Weather information for

sustainable agriculture

by

L. S. Rathore Director General of Meteorology

Talk Outline

Meteorological Information

for Agriculture

Observe & Predict Weather

Agromet advisory services

Future needs and strategies

The Challenge of Sustainable Agriculture:

ENV ECON

SOCIAL

Optimize natural resource management

Protect the environment and biodiversity

Produce sufficient, affordable, safe, high quality

food, feed and fiber for all without compromising

health of the natural resources

Maintain & enhance economic viability of farming

Produce food economically

Contribute to well-being of local communities

Protect health of

consumers & farm workers

Sustainable Agriculture Management

includes

Soil management

Crop management

Livestock management

Water management

Integrated Pest Management (IPM)

All these components of

Sustainable Agriculture require

meteorological information

Use of Met. information in Agriculture

Increase crop yields through weather based crop management

Lower production costs (Input management)

Environmental protection

Better product quality

Land management: diversification, soil conservation

Water management

Combat unfavourable consequences of weather

Cultivar selection adapted to local agro-climatic conditions

Live-stock food, shelter, behaviour and health

Integrated pest management

Soil and water conservation

Watershed management

Agroforestry systems management

Intercultural operation management, etc. etc.

What IMD Does Produce Weather and Climate Forecasts and

Warnings - To Protect Life and Property

- To Enhance the National Economy including Agriculture

Also provide Data and Products: This problem has three parts . . .

Observe & Analyze: Current state of atmosphere

Forecast: What’s going to happen?

Communicate: Forecast/warning dissemination

Meteorological information is becoming more important to Agriculture due to climate change & increase climatic variability

Weather Observation Network • Conventional

• Non-conventional

Weather observation & monitoring services

PILLOT

BALOON

NETWORK

RS/RW NETWORK

BuoysTWORK AWS ARG

RADAR

55 DWRs by 2018

Type of Observatory

Installed Proposed

AWS 675 400

ARG 1350 2000

DWR 13 42

Observational network

CTT QPE

AMV

OLR SST

11

INSAT-3D

INDIA’s Advanced Weather Satellite

India's advanced weather satellite INSAT-3D

launched in the early hours of July 26, 2013 from

Kourou, French Guiana, and has successfully

been placed in Geosynchronous orbit.

It carries four payloads

Imager (Six Channels)

Sounder (Nineteen Channels)

Data Relay Transponder(DRT)

Satellite Aided Search and Rescue (SAS & R)

Imager: The Imager will generate images of the earth disk from geostationary

altitude of 36,000 km every 26 minutes and provide information on

various parameters, namely, outgoing long-wave radiation,

quantitative precipitation estimation, sea surface temperature, snow

cover, cloud motion winds, etc

Atmospheric Sounder: It will provide information on the vertical profiles

of temperature, humidity and integrated ozone. These profiles will be

available for a selected region over Indian landmass every one hr and

for the entire Indian Ocean Region every six hrs.

Data Relay Transponder: It will be used for receiving meteorological,

hydrological and oceanographic data from remote, uninhabited

locations over the coverage area from Data Collection Platforms

(DCPs) like Automatic Weather Station (AWS), Automatic Rain Gauge

(ARG) and Agro Met Stations (AMS).

Satellite Aided Search and Rescue Transponder : The major users of Satellite Aided Search and Rescue service in India

are the Indian Coast Guard, Airports Authority of India (AAI),

Directorate General of Shipping, Defence Services and fishermen.

INSAT-3D

IMPROVEMENTS IN INSAT -3D OVER

KALPANA-1 AND INSAT-3A

• Imaging in Middle Infrared band to provide night time

images of low clouds and fog.

• Imaging in two Thermal Infrared bands for estimation

of Sea Surface Temperature (SST) with better

accuracy.

• Higher Spatial Resolution in the Visible and Thermal

Infrared band.

• Sounder derived profiles include temperature at 40

vertical pressure levels from surface to about 70 km

and water vapor in 21 levels from surface to around

15 km above along with following derived Products.

