SlideShare a Scribd company logo
1 of 21
National Collateral Management Services Limited


 Weather Data Infrastructure: Challenges
           and W Forward
                  ay
        Accelerating Agri Insurance in India


                   5th February, 2011

                                               1
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% 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
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)
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
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
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
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
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
Progress Since May 2005




                  10
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
Weather Data Collection
   Near real time climate
    data collection from
     remote locations




            QC              WeatherMan




         Database                  Dissemination
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 process
 Data quality check based on predefined
  parameters
 Storage and retrieval of data in desired format
  for dissemination
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
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
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
Thank You




    21

More Related Content

Similar to 5 Feb 2011 Sanjay Kaul NCSML Agri Insurance

Weather insurance in India - A snaphot (2010)
Weather insurance in India - A snaphot (2010)Weather insurance in India - A snaphot (2010)
Weather insurance in India - A snaphot (2010)
Outenga
 

Similar to 5 Feb 2011 Sanjay Kaul NCSML Agri Insurance (20)

Hyderabad presentation
Hyderabad presentationHyderabad presentation
Hyderabad presentation
 
Weather insurance in India - A snaphot (2010)
Weather insurance in India - A snaphot (2010)Weather insurance in India - A snaphot (2010)
Weather insurance in India - A snaphot (2010)
 
Climate-Smart Agriculture: Weather Data for Index Insurance, enhanced effici...
Climate-Smart Agriculture: Weather Data for  Index Insurance, enhanced effici...Climate-Smart Agriculture: Weather Data for  Index Insurance, enhanced effici...
Climate-Smart Agriculture: Weather Data for Index Insurance, enhanced effici...
 
SKyClaim: Using Drones for Crop Insurance
SKyClaim: Using Drones for Crop InsuranceSKyClaim: Using Drones for Crop Insurance
SKyClaim: Using Drones for Crop Insurance
 
An overview of rainfall insurance in india
An overview of rainfall insurance in indiaAn overview of rainfall insurance in india
An overview of rainfall insurance in india
 
An overview of rainfall insurance in india
An overview of rainfall insurance in indiaAn overview of rainfall insurance in india
An overview of rainfall insurance in india
 
IFPRI-Using Remote Sensing technologies to improve sampling-Mangesh Patankar
IFPRI-Using Remote Sensing technologies to improve sampling-Mangesh PatankarIFPRI-Using Remote Sensing technologies to improve sampling-Mangesh Patankar
IFPRI-Using Remote Sensing technologies to improve sampling-Mangesh Patankar
 
Crop insurance in India
Crop insurance in IndiaCrop insurance in India
Crop insurance in India
 
South Asia Drought Monitoring System (SADMS)
South Asia Drought Monitoring System (SADMS)South Asia Drought Monitoring System (SADMS)
South Asia Drought Monitoring System (SADMS)
 
Delivering Efficiency and Customer Satisfaction to Claims and Underwriting wi...
Delivering Efficiency and Customer Satisfaction to Claims and Underwriting wi...Delivering Efficiency and Customer Satisfaction to Claims and Underwriting wi...
Delivering Efficiency and Customer Satisfaction to Claims and Underwriting wi...
 
IBM The Weather Company: weather means business 2019
IBM The Weather Company: weather means business 2019IBM The Weather Company: weather means business 2019
IBM The Weather Company: weather means business 2019
 
Weather Based Index Insurance
Weather Based Index InsuranceWeather Based Index Insurance
Weather Based Index Insurance
 
Pacific Regional Agritourism Workshop 2019: Using Satellite Technology to Boo...
Pacific Regional Agritourism Workshop 2019: Using Satellite Technology to Boo...Pacific Regional Agritourism Workshop 2019: Using Satellite Technology to Boo...
Pacific Regional Agritourism Workshop 2019: Using Satellite Technology to Boo...
 
360-Degree Approach to DR / BC
360-Degree Approach to DR / BC360-Degree Approach to DR / BC
360-Degree Approach to DR / BC
 
Comprehensive Risk Cover through Remote Sensing Techniques in Agriculture Ins...
Comprehensive Risk Cover through Remote Sensing Techniques in Agriculture Ins...Comprehensive Risk Cover through Remote Sensing Techniques in Agriculture Ins...
Comprehensive Risk Cover through Remote Sensing Techniques in Agriculture Ins...
 
