Shu-Chih Yang ◽  
Huiling Yuan ◽  
Pay-Liam Lin ◽  
Yu-Chieng Liou
Keyword(s):  
Decision Making ◽  
Operating Characteristic ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast System ◽  
Relative Operating Characteristic ◽  
Ensemble Prediction System ◽  
Discrimination Ability ◽  
Probabilistic Forecasts

Abstract Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system. Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

scholarly journals Relative Merit of Model Improvement versus Availability of Retrospective Forecasts: The Case of Climate Forecast System MJO Prediction

2012 ◽  
Vol 27 (4) ◽  
pp. 1045-1051 ◽  
Author(s):  
Qin Zhang ◽  
Huug van den Dool
Keyword(s):  
Systematic Error ◽  
Error Correction ◽  
Initial Conditions ◽  
Climate Forecast ◽  
Prediction System ◽  
Madden Julian Oscillation ◽  
Forecast System ◽  
Climate Forecast System ◽  
Model Improvement ◽  
Ncep Climate Forecast System

Abstract Retrospective forecasts of the new NCEP Climate Forecast System (CFS) have been analyzed out to 45 days from 1999 to 2009 with four members (0000, 0600, 1200, and 1800 UTC) each day. The new version of CFS [CFS, version 2 (CFSv2)] shows significant improvement over the older CFS [CFS, version 1 (CFSv1)] in predicting the Madden–Julian oscillation (MJO), with skill reaching 2–3 weeks in comparison with the CFSv1’s skill of nearly 1 week. Diagnostics of experiments related to the MJO forecast show that the systematic error correction, possible only because of the enormous hindcast dataset and the ensemble aspects of the prediction system (4 times a day), do contribute to improved forecasts. But the main reason is the improvement in the model and initial conditions between 1995 and 2010.

scholarly journals Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

2015 ◽  
Vol 54 (7) ◽  
pp. 1569-1578 ◽  
Author(s):  
S. Abhilash ◽  
A. K. Sahai ◽  
N. Borah ◽  
S. Joseph ◽  
R. Chattopadhyay ◽  
...  
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Probabilistic Prediction ◽  
Forecast System ◽  
Model Framework ◽  
Ensemble Prediction System ◽  
Physical Mechanisms ◽  
Uncertainty Estimates ◽  
Model Ensembles ◽  
Environmental Prediction

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.

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