OnPoint Climatology is created by statistically processing many years of hourly and daily OnPoint Past weather data to produce means, standard deviations, and other statistics that together quantify the statistical nature of OnPoint Past weather on hourly and daily increments throughout a calendar year.
OnPoint Climatology can be used in combination with OnPoint Past weather and Forecast data to identify and rank departures from normal. For example, by differencing the OnPoint Forecast of temperature with the associated OnPoint Climatology mean, customers are able to produce a forecast of temperatures in terms of departure from normal.
Taking this a step further, one can divide the departure from normal forecast by the associated OnPoint Climatology standard deviation to determine where the forecast temperatures may be significantly different from normal. The result of this processing is often referred to as a ‘standardized anomaly’. Weather Source provides many pre-computed standardized anomaly results as geospatial files (our OnPoint Graphics product) that are available from the OnPoint API.
In addition to using the OnPoint Climatology to quantify departures from normal, it can also be useful as a ‘climatological’ long range forecast. Most dependable forecasts can only provide a reliable forward view of several days. A few more advanced forecasts can look forward a few weeks with some skill and beyond that, longer range forecasts tend to be less accurate. OnPoint Climatology allows businesses to immediately identify what weather to expect for any location at any point in time.
In addition, OnPoint Climatology provides valuable ‘frequency of occurrence’ information for parameters like precipitation and snowfall. The frequency of occurrence provides insight into how often certain precipitation amounts occur (e.g. how often does snowfall or precipitation in the range of 1.0 to 2.5 inches occur).