04/01/2014 15

Geophysical Parameters from INSAT -3D satellite No. Parameters No. Parameters

1. Outgoing Long wave Radiation

(OLR)

9. Water Vapor Wind (WVW)

2. Quantitative Precipitation

Estimation ( QPE)

10. Upper Tropospheric Humidity

(UTH)

3. Sea Surface Temperature (SST) 11. Temperature, Humidity profile &

Total ozone

4. Snow Cover 12. Stability indices from sounder

data

5. Fire 13. Normalized Difference snow

Index

6. Smoke 14. Flash Flood Analyzer

7. Aerosol 15. FOG (day and night)

8. Cloud Motion Vector (CMV) 16. Tropical Cyclone-intensity

/position

Daily average data statistics in GFS

T574L64 for August-2012

Parameter P-surface uv t q Radiance

Data

Received 29339 408147 126947 51958 2982385

Data

Assimilated 25610 292369 101473 15367 744426

Data

Assimilated

(%)

87% 71% 79% 30% 29%

IMD has long time series of various climate data in its archive. Data rescue and data services are mainly provided by National Data Center (NDC) at Pune.

NCC generates, many climate data products for smaller spatial and temporal scales for the user community.

These data products include followings:

Daily gridded (1o X 1o) rainfall and temperature data

Daily gridded(0.5o X 0.5o and 0.250x 0.250 [long series]) rainfall data

District wise normal for various surface parameters, marine climate summaries for Indian Ocean region etc

Climate data products

Weather forecasting in India

In recent times, weather forecasts & services have improved significantly due to …

enhanced understanding of Earth's atmosphere,

better observing systems better communication systems better analytical techniques improved numerical models faster computers better dissemination systems

Surface Observations- Manned/ Automatic

Upper-Air Observations

Satellite Observations

Aircraft Reports

Ship Reports

Ocean Buoys data

Global Data

Data-flow on real-

time mode

Regional Telecom

Hub/ Regional

AMSS

IMD’s Observational & Forecasting System

Data ported to NCMRWF

IMD, Delhi

Northern Hemispheric Analysis Centre

Generation of Daily Forecasts/warnings

IMD, Pune

Weather Central

Southern Hemispheric Analysis Centre

Regional Forecasts

IMD, Chennai

IMD, Guwahati

IMD, Kolkata

IMD, Mumbai

IMD, Nagpur

IMD, Delhi

Area Cyclone Warning Centres

Specialized products

State Level

Met. Centres/ Offices/ Airport

Met. Offices

Radar

Network

Distric

t Mete

oro

logic

al In

form

atio

n

Cente

rs

IMD, Delhi

LAFS NWP forecasts

Meteorological Applications

Today's improved forecasts support

Enhance economic competitiveness while reducing risk

Providing vital warnings about severe and hazardous weather to protect lives and mitigate property loss

Supporting critical sectors such as agriculture, aviation, marine, hydrology, tourism etc.

Operational Weather Forecasts • Weather Bulletins: All India, Regional, State level

• District Level Forecasts

• Cyclone prediction

• Aviation Forecast

• Hydromet Forecast

• Marine Forecast

• City Forecast

• Tourism Forecast

• High Way Forecast

• Forecast for Antarctica

• Air Quality Forecast

• Now-cast: Hourly venue specific forecast

• Extended Range Forecast

• Seasonal Forecast

Operational NWP System

Medium Range Forecast

> GFS T-574/L64 with GDAS ( 00 & 12 UTC)

> MME based District Level Forecasts

Short Range Forecast > WRF (ARW) VAR at 27 km and 9 km

> HWRF

> MME based cyclone track prediction

> Polar WRF for Antarctica

Nowcast and Very Short Range Forecast

> Hourly venue specific forecast- WRF (3 km)

> ARPS with assimilation of DWR

> Nowcast System with assimilation of DWR

CC: 7 DAY CUM RAIN: MONSOON 2011

0.3

0.4

0.5

0.6

0.7

0.8

0.9

CENTRAL

INDIA

NW INDIA NE INDIA EAST

INDIA

SP INDIA WEST

COAST

INDIA

ALLINDIA

CC

T382

T574

Domain mean

correlation

coefficient (CC) of

weekly (seven

days) cumulative

observed and

forecasts for day-1

to day-5 of rainfall

for GFS T382 and

T574 over different

homogeneous

regions of India

during monsoon

2011

Domain mean correlation coefficient

DISTRICT LEVEL FORECAST

Generation of district level weather

forecast (DLWF)

