भारतीय उष्णदेशीय मौसम विज्ञान संस्थान
Indian Institute of Tropical Meteorology
पृथ्वी विज्ञान मंत्रालय, भारत सरकार का एक स्वायत्त संस्थान An Autonomous Institute of the Ministry of Earth Sciences, Govt. of India
Seasonal Prediction
Objectives
To develop an Indian model based on CFS coupled model for seasonal prediction of monsoon over Indian region. (It is a part of Monsoon mission.)
To implement Assimilation Modules at IITM and to perform experimental assimilation of ocean and atmospheric data.
To carry out research for better understanding of monsoon coupled ocean-land-atmosphere interactions using observational datasets and diagnostics of coupled ocean – atmosphere model.
To improve prediction skill for both summer (SW) and winter (NE) monsoon over Indian region by improving the parameterization schemes of the model and replacing different modules in the system.
To transfer the model to IMD for operational forecast of Indian monsoon.
To develop Multi-Model Ensemble (MME) system for monsoon prediction.
Implementation of coupled data assimilation to seasonal prediction system.
Develop application for sectors like agriculture, hydrology, dam management, wind energy using seasonal prediction model
About Us
The seasonal prediction of the Indian summer monsoon rainfall (ISMR) is very important for India, especially for planning strategies towards management of agricultural production and water resources. The seasonal prediction of the monsoon by dynamical models is based on the fact that the slowly varying boundary conditions like sea surface temperature (SST), soil moisture, snow cover etc. exert significant influence on atmospheric development on seasonal time-scales in the tropics. Although the seasonal mean monsoon seems to be potentially predictable, atmospheric GCM simulations have not shown enough skill in capturing the inter-annual variations in the monsoon rainfall. Indian Summer Monsoon has limited potential predictability. It has also been recognized that ocean-atmosphere coupling is crucial in determining the potential predictability of the monsoon. Therefore, a coupled ocean-atmosphere climate model is required for predicting the monsoon. It is essential to develop and improve a system of fully coupled ocean-atmosphere-land modelling system for dynamical prediction of the seasonal mean monsoon rainfall. IITM is developing such a system and it will be transferred to the India Meteorological Department (IMD). This model is useful for giving lot of spin-off in science, e.g., one should be able to study the role of air-sea interactions on monsoon variability and predictability in more details.
Recent studies have demonstrated the possibility of achieving improved skills in simulating the seasonal mean monsoon rainfall by using ocean-atmosphere coupled models. This improvement appears to result from more accurate representation of the coupled interactions between the Indian monsoon and the tropical oceans. During 11th and 12th Five Year Plan periods, IITM scientists have setup an ocean-atmosphere coupled model on its IBM P6 575 (Prithvi) & Aaditya High Performance Computing (HPC) system and made long period free runs as well as hindcast (retrospective) experiments to test the model with set of initial conditions (e.g., with ensembles of atmospheric and oceanic initial conditions). The model outputs have been analyzed and its performance for simulating Indian Summer Monsoon Rainfall(ISMR) was examined. Certain biases in model simulations have been identified and efforts are being made to reduce these model biases. In addition to the research efforts on the coupled model, IITM has provided, for the first time in India, reliable experimental coupled dynamical monsoon prediction to IMD for further dissemination to general public. Certain modifications (e.g., better physical parameterizations and better representation of air-sea interaction processes, higher resolution) has been incorporated for making this model better suitable for our region, leading to enhanced model skill of simulating ISMR. At present the model using for seasonal prediction (CFSv2) at a horizontal resolution of 38km for atmosphere is the one of the highest resolution in the world and is able to achieve a better skill for ISMR. The model is set up under “monsoon mission” program of Ministry of Earth sciences and is handed over to IMD for operational use by 2017. In the succeeding phase of monsoon mission, the group is working further on model development and use these model improvements for developing application for sectors like agriculture, hydrology, wind energy forecasts etc which has crucial role in the life and economy of the people.
