||Price, Crop & Weather Intelligence Group (CWIG)|
CWIG offers you record of weather data from the locations exactly where it is applicable, crop acreage and yield forecast for unparalleled competitive advantage of pro-actively planning your business well in advance!
We help farmers, agriculture trading professionals and other stakeholders in the market with most comprehensive weather services available by a single point of access to weather data and crop status to mitigate risk and cultivate an edge in the market.
||Price Intelligence |
CWIG gathers market pricing information for 24 important commodities from various National / International markets with the participation of around 600 leading participants. This data is used to monitor daily price variation, which is useful for commodity market and commodity lending banks.
||Crop Intelligence |
Information about the surface of the earth is vital to everybody. We all need to know what is happening around us, with our environment, with our infrastructure, with our forests and farmland, with our rivers and oceans. People want to be informed about their own region, about their country but also about the rest of the world.
A timely crop forecasting system is essential to strengthen any country’s food security. Periodic within-season estimates of crop acreage and yield and timely monitoring of crop health status could greatly help in organizing for the availability of inputs and for formulating optimal prices and strategies for marketing, procurement, transportation and storage. Being able to monitor fields on a larger scale and to accurately predict the yield for different crops will revolutionize the agricultural (precision farming), industrial (food industry) and trading (commodity markets) sectors. Creating and maintaining up-to-date maps of the world is currently impossible but will be achievable through our service.
We monitor crops in the country to provide accurate, timely and reliable information on the area of the major crops, their development and problems (focusing to drought assessment), plus reliable yield forecast and final yield estimates. These data is available at the country, state, district as well as at block levels. The crops area assessment is based on the multitemporal image analysis of various cloud free satellite data available. The crop yield forecast is through the application of simulation models which combines high-resolution satellite (IRS series and Landsat TM) data and time series data from MODIS and NOAA AVHRR. Vegetation indices and more precise indicators like canopy chlorophyll index, red shift, and thermal infrared-based crop water stress index, etc., are more promising for diagnosing the nutrient and water stress. These remotely sensed methods are now finding place in crop management through precision farming. Such an approach can aid sustainability by assisting the farmer in deciding the quantity of inputs to be applied to ensure food production and profits from agriculture as an enterprise, thus minimizing farmers’ input costs and reducing environmental degradation.
CWIG’s high-level products and services will consist of crop mapping, identification and monitoring as well as yield predictions. These products are based on multi-temporal image analysis, geographical databases and agro meteorological simulation models.
Crop area measurement is a very common practice in agriculture. Remote sensing is often used for this purpose because of its strengths in regard to spatial and temporal resolution, relative low costs and potential for rapid assessment of spatial features. Many of the same issues concerning crop type identification also affect crop area measurement from remotely sensed data. This is because crop type identification is a necessary first step to area estimation. In many cases, though, crop type identification is more concerned with classifying all crop types from each other, where area estimation often is concerned with only a few target crops. In either case, these two applications are frequently performed in sequence: first crop identification and then area estimation.
Crop yield forecasts can greatly influence management decisions and provide a means for farm income assessment. Consequently, individual farmers and district-level administrators required rapid and accurate estimates of crop yield, both locally and regionally. In the past, the standard yield estimation procedure included the analysis of crop cuttings at randomly sampled ground plots during harvest or meteorological regression models using rainfall and past yield data. These methods often produce results that are neither not timely nor spatially explicit. Though still used, these methods are being replaced by estimation of crop yields using remote sensing because of its ability to produce results quickly and spatially. Using this technology, it was found that spatially meaningful estimates of yield can be made as early as 1 to 3 months prior to harvest, thus impacting management reaction time to yield forecasts.
Potential Yields of Wheat
||Weather Intelligence |
CWIG is the country’s largest private weather data provider headquartered in Hyderabad. CWIG is the only country level weather content maker that employs over 80 staff with our presence in 16 states, including Andhra Pradesh, Bihar, Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Kerala, Maharashtra, Madhya Pradesh, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and Uttarakhand. We offer a full range of weather content (viz., temperature (minimum, maximum and average), rainfall (amount and intensity), dew point, wind direction, wind speed (hourly average and high speed), relative humidity, atmospheric pressure, heat degree-day, cool degree-day, heat index etc.) with our "network of automatic weather stations" spread across the country.
CWIG differentiates itself from other weather companies by utilizing all latest available technologies to provide better and accurate services to help our clients to take direct actions to mitigate their potential weather risk. As a full weather service group, CWIG is offering services to various industries such as agriculture, energy and disaster mitigation for corporate and government agencies that are subjected to weather risks. By taking advantage of our countrywide presence, CWIG can provide the highest quality services and support them in the most cost-effective manner.