The same was

started since June

2008

Parameters:

Rainfall

Max and Min temperature

Total cloud cover

Surface Relative humidity

Surface Wind

Forecast Track

Observed Track

Date and Time in Track

are given in UTC

IST = UTC + 0530 hrs

Phailin: Forecast & Observed Track

PHAILIN: • Category: Very Severe Cyclonic

Storm

• Crossed India coast on 12th

Oct. 2013 near Gopalpur

• Winds: 110 knots (200-210

kmph) gusting up to 230 kmph

• Estimated central pressure was

940 hPa

• Pressure drop of 66 hPa at the

centre.

• Intensity scale-T6.0

PHAILIN : RAPID

INTENSIFICATION

• There was rapid

intensification of the

system from 10th Oct.

morning to 11th

morning leading to an

increase in wind speed

from 45 knots to 115

knots (70 knots in 24

hrs)

• Satellite imagery

depicted a well-defined

eye with a diameter of

15 km surrounded by

wall cloud region of

about 100 km

08/09UTC 10/00UTC

09/00UTC 11/00UTC

900

920

940

960

980

1000

1020

03 12 00 06 12 18 00 06 12 18 00 06 12 18 00 06 12 18 00 06 12 00 06

08-10-2013 09-10-2013 10-10-2013 11-10-2013 12-10-2013 13-10-2013 14-10-2013

Estimated Central Pressure (hPa)

0

20

40

60

80

100

120

140

03 12 00 06 12 18 00 06 12 18 00 06 12 18 00 06 12 18 00 06 12 00 06

08-10-2013 09-10-2013 10-10-2013 11-10-2013 12-10-2013 13-10-2013 14-10-2013

Maximum Sustained Wind (knots)

920

940

960

980

1000

1020

10

/0

10

/3

10

/6

10

/9

10

/12

10

/15

10

/18

10

/21

11/0

11/3

11/6

11/9

11/1

2

11/1

5

11/1

8

11/2

1

12

/0

12

/3

12

/6

12

/9

12

/12

12

/15

12

/18

12

/21

M

S

L

P

TIME UTC

GOPALPUR MSLP

Observation at Gopalpur suggests maximum wind

speed of 115 knots or 215 kmph with pressure drop

of 66 hPa at centre

0 10 20 30 40 50 60 70 80 90

100 110 120

10

/0

10

/3

10

/6

10

/9

10

/12

10

/15

10

/18

10

/21

11

/0

11

/3

11

/6

11

/9

11

/12

11

/15

11

/18

11

/21

12

/0

12

/3

12

/6

12

/9

12

/12

12

/15

12

/18

12

/21

K

N

O

T

S

TIME UTC

GOPALPUR WIND

FORECAST ON CYCLONE PHAILIN Genesis, Track, Intensity and landfall

3rd October : Forecast of Formation of low pressure area on low pressure

over North Andaman Sea around 6th October.

7 October : Forecast for intensification of low over Andaman Sea

8 October : Depression formed and regular special bulletin commenced.

Forecast for further intensification into a cyclonic storm by 9th October and

move towards North Andhra Pradesh and Odisha Coast during next 72 hrs.

9th October (morning): Forecast for Cyclonic Storm to intensify further to

a very severe cyclonic storm with a wind speed of 175-185 kmph gusting to

200 kmph at the time of landfall between Kalingapatnam and Paradip by

night of 12th October . The forecast track indicated the landfall near

Gopalpur

11th October 2013 : Forecast wind intensity was increased to 210-220 kmph

at the time of landfall

Forecast Track

Observed Track

Date and Time in Track

are given in UTC

IST = UTC + 0530 hrs

Forecast Performance Verification

Cyclogenesis

Prediction

Track

Prediction

Intensity

Prediction

Rapid

Intensification

Decay after

Landfall

Decay Model

RI-Index

SCIP Model

Multimodel

Ensemble(MME)