Project Details
Developmental Activities:
· Seasonal prediction and experiments using fully coupled General Circulation Model: The resolution of the CFSv2 model has been increased from original T126 spectral resolution (equivalent to about 110 km resolution) to T382 spectral resolution (equivalent to about 38 km horizontal resolution). This is first time in the world a seasonal prediction system was run at this high resolution globally. Retrospective forecast (hindcast) experiments have been carried out using the coupled model CFSv2 with this higher spectral resolution of T382 from 1981-2018, using various initial conditions (of different months) with 2 ensembles (using 00 and 12UTC data). After acquiring the new HPC, model hindcast runs are completed with initial conditions from different months extending throughout the year. The IITM seasonal prediction system with high resolution and model physics improvements demonstrated that seasonal prediction of Indian monsoon rainfall with useful skill (> 0.6) is realizable. Using this model, India meteorological department (IMD) predicted for the first time the deficit monsoon (monsoon of year 2015, 14% lower than long term mean) successfully at a lead time of 3 month (Feb IC) with a large degree of spatial agreement when all other world leading climate centers were suggesting that it would be near normal monsoon during that year.
Hindcast skill improvement in Climate Forecast System (CFSv2) using modified cloud scheme[Samir Pokhrel, Anupam Hazra, Hemantkumar S. Chaudhari, Subodh K. Saha, Febin Paulose, Sujith Krishna, Phani Murli Krishna, Suryachandra A. Rao Int. j. Climatol., DOI: 10.1002/joc.5478]
A modified cloud scheme is implemented in the present version of operational CFSv2. The modification includes major changes in terms of the different cumulus parameterization scheme, modified cloud microphysics scheme and the variable critical relative humidity. Two sets of CFSv2 retrospective forecast experiments are performed to check the model’s fidelity for operational forecast usage for the prediction of Indian summer monsoon rainfall (ISMR). The first experiment (Exp1) is identical to the present operational mode of the model. The second experiment (Exp2) includes the modified cloud scheme. All the individual changes have already shown enhancement in the seasonal viability of the model in the free-run mode in previous studies. This study has carried out exclusive hindcast experiments by combining the above mentioned major changes. There is a marked improvement in the spatial distribution of the precipitation and the amplitude of the annual cycle of ISMR. The underestimation of the peak of the annual cycle of ISMR in Exp1 is enhanced by 23% in Exp2. Because of better simulations of clouds and tropospheric temperature gradient, the point of maximum precipitation has migrated northwards from equator (Exp1) to 20 °N (Exp2) as seen in Fig-1. These improvements also impress upon all the other aspect of the ocean–atmosphere coupled interaction, namely planetary-scale Hadley circulation, air–sea interactions and most of the facets of monsoon teleconnections. The skill of extended Indian monsoon rainfall region (65 °E–95 °E, 5 °N –35 °N) has increased from 0.50 in Exp1 to 0.67 in Exp2 and the same holds true for other regions as well. The skill of Niño3.4 index enhances from 0.58 in Exp1 to 0.67 in Exp2. The dynamical wind shear based monsoon performance indices also show the surge in the skill score.
Fig 1. Latitude–time spatial annual cycle for the meridional average over the Indian monsoon region (70 °E –90 °E) overlaid with the point of maximum precipitation (black curve) from GPCP, Exp1 and Exp2.
Structure, characteristics, and simulation of monsoon low‐pressure systems in CFSv2 coupled model.[ Ankur Srivastava, Suryachandra A. Rao, D. Nagarjuna Rao, Gibies George, Maheswar Pradhan, JGR Oceans, https://doi.org/10.1002/2016JC012322]
Fidelity of the Climate Forecast System version 2 (CFSv2) at simulating the LPS and their characteristics was evaluated using a feature tracking algorithm. The model is able to reproduce the clustering of LPS by monsoon intraseasonal oscillations and the associated precipitation over eastern-central India. It is found that mean biases in circulation and moisture stem from cold sea surface temperature (SST) bias in the model which results in weak LPS linked rainfall events over central India. Two sensitivity experiments were carried out to study the effect of coupled dynamics of tropical basins on LPS. Suppression of active dynamics of the tropical Indian Ocean in CFSv2 causes a reduction in cold SST bias and enhanced cyclogenesis in the northern Bay of Bengal. The reduced low-level anticyclonic bias and enhanced moisture availability result in a better simulation of LPS structure, and associated precipitation over CI. Suppression of active ocean dynamics in tropical Pacific Ocean causes a perennial El-Nino type bias which restricts LPS propagation over the Indian landmass, possibly due to time mean subsidence induced by remote El-Nino forcing. Sensitivity experiments indicate the need for improvements in the representation of tropical Indian Ocean coupled dynamics as well as convective parameterization schemes in the model for subsequent improvements in the simulation of ISM at various time scales.
