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- Author or Editor: N. Prasad x
- Bulletin of the American Meteorological Society x
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Abstract
A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.
Abstract
A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.
Abstract
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.
Abstract
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.
