Value of the NC ECONet

The ECONet/NC CRONOS Database: Its Value to the State of North Carolina

A paper by Sylvia M. R. Dake
November 2003

Executive Summary

While the program is named after the Greek God of agriculture, CRONOS, and the initial observation sites were funded because of agricultural needs within the state, this program evaluation will show that the system has value for a wide range of citizens and interest.

The core of North Carolina's climate and environmental observation network is composed of 99 - state of the art - weather observation sites scattered in and around the state. (Plans for the future include the installation of additional sites so that every county in the state has at least one site.) These sites transmit a variety of weather parameters via radio signals and intranet communication links to a series of networked computer servers. The sites measure temperature, dew point, wind direction and speed, atmospheric pressure, precipitation, soil temperature, soil moisture, and solar radiation parameters at least once every hour. Programs within the database calculate humidity, wind chill, temporal averages - mean temperatures for the hour/day/month, standard deviations of wind direction and heating and cooling degree day statistics. Most of this information is available, on-line, for immediate retrieval via the internet from as long ago as 7 days. Older data sets can be requested by email, fax, or phone. Custom data summaries or special calculations are also available (sometimes for a small fee) and are processes by a staff of meteorologists and students working within the office of the State Climatologist at North Carolina State University.

The information provided by the database and the staff is used by a growing number of state agencies, municipalities, education interests, agribusinesses, transportation entities, and private companies. In October of 1998, Dr. Sethu Raman - the State Climatologist, wrote a preliminary proposal for the expansion of the original agri-weather observation network and database capabilities. This proposal included a conservative assessment of its economic benefits. Dr. Raman estimated that at least 91 million dollars a year could be saved by state users and, in years with significant weather events - droughts/hurricanes/etc., there would be even larger savings. This evaluation utilizes the client contact database that has been developed in the past two years to refine the original estimates and explore some of the outcome and benefit pathways. Arguments will be presented to support the outcomes that are difficult to monetize due to time and personnel constraints. Agency utilization estimates and extrapolations will yield some dollar figures that continue to validate the original proposal. It is clear that the ECONet/NC CRONOS Database should continue to be funded based on the cost benefit analysis of its outcomes.


At the onset of a discussion of outcomes, comparisons should be noted between NC's network and database and those that exist in other states. While the National Weather Service (NWS), Department of Defense (DOD), some State Departments of Transportation, and even some private companies maintain observing networks of their own, several State Climatology Offices (SCOs) have spearheaded efforts to interconnect, expand and develop meso-networks (small networks) that allow for greater data reporting density. Studies in these other states have implied that improvements in profitability can be made in industries like transportation, energy, and construction and that the general effectiveness of emergency management operations will also improve. NJ, PA, CO, TX, and OK are just a few of the states with networks similar to NC's. Supportive material from those states should not be ignored.

Oklahoma has the largest and most developed network and is the premier model for most other states. The National Science Foundation, through its Experimental Program to Stimulate Competitive Research (EPSCoR), said in the outcomes and impacts paragraph of one of it's awards summaries, "The Oklahoma Mesonetwork's economic benefit to Oklahoma is approximately $1000.00 per customer (state agencies, farmers, and businesses). Mesonet weather data also assists Oklahoma's utility suppliers in making informed decisions regarding the generation, transmission and distribution of electricity, potentially saving Oklahoma utilities and their customers as much as two million dollars annually." A comparison can be made based on the fact that OK and NC have similar sized networks and state land areas. With over 2,387 customer requests logged for the NC system in the past 2 years, a roughly estimated economic benefit would be a little under 1.2 million dollars per year. This rough estimate, alone, is more than twice the yearly operating budget and supports a high value for the database. In the following paragraphs and charts, this analysis will refine that approximation in a few key areas. A limited survey of the SCO's clients was conducted in late October and early November of 2003. Again, time does not allow for the full exploration of all the pathways that could be identified.

As we begin to look at more specific outcomes and benefits, sensitivity, methodology, and objectivity should be discussed. It was clear from an interview with the climate office staff that utilization of the network has been increasing each year. It was also clear, from the client survey work, that the data was being used with positive results and the staff was reported to be friendly and helpful. This positive impression has been spread by "word of mouth" and it is expected that time will show continued growth in the number of clients. That is why the marginal analysis extends out over an 11-year period and shows trends by variations in industry behavior and utilization rates. The Client Count by Classification list shows that there were over 2300 contacts in the last two years. This chart and other industry specific information, usually identified in the discussion or in footnotes, allowed for the derivation of the utilization rates.

