A global forecast system model at a horizontal resolution of T1534 (∼12.5 km) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the northwest Indian region. Although the model has a reasonable fidelity in capturing the spatiotemporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

During August 2018 and 2019 the southern state of India, Kerala received unprecedented heavy rainfall which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state of the art global prediction systems, the National Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (ECMWF) integrated forecast system (IFS) and the Unified Model based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF).Satellite, radar and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range, sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the ERA5 (ECMWF Reanalyses version 5) reanalyses and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column integrated precipitable water tendency, however is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019.Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

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Realistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the historical run of 26 models which participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and it is shown that the current state of the art general circulation models (GCMs) still have difficulties in properly simulating the PIC. The pool covers an area greater than 1 million km2 between 3°–13°N and 77°–90°E over the southwest Bay of Bengal (BoB); persisting throughout the ASM and interestingly it is surrounded by the deep convective region. The majority of the models could not simulate cloud variability over the ASM. The performance of National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) version 2.0, which is being used for operational monsoon prediction over the Indian region, is also tested to evaluate its fidelity in capturing PIC. The moist convective process in the default CFSv2 is found to be one of the major sources of uncertainty in its monsoon prediction. In this study, evaluation of the default CFSv2 (CTRL) and suites of modified CFSv2 have been carried out. The default version of CFSv2 has the simplified Arakawa Schubert (SAS) as convection scheme and Zhao and Carr (ZC) as microphysics. In another modification, the SAS is changed to revised simplified Arakawa Schubert (RSAS) keeping the microphysics unchanged. Further, a more physically based cloud scheme (WRF Single Moment 6‐class microphysics—WSM6) is used with SAS and RSAS for comparison of simulation of PIC. Among the CMIP5 models, ACESS‐1‐0, GFDL‐CM3, HadGEM2‐CC, HadGEM2‐ES are able to represent the PIC reasonably well. CFSCR has shown an improved fidelity in comparison to the CTRL, CTRL‐WSM and RSAS and other CMIP5 models. The impact of cloud microphysics in CTRL‐WSM and CFSCR appears to play an important role in the simulation of the PIC.