Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~ 60km) and lead (2–5 months) considered here the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June-August) scale. 8-member ensemble average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.
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Posted 09 Mar, 2021
On 17 Feb, 2021
On 11 Feb, 2021
Posted 09 Mar, 2021
On 17 Feb, 2021
On 11 Feb, 2021
Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~ 60km) and lead (2–5 months) considered here the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June-August) scale. 8-member ensemble average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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