Genesis Potential

Parameter(GPP)

STEP-I

STEP-II

STEP-III

STEP-IV

STEP-V

NWP based Objective Cyclone Prediction System

0

50

100

150

200

250

300

350

400

450

500

550

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 12 hr forecast error(km) 24 hr forecast error (km) 36 hr forecast error (km) 48 hr forecast error (km) 60 hr forecast error (km) 72 hr forecast error (km) Linear (12 hr forecast error(km)) Linear (24 hr forecast error (km))

Track forecast error (km) Average during last five (2008-12)

24 hr- 133 km,

48 hr-254 km

72 hr- 376 km

Trend in improvement in track

forecast (km/Year) during 2003-12

12 hr- 5.1 km per year

24 hr- 7.2 km per year

0

100

200

300

400

500

600

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

12 hr 24 hr 36 hr 48 hr 60 hr 72 hr

0

5

10

15

20

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

12 hr 24 hr 36 hr 48 hr 60 hr

Mean landfall point forecast error (km)

Mean landfall time forecast error (hr)

Average (2008-12)

24 hr- 91 km,

48 hr- 96 km

72 hr- 135 km

Average (2008-12)

24 hr- 5.5 hrs

48 hr- 7.3 hrs

72 hr- 1.2 hrs

Trend in

improveme

nt in

Landfall

forecast

during

2003-12

Landfall

point

12 hr- 16

km per year

24 hr- 33

km per year

Landfall

Time

12 hr- 0.5hr

per year

24 hr-0.0hr

per year

Average (2008-12)

Skill compared to persistence method

AE

12 hr- 34%,

24 hr- 40%,

-40

-20

0

20

40

60

80

2005 2006 2007 2008 2009 2010 2011 2012

Skill (%) of maximum sustained surface wind forecast based on absolute error compared to

persistence forecast error

12 hr 24 hr Linear (12 hr) Linear (24 hr)

Trend in improvement in skill

during 2003-12 (decrease in AE

per year)

12 hr- 3.4%,

24 hr- 8.4%,

TRIGGERING FOR WATER MANAGEMENT

The full reservoir level of Hirakud Dam is 630 ft.

DGM IMD ADVISED ON 9TH October to Hirakud Dam authorities

through MHA to release water in view of potential threat due to

cyclone

Due to release water level came down to 621 ft. on 11th October.

As the released water was increasing over the plain area of

coastal Odisha, DGM, IMD further advised to stop releasing water

on 11th October

As a result the reservoir was well managed

Due to extremely heavy rain over Mahandi catchment the

reservoir level increased from 621ft on 11th to 629ft after the

cyclone

As it was within the full capacity, it did not worsen the flood

situation in Mahanadi

ACCURACY ACHIEVED MAINLY BECAUSE:

Science and Technological Upgradation

• Improvement in observational network (Ocean, land and atmosphere)

and quality of data (DWR, Ship, Buoys, AWS, High wind speed

recorders etc.)

• Satellite images and derived products (Kalpana, INSAT 3A, Oceansat-

II) other international satellites

• Fast communication and data Exchange system

• Superior computational capabilities, super computer facilities

• Improved Numerical modelling capabilities ( GFS, WRF, HWRF)

• Skilled Human Resource Capabilities

• Improved tools and techniques of forecasting

• Excellent support and Inter- ministerial collaborations within different

sister organisations of MoES + IIT, IISc and Universities etc

• International collaborations

• Research and Development

EXTENDED RANGE WEATHER FORECAST

(NATIONAL MONSOON MISSION)

Scientific Approach for Implementation of ERFS over India

• Performance evaluation over India with respect to precipitation and surface temperature using IMD’s surface

observational network data.

• Estimation of reliability, systematic errors in prediction of these parameters and selection of global products

for dynamical downscaling

Estimation / removal of systematic biases in the global products to be used as input for Dynamical / Statistical Downscaling

Dynamical downscaling with use of RCMs Initial and boundary conditions from global model outputs

Surface boundary condition from Space based

observations and climatological data

Evaluation of performance of RCMs and selection for

super- ensemble

Statistical downscaling

• Use of AGCM/CGCM products as predictors

• Use of IMD observations as predictands

• Deterministic and Probabilistic approaches and

performance evaluation.

