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Relevant bibliographies by topics / Monsoon rain prediction

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Academic literature on the topic 'Monsoon rain prediction'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Monsoon rain prediction"

1

Krishnamurti, T. N., S. Pattnaik, and D. V. Bhaskar Rao. "Mesoscale Moisture Initialization for Monsoon and Hurricane Forecasts." Monthly Weather Review 135, no. 7 (2007): 2716–36. http://dx.doi.org/10.1175/mwr3417.1.

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Abstract This paper addresses physical initialization of precipitation rates for a mesoscale numerical weather prediction model. This entails a slight modification of the vertical profile of the humidity variable that provides a close match between the satellite and model-based rain rates. This is based on the premise that the rain rate from a cumulus parameterization scheme such as the Arakawa–Schubert scheme is most sensitive to the vertical profiles of moist static stability. It is possible to adjust the vertical profile of moisture by a small linear perturbation by making it wetter (or dri

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Bhomia, Swati, Neeru Jaiswal, C. M. Kishtawal, and Raj Kumar. "Multimodel Prediction of Monsoon Rain Using Dynamical Model Selection." IEEE Transactions on Geoscience and Remote Sensing 54, no. 5 (2016): 2911–17. http://dx.doi.org/10.1109/tgrs.2015.2507779.

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Haris, Mohd Fauzi, Norita Mohd Norwawi, Mohd Hafez Mohd Isa, and Muhammad Rawi Mohamed Zin. "Cosmogenic Radionuclide-Beryllium 7 (7Be) for Monsoon Rainfall Forecasting in Malaysia: A Systematic Literature Review." Malaysian Journal of Science Health & Technology 9, no. 1 (2023): 46–55. http://dx.doi.org/10.33102/mjosht.v9i1.344.

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Within the region of India, monsoon prediction was reported to use the concentration of 7Be cosmogenic radionuclide that contains crucial information such as atmospheric behaviour change. This review aims to identify and analyse the existing research on the usage of 7Be Cosmogenic radionuclide in assisting weather forecasts, especially rain onset and withdrawal in the monsoon season and the correlations of rainfall and monsoonal variation in Malaysia. A comprehensive search of major scientific databases was conducted, and relevant studies were selected based on inclusion and exclusion criteria

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Sunilkumar, K., T. Narayana Rao, and S. Satheeshkumar. "Assessment of small-scale variability of rainfall and multisatellite precipitation estimates using a meso-rain gauge network measurements from southern peninsular India." Hydrology and Earth System Sciences Discussions 12, no. 10 (2015): 10389–429. http://dx.doi.org/10.5194/hessd-12-10389-2015.

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Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain storms (both in space and time) over a complex hilly terrain in southeast peninsular India. Three years of high-resolution gauge measurements are used to evaluate 3 hourly rainfall and sub-daily variations of four widely used multisatellite precipitation estimates (MPEs). The network consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of ~ 10 km. Morphological features of rainfall in two principal monsoon seasons (southwest m

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Rehman, Asid, Farrukh Chishtie, Waqas Qazi, Sajid Ghuffar, and Imran Fatima. "Evaluation of Three-Hourly TMPA Rainfall Products Using Telemetric Rain Gauge Observations at Lai Nullah Basin in Islamabad, Pakistan." Remote Sensing 10, no. 12 (2018): 2040. http://dx.doi.org/10.3390/rs10122040.

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Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical in

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ANAND, M. SHARAN. "MONSOON SEASON RAIN PREDICTION FOR THE YEAR 2017 FOR TELANGANA, INDIA." Asian Journal of Current Research 2, no. 2 (2017): 75–80. https://doi.org/10.5281/zenodo.1408506.

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In this work the rainfall in Telangana has been calculated based on rainfall data between 1985 to 2016.The calculations have been performed using Time Series method and Fast Fourier Transform (FFT) method. The results of these two methods are averaged for better reliability. The rainfall data is also analyzed in the frequency domain to identify the causes which are significantly contribute to the rainfall.

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Rodrigo, Channa, Sangil Kim, and Il Jung. "Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka." Atmosphere 9, no. 10 (2018): 378. http://dx.doi.org/10.3390/atmos9100378.

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This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon as

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Saiuparad, Sunisa. "Improvement the Largest Lyapunov Exponent for Measurement the Northeast Monsoon Prediction." Applied Mechanics and Materials 879 (March 2018): 217–21. http://dx.doi.org/10.4028/www.scientific.net/amm.879.217.

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Thailand is an agricultural country. So that, the water resources are important. The water management is very important for keep the water used in necessary time. The monsoon is causes a heavy rain. So that, the monsoon prediction by the global climate model is important. The accuracy of the forecasts by the predictability measurement method is very important. In this research, the northeast monsoon prediction in Thailand by the global climate model. The data from The Bjerknes Centre for Climate Research (BCCR), University of Bergen, Norway. The global climate model is Bergen Climate Model (BC

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Chakraborty, Arindam, and T. N. Krishnamurti. "Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate." Monthly Weather Review 137, no. 9 (2009): 2736–57. http://dx.doi.org/10.1175/2009mwr2736.1.

