• To study the monsoon variability over different spatio-temporal scales.
• To study the air-sea interaction associated with monsoon intra-seasonal oscillations.
• To carry out basic research in understanding complex atmospheric/oceanic processes and model parameterization schemes in order to improve the forecast skills using global and regional dynamical models.
• To evaluate the simulation of global climate and monsoon climate by the coupled model and diagnosis of systematic bias.
• To develop a system for prediction of the intraseasonal oscillations of Indian summer monsoon that involve the onset, progression, active/break spells, and its withdrawal, on extended range (2-3 weeks in advance).
• To develop methods for extended range prediction of severe  weather, i.e. heavy rainfall events, heat wave, cold wave, cyclone etc. 
• To develop methods for the extended range prediction beyond southwest monsoon season (pre-monsoon, post-monsoon, winter etc.).
• To develop bias correction techniques for improving the extended range prediction skill.
• To develop downscaling techniques using empirical models to improve the extended range prediction skill.
• To develop dedicated manpower to take up research challenges to improve quality of forecast.
• To generate various application oriented forecast products.

Indian summer monsoon season has periods of active (above normal rainfall) and break (below normal rainfall) epochs, which constitute the monsoon intraseasonal oscillations (MISOs). Prediction of these MISOs, two to three weeks in advance, therefore assumes great importance for agricultural planning (sowing, harvesting, etc.) and water management. Hence, there is a need for development of techniques based on statistical and dynamical methods for the forecasting of active/break periods in ensuing monsoon season in the extended range time scale.

Extended Range Prediction group of IITM has been providing experimental real-time forecast of the active-break spells of Indian Summer Monsoon Rainfall (ISMR) since 2011 up to 20 days lead using an indigenously developed Ensemble Prediction system (EPS) based on the state-of-the-art Climate Forecast System Model Version 2 (CFSv2). The EPS generates a large number of forecasts from different initial conditions so that the expected forecast and also the expected spreads or uncertainties in terms of probability from this forecast can be informed. Forecast is generated  at 5 day interval for the next 20 days. The pentad (5-day mean) prediction skill may be considered as the intraseasonal variability prediction skill and is a more rigorous way of evaluating the model's hindcast skill. Predictions and verification are done over different homogeneous regions of India where ISMR is more or less homogeneous. The selected regions are Central India (CEI), North-East India (NEI), North-West India (NWI), South peninsula (SPI) and a broader region, monsoon core zone of India (MZI).

Initially the forecasts were being generated only during Southwest monsoon season (June-September). Realizing the potential skill and applicability, now the forecasts have been extended to whole year from 2015 onwards. This includes the prediction of Northeast monsoon, Madden-Julian Oscillation and extreme weather like heavy rainfall events, heat waves, cold waves, cyclones etc.

Developmental Activities:

• Development of  CFS based Grand Ensemble Prediction System

  Using single model ensembles (SMEs) based on a suite of different variants of CFSv2 model to increase the spread without relying on very different codes or potentially inferior models, a multi-model ensemble prediction system is described. The SMEs are generated not only by perturbing the initial condition, but also using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, GFS). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10-20 day time-scale. Finally, the multi-model consensus forecast, including ensemble-based uncertainty estimates, is developed. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating single model ensemble (SME) configuration, because increased ensemble spread reduces over-confidence errors (Fig. 1). [Abhilash S., Sahai A.K., Borah N., Joseph S., Chattopadhyay R., Sharmila S., Rajeevan M., Mapes B.E., Kumar A., Improved Spread-Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System, Journal of Applied Meteorology and Climatology, 54, July 2015, DOI:10.1175/JAMC-D-14-0200.1, 1569-1578]

Fig. 1: Bivariate RMSE and spread of MISO indices from CFST126, CFST382, GFSbc and MME.

• Development and evaluation of an objective criterion for the real-time prediction of Indian summer monsoon onset in a coupled model framework

  This study reports an objective criterion for the real-time extended range prediction of Monsoon Onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model – CFSv2. Three indices are defined – one from rainfall measured over Kerala, and others based on the strength and depth of low level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low level wind post MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorised principal weather forecasting agency under Govt. of India, for the period 2001-2014 (Fig. 2). The proposed criterion successfully avoids predicting “bogus” onsets, which is a major challenge in the prediction of MOK. Furthermore, the evolution of various model-predicted large scale and local meteorological parameters corresponding to the predicted MOK date are in good agreement with that of the observation, advocating the robustness of the devised criterion and the suitability of CFSv2 model for MOK prediction. However, it is to be noted that the criterion proposed here can be used only in the dynamical prediction framework, as it necessitates input data on the future evolution of rainfall and low level wind. [Joseph S., Sahai A.K., Abhilash S., Chattopadhyay R., Borah N., Mapes B.E., Rajeevan M., Kumar A., Development and evaluation of an objective criterion for the real-time prediction of Indian Summer Monsoon onset in a coupled model framework, Journal of Climate, 28, August 2015, DOI:10.1175/JCLI-D-14-00842.1, 6234-6248]

Fig. 2: Evolution of rainfall (ROK index) and low level zonal wind (UARAB index) used for formulating the criteria in (a) OBS and (b) MME. For each year, the ROK index is shown as solid bars, UARAB index as thick solid line, and the MOK date is shown as open circle.

