Multi-physics schema for sub-seasonal prediction of Indian summer monsoon

Kaur, Manpreet; Sahai, A. K.; Phani, R.; Joseph, Susmitha; Mandal, Raju; Dey, Avijit; Chattopadhyay, Rajib

doi:10.1007/s00382-021-05926-2

Cited by 4 publications

(7 citation statements)

References 64 publications

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“…Overall, in all the pentad leads, the CFS_nsas members perform comparatively better than other MPMME members (pattern correlations are marked in the figure). These findings are similar to Kaur et al (2022). The observed northward propagation of ISOs can be shown using a phase-composite diagram (Supporting Information Figure S3).…”

Section: Representation Of Rainfall Isos In Mpmmesupporting

confidence: 80%

“…Using the National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) model version 2 (CFSv2) and its atmospheric component Global Forecast System (GFS) model with real‐time bias‐corrected sea‐surface temperature from CFS (Saha et al ., 2014), simulations are carried out for 15 years (2001–2015) (Sahai et al ., 2021; Kaur et al ., 2022). Every 1st, 6th, 11th, 16th, 21st, and 26th day of each month from May to September the models are initialized with NCEP climate forecast system reanalysis (Saha et al ., 2014).…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…This multiphysics‐based ensemble of simulations with multimodel is hereafter called the MPMME. Deep convection is stronger in the nsas parametrization than in sas, which modifies the precipitation to a greater extent in the former than in the latter in seasonal mean over the Indian region (Kaur et al ., 2022). Moreover, fer and zc differ in the treatment of water or ice, which contributes to the changes in precipitation—see Kaur et al .…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…Moreover, fer and zc differ in the treatment of water or ice, which contributes to the changes in precipitation—see Kaur et al . (2022, tables 1 and 2) for further details on the differences.…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…MPMME is a part of the development of a new extended‐range ensemble forecasting framework under the National Monsoon Mission project (Rao et al ., 2019). The superiority of the MPMME strategy in simulating mean rainfall characteristics compared with the existing operational version for extended‐range prediction is documented in earlier studies (Sahai et al ., 2021; Kaur et al ., 2022). MPMME is under development, and preliminary simulations are generated from unperturbed ICs using a multiphysics approach along with the multimodel framework.…”

Section: Introductionmentioning

confidence: 99%

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Northward propagation of convection over the Indian region in multiphysics multimodel ensemble

Karmakar

Joseph

Sahai

et al. 2022

Quart J Royal Meteoro Soc

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An assessment of a multiphysics multimodel ensemble (MPMME) strategy is provided to simulate the critical aspects of the Indian summer monsoon and its intraseasonal variability. Using various physics combinations of Climate Forecast System (CFS) and its atmospheric component Global Forecast System (GFS), a 15‐year hindcast for May–October is generated. Three different convective parametrizations, simplified Arakawa–Shubert (sas), revised deep‐convection sas (nsas), and revised sas with modified shallow‐convection (nsas_sc) are coupled with two microphysics schemes Zhao and Carr (zc) and Ferrier. Spatiotemporal characteristics of predicted Indian summer monsoon climatology and 20–70‐day periodic intraseasonal oscillations (ISOs) are evaluated using observations. MPMME members reproduce the overall characteristics of the seasonal mean, but they have significant biases over different regions. Pattern correlations reveal that CFS_nsaszc performs best among MPMME in simulating the observed characteristics of rainfall ISOs and providing significant ISO forecast up to pentad 3 lead. A diagnostic based on the vorticity budget equation during strong convective events (SCEs) associated with ISOs is used to understand better the mechanism of northward‐propagating ISOs and the responsible factors that develop a vorticity tendency to the north of convection maxima. The tilting term in the vorticity equation shows northward propagation and leads precipitation maxima by about a week over the Bay of Bengal. Vertical shear of mean zonal winds and meridional gradients of vertical winds are found to be essential in developing vorticity tendency. SCEs are better represented in CFS than in GFS. Notably, along with CFS_nsaszc, two CFS_sas members capture the occurrence of SCEs reasonably well. However, errors in vertical shear of mean zonal winds are remarkably high after pentad 2 lead in CFS_sas and GFS, explaining their relative weakness in simulating ISOs during June–September. This study demonstrates that the MPMME strategy could utilize individual physical schemes' strengths to provide better subseasonal forecasts.

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Section: Representation Of Rainfall Isos In Mpmmesupporting

confidence: 80%