Multi-model super ensemble approach: based on dynamical and statistical predictions

Precipitation

Surface temperature

PREDICTION • Categorical prediction of precipitation and surface temperature in terms of below normal, normal and above normal (in

monthly and seasonal scales) for meteorological sub-divisions (36 sub-divisions) / major agro-climatic zones.

• Evaluation of the performance of the forecast and improvement of the model products

Application of experimental ERF for user oriented advisories for

farmers for different agricultural practices

Various Atmospheric General Circulation Models (AGCMs)/Coupled

General Circulation Models (CGCMs) outputs from different

operational/research organizations

Estimation / removal of systematic biases in RCM products

Monthly weather forecast

Month-wise forecast evaluation for 2011 and 2012

List of demonstration sites for pilot Study

S.

No.

Organization Districts Rabi Crops Kharif Crops

1 CSK Himachal Pradesh Krishi Vishwa

Vidyalaya, Palampur- 176062 (HP)

Kangra

Kullu

Wheat Apple, Maize

2 G.B. Pant Univ. of Agri.& Tech.

Pantnagar- 263145 (Uttarakhand)

U.S. Nagar Wheat Rice

3 Anand Agricultural University

Anand - 388 110, Gujarat,

Anand

kheda

Tobacco and potato Rice and Castor

4 Central Arid Zone Research Institute, ICAR,

Jodhpur- 342003 (Rajasthan)

Jodhpur Wheat and Mustard Pearl millet, Cluster

bean and Cumin,

Livestock

5 Orissa University of Agri.& Tech.

Bhubaneshwar- 751003 (Orissa)

Angul

Khorda

Groundnut Rice and Groundnut

6 Acharya N.G. Ranga Agriculture Uni.,

Hyderabad-(A P)

Mahabubnagar Maize Maize and Cotton

7 Tamil Nadu Agricultural University,

Coimbatore - 641 003 (TN)

Coimbatore

Nagapattinum

Maize Maize and Cotton

8 Dr. Panjabrao Deshmukh Krishi Vidyapeeth,

Akola-444104, (Maharashtra)

Akola Sorghum (Kharif

crop in N india)

Cotton and Soybean

9 J.N. Krishi Vishwa Vidyalaya

Jabalpur- 482004 (MP)

Jabalpur Chickpea Rice

IMD’S DYNAMICAL FORECASTING MODEL

The Seasonal Forecast Model (SFM) of Experimental Climate Prediction Center (ECPC) is used for this purpose

The model showed some useful skill during hindcast mode (1985-2004).

The dynamical model needs more testing and refinement.

SFM Hindcast (1985-2004) with monthly sst

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

YEAR

Rai

nfa

ll (S

tan

dar

diz

ed A

no

mal

y in

mm

)

ACTUAL Hindcast

Correl.Coeff: 0.37

Latest Forecast

Recent Forecasts are available in in the following link

http://202.54.31.51/bias/pune/

April June

All India June – September

Rainfall

Update for All India June – September

Rainfall

All India July Rainfall

June – September Rainfall for Four

Homogeneous Regions

IMD’s Present Two Stage Forecast

Strategy for SW Monsoon Rainfall

NATIONAL CLIMATE CENTER

New LRF system based on ensemble

technique for the seasonal rainfall over

the country as a whole

IMD has been consistently working to improve the Long range forecast models.

The new forecasting system is different from the existing models in three major points New Predictor data set

Use of a new Nonlinear technique

Introduction of the concept of Ensemble average

NATIONAL CLIMATE CENTER

AAS BULLETIN BASED ON EXTENDED RANGE WEATHER

FORECAST ISSUED BY IMD ON EXPERIMENTAL MODE

IMD in Collaboration with Indian Institute of

Tropical Meteorology, Pune started on a pilot

mode experimental Agromet Advisory bulletin

started from southwest monsoon 2013 based on

the need of farmers and other users. Bulletin is

being issued on every Friday.