The telephone surveys were neither scientifically random nor exhaustive. Industries or categories were selected by the likelihood of finding a definable pathway for valuation. Some of the categories with the largest counts were avoided to insure that the results were not skewed to the most frequent users. Time did not allow for much more than 50 calls to clients from six or seven different industries. There were difficulties in contacting the right individuals (those who used the data or knew of the profit outcomes) and some resistance to inquiries (some were afraid to release private information and others were afraid the survey would result in increased charges for the data). The surveys generally asked the clients what data products were used, how frequently they accessed the network and if any benefits were realized from the use of the data (within a one year time period). Casual remarks picked up the favorable comments mentioned above and only two negative comments. One former client, an agricultural management firm, decided to set up their own observing equipment and stopped using the state network. Another client decided that the fees they were charged and the data they needed were duplicated within reports they paid for from a private weather forecasting service. This response indicated another pathway for evaluation. (To explain this client's actions it should be noted that the database does include some NWS and DOD sites that are a part of other data networks.) The details of the exploration of 7 different industries (benefit pathways) follow.


This industry figured prominently in the SCO contacts list. It was very easy to survey the clients and get direct answers to questions about how they valued the weather network. Seeing 133 requests in 2003 for data (from companies of 10 to 50 employees) and noting that there are approximately 4,440 companies of this size in NC, a utilization rate of close to 3% can be shown1. Five companies surveyed reported an average saving of $1,500.00/day for days when they could support a work delay due to weather. With an estimate of 10 such days affecting each company each year, the total benefit can be set at 15K per company per year.

Over the time period of the marginal analysis, it is reasonable to assume that there has been slowly increasing awareness of the network and its potential use in the construction industry. This, already large, user group could grow as much as 1% each year. The monetization table shows that the analysis extrapolates backwards a few years to .1%, then goes to 3% for this year, and then increases to a maximum of 10% utilization in the year 2009. Sensitivity could still be reflected by variations in the total number of construction companies (of this size) in NC, but increased utilization may mask it. If the average number of companies remains near 4,440 and the benefits per company remain about the same, the benefits total would be close to $6.6 million in 2009 (for 440 companies). Switching out the "per company benefit average" still shows that this pathway is robust. An average saving of 10k per company, on the low side, still results in 1.3 million in benefits at 3% utilization (133 companies). At 16k the benefit is 2.1million. These are still large and robust numbers. Finally, it should be mentioned that there are no significant opportunity costs to balance, here.


Meso-networks are developed, in part, to add raw weather data to forecast models. It is easily understood that with modern high-speed computers and communication capability, the more raw data that we can pump into our forecasts, the better the model output becomes. Statistical measures of forecast improvement, tied to increased data input, have been the subject of many scientific papers but none are specific enough to allow a monetizable chain. Other papers have linked improved forecast skill to improved crop management outcomes and monetizable benefits to farmers but they are difficult to link to crops grown in North Carolina. Thus, connecting and extrapolating a data rich meso-network to improved crop yields is not immediately possible due to the time constraints for this project. This analysis returns to the original proposal and uses the new survey indicators to find monetizable outcomes.

Dr. Raman previously estimated that 43.4 million dollars per year could be saved by use of the network's data just in the areas of crop management, pesticide application, and drought effect mitigation. While these estimates still hold true, a survey of the current users of the database indicates, more specifically, some of the pathways for savings. The marginal analysis in this paper will not include Dr. Raman's estimate. It will focus on some newly identified pathways like agricultural management/consulting firms. These services frequently request soil moisture and temperature information to be used in the preparation of their management reports for farmers. Although the line item for suggested planting times is only a small part of the report, with some reports costing upwards of $2,500.00, there is some small profit to the consulting company as a result of using the network. Even considering the number of such firms in NC, the monetized outcome is very small and thus not included in this analysis. (Of course, there is more profit to be made for the farmer if the report is useful in improving crop yields.)

Another income path that emerged in the survey work was through Federal disaster payments. Again, as Dr. Raman had noted, in severe weather (hail storms, droughts, floods, etc.), network data would prove even more valuable. The survey found two cooperatives and a Farm Service Agency that reported using the network data to validate loss payments of $2 million dollars - to livestock interests in one county in 1998-99 and $800k - in that same county in 2002. I used these numbers and years to set up a trend and extrapolate. There are other payments pending - amounting to $1,000.00 for corn crop losses, for this year, in another county. I will not monetize the corn crop loss because it is very small and only one data point is not indicative of any kind of trend. Given the nature of farming, however, it can easily be argued that all NC farmers can use the network to justify loss payments.

