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.
This study utilizes cluster analysis to produce sets of weather patterns for the Indian subcontinent. These patterns have been developed with future applications in mind; specifically relating to the occurrence of high-impact weather and meteorologically induced hazards such as landslides. The weather patterns are also suited for use within probabilistic medium-to long-range weather pattern forecasting tools driven by ensemble prediction systems. A total of 192 sets of weather patterns have been generated by varying the parameter which is clustered, the spatial domain and the number of weather patterns. Non-hierarchical k-means clustering was applied to daily 1200 UTC ERA-Interim reanalysis data between 1979 and 2016 using pressure at mean sea level (PMSL) and u-and v-component winds at 10-m, 925-hPa and 850-hPa. The resultant weather pattern sets (clusters) were analysed for their ability to represent the main climatic precipitation patterns over India using the explained variation score. Weather patterns generated using 850-hPa winds are among the most representative, with 30 patterns being enough to represent variability within different phases of the Indian climate. For example, several weather pattern variants are evident within the active monsoon, break monsoon and retreating monsoon. There are also several variants of weather patterns susceptible to western disturbances. These weather pattern variants are useful when it comes to identifying periods most susceptible to high-impact weather within a large-scale regime, such as identifying the most flood prone periods within the active monsoon. They hence have potentially many forecasting applications.
In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.
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