The ERFS bulletin has mainly three components:

i. Realized rainfall for the preceding two weeks.

ii. Rainfall forecast of 4 pentads (5 days each).

iii. Broad Agromet Advisories based on the

realized and forecasted rainfall along with

crop status for six homogeneous regions viz.

South India, West India, Central India, East

India, North India and Northeast India

Use of LRF in Agriculture

For seasonal planning on

• Type of crop/variety to be sown

• Proportion of area under different crops

• How much of land, if any, to keep fallow

• Redistribution of inputs ( seed, fertilizer,

pesticides etc. )

• Arranging for Power & Water Resources

• Preparation of Contingency Plans

• Preliminary enquiries on exports/imports

Help make the best use of a good season and minimize

the harmful impacts of the adverse one

Aridity Anomaly Maps

Aridity Anomaly Map gives

information about the

moisture stress experienced

by growing plant. This

analysis would indicate

qualitatively retardation in the

plants growth and so poor

yields. Indirectly, this may

also be helpful for irrigation

scheduling, the amount and

the time at which the water is

badly needed by the plant.

Standard Precipitation Index

(SPI) Maps

Bi-weekly Cumulative Weekly

Real Time Data availability

• past weather

• current weather

• forecasts

• crop (stage and state)

• soil

• management

Decision Support system(s) for major crops

Agromet

Advisories

Decision Support System

Preparation of value

added medium range

forecast at district level Tuesday

Friday

NWP products

State Met Centre (SAMC)

Issuing State Level Composite Bulletin

Value addition

Agromet Field Units (AMFUs)

Dissemination of Agromet Advisory through Multi-Channel

Dissemination System

Parameters

Rainfall, Wind speed and direction, Maximum temperature, Relative humidity, Minimum temperature, Cloud cover

Agromet

Advisory

4.63

million

farmers

Receiving

SMS

• Conducting State Level Meeting

• Completed meetings in 6 States

in 2012

• Issuing District Level Bulletin

• In all total 596 bulletins in 13

languages &uploading in the

website of Agrimet Division

(http://imdagrimet.gov.in)

• Conducting Farmer Awareness

Programme

• Completed at 106 stations

Also Brochures

for Awareness

were

completed for

14 languages

From Composite State

Level Bulletin, Agrimet

Division, IMD preparing

National AAS bulletin

Organised different

training programmes.

Established feedback

mechanism

Economic

benefits from

savings in

farm inputs.

Increased farm

productivity

Farmer Portal

Dissemination of Agromet Advisory

1. Mass Mode

All India radio, Television, Print Media

2. Outreach at Village level

Ministry of IT Internet based Village Connectivity

Web Pages: IMD, SAUs, ICAR Web Pages

Mobile Phones (SMS & IVRS) through Public & private agencies

“Kisan SMS”, a portal for farmers under www.farmer.gov.in

4.8 million farmers

Kisan Call Centres

3. Human face for advisory dissemination

KVK (ICAR): Training + interaction

DAO (SDA): Coordinate Farm inputs with Line Dept. in rhythm of

weather forecast

NGOs & other intermediary groups, Awareness Programme

Operational communication linkage between Agromet Advisory Service Unit and end-users (farmers) for effective

communication

Forecast from IMD, New Delhi

State Met Centres

Agromet Advisory Bulletin

by AMFUs Postal Contact

Personal

Contact

Radio News Papers

KVK

State Agril. Dept.

Farmer

Television

SMS on

mobile

Website

Capacity building through Farmer Awareness Programmes

• The objective of these programmes is to make

farmers become more self-reliant in dealing with

weather and climate issues that affect

agricultural production on their farms and to

increase the interaction between the farmers and

the AgroMeteorological Service providing

agencies i.e. IMD, SAUs, ICAR etc.

• Such programs help increase the interaction

between the local farming communities and the

Meteorological Centres (MCs),

AgroMeteorological Field Units (AMFUs) and

Krishi Vigyan Kendra (KVK).

• Considering above, a large number of such

seminars are organized in different agro-climates

of the country to sensitize farmers about the

weather and climate information and it’s

applications in operational farm management.