Spreading these returns out over total cash receipts for NC livestock would give a better average for the state but mask general agricultural variations in profits2. The trend on the marginal analysis simply shows some years with benefit claims and some years without them. Sensitivity analysis would also be served by using only the known benefit numbers in the marginal analysis and classifying them as conservative or low. Again, it appears robust simply because the benefits use are such large numbers. Since most of this money comes from outside of the state, opportunity costs were not considered, here.

An interesting duality was suggested, however. The survey found that when livestock waste fines are levied, agri-interests will use the network to defend against payments and the NC Department of the Environment and Natural Resources (NC DENR) will use the data to support their claims. Since this represents two sides of the same coin to society (a simultaneous +/- situation) and since both entities are in the state, the complexity of analysis of this pathway will be noted but the survey result will not be included in the totals.


Survey respondents from this sector excitedly spoke of the cost savings to their offices from the use of network data. The savings were realized in claims for damages to city parks, sewer and water facilities, and in the prevention of the payment of a claim related to water damage (supposedly cause by bad city planning). The cities involved were of three different sizes3. They indicated savings of 1k, 10k and 21k in a single year. A total of 32k for 2003 was used in the marginal analysis. Other responses indicated that savings of only 10k per year were more routine. The marginal analysis reflects this trend. As in the agricultural example, sensitivity was checked by using only the lowest defensible benefits. Higher numbers would result from multiplying the benefit by other cities of similar size. This lowest number is large enough to show great value. There were some survey problems. The respondents in several cities knew of savings but had no idea of the exact amounts. (The benefit calculators were on vacation or no longer working in that office.) Some opportunity costs were involved in one case but the amount would have been small and is therefore not included.


It was expected that one of the single largest monetizable benefits could be substantiated in this industry. Unfortunately, neither of the two large power companies in the state would respond to the survey questions. The estimates from Dr. Raman's 1998 proposal should stand however, because smaller companies in the state reported savings in energy distribution (keeping the power flowing when it is need). Responses from the NC electric cooperatives and municipalities that run their own distribution operations indicated that the network data was key to daily operations. Of the two agencies that responded, each reported savings of approximately $600.00 per year in this area. One also reported savings of $10k per year, in two of the last three years - in lieu of paying weather consultant/forecast fees. The total that is included in the marginal analysis is $10,600.00 per year but only for two years. Energy sector opportunity costs should be considered here. Some of the coops have redirected this money to infrastructure, since the beginning of 2003, and they have stopped using the network as frequently as they have in the past. That is why this benefit is dropped from the marginal analysis time stream after 2002.

The generation side of the energy business (power production) is affected by longer-term weather trends. Climate network data has been proven to be very important in planning generation efforts. If a sudden or significant rise or drop in temperatures is forecast, energy must be purchased from producers outside of the state (often at higher prices) or older (high pollution and thus very expensive) coal burning plants must be brought on line to feed the distribution channels. The energy companies rely on accurate weather forecasts to make accurate power generation forecasts so that they can make a profit. It is understandable that there might be resistance to revealing the details of this process.

The smaller cooperatives also use the historical weather data (heating/cooling degree-day trends) to educate their customers. The conservation effort, of each coop member, goes a long way toward seeing returns for the whole group. This same information has helped some of the coops defend their bills to meticulous members and thus avoid lawsuits. Both large and small energy agencies have also discovered the importance of historical wind reports. In the last few years - in the aftermath of hurricanes, thunderstorm down bursts, and ice storms - near real time wind reports have become key to power restoration efforts. If a power company can preposition crews near the area where winds are forecast to be the strongest or can make that determination shortly after the event, restoration is swifter, customer satisfaction is higher, and losses are lower. All of these reasons substantiate the original 12 million-dollar savings estimate from this sector. Without further information on how this figure can vary, the sensitivity of this estimate is high and questionable.


The staff in the NC SCO provides educational services to K-12 entities, public and private, throughout the state. These services primarily take the form of classroom presentations about the network and the use of its data. The student contact amounted to 88 hours of work in the past year. It is difficult to monetize the outcomes related to science education - it requires the isolation of resulting changes in graduation rates, the potential for higher SAT/ACT scores and/or future income earning potential. It is; however, very easy to look at what this service might cost our schools if they had to pay for it. This private industry analogy comes from using a rate of $175.00/hour for a consulting meteorologist to make an hour-long presentation and results in a $15,400.00 value, per year, for this line item4. Over a ten year time span, only slow growth - of the number of contact hours - is expected since additional personnel will not be added (cost control). Sensitivity analysis is accomplished by varying the expert's hourly rate. You might expect a range of $75.00 to $200.00 per hour. Switching these numbers only gives a range of 6.5k to17.6k. In either case, the numbers are sufficiently large to add significant support to the value of the network.

