When I worked as an Energy Trading Floor meteorologist, there was a period of time every morning when we would evaluate different vendor forecasts that would roll in. This would be in addition to evaluating the outputs of various numerical weather prediction models such as the GFS and the European model (ECMWF).
We were endeavoring to sift through the following 4 items:
What overall behaviors were the models pointing to? This would require examining each model separately.
Based upon what we knew about how the models were performing, what would be our call?
What were the external vendor forecasts saying would happen? This would point to what the markets might do.
How divergent were those vendors? This would point to the certainty of what the markets might do. For example, if vendors are all in agreement, chances are that the markets would trade according to this trend.
This 4-stage process would be repeated operationally each day. I have a great deal of respect for energy and commodities trading floor meteorologists, because this job isn't easy. There are deadlines and tight time constraints, and these meteorologists are usually held accountable for their calls. In my 15 years of both being on the job, consulting, and evaluating lots of in-house forecasts, the best meteorologists are lucky if they can improve upon a 1-15 day forecast by an average of 1 degree over the public and private vendors when it's all said and done. It is notoriously difficult to do, but our job as trading floor meteorologist is to squeeze as much accuracy as we can---because this can amount to millions of dollars for a company.
When I started my company StatWeather in 2009, I had one goal in mind: to help people in energy and commodities make better decisions with less effort. We then set about to develop a streamlined, automated system that anybody could use to accomplish the above 4-stage process. It would help to make the meteorologist's job easier, and for companies that don't have the luxury of a meteorologist, it would serve to inform trading decisions in a reliable manner.
First we developed the software tool, “Forecast Skill.” A trader with this software tool can upload any of their vendor data (including their own in-house forecasts), or use StatWeather's collection of free forecaster data (such as the National Weather Service), and the software will automatically compute and show forecaster errors and biases, averaged over different time frames. From looking at “Forecast Skill”, a trader can see when certain forecasts have been too hot or too cool for extended periods of time. It is not unusual for a forecast to be running as much as an 8 or 10 degree bias for 5 or 6 weeks straight, as odd as this sounds!
Then we developed the software tool, “Forecast Consensus.” A trader can see how their selection of forecasts have been trending as a whole, and exactly where those forecasts are converging or diverging, pointing to possible volatility in the markets. It is a quick reference, but a very powerful tool.
Then we developed the software tool, “Forecast Optimization #1 and #2.” Forecast Optimization #1 will take a collection of numerical weather prediction models and forecast ensembles, and combine them into an “optimal” forecast taking into account StatWeather's internal computations of biases and errors. It does basically what a human meteorologist would do in examining various model outputs, but it makes the process more analytical and robust, keeping systematic track of model biases.
Forecast Optimization #2 will operate on the collection of vendor data which the user has uploaded to the software and present a “vendor optimized forecast.”
Now a trader has the complete picture in a “one stop shop” that he or she can see at-a-glance: the trader knows where their vendors are trending, what the models are doing, how accurate their vendors have been, and what a good or “optimized” call would be. This “optimized” call is generally 25% more accurate than going with a straight consensus of vendor forecasts. Sometimes it's only 20% better. Other times, it's as much as 33% better. But the point is that, this computerized system, without any subjectivity from a human meteorologist, basically gives you the accuracy of what the very best in-house meteorologists in the business could get you, or even better.
What does such a software subscription cost? It is about 25% the cost of hiring an analyst. We hope that you will try out our system and see what StatWeather can do to help you trade better with less work and greater returns!
To learn more about the many things that StatWeather offers, please go to www.statweather.com.
Thank you for reading. Best wishes and happy trading!