Figure. The tracks of LPS during June through September for the period (1982–2009) in (a–c) JRA55; (d–f) CTL run; (i–k) ISLAB run, and (l–n) PSLAB run. The black dot represents the location of genesis and the curves indicate their propagation. MISI>1 implies tracks during monsoon active phase, and MISI<-1 implies tracks during monsoon break phase. Shading indicates the JJAS mean SSTs (°C) in (Figures a, d, i, and l). The shading in Figures b, e, j, and m and c, f, k, and n represent the composite SST anomaly (°C) during active and break phase, respectively.
Dutta U., Hazra A., Chaudhari H.S., Saha Subodh K., Samir Pokhrel, Verma U., Unraveling the global teleconnections of Indian summer monsoon clouds: expedition from CMIP5 to CMIP6, Global and Planetary Change, 215: 103873, August 2022, DOI:10.1016/j.gloplacha.2022.103873, 1-12 (Impact Factor 5.114)
Gade S.V., Sreenivas P., Rao Suryachandra A., Srivastava Ankur, Pradhan M., Impact of the Ensemble Kalman Filter based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall, Geophysical Research Letters, 49, August 2022, DOI:10.1029/2021GL097184, 1-11 (Impact Factor 4.720)
Gayatri V.K., Mohan G.M., Hazra A., Pawar S.D., Pokhrel S., Chaudhari H.S., Konwar M., Saha Subodh K., Mallick C., Das Subrata K., Deshpande S.M., Ghude S.D., Domkawale M., Rao Suryachandra A., Nanjundiah R.S., Rajeevan M., Evaluation and Usefulness of Lightning Forecasts Made with Lightning Parameterization Schemes Coupled with the WRF Model, Weather and Forecasting, 37, May 2022, DOI:10.1175/WAF-D-21-0080.1, 709–726 (Impact Factor 3.025)
Dhakate A.R., Pillai P.A., Seasonal extreme rainfall over Indian monsoon region: a moisture budget analysis to distinguish the role of ENSO and non-ENSO forcing , Theoretical and Applied Climatology, 148, May 2022, DOI:10.1007/s00704-022-04016-5, 1603–1613 (Impact Factor 3.179)
Das Renu S., Rao Suryachandra A., Pillai P.A., Srivastava Ankur, Pradhan M., Dandi R.A., Why coupled general circulation models overestimate the ENSO and Indian Summer Monsoon Rainfall (ISMR) relationship?, Climate Dynamics, Online, April 2022, DOI:10.1007/s00382-022-06253-w, 1-17 (Impact Factor 4.375)
Mallick C., Hazra A., Saha Subodh K., Chaudhari H.S., Pokhrel S., Konwar M., Dutta U., Mohan G.M., Vani K.G., Seasonal predictability of lightning over the global hotspot regions , Geophysical Research Letters, 49: e2021GL096489, January 2022, DOI:10.1029/2021GL096489, 1-11 (Impact Factor 4.720)
Team
Project: Monsoon Mission
Project Directors: Dr. A.K.Sahai, Scientist-G, Dr. Suryachandra Rao, Scientist-G
Deputy Project Director: Dr. P. Mukhopadhyay, Scientist-F
Sub-project: Seasonal Prediction
Dr. A. Suryachandra Rao Scientist-G & Program Director of Monsoon Mission
Climate Variability and Prediction
surya@tropmet.res.in
Phone No - +91-(0)20-25904245 View profile
Dr. Anupam Hazra Scientist-F
Cloud Microphysics
hazra@tropmet.res.in
Phone No - +91-(0)20-25904475 View profile
Shri. S. Mahapatra Scientist-E
Seasonal and Extended Range Prediction
mahap@tropmet.res.in
Phone No - +91-(0)20-25904308 View profile
Dr. Subodh Saha Scientist-F
Land surface process studies
subodh@tropmet.res.in
Phone No - +91-(0)20-25904422 View profile
Dr. Hemant kumar Chaudhari Scientist-F
Climate Modelling
hemantkumar@tropmet.res.in
Phone No - +91-(0)20-25904374 View profile
Dr. Samir Pokhrel Scientist-E
Monsoon coupled ocean-atmosphere dynamics
samir@tropmet.res.in
Phone No - +91-(0)20-25904307(o) View profile
Dr. Prashant Pillai Scientist-E
ENSO-monsoon teleconnection,Diagnostic and modeling studies
prasanth@tropmet.res.in
Phone No - +91-(0)20-25904309 View profile
Dr. Phani Murali Krishna Scientist-E
Atmospheric modelling, HPC Computing
rphani@tropmet.res.in
Phone No - +91-(0)20-25904310 View profile
