What is Flowx?

To get the most out of Flowx and avoid frustrations, it is super important to know what it is good for and what it's not so good for.

Flowx is good for watching large weather systems and planning activities. For example:

• watching a band of rain travel across the country and planning a bike ride.
• watching a incoming storm and planning a hike
• watching a nice weather system and planning a BBQ on the weekend.
• watching a swell arriving  and planning a surf trip

Flowx is not as good for current conditions in some areas, especially temperatures in micro-climates. That said, if the temperatures are wrong, the error tends to be a bias and the trends tend to be correct. For example, the temperature is always 5°C lower than the actual temperature. One day we hope to improve on this issue in Flowx.

We recommend you use Flowx alongside your typical icon-based weather app, e.g., AccuWeather, WeatherBug or Yahoo Weather.

To understand why Flowx is good for watching large weather systems and not as good for current conditions, we need to understand how weather predictions work. These are the basic steps:

1. Measure weather data using weather stations and satellites.
2. Solve global weather simulation. Flowx uses the raw data from weather simulations.
3. Post-process the simulation results using statistics (MOS) to improve the forecasts. Most other weather apps use post-processed data.

The advantage of simulation data is that it is global. You get it anywhere in the world. With a little bit of swiping goodness, this gives you the nice map views of large weather systems and how they progress over time.

The disadvantage of simulation data is that it doesn't model details very well since the data points are ~25km apart, especially at the earth's surface where there is a lot of detail, like mountains, lakes, islands, valleys, micro-climates and cities. For example, the simulation may predict a lower temperature than the actual temperature in a sheltered area, like a valley.

This is the advantage of post-processed (MOS) data, it provides better predictions at fixed locations where there is measured data, i.e., in cities, towns and airports with weather stations.

The disadvantage of post-processed (MOS) data is that it is only at fixed location where measured data exists. This tends to be in cities, towns and airports. So you lose the nice map views with the swiping goodness.