Our commitment is to perform a variety of weather services to meet the varying needs of all stakeholders in the market and offering better services for each and every one of them is our final goal.
Weather Information Management System (WIMS)
One critical issue with all weather data is quality and reliability. Hence we have developed an extensive set of algorithms and procedures for processing the data to ensure better quality and reliability for various weather applications. Key attributes of “Weather Information Management System (WIMS)” are discussed below.
Correct and accurate weather data is the lifeblood of the weather market. For structuring and pricing weather insurance products or weather derivatives and for tracking positions during a season, traders, end-users and portfolio risk managers require a) weather observations, typically for at least past 30 years and b) daily observations of current weather available at near real time. WIMS provide this full range of current weather data for a series of primary weather stations in almost in all states. CWIG can also provide data for clients who have specific data requirements.
The “real-time” data published by India Meteorological Department (IMD) regularly includes missing or erroneous values due to various reasons (viz., instrument errors, communication system failures, or other glitches) in the process of measuring, transmitting and recording weather conditions. Such values are almost always identified and corrected before publication of the final, official climate records anywhere from a week to several months later, however they pose a problem for data usage that depend on accurate and timely information. Incorrect data for recent periods can result in incorrect valuations of current positions, leading to erroneous insurance settlement or trading decisions.
To provide the market with more reliable data, we conduct overnight processing of prior-day data feeds from individual meteorological stations across the country. All data is run through a series of algorithms to detect missing or erroneous values, and experienced meteorologists verify and estimate a replacement value when a problematic observation is detected. Cleaned data is loaded into CITRIX server and delivered to clients on regular basis.
Even though IMD has compiled extensive records of historical weather data over several decades or more, such data often is not suitable for underwrite an insurance product or the weather trading purposes in its raw form. To provide weather market participants with a more reliable basis for pricing and risk management, CWIG provide two types of services on historical data recorded from individual locations.
Occasional missing values are common in most historical records, and CWIG also analyze the records to detect any erroneous data, such as minimum values that are greater than maximum or precipitation when there is no change in relative humidity, etc. All missing or erroneous values are replaced with simulated values or estimated values derived from comparisons with neighboring station recordings, analyses of local micro-climate biases and satellite cloud pictures. The final cleaned data provides a continuous and complete historical time series of daily values.
Enhanced data is a version of daily historical values that has been adjusted to be consistent with how temperatures are being recorded by the current instrumentation at each individual weather station. Periodic changes in weather station location, instrumentation or environment over time have introduced measurement discontinuities – permanent increases or decreases in temperature observations – in the historical records for many stations. The existence of such discontinuities in historical data can make the data unreliable for underwriting insurance product or valuing weather derivatives that will be settled based on observations taken with current instrumentation.
Over the past 4 years, CWIG has developed and continued to refine a complex methodology for identifying and quantifying discontinuities in historical data. This data enhancement methodology involves an extensive series of statistical tests that compare historical temperature recordings at a particular weather station to recordings at a series of highly-correlated neighboring stations. Manual analyses and checks of the data by meteorologists serve as a final step to confirm the existence and magnitude of discontinuities in historical data.
Data format and availability
WIMS users have access to a broad array of weather data necessary for analyzing and tracking risk, including databases of cleaned weather data and automatic feeds of daily cleaned data for hundreds of weather stations countrywide in their desired format.
Security of client data has been treated as an issue of utmost priority in the design of the application. CWIG operates the system through CIRTIX server that provides extremely high physical security as well as high speed and reliable internet connectivity. The system itself incorporates features such as multiple firewalls and encryption of data transmissions to maximize security in all dimensions.
Spatial distribution of NCML Weather Stations Network.
||Our Team |
CWIG has been active in the weather risk market since 2005 and has a dedicated team focused on the development of overall weather risk management business. Located in New Delhi, Mumbai, Hyderabad and across various state capitals the team includes approximately 80 professionals with expertise in agriculture, meteorology, climatology, remote sensing, GIS, software development, electronics and instrumentation.