IRJET- Crop Yield Prediction based on Climatic Parameters
IRJET- Crop Yield Prediction based on Climatic ParametersIRJET- Crop Yield Prediction based on Climatic Parameters
IRJET- Crop Yield Prediction based on Climatic Parameters
 
CLOUD_SEEDING.pptx
CLOUD_SEEDING.pptxCLOUD_SEEDING.pptx
CLOUD_SEEDING.pptx
 
FFM Flood Forecasting Model Using Federated Learning.docx
FFM Flood Forecasting Model Using Federated Learning.docxFFM Flood Forecasting Model Using Federated Learning.docx
FFM Flood Forecasting Model Using Federated Learning.docx
 
Pramod Aggarwal: Technologies and big data for improved crop insurance
Pramod Aggarwal: Technologies and big data for improved crop insurancePramod Aggarwal: Technologies and big data for improved crop insurance
Pramod Aggarwal: Technologies and big data for improved crop insurance
 
robotics iot in agriculture.pptx
robotics iot in agriculture.pptxrobotics iot in agriculture.pptx
robotics iot in agriculture.pptx
 

More from CSISA

Csisa obj 1 wp.ktm.jan.13
Csisa obj 1 wp.ktm.jan.13Csisa obj 1 wp.ktm.jan.13
Csisa obj 1 wp.ktm.jan.13
CSISA
 
23 25 jan 2013 csisa kathmandu service provider survey aanand
23  25 jan 2013 csisa kathmandu service provider survey aanand23  25 jan 2013 csisa kathmandu service provider survey aanand
23 25 jan 2013 csisa kathmandu service provider survey aanand
CSISA
 
23 25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
23  25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...23  25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
23 25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
CSISA
 
23 25 jan 2013 csisa kathmandu arun joshi
23  25 jan 2013 csisa kathmandu arun joshi23  25 jan 2013 csisa kathmandu arun joshi
23 25 jan 2013 csisa kathmandu arun joshi
CSISA
 
23 25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
23  25 jan 2013 csisa kathmandu engaging sh gs ilri arindam23  25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
23 25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
CSISA
 
23 25 jan 2013 csisa kathmandu ict possibilities poornima
23  25 jan 2013 csisa kathmandu ict possibilities poornima23  25 jan 2013 csisa kathmandu ict possibilities poornima
23 25 jan 2013 csisa kathmandu ict possibilities poornima
CSISA
 
23 25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
23  25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz23  25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
23 25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
CSISA
 
23 25 jan 2013 csisa kathmandu new business models aanand
23  25 jan 2013 csisa kathmandu new business models aanand23  25 jan 2013 csisa kathmandu new business models aanand
23 25 jan 2013 csisa kathmandu new business models aanand
CSISA
 
23 25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
23  25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne23  25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
23 25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
CSISA
 
23 25 jan 2013 csisa kathmandu odisha c.m. khanda
23  25 jan 2013 csisa kathmandu odisha c.m. khanda23  25 jan 2013 csisa kathmandu odisha c.m. khanda
23 25 jan 2013 csisa kathmandu odisha c.m. khanda
CSISA
 
23 25 jan 2013 csisa kathmandu overview eup bihar dr malik
23  25 jan 2013 csisa kathmandu overview eup bihar dr malik23  25 jan 2013 csisa kathmandu overview eup bihar dr malik
23 25 jan 2013 csisa kathmandu overview eup bihar dr malik
CSISA
 
23 25 jan 2013 csisa kathmandu partnership issues noel
23  25 jan 2013 csisa kathmandu partnership issues noel23  25 jan 2013 csisa kathmandu partnership issues noel
23 25 jan 2013 csisa kathmandu partnership issues noel
CSISA
 
23 25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
23  25 jan 2013 csisa kathmandu ph assessment bihar al schmidley23  25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
23 25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
CSISA
 
23 25 jan 2013 csisa kathmandu ppp dealer training ravikanth
23  25 jan 2013 csisa kathmandu ppp dealer training ravikanth23  25 jan 2013 csisa kathmandu ppp dealer training ravikanth
23 25 jan 2013 csisa kathmandu ppp dealer training ravikanth
CSISA
 
23 25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
23  25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....23  25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
23 25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
CSISA
 
23 25 jan 2013 csisa kathmandu ssnm ipni kaushik
23  25 jan 2013 csisa kathmandu ssnm ipni kaushik23  25 jan 2013 csisa kathmandu ssnm ipni kaushik
23 25 jan 2013 csisa kathmandu ssnm ipni kaushik
CSISA
 
23 25 jan 2013 csisa kathmandu technology adoption takashi
23  25 jan 2013 csisa kathmandu technology adoption takashi23  25 jan 2013 csisa kathmandu technology adoption takashi
23 25 jan 2013 csisa kathmandu technology adoption takashi
CSISA
 
23 25 jan 2013 csisa kathmandu agro-advisory services surabhi
23  25 jan 2013 csisa kathmandu agro-advisory services surabhi23  25 jan 2013 csisa kathmandu agro-advisory services surabhi
23 25 jan 2013 csisa kathmandu agro-advisory services surabhi
CSISA
 

More from CSISA (20)

Mukesh khullar
Mukesh khullar Mukesh khullar
Mukesh khullar
 
Csisa obj 1 wp.ktm.jan.13
Csisa obj 1 wp.ktm.jan.13Csisa obj 1 wp.ktm.jan.13
Csisa obj 1 wp.ktm.jan.13
 
23 25 jan 2013 csisa kathmandu service provider survey aanand
23  25 jan 2013 csisa kathmandu service provider survey aanand23  25 jan 2013 csisa kathmandu service provider survey aanand
23 25 jan 2013 csisa kathmandu service provider survey aanand
 
23 25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
23  25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...23  25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
23 25 jan 2013 csisa kathmandu acclerated mechanisation lessons from banglad...
 
