Abstract
The authors assess the predictability of large-scale monsoon intraseasonal oscillations (MISOs) as measured by precipitation. An advanced nonlinear data analysis technique, nonlinear Laplacian spectral analysis (NLSA), is applied to the daily precipitation data, resulting in two spatial modes associated with the MISO. The large-scale MISO patterns are predicted in two steps. First, a physics-constrained low-order nonlinear stochastic model is developed to predict the highly intermittent time series of these two MISO modes. The model involves two observed MISO variables and two hidden variables that characterize the strong intermittency and random oscillations in the MISO time series. It is shown that the precipitation MISO indices can be skillfully predicted from 20 to 50 days in advance. Second, an effective and practical spatiotemporal reconstruction algorithm is designed, which overcomes the fundamental difficulty in most data decomposition techniques with lagged embedding that requires extra information in the future beyond the predicted range of the time series. The predicted spatiotemporal patterns often have comparable skill to the MISO indices. One of the main advantages of the proposed model is that a short (3 year) training period is sufficient to describe the essential characteristics of the MISO and retain skillful predictions. In addition, both model statistics and prediction skill indicate that outgoing longwave radiation is an accurate proxy for precipitation in describing the MISO. Notably, the length of the lagged embedding window used in NLSA is crucial in capturing the main features and assessing the predictability of MISOs.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4403-4427 |
| Number of pages | 25 |
| Journal | Journal of Climate |
| Volume | 31 |
| Issue number | 11 |
| DOIs | |
| State | Published - Jun 1 2018 |
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Keywords
- Monsoons
- Spectral analysis/models/distribution
- Statistical forecasting
- Stochastic models
- Time series
ASJC Scopus subject areas
- Atmospheric Science
Cite this
Predicting monsoon intraseasonal precipitation using a low-order nonlinear stochastic model. / Chen, Nan; Majda, Andrew; Sabeerali, C. T.; Ravindran, Ajaya.
In: Journal of Climate, Vol. 31, No. 11, 01.06.2018, p. 4403-4427.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predicting monsoon intraseasonal precipitation using a low-order nonlinear stochastic model
AU - Chen, Nan
AU - Majda, Andrew
AU - Sabeerali, C. T.
AU - Ravindran, Ajaya
PY - 2018/6/1
Y1 - 2018/6/1
N2 - The authors assess the predictability of large-scale monsoon intraseasonal oscillations (MISOs) as measured by precipitation. An advanced nonlinear data analysis technique, nonlinear Laplacian spectral analysis (NLSA), is applied to the daily precipitation data, resulting in two spatial modes associated with the MISO. The large-scale MISO patterns are predicted in two steps. First, a physics-constrained low-order nonlinear stochastic model is developed to predict the highly intermittent time series of these two MISO modes. The model involves two observed MISO variables and two hidden variables that characterize the strong intermittency and random oscillations in the MISO time series. It is shown that the precipitation MISO indices can be skillfully predicted from 20 to 50 days in advance. Second, an effective and practical spatiotemporal reconstruction algorithm is designed, which overcomes the fundamental difficulty in most data decomposition techniques with lagged embedding that requires extra information in the future beyond the predicted range of the time series. The predicted spatiotemporal patterns often have comparable skill to the MISO indices. One of the main advantages of the proposed model is that a short (3 year) training period is sufficient to describe the essential characteristics of the MISO and retain skillful predictions. In addition, both model statistics and prediction skill indicate that outgoing longwave radiation is an accurate proxy for precipitation in describing the MISO. Notably, the length of the lagged embedding window used in NLSA is crucial in capturing the main features and assessing the predictability of MISOs.
AB - The authors assess the predictability of large-scale monsoon intraseasonal oscillations (MISOs) as measured by precipitation. An advanced nonlinear data analysis technique, nonlinear Laplacian spectral analysis (NLSA), is applied to the daily precipitation data, resulting in two spatial modes associated with the MISO. The large-scale MISO patterns are predicted in two steps. First, a physics-constrained low-order nonlinear stochastic model is developed to predict the highly intermittent time series of these two MISO modes. The model involves two observed MISO variables and two hidden variables that characterize the strong intermittency and random oscillations in the MISO time series. It is shown that the precipitation MISO indices can be skillfully predicted from 20 to 50 days in advance. Second, an effective and practical spatiotemporal reconstruction algorithm is designed, which overcomes the fundamental difficulty in most data decomposition techniques with lagged embedding that requires extra information in the future beyond the predicted range of the time series. The predicted spatiotemporal patterns often have comparable skill to the MISO indices. One of the main advantages of the proposed model is that a short (3 year) training period is sufficient to describe the essential characteristics of the MISO and retain skillful predictions. In addition, both model statistics and prediction skill indicate that outgoing longwave radiation is an accurate proxy for precipitation in describing the MISO. Notably, the length of the lagged embedding window used in NLSA is crucial in capturing the main features and assessing the predictability of MISOs.
KW - Monsoons
KW - Spectral analysis/models/distribution
KW - Statistical forecasting
KW - Stochastic models
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85047061594&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047061594&partnerID=8YFLogxK
U2 - 10.1175/JCLI-D-17-0411.1
DO - 10.1175/JCLI-D-17-0411.1
M3 - Article
AN - SCOPUS:85047061594
VL - 31
SP - 4403
EP - 4427
JO - Journal of Climate
JF - Journal of Climate
SN - 0894-8755
IS - 11
ER -