Extremes in June rainfall during the Indian summer monsoons of 2013 and 2014: Observational analysis and extended-range prediction

The onset/progression phase of the Indian summer monsoon (ISM) is very crucial for the agricultural sector of the country as it has strong bearing on the sowing of kharif crops, which in turn, affects overall food grain production and hence, food security. The recent ISMs of 2013 and 2014 exhibited quite distinct progression phases. While 2013 had one of the fastest advancement in the last 70 years, 2014 witnessed a comparatively lethargic progression phase. The major difference was felt in the early monsoon month of June, with 2013 (2014) monthly rainfall being +34% (−43%) of its long period average. Observational investigations reveal that, during June 2013, the monsoon trough was very active in its normal position favouring low-level positive vorticity generation and moisture convergence, whereas the absence of monsoon trough during June 2014 facilitated the prevalence of a strong low-level anticyclonic circulation over central India hampering the northward progression of the ISM (Fig. 3). It is found that June 2013 (2014) was associated with (i) stronger (weaker) north-south tropospheric temperature (TT) gradient with positive (negative) TT anomalies over Eurasia and north of 60°N; (ii) negative (positive) SST anomalies over the equatorial Indian Ocean, northwestern Arabian Sea and equatorial eastern Pacific; (iii) stronger (weaker) monsoonal Hadley circulation; and (iv) stronger (weaker) Walker circulation in response to the negative (positive) SST anomalies over the equatorial Pacific. The study also examines the skill of an Ensemble Prediction System (EPS) in predicting the observed contrasting behaviour during June 2013/2014 on extended range (∼15–20 days in advance) in real time. The EPS not only forecasted the observed discrepancy, but also predicted the influential role of the large-scale meteorological conditions prevalent during June 2013 (2014), thus demonstrating the remarkable skill of the EPS in predicting June extremes. [Joseph S., Sahai A. K., Chattopadhyay R., Sharmila S., Abhilash S., Rajeevan M., Mandal R., Dey A., Borah N., Phani R., Extremes in June rainfall during Indian summer monsoons of 2013 and 2014: Observational Analysis and Extended range prediction, Quarterly Journal of Royal Meteorological Society, online, February 2016, DOI:10.1002/qj.2730]

Fig. 3: (a) Rainfall anomaly (shading, mm day⁻¹) and (b) 850 hPa anomalous wind (streamlines) and the KE (shading, m2 s⁻²) of its rotational component, during June 2013. (c) and (d) are same as (a) and (b), but for June 2014.

• Ganesh S.S., Sahai A.K., Abhilash S., Joseph S., Dey A., Mandal R., Chattopadhyay R., Phani R., New approach to improve the track prediction of Tropical cyclones over North Indian Ocean, Geophysical Research Letters, 45, April 2018, DOI:10.1029/2018GL077650, 1-9 (Impact Factor 4.253)
• Shrivastava S., Kar S. C., Sahai A. K., Sharma A.R., Identification of drought occurrences using Ensemble predictions up to 20-Days in advance , Water Resources Management, 32, April 2018, DOI: 10.1007/s11269-018-1921-9, 2113-2130 (Impact Factor 2.848)
• Chattopadhyay N., Rao K.V., Sahai A.K., Balasubramanian R., Pai D.S., Pattanaik D.R., Chandras S.V., Khedikar S., Usability of extended range and seasonal weather forecast in Indian agriculture, Mausam, 69, January 2018, 29-44 (Impact Factor 0.467)

Project: Monsoon Mission
Project Directors: Dr. A.K.Sahai, Scientist-G, Dr. Suryachandra Rao, Scientist-F
Deputy Project Director: Dr. P. Mukhopadhyay, Scientist-E
Sub-Project: Extended Range Prediction

Dr. A.K. Sahai
Scientist-G & Program Director of ERPAS
Monsoon prediction and variability
sahai@tropmet.res.in
Phone No - +91-(0)20-25904520

Dr. Sujata Mandke
Scientist- D
Monsoon variability and Prediction
amin@tropmet.res.in
Phone No - +91-(0)20-25904508
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Dr. (Smt) A.A. Deo
Scientist-D
Application of Ocean modeling, Upper Oceanic Process studies
aad@tropmet.res.in
Phone No - +91-(0)20-25904279
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Dr. Susmitha Joseph
Scientist-E
Interactions between intraseasonal & interannual variabilities of ISM
susmitha@tropmet.res.in
Phone No - +91-(0)20-25904521
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Shri Soumendyu De
Scientist-D
Atmospheric Physics and Modelling
sde@tropmet.res.in
Phone No - +91-(0)20-25904278
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Dr. Rajib Chattopadhyay
Scientist-D
Monsoon Seasonal Prediction
rajib@tropmet.res.in
Phone No - +91-(0)20-25904523
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Shri N.K.Agarwal
Scientist-D
Atmospheric Energetics in Wavenumber/Frequency Domain
nka@tropmet.res.in
Phone No - +91-(0)20-25904276
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Mr. Avijit Dey
Scientist-C
Intra-seasonal variability of Indian summer monsoon
avijit.cat@tropmet.res.in
Phone No - +91-(0)20-25904916
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Shri Raju mandal
Scientist-C
Monsoon variability and predictability
raju.cat@tropmet.res.in
Phone No - +91-(0)20-25904912
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Smt. S S Naik
Scientific Officer Gr. II
snaik@tropmet.res.in
Phone No - +91-(0)20-25904277

Shri. D. W. Ganer
Scientific Officer Gr. II
tsd@tropmet.res.in
Phone No - +91-(0)20-25904275

Associates :

Dr. Phani Murali Krishna
Scientist-D
Atmospheric modelling, HPC Computing
rphani@tropmet.res.in
Phone No - +91-(0)20-25904310
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