23 25 jan 2013 csisa kathmandu arun joshi
23  25 jan 2013 csisa kathmandu arun joshi23  25 jan 2013 csisa kathmandu arun joshi
23 25 jan 2013 csisa kathmandu arun joshi
 
23 25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
23  25 jan 2013 csisa kathmandu engaging sh gs ilri arindam23  25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
23 25 jan 2013 csisa kathmandu engaging sh gs ilri arindam
 
23 25 jan 2013 csisa kathmandu ict possibilities poornima
23  25 jan 2013 csisa kathmandu ict possibilities poornima23  25 jan 2013 csisa kathmandu ict possibilities poornima
23 25 jan 2013 csisa kathmandu ict possibilities poornima
 
23 25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
23  25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz23  25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
23 25 jan 2013 csisa kathmandu lessons from scaleup of zt bihar shahnawaz
 
23 25 jan 2013 csisa kathmandu new business models aanand
23  25 jan 2013 csisa kathmandu new business models aanand23  25 jan 2013 csisa kathmandu new business models aanand
23 25 jan 2013 csisa kathmandu new business models aanand
 
23 25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
23  25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne23  25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
23 25 jan 2013 csisa kathmandu obj 1 planning- big picture etienne
 
23 25 jan 2013 csisa kathmandu odisha c.m. khanda
23  25 jan 2013 csisa kathmandu odisha c.m. khanda23  25 jan 2013 csisa kathmandu odisha c.m. khanda
23 25 jan 2013 csisa kathmandu odisha c.m. khanda
 
23 25 jan 2013 csisa kathmandu overview eup bihar dr malik
23  25 jan 2013 csisa kathmandu overview eup bihar dr malik23  25 jan 2013 csisa kathmandu overview eup bihar dr malik
23 25 jan 2013 csisa kathmandu overview eup bihar dr malik
 
23 25 jan 2013 csisa kathmandu partnership issues noel
23  25 jan 2013 csisa kathmandu partnership issues noel23  25 jan 2013 csisa kathmandu partnership issues noel
23 25 jan 2013 csisa kathmandu partnership issues noel
 
23 25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
23  25 jan 2013 csisa kathmandu ph assessment bihar al schmidley23  25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
23 25 jan 2013 csisa kathmandu ph assessment bihar al schmidley
 
23 25 jan 2013 csisa kathmandu ppp dealer training ravikanth
23  25 jan 2013 csisa kathmandu ppp dealer training ravikanth23  25 jan 2013 csisa kathmandu ppp dealer training ravikanth
23 25 jan 2013 csisa kathmandu ppp dealer training ravikanth
 
23 25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
23  25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....23  25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
23 25 jan 2013 csisa kathmandu segmenting training priorities k ambij and v....
 
23 25 jan 2013 csisa kathmandu ssnm ipni kaushik
23  25 jan 2013 csisa kathmandu ssnm ipni kaushik23  25 jan 2013 csisa kathmandu ssnm ipni kaushik
23 25 jan 2013 csisa kathmandu ssnm ipni kaushik
 
23 25 jan 2013 csisa kathmandu technology adoption takashi
23  25 jan 2013 csisa kathmandu technology adoption takashi23  25 jan 2013 csisa kathmandu technology adoption takashi
23 25 jan 2013 csisa kathmandu technology adoption takashi
 
23 25 jan 2013 csisa kathmandu agro-advisory services surabhi
23  25 jan 2013 csisa kathmandu agro-advisory services surabhi23  25 jan 2013 csisa kathmandu agro-advisory services surabhi
23 25 jan 2013 csisa kathmandu agro-advisory services surabhi
 
Conservation Agriculture - Sense & Non Sense
Conservation Agriculture - Sense & Non SenseConservation Agriculture - Sense & Non Sense
Conservation Agriculture - Sense & Non Sense
 

Recently uploaded

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Recently uploaded (20)

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 

5 Feb 2011 Sanjay Kaul NCSML Agri Insurance

  • 1. National Collateral Management Services Limited Weather Data Infrastructure: Challenges and W Forward ay Accelerating Agri Insurance in India 5th February, 2011 1
  • 2. Our Services End-to-end services across the value chain 2
  • 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. 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 5
  • 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. 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. 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. 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
  • 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 18
  • 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. 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. Thank You 21