WXSIM is a very unique software package, in many ways - in terms of what it's for, how it works, and how and why it came into existence.
What it's for
WXSIM generates accurate weather forecasts for your specific, customized location(s), using a variey of surface and upper air data and forecast model data, plus optionally current and past data from your home weather station. WXSIM also includes tools for interacting with the forecast - either before or during the run - to inject your own skill and/or other sources of information. Other uses of WXSIM include hypothetical weather modeling for education or experimental purposes.
WXSIM occupies a niche filled by no other program. Used properly, its forecasts are of professional quality, often as good as or better than anything else you'll see, especially since customization allows the forecasts to be highly specific for your exact site. Its interactive nature also makes it a great learning tool, either for amatuer weather enthusiasts or meteorology students.
Here are a few more things you can do with it, besides actually making forecasts:
How it works
WXSIM has sophisticated native routines for forecasting various weather elements (mainly temperature and humidity) for a single site, plus surface advection (transport of conditions via wind). However, it combines this with a variety of external model data, especially NOAA's GFS (Global Forecast System) to make the forecast more accurate and very specific to your location.
A vast amount of work has gone into the development of WXSIM. Space does not allow a full discussion of all the program's algorithms, but just to provide a notion of what is involved, here are some of the variables modeled or used by the program: Date, time, sun angle, distance from sun, latitude, longitude, elevation, proximity to large bodies of water, climatological temperature and dew point data, heat capacity of the surface, latent heat of condensation, incoming shortwave solar radiation, outgoing longwave terrestrial radiation, cloud albedo and emissivity, mixing due to winds and convection, advection of temperature and dew point, upper level temperatures and dew points (in 5 atmospheric layers), formation of dew, frost, and fog, formation of sea breezes, accumulation and melting of snow, soil temperature and moisture, ... and much more.
Why it exists
OK, this is more of a story! I (Tom Ehrensperger, the creator fo WXSIM) was an astronomy and weather obsessed kid, building telescopes and weather instruments, and recording and analyzing weather data, in a continual effort to improve my own forecasts, especially of temperature. I naturally majored in physics, earning BS and MS degrees in Physics from Georgia Tech, by 1984. I went on to have a happy career (33 years and counting) teaching high school physics, astronomy, and meteorolgy, mainly at Woodward Academy (www.woodward.edu), where I'm the first person you see in the opening video. I've had a "second life", though, developing this (and other) software. It started as a fun little attempt to model the daily temperature curve, using astronomically calculated sun angle and radiation laws, first on an HP41-C calculator, and then (for years) on a Commodore 64 computer. It could be a long story, but I'll try to cut to the chase: over the succeeding decades, this little model grew to encompass more and more variables and became an accurate temperature forecasting tool. In 1994, I named it and started "marketing" it on a small scale, which started the process of getting user feedback, which helped drive my development of it. About the same time, the internet came along, with weather data that could be ingested and used, so it became a sort of hybrid - of a painstakingly and originally derived temperature and humidity model along with a mixer/user of real-time surface and upper air data and "big" model forecast data It is perhaps best described as an "interactive local atmospheric model". I continued tweaking it for accuracy and adding features. Perhaps the most significant improvements in the past few years are two separate (though they can be used together) "learning" features, which allow the program to learn from its mistakes, and correct its own biases, with regards to temperature and humidity forecasts.