Lok Sabha Question No. 3302 (UNSTARRED), answered on 2019-07-12
Subject: Extreme Weather Phenomena
Ministry: EARTH SCIENCES
Asked by: Imtiaz Jaleel Syed, Asaduddin Owaisi

## GOVERNMENT OF INDIA

## MINISTRY OF EARTH SCIENCES LOK SABHA

## UNSTARRED QUESTION No. 3302

## TO BE ANSWERED ON FRIDAY, JULY 12, 2019

## EXTREME WEATHER PHENOMENA

## 3302. SHRI SYED IMTIAZ JALEEL:

## SHRI ASADUDDIN OWAISI:

Will the Minister of EARTH SCIENCES be pleased to state:

- (a) whether  at present India's capacity to predict  extreme  weather phenomena is restricted to the district level which elude the benefit to farmers and citizens, if so, the details thereof;
- (b) whether Indian Meteorological Department (IMD) is going to extent its high tech forecast facilities down  to  block  and  village level by extendingits SMS alert system to 21 million farmers, if so, the details thereof;
- (c) whether the Government proposes to commission Mihir a high performance computer for weather forecast, if so, the details thereof;
- the  time  by  which  its  benefits  are  likely  to  be  derived  to  give  boost
- (d) to farmers and citizens; and
- (e) the  other  steps  taken/being  taken  by  the  Government  for  accurate weather forecast?

## ANSWER

## MINISTER FOR MINISTRY OF SCIENCE AND TECHNOLOGY AND

## MINISTRY OF EARTH SCIENCES (DR. HARSH VARDHAN)

- (a)-(d) India Meteorological Department (IMD) is planning to introduce experimental block level forecasts in some districts and their dissemination of advisories to farmers through SMS.  Efforts for Pilot mode initiative for sub-district scale (block level) forecast generation and evaluation are underway.

Under Gramin Krishi Mausam Sewa (GKMS) scheme, District AgroMet Units (DAMUs) are being established at Krishi Vigyan Kendras (KVKs) in collaboration with Indian Council of Agricultural Research (ICAR) to implement block level Agromet Advisory Services (AAS). Experimental block  level  forecast  and  advisories  in  50  blocks  of  the  country  are already  made  functional.  The  GKMS  of  IMD  has  been  successful  in providing the crop specific advisories to the farmers through different print/visual/Radio/  IT  based  media  including  short  message  service (SMS)  and  Interactive  Voice  Response  Service  (IVRS)  facilitating  for appropriate field level actions.

- (e)

Block  level  weather  forecast  and  AgroMet  Advisories  will  aid  the farmers in taking decision on day-to-day agricultural operations, based on  the  crop  pattern  at  block  level.  AAS  rendered  by  IMD  is  a  step towards weather-based crop and livestock management strategies and operations dedicated to enhancing crop production. It is also expected to reduce crop damage and loss due to extreme weather events. At  present,  42  million  farmers  in  the  country  receive  the  Agromet Advisories through SMS directly. Mihir and Pratyush High Performance Computer clusters, have already been  commissioned  for  initiating  experimental  block  level  advisories for farmers. Under  the  National  Monsoon  Mission,  Ministry  of  Earth  Sciences (MoES)  has  implemented  two  state  of  the  art  dynamical  prediction systems for short, medium & extended range forecasts and seasonal forecasts. All these initiatives have helped to improve the accuracy of forecast.  An  improved  suite  of  prediction  models  has  already  been implemented  operationally  at  IMD  for  enhanced  short  range  forecast through assimilation of all available Indian and global satellite data in real time. Since  December  2016,  IMD  used Global Forecast System  (GFS) operationally  to  generate  forecast  at  12  Km  horizontal  resolution  in short  to  medium  range  upto  10  days.  GFS  assimilates  conventional data as well data from satellite & weather radars for better predictions. Additionally  a  12  Km  grid  scale  state  of  the  art  Global  Ensemble Predictions system was commissioned on 1 st  June 2018 for generating operational  forecast  for  10  days.  This  system  is  in  the  process  of further improvement for better accuracy of forecast.

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