||Anatomy of a Weather Index / Weather Derivative |
Structure of index insurance contracts (taken from Weather index insurance for coping with risks in agricultural production by Ulrich Hess)
The terminology used to describe features of index insurance contracts resembles that used for futures and options contracts rather than for other insurance contracts. Rather than referring to the point at which payments begin as a trigger, for example, index contracts typically refer to it as a strike. They also pay in increments called ticks. Consider a contract being written to protect against deficient cumulative rainfall during a cropping season. The writer of the contract may choose to make a fixed payment for every one millimeter of rainfall below the strike. If an individual purchases a contract where the strike is one hundred millimeters of rain and the limit is fifty millimeters, the amount of payment for each tick would be a function of how much liability is purchased. There are fifty ticks between the one hundred millimeter strike and fifty millimeter limit. Thus, if $50,000 of liability were purchased, the payment for each one millimeter below one hundred millimeters would be equal to $50,000/(100 – 50), or $1,000. Once the tick and the payment for each tick are known, the indemnity payments are easy to calculate. A realized rainfall of ninety millimeters, for example, results in ten payment ticks of $1,000 each, for an indemnity payment of $10,000. The figure below maps the payout structure for a hypothetical $50,000 rainfall contract with a strike of one hundred millimeters and a limit of fifty millimeters.
How Weather-Based Index Based Insurance Works (taken from Risk Management: Pricing, Insurance, Guarantee. The Use of Price and Weather Risk Management Instruments presented by Erin Bryla)
Risk management products based on weather events avoid the problems of traditional crop insurance because they rely on objective observations of specific weather events that are outside the control of either farmers or insurance companies. They are also less costly to administer because they do not require individual contracts and on-field inspections and loss adjustments. Although these are often called weather-based index insurance products, they are strictly risk management tools rather than traditional insurance.
Weather-based index insurance compares a measurable, objective, correlated risk (e.g. rainfall, temperature, wind speed etc.) to yields. In the case of rainfall as the correlated risk, historical data gathered from regional weather stations is used to determine the mean rainfall for a given period in the farmer’s area. Once the appropriate period has been selected, the issue becomes structuring the rainfall index.
A weather (rainfall) “index” should be carefully designed to weight the more important periods for rainfall in the crop cycle more heavily and than those periods where rainfall is not as important to production. Precipitation in different stages contributes in different measures to plant growth and an excess of rain may be of no use for production. Hence, it is useful to develop a weighting system that allows to differentiate the importance of rainfall in different growth periods and to shape the model so as to take into account the fact that excess rain may be wasted without contributing to plant growth. The final value of the index (the value which, when compared with the threshold, indicates if the insured should be granted an indemnity or not) is calculated by summing the values obtained by multiplying rainfall levels in each period by the specific weight assigned to the period.
Once a sufficient degree of correlation is established between rainfall and yield, and the index has been weighted properly an agricultural producer can hedge his production risk by purchasing a contract that pays in the case rainfall falls below a certain threshold. Farmers can elect coverage for a given period taking into consideration the crop cycle and the marketing cycle. Using this historical index the program is designed, where the option premium is the cost of the coverage and the strike is the rainfall threshold below which indemnity is triggered. The insurance is set up on a proportional basis allowing farmers to choose their rainfall trigger level or threshold.
Customers participating receive a payment if the rainfall index level falls below the threshold. The higher the threshold set for the contract the better the coverage provided the trade off being the higher the threshold the higher the cost of the coverage. In essence a farmer can elect a lower trigger amount of rainfall in order to lower his premium or he can elect a much higher trigger that will give him greater protection but will cost more in premium. Customers can also elect the comprehension of their insurance so they can partially or fully insure their revenue.
Their payment from the insurance is ultimately determined by the combination of these two factors - the rainfall threshold that they wish to be their trigger and the comprehensiveness of the coverage they want. Payment is equivalent to the percentage of rainfall-index shortage multiplied by the level of coverage selected. In the case where rainfall does not fall below the trigger no payment is made to the farmer and the premium is not returned.
Small farmer payout structure (Source: Innovative Financial Services for Rural India by Ulrich Hess).
Anatomy of Weather Derivatives
Basic concept (taken from Weather Derivatives and Weather Insurance: Concept, Application, and Analysis by Lixin Zeng)
A weather derivative is a contract between two parties that stipulates how payment will be exchanged between the parties depending on certain meteorological conditions during the contract period. There are three commonly used forms of weather derivatives: call, put, and swap.
A call contract involves a buyer and a seller. They first agree on a contract period and a weather index that serves as the basis of the contract (denoted W). For example, W can be the total precipitation during the contract period. At the beginning of the contract, the seller receives a premium from the buyer. In return, at the end of the contract, if W is greater than a pre-negotiated threshold S, the seller will pay the buyer an amount equal to P = k(W - S), where k is a pre-agreed-upon constant factor that determines the amount of payment per unit of weather index. The threshold S and factor k are known as the “strike” and “tick” of the contract, respectively. The payment can sometimes be structured as “binary”: a fixed amount P0 will be paid if W is greater than S or no payment will be made otherwise.
