Novel evaluation of sub-seasonal precipitation ensemble forecasts for identifying high-impact weather events associated with the Indian monsoon

We assess the skill of the fully coupled lagged ensemble forecasts from GloSea5-GC2, for the sub-seasonal to seasonal (S2S) timescale up to 4 weeks, with the aim of understanding how these forecasts might be used in a Ready-Set-Go style decision-making framework. Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) are used to seamlessly verify these ensemble forecasts up to monthly timescales whereby forecast and observed precipitation fields are summed over a sequence of increasing lead time accumulation windows (LTAWs), from 1d1d up to 2w2w. Results show that model biases grow with increasing LTAW and with ensemble member age. The S2S model exhibits both wet and dry biases across different parts of the Indian domain. The S2S model error grows from around 10 mm for a 24-h accumulation to 50 mm for the 2-week LTAWs. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than actual skill everywhere. The sensitivity to the number and age of ensemble members was tested, with potential skill showing more impact from the exclusion of older members at all LTAWs. We conclude that the older lagged members do not necessarily add value by being included in the short to medium range or even for the extended range forecasts. GloSea5-GC2 shows some skill in detecting large accumulations, which are not always tied to locations where they are climatologically frequent.

Automated summary

This study assesses the skill of the fully coupled lagged ensemble forecasts from GloSea5-GC2 for the sub-seasonal to seasonal timescale. The results show that model biases increase with lead time accumulation windows and ensemble member age. The S2S model exhibits wet and dry biases across different parts of the Indian domain, and the model error grows as the lead time increases. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than the actual skill.