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Abstract This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of R

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Tu, Chuan-Chi, Yi-Leng Chen, Shu-Ya Chen, Ying-Hwa Kuo, and Pay-Liam Lin. "Impacts of Including Rain-Evaporative Cooling in the Initial Conditions on the Prediction of a Coastal Heavy Rainfall Event during TiMREX." Monthly Weather Review 145, no. 1 (2017): 253–77. http://dx.doi.org/10.1175/mwr-d-16-0224.1.

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Abstract A cycling run, which began 36 h before the model forecast, was employed to assimilate special Terrain-influenced Monsoon Rainfall Experiment (TiMREX) soundings, Global Telecommunications System (GTS) data, and Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) global positioning system (GPS) radio occultation (RO) refractivity profiles to improve the model initial conditions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) to study a coastal, heavy rainfall event over southwestern Taiwan during 15–16 June 20

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Books on the topic "Monsoon rain prediction"

1

Kelkar, R. R. Monsoon prediction. BS Publications, 2009.

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2

K, Sahai A., and Indian Institute of Tropical Meteorology., eds. Prospects of prediction of Indian summer monsoon rainfall using global SST anomalies. [Indian Institute of Tropical Meteorology], 2002.

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Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.

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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in i

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Kucharski, Fred, and Muhammad Adnan Abid. Interannual Variability of the Indian Monsoon and Its Link to ENSO. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.615.

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The interannual variability of Indian summer monsoon is probably one of the most intensively studied phenomena in the research area of climate variability. This is because even relatively small variations of about 10% to 20% from the mean rainfall may have dramatic consequences for regional agricultural production. Forecasting such variations months in advance could help agricultural planning substantially. Unfortunately, a perfect forecast of Indian monsoon variations, like any other regional climate variations, is impossible in a long-term prediction (that is, more than 2 weeks or so in adva

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Goswami, B. N., and Soumi Chakravorty. Dynamics of the Indian Summer Monsoon Climate. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.613.

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Lifeline for about one-sixth of the world’s population in the subcontinent, the Indian summer monsoon (ISM) is an integral part of the annual cycle of the winds (reversal of winds with seasons), coupled with a strong annual cycle of precipitation (wet summer and dry winter). For over a century, high socioeconomic impacts of ISM rainfall (ISMR) in the region have driven scientists to attempt to predict the year-to-year variations of ISM rainfall. A remarkably stable phenomenon, making its appearance every year without fail, the ISM climate exhibits a rather small year-to-year variation (the sta

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Book chapters on the topic "Monsoon rain prediction"

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Rao, A. S., and Vijendra K. Boken. "Monitoring and Managing Agricultural Drought in India." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0034.

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Agriculture is the mainstay of more than 70% of India’s more than 1 billion population. Indian agriculture is predominantly rain-fed and depends on the spatial and temporal distribution of rains from southwest (June– September) and northeast (October–December) monsoons. A monsoon refers to seasonal alteration of atmospheric flow. The Indian subcontinent is predominantly characterized by a tropical monsoon climate, where climatic regimes are governed by rainfall rather than by temperature. The southwest monsoon accounts for 70–90% of the annual rainfall. The Technical Committee on Drought Prone

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"Future Monsoon Predictions." In Monsoon Rains, Great Rivers and the Development of Farming Civilisations in Asia. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781139342889.007.

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Byun, Hi-Ryong, and Suk-Young Hong. "Monitoring Agricultural Drought in South Korea." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0041.

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South Korea (hereinafter referred to as Korea) lies in the middle latitudes of the Northern Hemisphere. Until the 1960s, Korea was a typical agrarian country, with agriculture generating roughly half of its gross national product (GNP) and employing more than half of the labor force. Agriculture still plays an important role in the Korean national economy, but it accounts for a relatively much lower share of the GNP (5.3% in 1997) and engages much less of the population (11.0%). The agricultural share of the national economy is declining continuously. Farms in Korea, as in many other Asian cou

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Ogallo, Laban A., and Silvery B. Otengi. "Monitoring Agricultural Drought: The Case of Kenya." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0028.

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Agriculture is the mainstay of Kenya’s economic development and accounts for about 30% of the country’s gross domestic product, 60% of export earnings, and 70% of the labor force. This sector is the largest source of employment (Government of Kenya, 1995). More than 85% of the population survives in one way or the other on agricultural activities (crops and livestock). Agriculture in Kenya is mainly rain-fed, with little irrigation. About 46% of the rural population live below the poverty line, with 70% of them below food poverty line. Like many parts of the tropics, the majority of agricultur

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Reports on the topic "Monsoon rain prediction"

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Shrestha, Sarthak, and Manish Shrestha. Hindu Kush Himalaya (HKH) monsoon outlook 2025. International Centre for Integrated Mountain Development (ICIMOD), 2025. https://doi.org/10.53055/icimod.1091.

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The Hindu Kush Himalaya (HKH) region is highly susceptible to the influence of monsoon, a periodic wind system, especially in the Indian Ocean and southern Asia. The summer monsoon, between June and September, is the major source of precipitation in the region with significant impacts on the hydrology of its rivers, which form the lifeline of nearly two billion people in the region. While a good monsoon is essential for replenishing these river systems, malevolence of water-related disasters such as floods, landslides, storms, heat waves, wildfires, droughts, glacial lake outburst floods (GLOF

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