Brochure for Awareness

Nepali Oriya Punjabi Tamil Marathi

Hindi Assamees Gujarati Manipuri English

Crop Yield Forecasting • Forecasting Agricultural Output using Space, Agrometeorology

and Land based observations (FASAL) involves developing

econometric, remote sensing and agromet based model to

generate multiple crop yield forecasts at national, state and

district level starting with crop sowing to end of season for 11

major Kharif and Rabi crops viz: Rice, Jowar , Maize, Bajra,

Jute, Ragi, Cotton, Sugarcane, Groundnut, Rapeseed & Mustard

and Wheat.

• IMD is implementing Agromet component of the scheme in

coordination with 23 State Agromet Centres (SAMCs), 46

principal Agro-Met Field Units (AMFUs) and IASRI in the country

to develop agromet models and issue in-season crop yield

forecast based on statistical and crop simulation models.

Rice

Wheat

Maize

Jowar

Bajra

Ragi

Groundnut

Sugarcane

Rape seed &Mustard

Cotton

Jute

Crops taken up under FASAL

Important activities being planned under GKMS

• Seamless weather forecast (Block level forecast,

Extended Range Forecast)

• Climatic Risk Management

• eAgromet system

• Smart Dissemination System

• Use of satellite data to derive new product including

alerts

• Activities under CREAM and R&D related to weather

and climate

• Economic impact assessment

• Gramin Rainfall Monitoring Mission (GRaMM)

• Rainfall Visualizer Tool

E-AGROMET: ICT-ENABLED INTEGRATED AGRO-

METEOROLOGICAL ADVISORY SYSTEM

Introduction

• Agromet advisory bulletin is an advice

bulletin to farmers regarding weather

sensitive agricultural operations.

•For mitigating the weather-based risk on crop

cultivation.

Key Concerns

• Currently, agromet bulletins are

being prepared manually.

• Difficult to scale for any crop

and/or region.

eAgromet System

Basic Idea

• Agromet bulletins can be prepared in advance

by analyzing past weather data of a region.

Methodology

• Identify the potential weather deviations by

analyzing weather data of 50 years.

• For each potential weather pattern, prepare

Agromet Advisory Bulletin in advance.

• Based on the weather prediction, system finds

appropriate Agromet bulletin and sends the

same to farmers.

Agromet System

Indian Meteorological Department

(IMD) provides weather prediction

data

Agricultural scientists prepare the

Agromet bulletins

Block Diagram of Proposed System

Objective

• Creation of ICT-based agro-

meteorological advisory system to

improve the quality, scalability and

reach.

Sample Agromet Advisory Bulletin

Delivered to farmers and stake

holders

AAS Bulletin for the district East Godavari (29th September to 3rd October, 2010) Issued

by IAAS, RARS, Chintapalli, Acharya N.G.Ranga Agricultural University

Weather forecast for Eat Godavari district Date: 28-09-2010

29-09-10 30-09-10 01-10-10 02-10-10 03-10-10

Rainfall (mm) 10 10 5 5 5

Max.Temp. 35 34 34 33 33

Min.Temp. 26 26 26 25 26

RH (%) Morning 86 84 86 85 86

Evening 70 81 75 79 76

Wind speed 3 4 4 9 8

Agro meteorological advisory for East Godavari district(Agency mandals):

Crop Advisory

Paddy • Paddy crop is at PI stage.

• Present weather condition is favourable for the incidence of

Blast, Sheath rot, leaf folder and stem borer.

• Suggested control measures, Drain out the water from field

• Spraying of Tricyclozole @ 120 g in 200 litres of water for one

acre

field (0.6 g/litre of water). Repeat the spray after one week.

• Spray validamycin or hexaconazole @2.0 ml per litre of water

twice in 15 days for sheath rot contol.

• Spray Chloripyriphos @ 2.5 ml per litre of water for the control

of leaf folder and stem borer

• Clear weather should be there for atleast 4-5 hours after spraying

of the chemical.

Preparation of Agromet Bulletins in an offline

manner

Identifying Potential Weather

Deviations by applying data mining

methods

Delivery of Online Agromet Advisory bulletin

to Stakeholders

eAgromet

System (Reposito

ry of Agromet Bulletins)

Feedback through stakeholders and field

visits by scientists

Approval/ adjustmen

t of Agromet

bulletin by experts

Weather forecast by Indian Meteorological Department

Determination of Soil Moisture over India using Space Borne

Passive Microwave Sensors onboard SMOS-Jodhpur

The main objective of the project is to validate 4% accuracy of the SMOS (Soil Moisture

and Ocean Salinity) satellite in soil moisture retrieval. In spite of this, we will also

compare brightness temperature (BT) estimated by radioactive transfer models with the

brightness temperature (BT) measured by SMOS sensor (MIRAS) over selected test

sites. Basically, the complete project is divided

into two phases

Pilot phase- validation of Rajasthan site

within 6 months.

Detail phase- validation of the rest of the

site selected in India.

Eart

h

SMO

S

Radiative

Transfer

Model

(RTM)

In-situ (Soil

Moisture)

Comparison

Radiative

Transfer

Model

(RTM)

(SM) In-Situ

(SM)

SMOS

Measured

Brightness

Temperature

(BT)

(BT)

SMOS

(BT)

simulated

1.4 GHz

Soil

Moisture

(SM) with

4%

accuracy

Simulated

Brightness

Temperature

(BT)

Satellite Data Based

Fusion Approach to

Develop Soil

Moisture Monitoring

System in India-IIT

Roorkee

Test sites over India Sr. No. State wise

Location

Longitude &

Latitude

1 Assam 26°63’, 94°21’

2 Rajasthan 26°55’, 70°57’

3 Andhra Pradesh 17°16’, 78°22’

4 Uttar Pradesh

(Allahabad)

25°28’, 81°54’

5 Madhya Pradesh

(Jabalpur)

23°16’,77°36’

6 West Bengal 23°42’,87°01’

7 Gujarat

(Junagadh)

23°03’, 72°40’

8 Kerala 10°00’, 76°25’

9 Tamil Nadu 11°13’, 77°02’

• Total 9 Test Sites Over India.

Red dots show the selected sites of

different soil types for the project

Ref.Online:http://www.mapsofindia.com/maps/in

dia/soilsofindia.htm

OPNC (August 19, 2013) "PROJECT REVIEW" ICRS, Jodhpur

64

High Resolution Soil Moisture map generation over Nine States (Dates for each state are different)

Economic Assessment by NCAP on IAAS estimated

10-25% economic benefit obtained by the farmers.

Potential economic benefit estimated by NCAER,

Rs.50,000 crores per year (used by 24% farmers).

Extrapolation can rise to Rs.211,000 crores if the entire

farming community were to apply Agromet information

to their agricultural activity.

In line with recommendations of PMO wrt the demands

raised by Bharat Krishak Samaj and MPs for expansion

of the service to village level

Economic Impact of IAAS

By 2020, India should have a coupled Ocean-Land-Atmosphere Model

for prediction of weather to seasonal climate.

Future numerical weather prediction systems- IMD

Global Modeling

•Hybrid data assimilation for the initial condition to

deterministic GFS-574 model

•GDAS with Ensemble Kalman Filter / Ensemble Transform

Rescaling perturbations

•High-resolution GFS: T1148-L64 (16 km) to T1500-L128 (13

km)

•Ensemble prediction system with GFS-574

Regional Modeling •Hybrid assimilation with WRFDA

•Assimilation of INSAT-3D radiance

•Assimilation of SAPHIR radiance from Meghatropique

satellite

•Nested domain at 3 km resolution over India in WRF

forecasting for 3 days

•Triple nested Hurricane WRF modeling system with hybrid

assimilation

•Coupled HWRF with POM or HYCOM in collaboration with

INCOIS

•Air quality – dispersion model HYSPLIT

•WRF-Chem model at 1 km over selected cities

•Implementation of Land-surface data assimilation system

Block level forecasting • Multi Model Ensemble approach

using global model forecasts

• EnKF bias corrected district

level and block level forecasts

using GFS model

• Use of high-resolution

mesoscale forecasts

Future needs and strategies…….

• Seamless weather forecast

• Improved crop/P&D models

• Decision Support system to prepare

advisories

• Improved dissemination

• Merging remote sensing and

conventional data merging

• GIS applications