Economic Development

The agencies in this sector say that sometimes, weather is literally half the battle in the struggle to attract new companies to NC. In the past two years, there have only been four requests for information from this sector. Most were requests for simple climate summaries that would become a part of their brochures and web pages promoting the region to all newcomers. The benefit, here, is small and difficult to determine. However, one of the four requests showed great significance. A media entity wanted to build a satellite uplink facility (generating $100k in local income in the first year and $60k in subsequent years) near, but not on, the eastern coast. (They are looking for the shortest distances for their signals to travel to reach certain satellites in orbit around the globe.) NC was within ideal latitudes but they had two concerns - one, hurricane wind damage to its facilities and two, sufficient local technical skill to supply workers. The wind observations (post Hurricane Fran) and the climatological estimate of the frequency of such storms, convinced the company that it was "OK" to build in NC. The state only lost out (to VA) due to the lack of residents with the proper skills to maintain such a high tech facility (near the city of Rocky Mount).

For this industry sector, it would be reasonable to think that other, similar, decisions could come down to weather concerns (some would have even higher dollar valuations). A conservative estimate - included in the marginal analysis - would be half of the potential indicated by the example above - or $50k for the first year with 30k in downstream benefits. Adding another new entity, every 6 years or so, would also be reasonable. (Similar dollar benefits are used and added into the time stream.) Since these would be completely new businesses in the state, there would be no significant opportunity costs. As utilization of the network increases and the efforts of our development agencies are supported, the chances of success should also increase. Sensitivity analysis appears to show that this is not a robust figure, however. There are just too many other variables involved in siting a new company.

Other Private Industries

There are at least 8 private weather-consulting operations in NC that might routinely access the network. Not all of their work requires data from the network but forensic investigations often look at historical or more recent climate data. Income for work on forensic projects (especially those related to litigation) can generate average fees of $400.00 per case6. Consulting meteorologist Jim Wirshborn of Mountain States Weather Services in Colorado provided this average and says that he has cases that involve his local network at least 6 times each year. (This firm finds mesonetwork data so important that they actually maintain their own observing sites to support their work in the highly variable terrain of the mountains of CO.) His expert opinion in the courtroom is like those of similar professionals in NC. The valuation of this outcome is small, but near $2,400.00 per year. Multiplied by the 8 professionals in NC, the value is just over $19k per year. Again, variations in consulting fees are a good way to check the sensitivity. A change of +/- $100.00 per case still yields significant benefits. This valuation is robust. With the uniqueness of the networks being a major reason for their use, opportunity costs do not seem to be a concern and this valuation pathway points to another area of consideration.

Often both law firms use the same information. It is unfortunate that time did not allow for the determination of a legal fee revenue average for these situations in NC. (These numbers could be in the hundreds of thousands of dollars.) There is, however, an opportunity cost trade off. When you look at the insurance claims that are usually at the center of these court cases, the decisions will either generate a claim (loss) by the insurance company and payment to the injured party or losses by the claimants. This +/- situation cuts both ways for the state in terms of gains and losses and will not be included in the totals.

The survey also looked at two hotels that requested data. Heating and cooling degree-day information, air temperature, winds and humidity data are critical to planning and on-the-fly adjustments of the heating and cooling systems of large buildings. This is especially true for large structures, with lots of glass, and those that see continuous indoor/outdoor traffic - like hotels. The energy cost savings can be enormous especially when you consider that customer comfort is one of their main priorities. The survey included calls to the Hilton Hotel chain that resulted in discussions with the house engineers and validation of the pathway mentioned above. A regional engineer did not return a call requesting benefit specifics, so this path remains unmonetized. Substantial savings are expected, however, since there are eight Hilton Hotels in NC that fit the description above. Of course, money not paid to our state energy vendors is an opportunity cost loss issue.

  1. Employment and Wages by Industry --> Go to Reporting Units and Employment by Industry and Size Groups. Check data between 2001 and 2003 for code 23 - construction for the state of NC private industry and all sizes.
  2. Cash Receipts From Farming By Commodity
  3. North Carolina State Demographics --> Look at number of cities and population size.
  4. This is the fee charged by Sylvia Dake, Consulting Meteorologist.
  5. Forensic Weather Services