Dynamical Seasonal Monsoon Forecasting at IITM

Transcription

1 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

2 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

3 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.

4 Program Structure Seasonal and Extended Range Prediction Training HPC & LIP Climate Variability & Change Cloud Physics and Dynamics

5 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

6 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!

7 MoES Vision To improve forecasts in the country for Weather on Short and Medium Range Climate, Seasonal Mean monsoon Climate Change, Decadal prediction

8 Implementation Framework IMD Operational Forecasts NCMRWF Short and Medium Range IITM Seasonal and Extended Range INCOIS Ocean Observations Data Assimilation

9 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

10 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

11 Rainfall skill Land points UKMO Depresys UKMO ECMWF CFS V2.0

12 Prediction Skill of ISMR in CFS V Obs. IMD Norm_imd_jjas_anom Norm_Jan_IC CFSv2 CFS v2 Jan IC Correlation= Norm_imd_jjas-anom Norm_imd_jjas-anom Norm_Feb_IC Norm_mar_IC CFS v2 Feb IC Correlation=0.59 CFS v2 Mar IC correlation= Norm_imd_jjas-anom Norm_Apr_IC CFS v2 Apr IC Correlation= Norm_imd_jjas-anom Norm_May_IC CFS v2 May IC correlation=0.36

13 Prediction of SW Monsoon seasonal (JJAS) Rainfall, using Dynamical models S.N. Year Predicted Rainfall ( % of LPMA) Actual Rainfall (% of LPA) Remark 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 % IITM CFS v.2 T382 (February IC) : 100 % +/- 4.5 % IMD (Observed) : 93 % IITM CFS v.2 T382 (February IC) : 104 % +/- 5 % IMD (Observed) : 106 % IITM CFS v.2 T382 (April IC) : 108 % +/- 5 % IITM CFS v.2 T382 (February IC) : 96 % +/- 5 % IMD (Observed) : 88% Good Good Overestimated Overestimated IITM CFS v.2 T382 (February IC) : 91 % +/- 5 % IITM CFS v.2 T382 (April IC) : 86 % +/- 5 % IMD (Observed) : 86% Good

14 Improvements in oceanic precipitation Bias

15 Monsoon Forecast for 2016 CFSv2 T382 JAN IC Forecast FEB IC MAR IC APR IC

16

17

18

19 Ensemble runs (minimum 40 ensemble members) for each Initial Conditions (ICs) Hindcast runs are carried out with 11 ensemble members for each ICs.

20 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).

21 JAN IC

22 FEB IC

23 MAR IC

24 APR IC

25 JAN IC

26 JAN IC c c

27 FEB IC

28 FEB IC c c

29 MAR IC

30 MAR IC

31 APR IC

32 APR IC

33 APR IC

34 FEB IC

35 FEB IC

36 FEB IC

37 FEB IC

38 APR IC

39 JAN IC FMA MAM AMJ MJJ JJA JAS ASO

40 FEB IC

41 FEB IC

42 APR IC

43 APR IC Indian Ocean Hadley circulation (70E-100E averaged ) for JJAS

44 Experimental Extended range prediction INITIAL CONDITION: 15 th June 2016

45

46 Predicted pentad wise rainfall (by MME)

47 Source: IMD (Dr. N. Chattopadhay, DDGM (Agrimet), Agricultural Meteorology Division)

48 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 million farmers through SMS using mobile phone. Source: IMD (Dr. N. Chattopadhay, DDGM, Agricultural Meteorology Division)

49 Source: IMD (Dr. N. Chattopadhay, DDGM, Agricultural Meteorology Division)

50