A put is the same as a call except that the seller pays the buyers when W is less than S. The payment, P, is equal to k(S - W) or P0 for a linear or binary payment scheme, respectively. The payment diagrams for a linear call and put contract are illustrated in the figure given below. A call or put is essentially equivalent to an insurance policy: the buyer pays a premium and, in return, receives a commitment of compensation if a predefined condition is met. A swap contract between parties A and B requires no up-front premium and, at the conclusion of the contract, party A makes a payment in the amount of P = k(W - S) to B. In the case of a negative P, the payment is actually made by B to A. In fact, a swap is a combination of (i) a call sold to B by A and (ii) a put sold to A by B. The strike S is selected such that the call and put command the same premium. Thus, this study will focus on the analysis of call and put contracts only.
The payment diagrams of a call and put contract.
A generic weather derivative contract can be formulated by specifying the following seven parameters:
• contract type (call or put),
• contract period,
• an official weather station from which the meteorological record is obtained,
• definition of weather index (W) underlying the contract,
• strike (S),
• tick (k) or constant payment (PO) for a linear or binary payment scheme, and
The parameters above determine the amount of payment (P) that the seller is obliged to make to the buyer. For a linear payment scheme,
Pput = kmax (S - W, 0) and
Pcall = kmax (W - S, 0), (1)
where the function max(x, y) returns the greater of values x or y. For a binary payment scheme,
Pput = P0 if W - S < 0; Pput = 0 if W - S ≥ 0 and
Pcall = P0 if W - S > 0; Pcall = 0 if W - S ≤ 0. (2)
Because the choice of W is extremely flexible, weather derivatives can be structured to meet a wide variety of risk management needs. The best-known examples are related to the consumption of electricity that is significantly affected by the seasonal temperature variations. An abnormally cool summer or warm winter decreases not only the number of kilowatt hours (KWHs) consumed but also the price per KWH in the unregulated energy trading market, reducing the revenue of utility companies that sell electricity. The same risk also applies to buyers of electricity, because an abnormally cold winter or hot summer can cause both the unit price and the consumption to rise. To manage these risks, derivative contracts based on seasonal accumulated heating degree days (HDD) and accumulated cooling degree days (CDD) are frequently used. HDD and CDD are defined as
where N is the number of days over the contract period and Ti is the arithmetic average of the observed daily maximum and minimum temperatures on the ith day of the contract. HDD and CDD measure the respective need for heating and cooling in order for people to stay comfortable. For example, a utility company can protect itself against revenue shortfalls due to a possible warm winter by buying an HDD put with a linear payment scheme [Eq. (1)]. The contract parameters are usually determined based on the company’s experience of the relationship between the revenue fluctuations and HDD variations. On the other hand, a major user of electricity may buy an HDD call to prepare for the high utility cost due to a winter colder than normal.
Besides the widely used HDD and CDD call and put contracts, there is a growing number of new and innovative uses of weather derivatives. In the following example, a snow blower retailer, in order to generate sales, promised its customers a sizable rebate if the total snowfall for the coming winter were less than a threshold. Consequently, the company would face a substantial liability if the snow level were below the threshold. The company, however, could completely transfer the risk by buying a total snowfall put with a strike equal to the threshold in the rebate contract.
In this case, a binary payment scheme [Eq. (2)] would serve the purpose better than a linear one does because the rebate would be triggered in a binary fashion.
The sellers of weather derivatives usually include major energy companies, who use these products to hedge their own risks and make trading profits. Insurance and reinsurance companies are also becoming important sellers, as they look for alternative ways to deploy their capital. Although the relatively short history of the weather derivative market does not allow a thorough analysis of the correlation between the performance of the weather derivatives and the general financial market, it is widely perceived that the correlation is negligible. Thus, they are appealing to a wide array of investors.
NCDEX: Trading of a Weather Index, an example
- Suppose we consider a small rice farmer
- Acreage: 1.5 hectares of land
- Yield of crop: 2000 kg/ha
- Output produced (yield * acreage)= 3 tones (valued at Rs. 33,000/-)
- Farmer hedges his risk against bad monsoon
- Farmer buys a weather index option
- Notional value of Rs. 10,000/-
- Premium priced at 3%
- Farmer buys a put option on August 15, paying a premium of Rs. 300/- at an index level of 1265
- Multiplier (for every unit shortfall in the index) will be Rs. 8/- (viz. contract value / index value)
- On expiry of the contract on September 20, the index drops to 1206 due to shortfall in rain
- Farmer exercises his option and gets paid Rs. 472/- and makes a gain of Rs. 172/-