Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems

Jain, Shipra; Scaife, Adam A.; Mitra, Ashis K.

doi:10.1007/s00382-018-4449-z

Cited by 37 publications

(35 citation statements)

References 57 publications

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“…Interestingly, we see an increase in the skill from 0.2 to 0.38 at Lead‐1 and 0.12 to 0.42 at Lead‐2 during peak summer monsoon time. Similar conclusions can be drawn for the full monsoon period (Figure 14b,d), which indicates that extended spatial averaging produces higher skill due to extended spatial coherence of peak monsoon precipitation variability (Jain, Scaife, & Mitra, 2019). This result is in agreement with the conclusion drawn by some recent studies (Jain et al, 2019).…”

Section: Resultssupporting

confidence: 78%

“…Similar conclusions can be drawn for the full monsoon period (Figure 14b,d), which indicates that extended spatial averaging produces higher skill due to extended spatial coherence of peak monsoon precipitation variability (Jain, Scaife, & Mitra, 2019). This result is in agreement with the conclusion drawn by some recent studies (Jain et al, 2019). However, our observed and model domains are similar in contrast to Jain et al (2019) who found high skill for precipitation by choosing a larger model domain for a fixed observed region.…”

Section: Resultssupporting

confidence: 78%

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Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Ehsan

Tippett

Kucharski

et al. 2020

Intl Journal of Climatology

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The potential and actual forecast skill of peak summer monsoon precipitation (July–August [JA]) over the core summer monsoon region of Pakistan (CSMRP: 68°–76°E, 30°–36°N) is investigated. The predictions from the latest version of the European Centre for Medium‐Range Weather Forecast System5 (SEAS5) and the North American Multimodel Ensemble (NMME) initialized in the beginning of June, May, and April are utilized. The potential skill is estimated by signal‐to‐noise ratio and perfect model correlation. The forecast skill is evaluated using the temporal anomaly correlation as well as the ranked probability skill score for probabilistic forecasts. The SEAS5 generally captures the observed climatological mean and variability patterns of peak summer monsoon precipitation over the region, with some biases mainly located over the complex topography zones. Observed SST anomalies in the El Niño–Southern Oscillation (ENSO) region are negatively correlated with observed peak summer monsoon precipitation over the region. The ENSO–precipitation connection is weakly reproduced in SEAS5 at Lead‐1 and fades gradually with increasing lead time. The potentially predictable component (signal) is small compared to the unpredictable part (noise). Nonetheless, statistically significant potential skill is found over the CSMRP. However, the forecast skill of peak summer monsoon precipitation over the region is lower than the potential skill in SEAS5. A multimodel ensemble average of five models shows little benefit in predicting peak monsoon precipitation over Pakistan. Extended spatial averaging has some impact on the skill; however, temporal averaging has no effect on the skill of the summer monsoon precipitation in SEAS5. Together these results show limitations of the current seasonal prediction models and demonstrate their inadequate performance in predicting peak summer monsoon precipitation over Pakistan.

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Section: Resultssupporting

confidence: 78%

Section: Resultssupporting

confidence: 78%

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Ehsan

Tippett

Kucharski

et al. 2020

Intl Journal of Climatology

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“…Because temperature influences snowmelt and runoff efficiency, skill in seasonal temperature forecasts can provide improved skill for seasonal water supply forecasts in this region (Lehner et al 2017). Seasonal forecast skill has also been demonstrated for monsoon rainfall (e.g., Jain et al 2019) and drought (Hao et al 2018) with potential to inform water management decisions in many regions of the globe. Decadal forecasts potentially can meet planning horizon needs but currently are less familiar to water managers than seasonal forecasts and long-term climate projections.…”

Section: E887mentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

164

113

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Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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“…The Met Office Global Seasonal Forecasting System version 5 (GloSea5) is a coupled initialized global operational seasonal forecasting system (MacLachlan et al, 2015). Previous studies have shown that GloSea5 provides skilful predictions of the large-scale monsoon circulation and modest skill for predicting monsoon rainfall (Johnson et al, 2017;Jain et al, 2018). A recent study (Chevuturi et al, 2018) demonstrated further that, while predictions of the exact date of monsoon onset over India remain elusive, GloSea5 has skill in predicting category-wise monsoon onset, using early, normal, or late tercile categories.…”

Section: Seasonal Outlook and Onsetmentioning

confidence: 99%

Forecasting the monsoon on daily to seasonal time‐scales in support of a field campaign

Martin

Brooks

Johnson

et al. 2019

Quart J Royal Meteoro Soc

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The successful planning and execution of a major field campaign relies on the availability and reliability of weather forecasts on a range of time-scales. Here, we describe the wide range of forecast products generated in support of a field campaign that took place in India in 2016 as part of the Interaction of Convective Organization with Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) project.We show examples of the suite of plots generated every day from the forecasts and supplied to the mission scientists, and describe how these were used to plan the flights. We highlight the benefits of having access to forecasts from a range of model resolutions and configurations; these allowed judgements to be made about uncertainty, particularly in the amount and location of deep convective rainfall, which is an important consideration for flight planning. Finally, we discuss the legacy of the forecasting activity, which has not only advanced our understanding of monsoon forecasting but also created a large database of targeted model forecast products for the whole of the 2016 monsoon season. These can be used by researchers for comparisons with in situ observations as well as future modelling studies.

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Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems

Cited by 37 publications

References 57 publications

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Current and Emerging Developments in Subseasonal to Decadal Prediction

Forecasting the monsoon on daily to seasonal time‐scales in support of a field campaign

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