In the beginning, his computer model only forecasted nighttime temperature on his property. Then he tested the model in another area where nighttime temperatures were known to be relatively cold, and discovered that the model performed well in forecasting temperatures elsewhere.
It was then he realized his model could be very valuable for many other uses – from agribusiness to energy consumption. For example, citrus growers could mitigate risk by better frost forecasts and energy companies could better predict energy demand – to generate more power during peak times, thus avoiding the need to purchase energy at higher prices from other sources. What this meant statistically is that with every 1% improvement in temperature forecasts, a utility company could save $1 million.
One of Matt’s professors, Fred Carr, had heard about the Collegiate Inventors Competition and urged him to submit his model. He did so and, to his surprise, won the grand prize award for 2006 beating out impressive competition with breakthrough inventions in technology and medicine from America’s top universities. With the prize money, he was able to start his own company, NanoWeather, Inc.
SHAN: What is a microclimate?
MATT: A microclimate is a small area that has a different climate from the larger area around it. By "small area," I mean anywhere from a few tens of feet up to the size of a large city.
I think the best examples of microclimates are in the San Francisco Bay Area where I grew up. The average maximum temperature during July in San Francisco is 68 degrees (F). Just 25 miles to the east, in the San Ramon Valley (e.g., Danville, CA), the average high in July is 90. San Francisco averages 23" of rain per year, while Kentfield, about 10 miles north of the Golden Gate, gets more than double that amount.
There are many examples of smaller microclimates within cities. For example, the eastern side of San Francisco is often sunnier and about 5 degrees warmer than the western side. The San Fernando Valley, near Los Angeles, is much warmer than areas closer to the coast.
I have five acres of land here in Norman, Oklahoma, which is where I did the research behind the forecast model. On some nights, one side of it can be as much as 20 degrees warmer/cooler than the other side, only 600 feet away.
There are even smaller microclimates if you want to get into botany and entomology. Microclimates that exist on the scale of a few inches can affect plants and bugs.
SHAN: What are the benefits of being able to forecast an environment closely?
MATT: There are many benefits, depending on who you are and what you do. If you are a farmer, you're likely in a different microclimate than the nearest airport (where most weather data and forecasts are for). A few degrees can be the difference between frost and no frost. Wine growers are especially sensitive to this. It is the variety of microclimates, after all, that allows California to produce so many different kinds of wine. Some grapes are extremely sensitive to temperature.
Wind farms tend to be built in microclimates that have unusually high wind speeds. Knowing the wind speed at their exact location lets them predict how much power they will generate.
For other types of energy companies, temperature is very important for predicting demand. If it's 85 degrees at the airport but 95 degrees where most people live, energy usage will be a lot more than expected.
Companies in a wide variety of industries have meteorologists on staff. But there's no way a single forecaster (or even a team of 100) could forecast for every microclimate they need to know about. So having an automated system that can forecast for hundreds of thousands of locations in a short period of time can save them a lot of time and money. It also can help the forecasters do their jobs better.
SHAN: How do you achieve optimum results and accuracy with your technology?
MATT: It is able to resolve microclimates as small as 300 feet across, so it can give a forecast for a precise location.
Automated forecasting systems are generally one of two things: a) low-resolution numerical models or b) statistical methods based on low-resolution model data combined with measurements from weather stations. The models are not very accurate because they assume the landscape is smooth and homogeneous. In other words, they assume microclimates don't exist. Statistical methods can represent microclimates, but only at locations with weather stations. That's why most forecasts are for airports.
My technology does not rely on statistics, so it works well everywhere. It uses the low-resolution models to determine the general, large-scale weather pattern. Then it uses physics to determine how the large-scale weather pattern will interact with each microclimate. So it has the advantages of both of the standard methods, but without either of their disadvantages.
NanoWeather, Inc., has diverse customers, from energy companies as Pacific Gas & Electric, to weather companies, including AccuWeather. Using the technology originally intended only for Matt’s five acres of land in Oklahoma, NanoWeather’s forecasts now reach approximately 200,000 paid subscribers and tens of millions of users around the world.
It is estimated that almost half of the world economy is sensitive to weather and climate. With climate change, these economical and timely forecasts are becoming even more important for weather sensitive industries, and the system’s unique design makes it easy to fit different customers’ needs.
Weatherwise Magazine has dubbed Matthew Haugland -
“Master of Microclimates.”
For more information about NanoWeather, Inc., please contact email@example.com
or visit: www.nanowx.com
For more about Microclimate examples visit:
Also, see MetLink resources for teaching weather and climate:
Check out the National Geographic Education archived lesson on Microclimate in the Schoolyard to see a Science Lesson for Kids: