1. National Collateral Management Services Limited
Weather Data Infrastructure: Challenges
and W Forward
ay
Accelerating Agri Insurance in India
5th February, 2011
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2. Our Services
End-to-end services across the value chain
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3. Weather and Indian Agriculture
 High dependence on Weather
• 60% of land holdings in India rain-fed
• 90% of crop losses attributable to weather
• Excessive wind speed
• High relative humidity
• Deficit or excess rainfall
• High or low temperatures
• Many areas prone to natural calamities like floods and drought
• Diminishing ground water resources
• Weather risk is the most significant volatile risk
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4. India & Rainfall
 Seasonal Distribution of Rainfall
No Season Duration Rainfall
1 Pre Monsoon March-May 10.4%
2 South West Monsoon June-September 73.4%
3 North East Monsoon October-December 13.3%
4 Winter Rain January-February 2.9%
 Cropped area Range various Classification
No Rainfall
under ranges of rainfall in IndiaArea
Cropped
1 < 750 mm Low Rainfall 33%
2 750-1125 mm Medium Rainfall 35%
3 1125-2000 mm High Rainfall 24%
4 > 2000 mm Very High Rainfall 8%
4 * Source (IMD & MoA, GOI)

5. Weather based Crop Insurance Scheme
 Weather Index based insurance product
 Premium subsidy shared by the Government
 Weather indices could be Maximum/
Minimum
Temperature, Relative Humidity, Excess/
Deficit
Rainfall and/ combination of above
or
 Replaces human subjective assessment with objective
weather parameters
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6. National Agricultural Insurance Scheme (NAIS) vs Weather based
Crop Insurance Scheme (W BCIS
SI No NAIS WBCIS
Practically all risk insurance Covers only parametric weather related
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cover risks like temperature, humidity, rainfall etc.
Technical challenges in designing weather
indices and also correlating weather indices
Easy to design if 10 years of
2 with ensuing yield losses. Needs up to 25
historical yield data is available
years’ historical weather data
Basis risk related to rainfall can be very high
3 High basis risk
but moderate for other weather parameters
Highly prone to
Less prone to tampering/administrative
4 tampering/administrative
influence
influence
High loss assessment cost
5 Low assessment cost
(Crop cutting experiments)
Lengthy/delayed claim
6 Faster claim settlement
settlement
7 Reinsurance not easy to get Reinsurance is available
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7. Weather Insurance - Key Challenges
 Lack of quality historical weather data other than
rainfall
 Delay in getting weather data from government
institutions
 High data cost of private data providers
 Immediate need to improve the weather station
density
 Questions over the data supplied by the private
players
 Accreditation of W eather Stations
 Lack of insurance education and awareness
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8. Weather Station Infrastructure
 3000 Automatic W eather Stations have been installed
across the country
Government Data Providers
 India Meteorological Department
 Revenue Dept, Water Resource Dept etc.
 Agriculture University
 Research Institutes/Stations
Private Data Providers
 NCMSL
 WRMS
 Express Weather
 Agro Com
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9. NCMSL Journey
 Creation of network of weather stations across the
country at relevant crop growing areas to monitor
weather parameters at hourly interval
 First AW installed in May 2005 at Khanapur,
S
Maharashtra for ICICI Lombard
 India’s largest & first private organization to establish
own network of 1000+ Automatic W eather Stations in
India.
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10. Progress Since May 2005
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11. Weather Parameters Tracked
 Rainfall (amount and intensity)
 Temperature (min. and max.)
 Relative humidity
 W speed and direction
ind
 Atmospheric pressure
 Heating Degree Day (HDD)
 Cooling Degree Day (CDD)
 Dew point

12. Weather Data Collection
Near real time climate
data collection from
remote locations
QC WeatherMan
Database Dissemination

13. Step 1:Importing the raw weather data to WeatherMan

14. Step 2: Software application to check the data quality

15. Step 3: Validation of imported weather data as per given
conditions

16. Step 4: Report generation as per the client
requirement

17. Operationalization
 Under the security of local host
 Trained Service Engineer – timely monitoring
 Automation of the process
 Data quality check based on predefined
parameters
 Storage and retrieval of data in desired format
for dissemination

18. Weather Data
SYNOP Data Climate Data
Data that are collected in real- Data that are quality controlled by
time at various stations around the respective agency where the
the globe and provided through data is collected
the GTS
Minimum Quality Checks Thorough Quality Checks
Normally provided four times a Provided within few hours to
day months
Used for Weather Forecast, Most appropriate for the Weather
Aviation industry Insurance/Derivative Industry
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19. Challenges
 Installation & Commissioning of Weather
Stations on short notice
 Retrieval of data on daily basis from remote
locations of India
 Tackling the possibility of data tampering
incidences
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20. W Forward
ay
 Offline CCTV/ ebcam with recording facility
W
 Dedicated Weather W Portal
eb
 Public Private Partnership (PPP)
 Accreditation of Weather Stations
 Apex Enforcement Authority
 Standardization in data collection, archival and
distribution
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21. Thank You
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