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National Collateral Management Services Limited


 Weather Data Infrastructure: Challenges
           and W Forward
      ...
Our Services




    End-to-end services across the value chain



                         2
Weather and Indian Agriculture

   High dependence on Weather
    •   60% of land holdings in India rain-fed

    •   90%...
India & Rainfall
   Seasonal Distribution of Rainfall
    No     Season               Duration                   Rainfall...
Weather based Crop Insurance Scheme

   Weather Index based insurance product

   Premium subsidy shared by the Governme...
National Agricultural Insurance Scheme (NAIS) vs Weather based
Crop Insurance Scheme (W   BCIS
SI No                 NAIS ...
Weather Insurance - Key Challenges
   Lack of quality historical weather data other than
    rainfall
   Delay in gettin...
Weather Station Infrastructure
   3000 Automatic W   eather Stations have been installed
    across the country

Governme...
NCMSL Journey
   Creation of network of weather stations across the
    country at relevant crop growing areas to monitor...
Progress Since May 2005




                  10
Weather Parameters Tracked
   Rainfall (amount and intensity)

   Temperature (min. and max.)

   Relative humidity

 ...
Weather Data Collection
   Near real time climate
    data collection from
     remote locations




            QC       ...
Step 1:Importing the raw weather data to WeatherMan
Step 2: Software application to check the data quality
Step 3: Validation of imported weather data as per given
                        conditions
Step 4: Report generation as per the client
requirement
Operationalization

 Under the security of local host
 Trained Service Engineer – timely monitoring
 Automation of the ...
Weather Data
          SYNOP Data                          Climate Data
Data that are collected in real- Data that are qua...
Challenges

 Installation   & Commissioning of Weather
  Stations on short notice

 Retrieval of   data on daily basis f...
W Forward
 ay

   Offline CCTV/ ebcam with recording facility
                W

   Dedicated Weather W Portal
         ...
Thank You




    21
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5 Feb 2011 Sanjay Kaul NCSML Agri Insurance

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5 Feb 2011 Sanjay Kaul NCSML Agri Insurance

  1. 1. National Collateral Management Services Limited Weather Data Infrastructure: Challenges and W Forward ay Accelerating Agri Insurance in India 5th February, 2011 1
  2. 2. Our Services End-to-end services across the value chain 2
  3. 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 3
  4. 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. 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 5
  6. 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 1 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 6
  7. 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 7
  8. 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 8
  9. 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. 9
  10. 10. Progress Since May 2005 10
  11. 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. 12. Weather Data Collection Near real time climate data collection from remote locations QC WeatherMan Database Dissemination
  13. 13. Step 1:Importing the raw weather data to WeatherMan
  14. 14. Step 2: Software application to check the data quality
  15. 15. Step 3: Validation of imported weather data as per given conditions
  16. 16. Step 4: Report generation as per the client requirement
  17. 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. 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 18
  19. 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 19
  20. 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 20
  21. 21. Thank You 21

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