Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Abstract

Subseasonal forecast skill over the broadly defined North American (NAM), West African (WAM) and Asian (AM) summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS) at sub-monthly lead times. Extended Logistic Regression (ELR) is used to produce probabilistic forecasts of weekly and week 3-4 averages of precipitation with starts in May-Aug, over the 1999-2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME) forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS) seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3-4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999-2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO) for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

Subseasonal Forecasts of the 2018 Indian Summer Monsoon Over Bihar

“…Skill ranges from 0.30–0.60 in Week 1, to 0.15–0.45 in Week 2, and 0.30 or less at Weeks 3 and 4. These low skill levels in monsoon precipitation beyond the first week are consistent (and particularly at Weeks 3 and 4) with those found using extended logistic regression (Vigaud, Robertson, Tippett, & Acharya, ). While we did not exclude, a priori, the potential value of Weeks 3 and 4 forecasts, in practice, the Real‐Time Weeks 3 and 4 forecasts were never sufficiently sharp (i.e., deviating strongly from 33%) for us to have sufficient confidence in them.…”

Subseasonal to Seasonal Prediction of Weather to Climate with Application to Tropical Cyclones

“…There is evidence that multimodel ensembles provide increased skill over individual models on subseasonal scales (Strazzo et al, ; Vigaud, Robertson, & Tippett, ; Vigaud, Robertson, Tippett, Acharya, ), as is well established in seasonal forecasting, so that better S2S hindcast protocols that enable better calibration and bias correction can be expected to improve skill. Research is required to revisit the trade‐off between spatial resolution and ensemble size at the subseasonal range.…”

Multimodel Subseasonal Forecasts of Spring Cold Spells: Potential Value for the Hazelnut Agribusiness

“…Efforts like the Subseasonal to Seasonal Prediction project (S2S) database (Vitart et al 2017), which freely provides a set of subseasonal forecasts and hindcasts produced by 11 different prediction systems, are helping the scientific community to advance understanding of sources of predictability, model improvement and forecast skill. Many studies have demonstrated the enhanced forecast quality of multimodel ensembles compared to a more conventional single-model ensemble approach (Hagedorn et al 2006;DelSole and Tippett 2014;Vigaud et al 2017). In addition, a large sample size allows the use of consensus between the different model forecasts to get some insight into the predictability (Piedelievre 2000), provides for an insightful evaluation of probabilistic skill (Krishnamurti et al 2006), and imparts a potential economic value to the forecast (Richardson 2000;Alessandri et al 2011).…”

“…Although a few studies have recently evaluated the subseasonal forecast quality and potentialities in a multisystem setting, both for precipitation (Vigaud et al 2017) and for temperature (Ferrone et al 2017), there is still no assessment regarding cold extremes at such time scale.…”