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Suryachandra A. Rao
,
B. N. Goswami
,
A. K. Sahai
,
E. N. Rajagopal
,
P. Mukhopadhyay
,
M. Rajeevan
,
S. Nayak
,
L. S. Rathore
,
S. S. C. Shenoi
,
K. J. Ramesh
,
R. S. Nanjundiah
,
M. Ravichandran
,
A. K. Mitra
,
D. S. Pai
,
S. K. R. Bhowmik
,
A. Hazra
,
S. Mahapatra
,
S. K. Saha
,
H. S. Chaudhari
,
S. Joseph
,
P. Sreenivas
,
S. Pokhrel
,
P. A. Pillai
,
R. Chattopadhyay
,
M. Deshpande
,
R. P. M. Krishna
,
Renu S. Das
,
V. S. Prasad
,
S. Abhilash
,
S. Panickal
,
R. Krishnan
,
S. Kumar
,
D. A. Ramu
,
S. S. Reddy
,
A. Arora
,
T. Goswami
,
A. Rai
,
A. Srivastava
,
M. Pradhan
,
S. Tirkey
,
M. Ganai
,
R. Mandal
,
A. Dey
,
S. Sarkar
,
S. Malviya
,
A. Dhakate
,
K. Salunke
, and
Parvinder Maini

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.

Open access
G. S. Bhat
,
S. Gadgil
,
P. V. Hareesh Kumar
,
S. R. Kalsi
,
P. Madhusoodanan
,
V. S. N. Murty
,
C. V. K. Prasada Rao
,
V. Ramesh Babu
,
L. V. G. Rao
,
R. R. Rao
,
M. Ravichandran
,
K. G. Reddy
,
P. Sanjeeva Rao
,
D. Sengupta
,
D. R. Sikka
,
J. Swain
, and
P. N. Vinayachandran

The first observational experiment under the Indian Climate Research Programme, called the Bay of Bengal Monsoon Experiment (BOBMEX), was carried out during July–August 1999. BOBMEX was aimed at measurements of important variables of the atmosphere, ocean, and their interface to gain deeper insight into some of the processes that govern the variability of organized convection over the bay. Simultaneous time series observations were carried out in the northern and southern Bay of Bengal from ships and moored buoys. About 80 scientists from 15 different institutions in India collaborated during BOBMEX to make observations in most-hostile conditions of the raging monsoon. In this paper, the objectives and the design of BOBMEX are described and some initial results presented.

During the BOBMEX field phase there were several active spells of convection over the bay, separated by weak spells. Observation with high-resolution radiosondes, launched for the first time over the northern bay, showed that the magnitudes of the convective available potential energy (CAPE) and the convective inhibition energy were comparable to those for the atmosphere over the west Pacific warm pool. CAPE decreased by 2–3 kg−1 following convection, and recovered in a time period of 1–2 days. The surface wind speed was generally higher than 8 m s−1.

The thermohaline structure as well as its time evolution during the BOBMEX field phase were found to be different in the northern bay than in the southern bay. Over both the regions, the SST decreased during rain events and increased in cloud-free conditions. Over the season as a whole, the upper-layer salinity decreased for the north bay and increased for the south bay. The variation in SST during 1999 was found to be of smaller amplitude than in 1998. Further analysis of the surface fluxes and currents is expected to give insight into the nature of coupling.

Full access
P. A. Francis
,
A. K. Jithin
,
J. B. Effy
,
A. Chatterjee
,
K. Chakraborty
,
A. Paul
,
B. Balaji
,
S. S. C. Shenoi
,
P. Biswamoy
,
A. Mukherjee
,
P. Singh
,
B. Deepsankar
,
S. Siva Reddy
,
P. N. Vinayachandran
,
M. S. Girish Kumar
,
T. V. S. Udaya Bhaskar
,
M. Ravichandran
,
A. S. Unnikrishnan
,
D. Shankar
,
A. Prakash
,
S. G. Aparna
,
R. Harikumar
,
K. Kaviyazhahu
,
K. Suprit
,
R. V. Shesu
,
N. Kiran Kumar
,
N. Srinivasa Rao
,
K. Annapurnaiah
,
R. Venkatesan
,
A. S. Rao
,
E. N. Rajagopal
,
V. S. Prasad
,
M. D. Gupta
,
T. M. Balakrishnan Nair
,
E. P. R. Rao
, and
B. V. Satyanarayana
Full access
P. A. Francis
,
A. K. Jithin
,
J. B. Effy
,
A. Chatterjee
,
K. Chakraborty
,
A. Paul
,
B. Balaji
,
S. S. C. Shenoi
,
P. Biswamoy
,
A. Mukherjee
,
P. Singh
,
B. Deepsankar
,
S. Siva Reddy
,
P. N. Vinayachandran
,
M. S. Girish Kumar
,
T. V. S. Udaya Bhaskar
,
M. Ravichandran
,
A. S. Unnikrishnan
,
D. Shankar
,
A. Prakash
,
S. G. Aparna
,
R. Harikumar
,
K. Kaviyazhahu
,
K. Suprit
,
R. V. Shesu
,
N. Kiran Kumar
,
N. Srinivasa Rao
,
K. Annapurnaiah
,
R. Venkatesan
,
A. S. Rao
,
E. N. Rajagopal
,
V. S. Prasad
,
M. D. Gupta
,
T. M. Balakrishnan Nair
,
E. P. R. Rao
, and
B. V. Satyanarayana

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.

Free access