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- Author or Editor: M. Rajeevan x
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Abstract
Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951–2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future.
Abstract
Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951–2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future.
Abstract
Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.
Abstract
Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.
Abstract
Rain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.
Abstract
Rain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.
