Gain of one-month lead time in seasonal prediction of Indian summer monsoon prediction: comparison of initialization str
- PDF / 5,620,520 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 10 Downloads / 217 Views
ORIGINAL PAPER
Gain of one-month lead time in seasonal prediction of Indian summer monsoon prediction: comparison of initialization strategies Ankur Srivastava 1
&
Suryachandra A. Rao 1
&
Maheswar Pradhan 1
&
Prasanth A. Pillai 1
&
V. S. Prasad 2
Received: 21 May 2020 / Accepted: 16 November 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the Monsoon Mission (MM) Seasonal Forecast model, at a lead time of 3 months. The ensembles in the MM model are generated by utilizing lagged initial conditions. The possibility of enhancing the lead time is explored by using the burst ensemble approach. Comprehensive seasonal hindcast experiments carried out in this study reveal that the two methods exhibit similar skill scores for the major tropical phenomenon which govern ISMR variability. In general, the model forecasts are slightly under-dispersive but satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. The ratio between the spread and RMSE is small for ISMR forecasts. Though the skill scores for the majority of indices are similar, the monsoon teleconnections seem to be quite sensitive to the initialization strategy. It is found that the burst initialization method provides a gain of 1-month lead time compared to lagged initialization strategy employed in previous studies without compromising the prediction skill. The gain of a months’ lead time with the burst ensemble approach is a tempting and useful proposition, which can be crucial for the policy- and decision-makers.
1 Introduction The uncertainty in the future evolution of a system (i.e., forecast) is valid for all the ranges of forecasts, be it a weather forecast, an extended range forecast, or a seasonal forecast (Tompkins et al. 2017). A seminal article by Prof. Ed Lorenz in 1963 (Lorenz 1963) demonstrated that even a small uncertainty in the initial condition could lead to large uncertainty in the forecast after a certain integration time, depending on the initial state of the atmosphere. This error growth limits the deterministic predictability to a few days only and is often referred to as the predictability of the first kind. Therefore, long-range weather forecasting should be impossible. Charney and Shukla (1981) hypothesized that there is another type of predictability in the ocean-atmosphere system, the predictability of the second kind. This predictability stems from the slowly varying nature of the boundary anomalies in * Suryachandra A. Rao [email protected] 1
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, New Delhi, India
2
National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, New Delhi, India
fields such as sea surface temperature, albedo, ice-cover, and soil moisture. The predictability of the second kind is the basis for seasonal forecasts. The numerical models employed for making a forecast denote the physical pro
Data Loading...
