Dynamical Seasonal Monsoon Forecasting at IITM H. S. Chaudhari, S. K. Saha, A. Hazra, S.Pokhrel, S. A. Rao, A. K. Sahai, R. Krishnan & Seasonal Prediction and Extended Range Prediction Group Indian Institute of Tropical Meteorology, Pune
Mandate and Vision of MoES To provide the country best possible weather forecast (short range ) and climate prediction (long range ) to society. To conduct the R & D required to improve the skill of both weather and climate forecasts and to improve Indian foresting system. To conduct regional climate change research to provide reliable projection of monsoon under climate change
The Vision of IITM To make IITM a Global Centre of Excellence through basic research on all aspects of Tropical Ocean- Atmosphere System required to improve Tropical Weather and Climate Forecasts.
Program Structure Seasonal and Extended Range Prediction Training HPC & LIP Climate Variability & Change Cloud Physics and Dynamics
Focused Science Plan: Missions Basic Res. in Variability & Predictability Coupled ocean-land-atmos. system Science of Regional & Global Climate Change HPC Prediction system for Seasonal mean and Active/Break cycles monsoon Physics and Dynamics of Tropical Clouds
The National Monsoon Mission Objectives To set up a high resolution short and medium range prediction system for monsoon weather and to conduct focused research to improve the present skill. To set up a dynamical seasonal prediction system and to set up a mechanism to enhance the current skill to a useful level!
MoES Vision To improve forecasts in the country for Weather on Short and Medium Range Climate, Seasonal Mean monsoon Climate Change, Decadal prediction
Implementation Framework IMD Operational Forecasts NCMRWF Short and Medium Range IITM Seasonal and Extended Range INCOIS Ocean Observations Data Assimilation
IITM Improving Prediction of Seasonal & Extended Range Monsoon Coupled Model CFS 2.0 It is important that all development work should be done on a specified model Basic Research Model Development & Improvement in Physical Parameterization Data Assimilation
CFSv2 Hybrid vertical coordinate (sigma-pressure) Noah Land Model : 4 soil levels. Improved treatment of snow/frozen soil Sea Ice Model : Fractional ice cover and depth allowed ESMF (3.0) AER RRTM Longwave radiation AER RRTM Shortwave Radiation The atmosphere and ocean models are coupled with no flux adjustment CFS V2 IITM CFSv2 AGCM resolution T126 L64 ( ~ 100 km) T382 L64 (~ 40 km) Ocean Model MOM4 (0.25 o 0.5 o grid spacing with 40 vertical layers) MOM 4 (0.25 o 0.5 o grid spacing with 40 vertical layers) Land Surface Model NOAH 4-layer model NOAH 4-layer model Sea Ice 2-layer Sea-ice model 2-layer Sea-ice model
Rainfall skill Land points UKMO Depresys UKMO ECMWF CFS V2.0
Prediction Skill of ISMR in CFS V2.0 3 2 1 0 Obs. IMD Norm_imd_jjas_anom Norm_Jan_IC CFSv2 CFS v2 Jan IC Correlation=0.37-1 -2-3 2.5 2 1.5 1 0.5 0-0.5-1 -1.5-2 -2.5 3 2 1 0 1982 1984 1982 1983 1984 1985 1986 1988 1990 1992 1994 1996 Norm_imd_jjas-anom 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Norm_imd_jjas-anom 1998 2000 Norm_Feb_IC Norm_mar_IC 2002 2004 2006 2008 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 CFS v2 Feb IC Correlation=0.59 CFS v2 Mar IC correlation=0.33-1 -2-3 2.5 2 1.5 1 0.5 0 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Norm_imd_jjas-anom 1997 1998 1999 Norm_Apr_IC 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 CFS v2 Apr IC Correlation=0.53-0.5-1 -1.5-2 -2.5 2.5 2 1.5 1 0.5 0-0.5-1 -1.5-2 -2.5 1982 1983 1984 1985 1986 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Norm_imd_jjas-anom Norm_May_IC 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 CFS v2 May IC correlation=0.36
Prediction of SW Monsoon seasonal (JJAS) Rainfall, using Dynamical models S.N. Year Predicted Rainfall ( % of LPMA) Actual Rainfall (% of LPA) Remark 1. 2011 CFS v.1 (March IC) : 102 % CFS v.1 (May IC) : 106 % IMD (Observed) : 102 % CFS v.2 (February IC) : 106 % CFS v.2 (May IC) : 117 % 2. 2012 IITM CFS v.2 T382 (February IC) : 100 % +/- 4.5 % IMD (Observed) : 93 % 3. 2013 IITM CFS v.2 T382 (February IC) : 104 % +/- 5 % IMD (Observed) : 106 % IITM CFS v.2 T382 (April IC) : 108 % +/- 5 % 4. 2014 IITM CFS v.2 T382 (February IC) : 96 % +/- 5 % IMD (Observed) : 88% Good Good Overestimated Overestimated 5. 2015 IITM CFS v.2 T382 (February IC) : 91 % +/- 5 % IITM CFS v.2 T382 (April IC) : 86 % +/- 5 % IMD (Observed) : 86% Good
Improvements in oceanic precipitation Bias
Monsoon Forecast for 2016 CFSv2 T382 JAN IC Forecast FEB IC MAR IC APR IC
Ensemble runs (minimum 40 ensemble members) for each Initial Conditions (ICs) Hindcast runs are carried out with 11 ensemble members for each ICs.
ESSO-INCOIS-GODAS SST Anomaly SST anomalies based on INCOIS-GODAS SST. Anomalies are based on 30 years mean OISST (Source: Indian National Centre for Ocean Information Services -GODAS).
JAN IC
FEB IC
MAR IC
APR IC
JAN IC
JAN IC c c
FEB IC
FEB IC c c
MAR IC
MAR IC
APR IC
APR IC
APR IC
FEB IC
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JAN IC FMA MAM AMJ MJJ JJA JAS ASO
FEB IC
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APR IC Indian Ocean Hadley circulation (70E-100E averaged ) for JJAS
Experimental Extended range prediction INITIAL CONDITION: 15 th June 2016
Predicted pentad wise rainfall (by MME)
Source: IMD (Dr. N. Chattopadhay, DDGM (Agrimet), Agricultural Meteorology Division)
IMD has contributed the various activities for the Indian Agriculture during southwest monsoon, 2015 like issuance of Agromet Advisories at district level in collaboration with Agromet Field Units for different agricultural operations especially contingent crop planning based on extended range weather forecast in collaboration with Indian Institute of Tropical Meteorology (IITM), Pune and Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad. In addition to that IMD was able to disseminate the agromet Advisories to 11.50 million farmers through SMS using mobile phone. Source: IMD (Dr. N. Chattopadhay, DDGM, Agricultural Meteorology Division)
Source: IMD (Dr. N. Chattopadhay, DDGM, Agricultural Meteorology Division)