Premium
A self‐organizing map–based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon
Author(s) -
Borah N.,
Sahai A. K.,
Chattopadhyay R.,
Joseph S.,
Abhilash S.,
Goswami B. N.
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50688
Subject(s) - hindcast , forecast skill , probabilistic logic , benchmark (surveying) , range (aeronautics) , scale (ratio) , computer science , climatology , meteorology , environmental science , mathematics , statistics , geography , machine learning , geology , engineering , cartography , geodesy , aerospace engineering
The paper describes a probabilistic prediction scheme of the intraseasonal oscillation of Indian summer monsoon (ISM) in the extended range (ER, ~3–4weeks) using a self‐organizing map (SOM)‐based technique. SOM is used to derive a set of patterns through empirical model reduction. An ensemble method of forecast is then developed for these reduced modes based on the principle of analogue prediction. A total of 900 ensembles is created based on the variations of one of the parameters, like length of the observation sample, number of patterns, number of lags, and number of input variables, keeping the others constant. Deterministic correlation skill at fourth pentad lead (15–20 days) from the current model is 0.47 (for development period, 1951–1999) and 0.43 (for hindcast period, 2000–2011) over the monsoon zone of India. This method effectively takes care of the stochastic uncertainties associated with a deterministic prediction scheme and provides better guidance to the user community. A large part of the uncertainty in the model's prediction skill is related to the interannual variability of the prediction skill of the active‐break spells. The model has problems in forecasting the unusually long active/break spells during the monsoon season, especially during September. Forecasts from certain initial conditions are less predictable than those from others. We describe some probable mechanisms from the literature for such problems in the model. This study will provide a benchmark to evaluate dynamical models' skills in predicting the ISM in ER time scale in future.