The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill metrics and subjective comparisons are carried out for the Indian region during the southwest monsoon season (June-September). Relative biases and skill metrics are documented at all-India and subregional scales. In the gauge-based (land-only) category, Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulties in representing rainfall over orographic regions including the Western Ghats mountains, in Northeast India and the Himalayan foothills. The wide range of skill metrics seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in Northeast India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research.

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The upgraded version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products is available to the user community. In this paper, two successive versions of the TMPA-3B42 research monitoring product, version 6 (V6) and V7, at the daily scale are evaluated over India during the southwest monsoon with gauge-based data for a 13-yr (1998-2010) period. Over typical monsoon rainfall zones, biases are improved by 5%-10% in V7 over the regions of higher rainfall like the west coast, northeastern, and central India. A similar reduced bias is seen in V7 over the rain-shadow region located in southeastern India. In terms of correlation, anomaly correlation, and RMSE, a marginal improvement is seen in V7. Additionally, in all-India summer monsoon rainfall amounts, mean, interannual values, and standard deviation show an overall improvement in V7. Different skill metrics over typical subregions within India show an improvement of the monsoon rainfall representation in V7. Rainfall frequency in different categories also indicates an overall improvement in V7 across all scales and subregions. Over central India regions associated with the monsoon transients, the sign of the bias has changed toward a positive bias. Even if the bias in the frequency of the occurrence of light rain has improved in V7, the values still show a large difference compared to observations. Though both V6 and V7 are able to represent the anomalous dry/wet regions during contrasting monsoon years, V7 shows some improvement in amplitude of those anomalies over V6. In general, V7 has considerably improved over V6 and will continue to be in demand from various sectors of observed rainfall data users.

A long-period (15 yr) simulation of sea surface salinity (SSS) obtained from a hindcast run of an ocean general circulation model (OGCM) forced by the NCEP-NCAR daily reanalysis product is analyzed in the tropical Indian Ocean (TIO). The objective of the study is twofold: assess the capability of the model to provide realistic simulations of SSS and characterize the SSS variability in view of upcoming satellite salinity missions. Model fields are evaluated in terms of mean, standard deviation, and characteristic temporal scales of SSS variability. Results show that the standard deviations range from 0.2 to 1.5 psu, with larger values in regions with strong seasonal transitions of surface currents (south of India) and along the coast in the Bay of Bengal (strong Kelvin-wave-induced currents). Comparison of simulated SSS with collocated SSS measurements from the National Oceanographic Data Center and Argo floats resulted in a high correlation of 0.85 and a root-mean-square error (RMSE) of 0.4 psu. The correlations are quite high (.0.75) up to a depth of 300 m. Daily simulations of SSS compare well with a Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoy in the eastern equatorial Indian Ocean (1.58S, 908E) with an RMSE of 0.3 psu and a correlation better than 0.6. Model SSS compares well with observations at all time scales (intraseasonal, seasonal, and interannual). The decorrelation scales computed from model and buoy SSS suggest that the proposed 10-day sampling of future salinity sensors would be able to resolve much of the salinity variability at time scales longer than intraseasonal. This inference is significant in view of satellite salinity sensors, such as Soil Moisture and Ocean Salinity (SMOS) and Aquarius.

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Abstract. A new regional coupled modelling framework is introduced – the Regional Coupled Suite (RCS). This provides a flexible research capability with which to study the interactions between atmosphere, land, ocean, and wave processes resolved at kilometre scale, and the effect of environmental feedbacks on the evolution and impacts of multi-hazard weather events. A configuration of the RCS focussed on the Indian region, termed RCS-IND1, is introduced. RCS-IND1 includes a regional configuration of the Unified Model (UM) atmosphere, directly coupled to the JULES land surface model, on a grid with horizontal spacing of 4.4 km, enabling convection to be explicitly simulated. These are coupled through OASIS3-MCT libraries to 2.2 km grid NEMO ocean and WAVEWATCH III wave model configurations. To examine a potential approach to reduce computation cost and simplify ocean initialization, the RCS includes an alternative approach to couple the atmosphere to a lower resolution Multi-Column K-Profile Parameterization (KPP) for the ocean. Through development of a flexible modelling framework, a variety of fully and partially coupled experiments can be defined, along with traceable uncoupled simulations and options to use external input forcing in place of missing coupled components. This offers a wide scope to researchers designing sensitivity and case study assessments. Case study results are presented and assessed to demonstrate the application of RCS-IND1 to simulate two tropical cyclone cases which developed in the Bay of Bengal, namely Titli in October 2018 and Fani in April 2019. Results show realistic cyclone simulations, and that coupling can improve the cyclone track and produces more realistic intensification than uncoupled simulations for Titli but prevents sufficient intensification for Fani. Atmosphere-only UM regional simulations omit the influence of frictional heating on the boundary layer to prevent cyclone over-intensification. However, it is shown that this term can improve coupled simulations, enabling a more rigorous treatment of the near-surface energy budget to be represented. For these cases, a 1D mixed layer scheme shows similar first-order SST cooling and feedback on the cyclones to a 3D ocean. Nevertheless, the 3D ocean generally shows stronger localized cooling than the 1D ocean. Coupling with the waves has limited feedback on the atmosphere for these cases. Priorities for future model development are discussed.

The South Asian region is dominated by summer monsoon rainfall which, in many years, is associated with severe floods leading to socio‐economic losses. In India, areas with high population are exposed to heavy rainfall events and their associated risks. Indian monsoon rainfall simulation and prediction at various spatial and temporal scales are challenging scientific tasks for the weather/climate modelling community. Skilful short‐ and medium‐range predictions of rainfall from global numerical models during monsoons are highly desirable for various hydro‐meteorological applications, including flood forecasting. For recent years (e.g. 2000–2016) higher resolution global advanced assimilation‐forecast systems have resulted in improved medium‐range rainfall forecasts. These model rainfall forecasts potentially could be used in hydro‐meteorological applications including water‐related disaster management/planning. In this study, deterministic rainfall forecasts from two state‐of‐the‐art global models (from the National Centers for Environmental Prediction and the Met Office) were evaluated against observed rainfall for the Indian summer monsoon season. Objective skill scores such as mean, bias, correlation co‐efficient, mean absolute error and root‐mean‐square error from Day 1 to Day 5 were examined for the monsoon season over India and at seven major river basins within India for 2013. It was seen that both of the models had useful skill for different regions and basins. For the Indus and Krishna basins, even the Day 5 forecasts were seen to be skilful. In general, the Met Office Day 5 forecasts were more skilful at all seven river basins. Finally, rainfall forecasts from the latest higher resolution Met Office Global Unified Model (implemented in July 2014) were compared with its immediate predecessor to document any further improvement during the monsoon period over India. It was seen that, at the medium range, the newer version of the Met Office model simulated Indian monsoon rainfall more realistically than the previous versions, with much reduced biases in the core monsoon trough region.